esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Effects of Plasma Pretreatment on the Microstructure of Peanuts
## 3 Results and Discussion
### Impact of Plasma Treatment on the Appearance and Structure of Peanut Embryos
#### 3.2.3 Impact of Plasma Treatment Gas Type on the Appearance and Structure of Embryos  
The results are presented in Figure 6. Figure 6c,e,g are SEM images at 500\(\times\) magnification, corresponding to plasma treatments with helium (He), argon (Ar), and oxygen (O\({}_{2}\)), respectively, at 200 W for 60 s. The structural changes observed in Figure 6c,d are consistent with the effects on peanut embryos at the same power level. As depicted in Figure 6e, the embryo surface exhibits tight arrangement, intensified etching, and significant overlapping wrinkles. Exuding lipid particles primarily accumulate in cell concavities and cracks, while relatively fewer protein particles and polymers escape to the surface or may be covered by lipids. In Figure 6g, the embryo surface texture appears chaotic, but cell structures remain clear. The depth of cross-linked concavities increases, and wrinkle severity intensifies. Micromolecule lipid particles stack up, resulting in the exudation of more protein particles and starch granules compared to Figure 6e,g, along with finer and more dense cracks.  
This is attributed to the interaction between abundant active species generated by the plasma and compounds on the peanut embryo surface. Three mechanisms--depolymerization, cross-linking, and etching--likely contribute to the surface modifications [39]. Helium (He), being a stable inert gas, experiences minimal ionization at this voltage, resulting in minor etching on peanut embryos. In contrast, argon (Ar) ionizes into high-energy Ar\({}^{+}\) ions and electrons, leading to depolymerization of macromolecule protein particles on the embryo surface and structural changes. The micromolecule protein particles and lipid particles formed by the decomposition of polymers are exposed on the embryo surface. Unlike He and Ar, oxygen (O\({}_{2}\)) ionization generates abundant reactive oxygen species (such as O\({}_{2}\)+, O\({}^{-}\), O\({}^{+}\), O\({}_{3}\), and H\({}_{2}\)O\({}_{2}\)) [16]. Even at low voltage, O\({}_{2}\) treatment induces significant oxidation, causing bond cleavage and chemical modifications on side chains, ultimately altering protein structures [35]. Therefore, O\({}_{2}\) treatment can also lead to etching and oxidation on peanut embryo surfaces.  
Figure 5: Appearance of peanut embryos under scanning electron microscope after different treatment durations. Note: The left column shows SEM images at 500\(\times\) magnification. (**a**) Corresponds to the control group peanuts. (**c.e,g.i,k**) Correspond to peanuts treated with cold plasma for 30 s, 60 s, 90 s, 120 s, and 150 s, respectively. The right column shows SEM images at 2000\(\times\) magnification. (**b**) Corresponds to the control group peanuts. (**d,f,h,j,l**) Correspond to peanuts treated with cold plasma for 30 s, 60 s, 90 s, 120 s, and 150 s, respectively.  
Figure 6: _Cont._
# EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
## 3 Bounded Number of Data Replication in Message Passing
### An Example  
In this part, we will show that data replication and the bounded number of message copies are quite useful in accelerating the information spread in a specific area. On the one hand, they can improve upon convergence speed of the network and realize network equilibrium. Besides, when compared with the basic broadcast mechanism, giving a restriction on the number of message copies helps to reduce redundant data dissemination. To explain how the bounded number of data replication works, an example is given as below.  
We consider three different scenarios of VANETs, urban environment, highway and parking lot, which respectively correspond to arbitrary graph, linear graph and complete graph. The way that the mechanism works in different scenarios is illustrated separately.  
Case 1: Assume in the common urban environment that the traffic can be described as arbitrary graph. If a vehicle carries message \(M\), it needs to spread the message to its neighborhood district. Assume the total number for which the message can be replicated is \(n=100\) and the number of vehicles in this area is \(20\). The number of messages can be copied by each vehicle is denoted by \(n_{i}\). We have \(n_{1}=100\) and \(n_{j}=0\) for \(j>1\). Then, Vehicle 1 with \(n_{1}=100\) communicates with a vehicle \(j\) within its communication area and \(n_{j}=0\), and then, the message parameters of the two vehicles take the average, with \(n_{1}=n_{j}=\frac{n_{1}+n_{j}}{2}=50\). The rest may be deduced by analogy. The ideal result is that each vehicle in this area carries message \(M\) with \(n_{i}=5\). Until then, the network enters into a balanced status.  
Case 2: Assume the message passing happens on the highway, where the traffic can be described as linear graph. We keep the same parameters. It takes less average operations than arbitrary graph for the network to enter into a balanced status.  
Case 3: Assume the message passing happens in a parking lot. As there are many vehicles in the parking lot, we consider the traffic as a complete graph. Then, there might be many vehicles in one vehicle's communication range. When the vehicle that carries message \(M\) with \(n_{i}=100\) receives more than one communication request, it chooses the vehicle with \(n_{j}\) if the gap between \(n_{i}\) and \(n_{j}\) is the largest. We hope that every vehicle in the neighborhood area receives message \(M\) and holds parameters with \(|n_{i}-n_{j}|\leq\epsilon\), which means the network enters into a \(\epsilon\)-balanced status and the network equilibrium is realized.
# Chosen Plaintext Combined Attack against SM4 Algorithm
## 2 Preliminaries
### Differential Characteristics of SM4 Algorithm S-box  
In reference [11], a differential fault attack based on random bytes was proposed for SM4 by using the S-box differential characteristics of SM4. The differential characteristics of S-box are described as follows. For the SM4 algorithm, let \(A_{i}=(a_{0j},a_{1j},a_{2j},\)\(a_{3j})\) as the s-box input of round \(I\), \(B_{i}=(b_{0j},b_{1,i},b_{2j},b_{3j})\) as the s-box output of round \(i\), and \(C_{j}=(c_{0j},c_{1j},c_{2j},c_{3j})\) (\(i=1,2,\ldots\) 32) as the output of L transformation in the \(i\) th round, where \(a_{j,i},b_{j,i},c_{j,i}\in Z_{2}^{8}\), \(j\in\{0,\ldots,4\}\) and \(i\in\{0,\ldots,31\}\). At the same time, let \(\triangle A_{i}=(\triangle a_{0j},\triangle a_{1j})\triangle a_{2j}\triangle a _{3j}\) as the input difference of S-box in round  
\(Z_{2}^{8}\). For \(j\in\{0,\ldots,4\}\) and \(i\in\{0,\ldots,31\}\), the set \(\Phi(\bigtriangleup a_{j,i},\bigtriangleup b_{j,i})\) is defined to satisfy: \(\Phi(\bigtriangleup a_{j,i},\bigtriangleup b_{j,i})=\big{\{}\big{|}x_{j,i} \in Z_{2}^{8}\big{|}Sbox(x_{j,i})\oplus Sbox(x_{j,i}\oplus\bigtriangleup a_{j,i })=\bigtriangleup b_{j,i}\big{\}}\), that is the set of input values of S-box when the difference between input and output of S-box in round \(i\) is \(\bigtriangleup a_{j,i}\) and \(\bigtriangleup b_{j,i}\) respectively. In addition, let the inverse transform of L be \(L^{-1}\). If \(\bigtriangleup A_{i}\) and \(\bigtriangleup C_{i}\) are known, \(\bigtriangleup B_{i}\) can be derived through \(\bigtriangleup B_{i}=L^{-1}(\bigtriangleup C_{i})\), and \(\Phi(\bigtriangleup a_{j,i},\bigtriangleup b_{j,i})\) can also be constructed. As mentioned in reference [11], if \(\Phi(\bigtriangleup a_{j,i},\bigtriangleup b_{j,i})\) is a non-empty set and the attacker knows \(\bigtriangleup a_{j,i}\) and \(\bigtriangleup b_{j,i}\), then \(x_{j,i}\) has at most 4 known candidate values. Moreover, the probability is 99.2% when there are 2 candidate values, and the probability is 0.8% is when there are 4. Based on the above differential characteristics, it can be seen that the input difference and output difference values of two pairs of different S-boxes need to be known to recover the round key of SM4 algorithm by using the differential characteristics.
## 4 Results
### Findings per Technical Debt Type
#### 4.3.1 Requirements Debt  
Definition: Problems related to the elicitation, specification, or management of requirements in AI competition platforms, often due to the evolving and data-driven nature of AI systems.  
Problem: Requirements in AI competitions are frequently underspecified or ambiguous. Traditional RE methods fall short in expressing goals such as explainability, fairness, or dataset constraints.  
Example: In a deep learning competition for medical imaging, vague requirements such as \"maximize Area under the Curve (AUC)\" may lead teams to overfit on artifacts, as organizers fail to define distributional shifts or minimum documentation standards.  
Stakeholder: Organizer  
Severity Score: 5--Critical impact on fairness, reproducibility, and outcome validity.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 3 Method
### BPSK Modulation  
Figure 6 shows the baseband digital signal along with the transmitted and received BPSK signals. The carrier frequency was chosen as 40 kHz. A 180\({}^{\circ}\) phase change was imposed when the symbol changed from 1 to 0 or 0 to 1. As shown in Figure 6c, there was a small \"bump\" when the symbol changed. The shape of the received BPSK signal was sinusoidal. Figure 7 is an enlargement of the 1  
Figure 4: (**a**) Baseband digital signal at 500 bps; (**b**) Carrier signal at 40 kHz.  
Figure 5: (**a**) Simulation of on-off keying (OOK) signals with 40 kHz carrier frequencies; (**b**) Transmitted OOK signal; (**c**) Received OOK signal in air environment.  
to 0 transition in the transmitted (Figure 7a) and received signals (Figure 7b). It can be seen that the phase of the transmitted signal rapidly changes with the digital signal. The received signal is obtained after the transmitted signal passes through the propagation medium and both ultrasonic transducers. In Figure 7b, it can be seen that the received signal remains in-phase with the reference carrier for some time, and the phase then changes along with an amplitude reduction. Finally, the amplitude recovers when a phase change of 180\({}^{\circ}\) is achieved. This causes the \"bump\" in the received signal.
## 5 Applications and Challenges
### Applications
#### 5.1.3 AR Display  
The core principle of AR optical devices is to project computer-generated virtual information (e.g., 3D models, text, etc.) into the user's field of view by superimposing it in real time with the real scene using an optical display device through an optical system, forming a visual effect of fusion of reality and virtual reality [87; 88; 89]. With virtual-reality symbiosis and real-time interaction, it has great industry, education, and consumption potential. [PERSON] et al. [84] proposed a tuneable planar liquid crystal Alvarez lens based on a DEA, as shown in Figure 13d. They succeeded in dynamically projecting the virtual character's light to depths of 55 cm, 65 cm, and 80 cm in the verification of the AR display. The virtual character 'light' was projected to a depth of 55 cm, 65 cm, and 80 cm in the verification of the AR display, which is a good example of the AR display. 65 cm, 65 cm, and 80 cm in depth, and achieve clear multi-plane fusion with real objects, which provides a new idea for lightweight AR devices. With the advantages of miniaturisation and fast response, dielectric elastomer-driven AR optical devices are expected to be applied in intelligent navigation, virtual meetings, and interactive education scenarios, bringing innovative experiences to various fields. In the future, DETL needs to continue its research in system integration, multisensory coordination, and material performance enhancement.
## 5 Conclusions  
This study optimized the minimum detectable difference (MDD) of gamma camera SPECT images through an indigenous V-shaped slit gauge and the Taguchi methodology. The eighteen groups of factor combinations were organized according to Taguchi's unique orthogonal array and ranked by three well-trained radiologists to derive the signal-to-noise ratio and obtain the fish-bone plot of average, stdev, and S/N, respectively. Then, the MDD was proposed to derive further analysis of the SPECT from gamma camera scanned images. The quantified MDD was 8.4, 7.9, and 7.1 mm for the second group of original L\({}_{\rm 18}\) preset, conventional, and the optimal preset, respectively, in this study. The fine MDD is beneficial in evaluating the spatial resolution than line pair/cm in routine quality control. However, these two indexes were defined according to different scenarios and could be converted via a simple equation. The clinical examination proved the optimal preset of a gamma camera to have a sharp delineation. The application of MDD to justify the imaging quality in clinical nuclear medicine is expected.  
Conceptualization, L-F.P. and L-K.P.; methodology, W.-J.L.; software, C.-H.K.; validation, B.-R.P., C.-H.K. and W.-J.L.; formal analysis, W.-J.L.; investigation, B.-R.P.; resources, W.-J.L.; data curation, B.-R.P.; writing--original draft preparation, L.-F.P.; writing--review and editing, L.-K.P.; visualization, L.-F.P.; supervision, L.-K.P.; project administration, B.-R.P.; funding acquisition, L.-K.P. All authors have read and agreed to the published version of the manuscript.  
Figure 7: The optimal preset yielded clearer images with greater contrast than the conventional preset. Specifically, the shadow of the ribs and pelvis could be identified, and the sacroiliac joint could also be delineated from the optimal figure.  
**Funding:** The authors highly appreciate the financial support of this study by the Ministry of Science and Technology of the Republic of China under contract No. MOST 110-2221-E-166-001 and the Taichung Armed Forces General Hospital in Taiwan (Contract No. 109A23).  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the Buddhist Dalin Tuzchi Hospital (B11002019 approved on 21 May 2021).  
**Informed Consent Statement:** Written informed consent has been obtained from the patient(s) to publish this paper.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion  
In this study, we compared the proposed residual network model with self-attention mechanisms to several common sequence models, including LSTM, GRU, ResNet, and CNN. LSTM and GRU are widely used in sequence modeling tasks, while ResNet and CNN are popular deep learning architectures for image processing and classification. Compared to these models, our proposed model achieved higher accuracy in all ventilation categories, especially for cooling ventilation, where it reached 99%.  
One of the advantages of the proposed model is its ability to capture the temporal and spatial dependencies in the ventilation data, which is important for accurately identifying different ventilation categories. Unlike the other models, our proposed model incorporates the self-attention mechanism, which allows it to focus on important features and enhance their representation. This mechanism also enables the model to learn more complex relationships between the inputs and outputs, which is crucial for achieving high accuracy in the ventilation classification task.  
However, there are still some limitations that need to be addressed in future work. The model exhibits high computational complexity due to its complex network structure and the inclusion of self-attention mechanisms within the residual block structure, especially considering the input sequence length of 425 and the computational demands of self-attention. In some cases, this high computational complexity may be even higher. The current model training and evaluation times are excessively long. The average training time exceeds 23 min and 16 s, while the evaluation time is approximately 1 min and 16 s. Moreover, the dataset used in this study only covers a certain range of grain conditions and ventilation scenarios, which may not fully represent real-world situations.  
Overall, our proposed model provides a promising approach to the development of more accurate and efficient ventilation control systems for grain storage by leveraging the principles of computer modeling and self-attention mechanisms. The model has demonstrated superior performance compared to several commonly used models, and future work can further improve its robustness and efficiency.  
Figure 12: Model evaluation chart under different learning rates. (**a**) Training loss curve. (**b**) Training accuracy curve.
## 3 Results and Discussions
### Phenolic Profile of Cassava Root Flour Naturally Fermented Using Different Times (mg/g)
The control sample starch granules remain intact, since there was no fermentation of the sample. Cassava starch granules are typically spherical to oval, with a size range of 5-35 \(\upmu\)m (average \(\sim\)15 \(\upmu\)m) [50], although the diameter was not measured in this study. Their smooth surface and truncated shape (Figure 1A) distinguish them from other starches like maize or potato. After 12 h (Figure 1B), significant morphological changes are observed. The starch granules lose their smooth, oval appearance and begin to exhibit surface irregularities, with some granules appearing shrunken or collapsed. This suggests the onset of structural degradation, possibly due to enzymatic activity or microorganism actions. There was a gradual disintegration of granules. The naturally fermented cassava root flour showed that the degradation process of the starch granules was intensified (Figure 1C). The starch granule surface becomes more irregular, since the enzymes and the microorganism have more resident time to break down bonds and work on the starch granules. The disintegration of the starch granules and other components of the sample (naturally fermented cassava root flour) continued at 48 h of fermentation (Figure 1D). The complete loss of structural integrity indicates advanced degradation, likely due to the combined effects of microbial activity and enzymatic actions. At this stage, the starch granules are broken into smaller parts due to the breaking of bonds (\(\alpha\)-1,4 and \(\beta\)-1,6 glycosidic bonds). Fermenting the cassava root increases the digestion rate and promotes good health [5]. Therefore, it aids the prebiotic potential for carbohydrates. These findings are in line with the report of [PERSON] et al. [17], who fermented cassava starch for 72 h.  
Figure 1: Scanning electron microscopy images showing the effects of natural fermentation (with different fermentation times) on cassava root flour. 0 h is the control (**A**), 12 h is 12 h of fermentation (**B**), 24 h is 24 h of fermentation (**C**), and 48 h is 48 h of fermentation (**D**). The SEM images reveal a progressive decline in cassava root flour's granule integrity over 48 h, from intact starch granules at 0 h to severely degraded structures by 48 h. These findings emphasize the susceptibility of cassava root flour granules under suboptimal conditions and provide a foundation for improving its cultivation in Africa, where food insecurity is an ongoing problem. Further studies are needed to correlate these morphological changes in cassava root flour granules with those of other root crops.
## 4 Discussion
### Variations of WUE  
Our analysis showed that the WUE exhibited a decreasing trend from northeast to southwest, which is consistent with the previous research results [15; 58]. The WUE of vegetation in Inner Mongolia showed some seasonal variability. Among the four seasons, the highest WUE was observed in summer, followed by autumn and winter, while the lowest WUE was observed in spring (Figure 6), which is inconsistent with the findings of [PERSON] et al. [59], who discovered that WUE is the highest in summer, followed by autumn, spring, and winter. These findings were most likely attributable to the seasonal differences in GPP and ET. Seasonal GPP values followed the order summer > autumn > spring > winter. Seasonal ET values followed the order summer > spring > autumn > winter. As the temperature rises in spring, snow or ice covering the land surface begins to melt, resulting in a significantly higher ET in spring than in autumn and winter [52]. In the meantime, vegetation would only be in the green-up stage in spring, with lower productivity [60]. Therefore, ET would have a significantly stronger effect than GPP, thus leading to the lowest WUE in spring.  
The mean values of GPP, ET, and WUE differed across various ecosystems. Our analysis shows that the broadleaf forest and coniferous forest had higher mean values of GPP, ET, and WUE than other vegetation types, but their contributions to GPP, ET, and WUE IAV were generally lower than those of typical steppe. This may be attributed to physiological properties, area proportion, and survival environment condition of vegetation types. The typical steppe contributes the most to GPP, ET, and WUE IAV in arid and semi-arid regions, which indicates that typical steppe ecosystems play an important role in arid and semi-arid ecosystems. The typical steppe in the study area is vast (23.36%), with relatively dense vegetation that allows for greater photosynthesis and efficient water exchange [58; 61], which has resulted in the highest contribution to GPP, ET, and WUE IAV. Cropland was the second most important contributor to WUE IAV. The rational use of irrigation and fertilization techniques, as well as the advancement of tillage techniques and crop types, all contribute to a significant increase of greening (GPP), which aids in the improvement of WUE on cropland [62; 63]. By contrast, the typical steppe ecosystem is more efficient than the forest at enhancing the use of water by vegetated ecosystems in arid and semi-arid zones.  
In addition, WUE variations in Inner Mongolia were found to be mainly controlled by GPP (accounting for 83.82% of all pixels) and spread in the central and western regions; this finding agrees with that of [PERSON] et al. [21] and [PERSON] et al. [61]. However, [PERSON] et al. [64] and [PERSON] et al. [7] noted that GPP dominated WUE variations in humid regions, while ET dominated WUE variations in arid regions. This discrepancy could be attributed to the differences in analysis methods.
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 5 Experimentation and Results
### Effect of Cache Size  
This section investigates the performance of different caching schemes across various router cache sizes, defined as the number of content items. Figure 4 presents the caching performance of GNN-DDQN, MLP-DDQN, LCD, PROB_CACHE, LCE, and CL4M under different caching scenarios. The router cache sizes ranged from 1 to 4, while the number of content items was fixed at 1000.  
GNN-DDQN exhibited a substantial performance advantage over MLP-DDQN when the cache size was limited to one item. For cache sizes of two and four, GNN-DDQN and MLP-DDQN performed similarly. However, when the cache size was set to three items, GNN-DDQN outperformed MLP-DDQN by achieving an 11.87% higher CHR, 3.57% lower ALT, 1.54% APS, and 2.20% lower ALL.  
Significantly, regardless of the router cache size, GNN-DDQN consistently reduced the latency time by at least 14.96%, 29.88%, 92.20%, and 76.37% compared to LCD, PROB_CACHE, LCE, and CL4M, respectively. The advantages of GNN-DDQN stem from its ability to predict popular content in advance and proactively cache them.
## 3 Results and Discussion
The \(x\) = 0.9 sample demonstrates EM absorption properties optimized for the X-band (8-12 GHz) with the lowest reflection loss (RL) of \(-\)45 dB and satisfying RL \(<\) -15 dB in the 8-13 GHz range, whereas the sample with \(x\) = 0.8 shows an excellent Ku band (12-18 GHz) absorption performance, satisfying RL \(<\) -19 dB in the 11-18 GHz range.