esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile Learning Technologies for Education: Benefits and Pending Issues
## 5 Results Analysis and Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline \multicolumn{1}{c}{**Generalization**} & \multicolumn{1}{c}{**Benefits of Mobile Learning**} \\ \hline Constructivist Learning & Learning methods, participation, contextual learning, new learning opportunities, challenge in education \\ \hline Student behavior & Improved retention, improved performance, involves the student, motivation and autonomy, experiential learning, self-directed, active participation, facilitates coordination, cooperation, collaboration \\ \hline Learning spaces & learning aligned with educational objectives, strategic learning, best/innovative learning methods, portability, ubiquity, connectivity, learning in multiple, conceptual and social physical spaces, lifelong learning \\ \hline Collaborative Learning & Improved interaction inside and outside the classroom, collaboration and communicationinteractive and accessible learning, project-based learning, improved teacher-student communication, improves student-student communication \\ \hline Informal and self-directed learning & Formal and informal learning, participation, convenience, and achievement, attractive learning, self-directed learning, informal learning \\ \hline Resources for teachers & Adapted to learning needs, innovative pedagogies to support teachers, natural and intuitive interface, immediate delivery of feedback, easier team work, help teachers with new literacy, new social interactions \\ \hline Technology and support & Service-oriented architectures, learning moments, commercial tools for creating and deploying content, improve the learning process, free access to educational platforms, high implementation speed \\ \hline Affordability and portability & Accessible and located learning, portable educational technology, affordable educational technology \\ \hline Availability and flexibility & Ubiquitous access to information, local and mobile learning, availability and accessibility, content available, encourage learning and participation in multiple physical spaces \\ \hline Motivational Learning & Easy and interesting learning, learning that generates achievement and enjoyment \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Generalization of the benefits of using mobile learning in education. The summary of these generalizations found in the analysis of each article and the findings discovered are shown in two concept matrices in Tables 4 and 5. The general analysis of the research indicates students are predisposed to use their mobile devices in the teaching-learning process because they are familiar with their utilization [32]. Moreover, the success of m-learning is highly dependent on the educational institution, the teachers involved, and the students [43]. Due to the physical characteristics of mobile devices, technological limitations are one of the main issues this technology has for its use in the educational area [34]. The generalizations proposed to identify the benefits of using m-learning found in the research are detailed below.
## 3 Maximum Power Point Tracking  
To ensure the extraction of the maximum power available from the PV array, an MPPT algorithm must be used [28; 29]. In this work, the perturb & observe (P&O) method for MPPT was implemented [30]. Figure 10 shows a scheme regarding the MPPT implementation for the proposed topology, where each converter module, and consequently each PV panel, uses an independent P&O algorithm to maximize the power extraction from the PV array. Figure 11 shows the control scheme applied to each PV panel, where the subscript \(i\) refers to the \(i\)-th flyback primary winding. The MPPT method generates a voltage reference  
Figure 8: Schematic of stages 7 to 12, time: \(T_{s}/3<t<2T_{s}/3\).  
Figure 9: Schematic of stages 13 to 18, time: \(2T_{S}/3<t<T_{s}\).  
for the corresponding PV panel output capacitor. This reference is processed by a closed-loop control strategy based on a proportional integral controller. The output of the voltage controller is a duty cycle to regulate the gating signals of the main and snubber switches.
# Defacement Detection with Passive Adversaries
## 2 Defacement Response
### Defacement Detection: Problem Definition
For example, changing the company logo or name, or inserting totally extraneous text, is normally considered a serious defacement, while the normal upgrade of Web site content and of the graphical evolution of the interface, as well as the changing advertising banners, should not generate continuous alarms. The above definitions must be understood in the context of a defacement response process, where prevention measures make the occurrence of a defacement instance unlikely, and alarm management and mitigation strategies are in place, normally with the support of an SOC. In this context, we are interested in two error measures for the defacement detector during a defined period of time:  
**UDR (Undetected Defacement Rate):**: the relative number of defacement occurrences, validated as such by human SOC operators, but not recognized by the defacement detector
**FAR (False Alarm Rate)**: : the relative number of generated alarms that do not correspond to real defacements  
We may then define the following requirements for a defacement detector:  
**Very low UDR**: Undetected defacements have extreme consequences, so their number should be as low as possible. **Low FAR**: False alarms cost, as they must be dealt with by the SOC. Based on available budget, thresholds can be set so as to have a desired FAR value. Obviously, lower FARs will make the expected UDR higher. **Unpredictability**: Only system administrators, responsible for the defacement detector, should have access to details and keys that make defacement alarms predictable. If adversaries know the alarm behaviour, they will be able to simulate it and break the defacement response infrastructure.
## 3 Results
### Profiles across Measures
#### 3.4.3 Profiling at 12 Months after Stroke  
At 12 months after stroke, the MoCA, MADRS, mRS, RAPA, WSAS, and SIS scores were used for the profiling. Among participants at 12 months after stroke, 43.9% showed at least one impairment with greater frequency than that of participants with impairment at day 3-7 or at 3 months after stroke. The participants were grouped into identified profiles by the AI algorithm, as shown in Figure 8 and Table 6. Four distinct impairment profiles were identified at this time point.  
Among the survivors of mild stroke at 12 months after stroke, a group of participants showed lowered cognitive abilities and low engagement in physical activities. These participants, highlighted in profile 1 (11%), scored less on the MoCA assessment (MoCA mean: profile 1 = 21.12, other participants = 26.59, \(p<0.05\)), measuring their cognitive abilities, and on the RAPA assessment (RAPA mean: profile 1 = 2.62, other participants = 4.2, \(p<0.05\)), which evaluated their level of physical activity.  
Another group of participants shown as profile 2 (9.6%) demonstrated an increased level of depression compared to other participants as they scored higher on the MADRS assessment for depression (MADRS mean: profile 2 = 9.71, other participants = 4.88, \(p<0.05\)). It is noteworthy that higher levels of depression among this group of participants were seen at all three time points.  
Profile 3 (11%) participants were separated from the rest given their increased level of functional disability. This group exhibited higher scores on the mRS assessment, which evaluates disability in stroke survivors for recovery and continued disability. All participants with this profile had disability symptoms, while a few reported moderate disability (mRS mean: profile 3 = 1.5, other participants = 0.75, \(p<0.05\)).  
In profile 4 (12.3%), participants indicated a poor quality of life, as indicated by their low scores on the SIS assessment, which evaluates disability and health-related quality of life after stroke. Their SIS assessment outcomes were significantly lower compared to those of the other participants (SIS Mean: profile 4 = 70.55, other participants = 87.67, \(p<0.05\)).  
Figure 8: Impairment profiles at 12 months after stroke.
## 2 Literature Review
### Communication Technologies for IoT Healthcare
#### 2.3.1 Latency and Bandwidth Challenges  
Significant difficulties arise from latency and bandwidth constraints, especially in settings with a high device density or unreliable network infrastructure. Many IoT healthcare applications require ultra-low latency to ensure timely alerts and immediate responses in many IoT health applications, like insulin administration systems, fall detection, and cardiac monitoring, to guarantee prompt reactions in emergency situations. The effectiveness of treatment and patient safety could be compromised by any delay in data transmission.  
These issues are addressed by several wireless technologies with differing trade-offs:  
* Wi-Fi's fast transmission rates make it ideal for sending massive amounts of medical data, including continuous vital sign monitoring or high-resolution imaging. It can be used for high-bandwidth medical image transfers and real-time video consultations, but its high-power consumption limits it from being used in battery-operated devices [37].
* BLE is intended for low-power consumption, making it perfect for wearable health devices and short-range communication between medical sensors and smartphones, yet its short range prevents widespread coverage in hospital settings or remote monitoring scenarios [38].
* LoRaWAN offers long-range connectivity with low power consumption, which makes it ideal for asset tracking in hospitals and rural healthcare applications for sending small health-related data packets over long distances. However, because of its low data rate, LoRaWAN is not appropriate for sending high-resolution medical data [39].  
Communication bottlenecks in high-density environments or unreliable network infrastructure can hinder real-time patient monitoring. In order to maximize effectiveness, reliability, and real-time responsiveness in IoT-driven healthcare systems, a hybrid strategy combining many wireless technologies is needed to address these latency and bandwidth limitations.
## 2 Results and Discussion
### Perceptual Interactions
#### 2.2.3 Electronic Nose Response to the Interaction of Aroma Perception  
The above experiment proved that MeSA did have a very important effect on the floral fragrance of tea. In order to observe its effect on tea fragrance, MeSA (0.5, 1, 5, and 10 \(\mu\)L), tea infusion, tea infusion, and MeSA (0.5, 1, 5, and 10 \(\mu\)L) of the mixture were set up and tested for the HERACLES electronic nose. JJM was selected as the tea sample for the experiment.  
Figure 2: Interactions between methyl salicylate and floral compounds: (**a**) MeSA + phenylacetaldehyde, masking effect, (**b**) MeSA + cis-jasmone, addition Effect. (**c**) MeSA + benzyl alcohol, synergistic effect.  
The results were unexpected: The addition of MeSA had a significant effect on the overall aroma. The masking effect of different concentrations of MeSA on tea aroma was confirmed. For a clearer and more intuitive representation, the electronic nose aroma profile of three samples of 5 \(\mu\)L MeSA, tea infusion, tea infusion and 5 \(\mu\)L MeSA mixture are shown (Figure 3).  
In order to highlight the effect, the small window showed the tea infusion and the mixture of tea infusion and MeSA. There was no doubt that the addition of MeSA reduced the response value of all sensors, which means that MeSA masked the floral in the tea, and also other aroma attributes.  
The electronic nose confirmed the masking effect of MeSA on the floral aroma of black tea. Combined with our analysis of the synergy/masking/additive effect of our binary mixture, the possible reasonable explanation was that MeSA and occupying higher concentrations of compounds such as linalool, phenethyl alcohol, and geraniol showed masking effects. The concentration of the floral compounds that had a synergistic effect with it was relatively low. Therefore, in summary, MeSA can achieve the effect of masking floral fragrance in black tea.
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 3 Methods
### Backbone Network with DLKA Attention Mechanism  
In ship detection tasks, targets exhibit diverse sizes and shapes and mutual occlusion in complex scenes often results in only partial visibility of targets. Traditional convolutions, constrained by fixed sampling patterns, struggle to adaptively adjust to irregular or occluded regions, leading to reduced detection accuracy. To address this, the dynamic large-kernel attention (DLKA) [35; 36] module combines deformable convolution and large-kernel convolution, enabling adaptive feature extraction for occluded targets.  
Deformable convolution dynamically learns spatial offsets during training, allowing the network to sample features from non-grid locations tailored to target morphology. This is particularly effective for irregular or partially occluded regions. Large-kernel convolution  
Figure 4: Structure of the CKSP module.  
expands the receptive field to capture broader contextual information, which is critical for long-range dependency modeling and cross-region feature fusion.  
The DLKA module is integrated into the PSABlock at the end of the backbone network. The structure diagram of DLKA is shown in Figure 5. The PSABlock employs parallel processing branches to handle multi-scale and multi-region features, leveraging diverse convolution kernel sizes, attention mechanisms, and dilation rates. Within this framework, DLKA introduces two deformable convolution branches:  
1. 5 \(\times\) 5 deformable convolution: Captures medium-range contextual information with a balance between detail preservation and computational efficiency.
2. 7 \(\times\) 7 dilated deformable convolution (dilation rate = 3): Achieves an expanded receptive field to aggregate long-range dependencies while maintaining resolution.  
These branches operate in parallel, adaptively fusing local details (e.g., ship edges and occluded parts) with global context (e.g., maritime backgrounds and distant targets). By learning dynamic offsets, DLKA enables the network to focus sampling on discriminative regions, overcoming the rigidity of traditional convolutions. This design ensures robust detection of multi-scale occluded ships in complex marine environments while maintaining network lightweightness.
## 1 Introduction
### Definition of Problems
#### 1.2.2 Security Aspect  
The dynamic encryption key technology that has resolved the availability problem in the low-power wireless communication IoT environment mentioned in Section 1.2.1 should also satisfy the security requirement. Users with malicious intent should not be able to guess the subsequent key (randomness) and predict the subsequent key (unpredictability) even if the current key is stolen. The key generation information delivery problem [22] should be prevented when both ends of communication key generate a key.
## 2 Specimens and Experimental Methods
### Coal Specimens and Their Characteristics
#### 2.1.2 Coal Specimen Properties  
The basic properties of the coal specimens are shown in Table 1. Vitrinite reflectance and macerals were carried on a microspectrophotometer (AXIO Imager M1m, Carl Zeiss, Germany). According to proximate analysis of coal (GB/T 212-2008), the proximate analyses of coal specimens were carried out by employing the Self Employed Industrial Instrument and Analyzer (SDTGA8000a, Hunan Sundy Technology Co., Ltd., China). The ultimate analyses were carried out by an element analyzer (PerkinElmer 2400 II). Table 1 shows that the maximum vitrinite reflectance varies from 0.9% to 1.17%, and the volatile content (wt.%) changes from 30.27% to 34.06%. Therefore, the collected specimens are highly volatile bituminous coal A.  
Macro- and microstructure deformation characteristics of four coal specimens were observed, including coal body structure, macro- and microfractures and folds, and etc. Then the three types of technically deformed coal specimens were recognized, i.e., undeformed coal, cataclastic coal, and crumpl coal; meanwhile, their deformation degree and deformation series were determined (Table 2 and Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Coal** \\ **Specimen** \\ \end{tabular} } & \multirow{2}{*}{**Type**} & \multirow{2}{*}{\(R_{o,max}\)/\%} & \multicolumn{3}{c}{**Proximate Analysis**} & \multicolumn{3}{c}{**Maceral Analysis**} & \multicolumn{3}{c}{**Ultimate Analysis (wt.\%)**} & \multicolumn{3}{c}{**d**} \\ \cline{3-14}  & & & **(wt.\%)** & & & **(vol.\%)** & & & & & **Ultimate Analysis (wt.\%)** & **d** \\ \cline{3-14}  & & & **Mad** & **Ad** & **VMdaf** & **V** & **E** & **I** & **C** & **H** & **O** & **N** & **S** \\ \hline S1 & Undeformed coal & 0.90 & 2.21 & 11.47 & 34.06 & 83.21 & 2.37 & 8.82 & 77.25 & 4.28 & 16.88 & 1.31 & 0.28 \\ S2 & Cataclastic coal & 0.96 & 1.82 & 8.92 & 33.14 & 82.33 & 2.56 & 7.62 & 77.61 & 4.32 & 16.42 & 1.26 & 0.39 \\ S3 & Granulitic coal & 1.17 & 2.04 & 10.16 & 31.68 & 82.75 & 2.36 & 7.14 & 76.39 & 4.23 & 17.94 & 1.24 & 0.20 \\ S4 & Crumple coal & 1.05 & 2.36 & 9.54 & 30.27 & 85.94 & 4.10 & 5.19 & 75.62 & 4.30 & 18.64 & 1.29 & 0.15 \\ \hline \hline \end{tabular} Note: Mad, moisture as air-dry basis; Ad, ash as dry basis; VMdaf, volatile matter as dry, ash-free basis; V, vitrine; E, exinite; I, inertriate; d, as dry basis.  
\end{table}
Table 1: Properties of coal specimens.  
Figure 1: Regional structure of the study area and sampling locations.
# Deep Neural Network Equalization for Optical Short Reach Communication
###### Abstract  
Nonlinear distortion has always been a challenge for optical communication due to the nonlinear transfer characteristics of the fiber itself. The next frontier for optical communication is a second type of nonlinearities, which results from optical and electrical components. They become the dominant nonlinearity for shorter reaches. The highest data rates cannot be achieved without effective compensation. A classical countermeasure is receiver-side equalization of nonlinear impairments and memory effects using Volterra series. However, such Volterra equalizers are architecturally complex and their parametrization can be numerical unstable. This contribution proposes an alternative nonlinear equalizer architecture based on machine learning. Its performance is evaluated experimentally on coherent 88 Gbaud dual polarization 16 QAM 600 Gb/s back-to-back measurements. The proposed equalizers outperform Volterra and memory polynomial Volterra equalizers up to 6 th orders at a target bit-error rate (BER) of \(10^{-2}\) by 0.5 dB and 0.8 dB in optical signal-to-noise ratio (OSNR), respectively.  
deep neural networks; volterra equalization; nonlinear systems; coherent optical communication +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Evaluation of Mass  
The findings obtained as a result of the experimental studies were evaluated using the ANOVA method in Section 2.3. Average mass values related to nozzle temperature are given in Figure 3. The mass of the tensile samples decreased with increasing temperature. The largest mass decrease appeared when the nozzle temperature increased from 240 to 250 \({}^{\circ}\)C. This result shows that the optimum operating temperature for the ABS sample is in this temperature range. After 240 \({}^{\circ}\)C, ruptures occurred on the material surface. A 55.95% decrease in the mass of the samples was observed between the lowest and highest temperature values.  
The ANOVA result, which determines the mass change in the samples against the nozzle temperature, is given in Table 5. The fact that the calculated F value (3049.628) is much higher than the critical F value (2.53) and the \(p\) value is much smaller than 0.05 makes the results significant. We should reject the null hypothesis of equality of means.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of** & **Sum of** & **Degrees of** & **Mean Square (MS)** & **F-Value** & **Critical F** & \(p\)**-Value** \\
**Variation** & **Squares (SS)** & **Freedom (DF)** & & & & \\ \hline Levels & \(SS_{Level}\) & \(\alpha-1\) & \(MS_{Level}\) & \(\frac{MS_{Level}}{MS_{E}}\) & \(F_{0,05,A-1,\alpha*(n-1)}\) & \(P\Big{(}F_{a-1,\alpha*(n-1)}>F\Big{)}\) \\ Error & \(SS_{E}\) & \(\alpha*(n-1)\) & \(MS_{E}\) & & & \\ Total & \(SS_{Total}\) & \(\alpha*n-1\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The analysis of variance for a one-factor experiment.  
Figure 3: The effect of nozzle temperature on mass data.  
AM is a manufacturing method used to create 3D objects layer by layer. In this process, thermoplastic or similar materials are melted and brought together in layers through a nozzle. Nozzle temperature directly affects the fluidity and adhesion properties of the material. A higher nozzle temperature can result in the material flowing more easily and providing a better bond between layers. However, increasing the nozzle temperature may cause thermal degradation of the material after a certain point. This thermal degradation can lead to changes in the chemical structures of the material and, consequently, a decrease in the mass of the parts. In particular, this effect may be more pronounced in thermally sensitive materials. Therefore, a decrease in the mass of the parts can be observed as the nozzle temperature increases in AM.  
The mass change in the tensile test samples was not random; changing the nozzle temperature parameter affected the mass. Porous polymers are defined as polymer materials with closed spaces (pores or cells) filled with air or gas, or with dispersed gas inside. Increasing the temperature in this porous structure results in larger cells. The extruded thread expands with the increase of cells. As a result, the mass of the samples decreases with increasing nozzle temperature.