esa-sceva/satcom-chunk-collection huggingface dataset:
# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 2 Related Work
### Deep-Learning-Based Plant Disease Detection  
Demand for deep-learning (DL)-based studies is increasing due to their promising results. Big data are used for DL model training for the prediction of automated detection. Therefore, a similar study used more than 54,000 images of 14 crop diseases with 26 different diseases types. A deep convolutional neural network (DCNN) was proposed with a 99.35% accuracy achieved on the held-out test dataset. Lastly, smartphone-assisted automatic crop disease detection was proposed and an app was suggested for development in [32]. A similar big data dataset was used for plant disease detection. The open dataset of more than 87,000 images was used with 25 different plant categories. Multiple DCNN architectures were used, and the best-performing network achieved a 99.53% accuracy. The reported results showed that this tool model can be used for real-time plant disease identification [33]. Symptom-based gaps found by researchers that cover it by proposing their own CNN with a visualization technique were also missing in previous architectures.  
Modified networks for plant disease identification were applied that improved the results of [34]. In-depth features and transfer learning using famous architectures were applied on famous models' architectures. Deep-feature-based classification using SVM and other ML classifiers showed better results than those of the transfer-learning method. Moreover, the fully connected layer of state-of-the-art architectures such as VGG-16, VGG-19, and AlexNet showed better accuracy than that of other fully connected layers [35]. The images of specific conditions and various symptoms were acquired in real time, and these were missed in public datasets. To tackle this limitation, data augmentation was performed, which took a single input leaf image from multiple views that covered certain conditions on the same leaf input image. It also covered multiple diseases affecting leaves. Augmentation-based predictions increased the accuracy by 12%. Furthermore, the data limitation suggested using data augmentation in [36]: the Plant-Village dataset contained differently annotated apple black rot images. Fine-tuned DL models were trained, and the best accuracy was achieved by VGG-16, at 90.4% [37]. Different ML- and DL-based methods are shown in Table 1.  
Although DL has shown excellent results in plant disease detection, it still faces some challenges. The big data challenge compromises previous studies because they used limited data. The data limitation can be reduced by using various strategies that also produce a more confident model by covering a different aspect of a specific input sample of plant disease [44].
## 4 Discussion
### Limitations  
Limited Sample Sizes: Some studies have relatively small sample sizes, which may limit the generalizability of the findings to larger populations. Larger sample sizes would provide more robust statistical power.  
Focus on Healthy Participants: While the studies include healthy participants, there is a limited focus on individuals with specific foot conditions or injuries. Future research could explore the applicability of lacing techniques to clinical populations.  
Short-Term Assessments: The studies predominantly provide short-term assessments of the effects of lacing techniques. Long-term studies could provide insights into the sustained impact of lacing on foot health.  
Variability in Lacing Patterns: There is variability in the specific lacing patterns tested across the studies. A standardized approach to lacing patterns could facilitate clearer comparisons.  
Single-Session Assessments: Some studies involve single-session assessments, which may not capture the long-term adaptation of the foot to different lacing techniques. Longitudinal studies could address this limitation.  
One of the key limitations of the studies reviewed is the small sample sizes, with several involving fewer than 20 participants. This limits the statistical power and generalizability of the findings. Additionally, the potential for bias in these studies, due to the  
Figure 2: Comparison of lacing techniques and their biomechanical impacts.  
heterogeneity of participant demographics and the lack of long-term follow-up, should be critically addressed in future research. While one of the included studies focuses on healthy adults, caution is needed in generalizing these findings to populations with specific health conditions, such as diabetes or musculoskeletal disorders. These populations may experience unique biomechanical responses due to factors like altered foot sensitivity, muscle weakness, or compromised joint stability. Future research should explore how lacing techniques impact individuals with these conditions, to provide more targeted recommendations.  
The limited number of studies (four) included in this review may affect the generalizability of the findings. The small sample size restricts the ability to draw broad conclusions, as the variability in study designs, populations, and outcome measures introduces a heterogeneity that could impact the robustness of the results. Additionally, differences in methodologies--such as variations in the pressure measurement techniques, participant characteristics, and specific lacing patterns analyzed--limit direct comparisons across studies. This variability suggests that while the initial findings on the biomechanical impact of lacing techniques are promising, further research with larger and more standardized samples is necessary, to confirm these effects and enhance their applicability across diverse populations.
## 5 Results and Discussion  
The proposed RO-PUF is verified using ten Intel Cyclone V chips (5 CSEMA5F31C6 and 5 CSEMA4U23C6) in the DE1-SoC and DE0-Nano board. Quartus is used to create and verify the design before downloading it onto the FPGA chip. The performance is analyzed over various routing densities of ROs and patterns. The study further evaluates the shift of performance by applying scenarios. The proposed RO-PUF can be applied to other FPGA chips or platforms with few modifications. The use of direct-link interconnects to guarantee routing equality may be replaced by direct connections or direct lines in Xilinx chips [45]. Another adjustment is that the counter size should be fitted to the maximum number of available flip-flops inside a LAB or configurable logic block (CLB) or to use a ripple counter when a counter is realized with more than one CLB.  
Figure 4: The relation between wire utilization and routing hotspots.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 3 Materials and Methods
### Proposed Framework for Natural Scene Classification
#### 3.2.1 Data Pre-Processing  
This process included tasks such as data augmentation, normalization, and train-test splitting. Before training the deep-learning model, we performed the pre-processing.  
_Data Augmentation:_ We used data augmentation to expand the size of the dataset while reducing overfitting. We randomly applied transformations such as rotation, scaling, and flipping to the images.  
_Normalization:_ We normalized the image pixel values to have a zero mean and unit variance. This contributed to faster convergence during training.  
_Splitting the Dataset:_ The dataset was separated into training and testing sets with an 80:20 train-test split. Training data were further divided into training and validation subsets. While training the model, the validation set was used for hyper-parameter tuning and to avoid overfitting, and the testing set was used to calculate the model's overall performance.
# Toxic Metals Migration from Plastic Food Contact Materials in Romania: A Health Risk Assessment
## 3 Results
### Health Risk Analysis
#### 3.2.2 Non-Carcinogenic Risk Analysis  
The assessment of non-carcinogenic risk was carried out by estimating the hazard quotient (HQ) and the hazard index (HI) for adults, using ingestion as the route of exposure.  
The hazard quotient (HQ) is a risk index developed by the U.S. EPA to assess potential health risks from exposure to chemical contaminants. The use of HQ provides an in-depth understanding of the possible health effects of chemical exposure, helping to create effective risk management strategies [21]. HQ values for ingestion obtained in our study are presented in Table 7.  
HQ values for metals in adults were less than 1, decreasing in the following order: Sb > Ni > Fe > Zn > Cu > Mn > Co > Ba > Li > Pb > Cd. These findings indicate that chronic exposure to the tested metals may pose a non-negligible non-carcinogenic risk. Table S4 presents the HQ values for all tested samples and elements.  
HI is an essential parameter to evaluate the overall health impact and safety of the tested samples [17]. The HI values obtained in our study ranged from 7.27 \(\times\) 10\({}^{-6}\) to 1.46 \(\times\) 10\({}^{-1}\) (Table S4). The cumulative hazard index of the 30 samples was found to be 6.82 to 137.552 times lower than the threshold value of 1, indicating that no adverse non-carcinogenic health effects are expected from exposure to the tested contaminants through food intake [21].  
Figure 1: The percentage contributions of hazard quotient of tested elements to the hazard index.  
As shown in Figure 1, Sb had the highest contribution to the hazard index (26.92%), followed by Ni (22.48%), Fe (16%), Zn (9.11%), Cu (8.90%), Mn (7.96%), Co (6.51%), Ba (1.70%), and Li (0.41%). Since Pb, Cd, and Cr were not detected in the food simulants, these elements did not contribute to the hazard index value.  
The results for average daily dose, hazard quotient, and hazard index indicate that the risk associated with the migration of toxic metals during long-term storage at 40 \({}^{\circ}\)C is low and negligible.
# Progress and Prospect of Saline-Alkaline Soil Management Technology: A Review
## 2 Traditional Management Techniques and Methods
### Phytoremediation
Its root system not only absorbs water, but also takes up salts from the soil, which are subsequently expelled through irrigation and drainage systems. This process effectively reduces soil salinity levels [63]. A notable example is the study by [PERSON] et al. [64], who integrated hybrid rice cultivation with saline-alkaline land management. They cultivated the two-line hybrid rice variety \"Chao you 1000\" in fields with soil salinity ranging from 0.2% to 0.6%, achieving a yield of 12.04 t/ha. This demonstrates the potential of rice cultivation as a sustainable approach to saline-alkaline soil reclamation. In areas with highly mineralized saltwater, another effective strategy involves the extraction of shallow saltwater for aquaculture, such as shrimp farming. This approach not only maximizes the utilization of groundwater resources, but also contributes to reducing salinity levels within the water body through the biological activities of aquatic organisms. Over time, this process can improve the groundwater environment, making it more suitable for agricultural and ecological purposes. Additionally, the growth and development of plants in such environments involve the absorption of soil nutrients, salts, and alkalis, which are subsequently stored in plant tissues. Harvesting these plants removes inorganic salts from the soil, further aiding in soil reclamation [23]. Phytoremediation offers multiple advantages, including cost savings compared to chemical amendments, improved soil structure and enhanced plant nutrient availability [65]. It also promoted environmental benefits such as carbon sequestration. While these methods exhibit strong environmental sustainability, it is important to note that the improvement of saline-alkaline soils is a gradual process. These approaches require sustained, long-term investments in planting, management, and infrastructure. Through careful planning of crop rotations, continuous field management, and the integration of aquaculture, soil salinity can be progressively reduced, and soil quality enhanced. This creates more favorable conditions for plant growth and long-term agricultural productivity. Consequently, these methods are particularly suitable for saline-alkaline land management projects where stakeholders are committed to long-term efforts and resource allocation.
Sensors_**2009**, \(9\), 8776-8809; doi:10.3390/s91108776  
_Article_  
**Feedback Power Control Strategies in Wireless Sensor Networks with Joint Channel Decoding**  
**Andrea Abrardo \({}^{1}\), Gianluigi Ferrari \({}^{2,\star}\), Marco Martalo \({}^{2}\) and Fabio Perna \({}^{1}\)**  
\({}^{1}\) Department of Information Engineering, University of Siena, Italy; E-Mails: abrardo@dii.unisi.it; perna@unisi.it  
\({}^{2}\) Wireless Ad hoc and Sensor Networks (WASN) Laboratory, Department of Information Engineering, University of Parma, Italy; E-Mail: martalo@tlc.unipr.it  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: gianluigi.ferrari@unipr.it; Tel.: +39-0521-906-513; Fax: +39-0521-905-758.  
_Received: 15 October 2009; in revised form: 22 October 2009 / Accepted: 23 October 2009 / Published: 3 November 2009_  
**Abstract:** In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources and joint channel decoding (JCD). In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a classical feedback power control strategy, which aims at equalizing all link SNRs at the access point (AP), and (ii) an innovative optimized feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest overall transmit energy consumption. These strategies will be referred to as \"balanced SNR\" and \"unbalanced SNR,\" respectively. While they require, in principle, an unlimited power control range at the sources, we also propose practical versions with a limited power control range. We preliminary consider a scenario with orthogonal links and ideal feedback. Then, we analyze the robustness of the proposed power control strategies to possible non-idealities, in terms of residual multiple access interference and noisy feedback channels. Finally, we successfully apply the proposed feedback power control strategies to a limiting case of the class of considered multiple access schemes, namely a central estimating officer (CEO) scenario, where the sensors observe noisy versions of a common binary information sequence and the AP's goal is to estimate this sequence by properly fusing the soft-output information output by the JCD algorithm.  
keywords: sensor networks; source correlation; feedback power control; joint channel decoding (JCD); serially concatenated convolutional codes (SCCCs); low-density parity-check (LDPC) codes; multiple access interference; noisy feedback; central estimating officer (CEO) problem; fusion
# A Simple Experimental Stand for Studying the Network Security Level of Power Electronic Devices
## 4 Proposed Experimental Testbed and Methodology to Study the Network Security Level of a PED
### Developed Methodology  
Figure 9 presents the developed methodology.  
The first stage of the study is finding out of open ports. This is an important study because every hacker always does this check first. Excess open ports are like open doors  
Figure 8: Topology of the experimental stand.  
Figure 9: Developed methodology.  
through which anyone can enter. Therefore, this vulnerability should be eliminated. To eliminate it, we need to know which ports are open and close the unnecessary ones.  
The next stage is to analyze the PED for well-known network vulnerabilities. In this stage, the PED will be subjected to special analysis through specialized scripts using a special tool. Through these scripts the PED is tested to find out if its embedded web server is vulnerable to some of the well-known vulnerabilities. After the execution of each script, the used tool presents an analysis report of the test performed.  
Step three of the proposed methodology is to carry out a study whereby it can be found out whether the information exchanged between the PED and the user/control and management center is secure or not--whether the transmitted data are encrypted or transmitted in plain text;  
Step four of the proposed methodology is carrying out penetration tests. Different attacks will be applied on the studied PED to find out how these attacks affect the PED performance. In this work, the PED will be subjected to different TCP DoS attacks to examine how different TCP DoS attacks affect the performance of the studied PED.  
The last stage is analysis of the results and recommendations. In this stage, the obtained results from the previous stages will be analyzed and, if necessary, recommendations will be proposed to enhance the network security of the studied PED.
# Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach
## 4 Results and Discussion
### Feature Importance Analysis
While our feature importance analysis provided valuable insights, it is important to discuss why certain features, such as renewable energy share and CO\({}_{2}\) emissions, received relatively low importance scores. Several factors may have contributed to this: (1) Time lag effects: The impact of changes in renewable energy share or CO\({}_{2}\) emissions on carbon tax prices may not be immediate. There could be a significant time lag between changes in these factors and their reflection in carbon tax policies, which our current model might not fully capture. (2) Indirect influences: These features may have indirect effects on carbon tax prices through their impact on other variables, such as GDP or energy consumption. Their influence might be partially absorbed by these intermediate variables in our model. (3) Policy inertia: Carbon tax policies may be slow to respond to changes in renewable energy adoption or emissions levels, due to political and economic considerations, reducing the direct observable impact of these factors on short-term price predictions. (4) Data granularity: Our current dataset may not have sufficient granularity or frequency of updates for these features to fully show their importance in carbon tax price dynamics. It is also worth considering how additional features could be incorporated in future versions of our model to enrich the analysis: (1) Technological advancements: Incorporating indicators of clean technology innovation, such as patents in renewable energy technologies or investment in clean tech R&D, could provide valuable insights into future carbon pricing trends. (2) International policy changes: Including variables that capture major international climate agreements or policy shifts in influential countries could help the model account for global policy trends that may impact domestic carbon tax decisions. (3) Public opinion data: Incorporating metrics of public sentiment towards climate change and carbon pricing could help capture the societal pressures that influence policy decisions. (4) Energy market dynamics: More detailed data on energy prices, particularly the price gap between renewable and fossil fuel energy sources, could provide additional context for carbon tax price predictions. (5) Climate change indicators: Including data on extreme weather events or climate change impacts could help capture the urgency driving carbon pricing policies. Incorporating these additional features in future iterations of our model could potentially improve its predictive power and provide a more comprehensive understanding of the factors influencing carbon tax prices. However, this would also increase the model's complexity and data requirements, necessitating careful consideration of the trade-offs between model sophistication and practical applicability.
# Fuzzy Logic-Based Controller for Bipedal Robot
## 4 The IDPD, IDPID Controller for Bipedal Robot
### IDPD Controller  
Assuming that the robot dynamical model was fully and accurately determined and that the motion trajectory was designed to ensure the relative equilibrium condition, ignoring noise and external forces, the dynamic model applied to the IDPD controller is the differential equations of motion of the robot (7). Applying the control law IDPD, the system of equations to determine the control force/moment is shown as follows:  
\[U_{(7\times 1)}=M(q)_{(7\times 7)}u_{(7\times 1)}+\psi(q,\dot{q})_{(7\times 1 )}+G(q)_{(7\times 1)}+Q(q)_{(7\times 1)};\quad U_{1}=0 \tag{11}\]  
Here, \(u\) is determined based on the proportional derivative control law (PD) as follows:  
\[u_{(7\times 1)}=\ddot{q}_{d(7\times 1)}+K_{v(7\times 7)}\dot{e}_{(7\times 1 )}+K_{p(7\times 7)}\dot{e}_{(7\times 1)} \tag{12}\]  
\[\left\{\begin{array}{l}e_{(7\times 1)}=[e_{1},e_{2},\ldots,e_{7}]^{T};\qquad e _{i}=q_{di}-q_{i}\\ \dot{e}_{(7\times 1)}=\left[\dot{e}_{1},\dot{e}_{2},\ldots,\dot{e}_{7}\right]^{T}; \qquad\dot{e}_{i}=\dot{q}_{di}-\dot{q}_{i}\end{array}\right. \tag{13}\]  
\[\left\{\begin{array}{l}K_{p(7\times 7)}=diag\{k_{P1},k_{P2},\ldots,k_{P7}\};\;k_{Pi }>0,\;(i=2,\ldots,7),\;k_{P1}=0\\ K_{V(7\times 7)}=diag\{k_{V1},k_{V2},\ldots,k_{V7}\};\;k_{Vi}>0\;(i=2,\ldots,7),\;k_{V1 }=0\end{array}\right. \tag{14}\]  
In which:  
\(q_{di},\ddot{q}_{di},\ddot{q}_{di}(i=1,\ldots 7)\) are the desired designed joint coordinates and their time derivatives, respectively; \(q_{i},\dot{q}_{i},\ddot{q}_{i}(i=1,\ldots 7)\) are the joint coordinates obtained by the control and their first and second derivatives with respect to time, respectively; \(k_{Pi}\), \(k_{Vi}\) are the proportional and derivative gain coefficients, respectively; \(K_{P}\), \(K_{V}\) are diagonal matrices. Since the coordinates \(q_{1}\) are not controlled, \(U_{1}=0\); for simplicity, we set \(k_{P1}=0\), \(k_{V1}=0\).  
Substituting (12)-(14) into (11) to find out the system of canonical equations of the controller is shown as follows:  
\[\ddot{e}_{(7\times 1)}+K_{V(7\times 7)}\dot{e}_{(7\times 1)}+K_{p(7\times 7 )}e_{(7\times 1)}=0 \tag{15}\]