esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Underserved and Remote Areas with Limited Connectivity
#### 5.10.3 Evaluation of the Existing Solutions  
Offline AI models are particularly effective in remote areas with unstable or no internet connection, as they can operate independently. However, they may face limitations in processing power and storage capacity, and data upload may be delayed [168]. Data compression and efficient transmission technologies allow effective data transfer under limited network conditions but may lead to information quality loss and increased computational resource consumption [169]. Lightweight AI models are suitable for resource-constrained environments but may not match the performance and accuracy of more complex models, requiring careful optimization to meet specific needs [170]. Low-bandwidth optimization methods can effectively transmit critical data in bandwidth-limited scenarios but may increase system complexity and computational overhead, and require ongoing monitoring and adjustment to adapt to changing network conditions [171]. In summary, the effectiveness of these methods depends on the specific application scenario and resource constraints. In underserved and remote areas, offline AI models and lightweight AI models offer good local processing capabilities, while data compression and low-bandwidth optimization techniques improve the feasibility of data transmission. The choice of the appropriate method should be based on specific needs, combining the strengths of different technologies to maintain data processing and transmission efficiency and reliability under various network conditions.
## 3 Results and Discussion
### Effect of the AgNPsDose  
The AgNPs' dosage effect on the adsorption of Cu\({}^{2+}\) was studied. As presented in Figure 6a, when the AgNPs' dosage increased, the Cu\({}^{2+}\) adsorption increased. Specifically, the percentage of Cu\({}^{2+}\) adsorption increased from 31% to 91% with a 10 mg/L concentration and from 23% to 73% with a 50 mg/L concentration. The increase in the percentage of Cu\({}^{2+}\) adsorption was due to the increase in the area of the AgNPs' surface and the consequent increase in the number of active sites and their availability for ion and electronic exchanges with the Cu\({}^{2+}\) ions that were dissolved in the aqueous solutions [25,26].
## 1 Introduction
### Motivation  
To the best of our knowledge, there are few deep-learning models specialized for leaf disease identification and detection. Existing models such as VGG, ResNet, YOLO, and Faster R-CNN are directly utilized, or with minor modifications. In other words, the TL approach dominates leaf disease identification and detection [15; 16; 17; 18; 19; 20; 21; 37; 38]. Unlike popular datasets such as ImageNet [1] and VOC2007 [26], which have been adopted for conventional computer vision problems such as classification and object detection, leaf disease datasets have characteristics in that there is a spatial region in which leaf diseases exist.  
Figure 1 illustrates an example of an apple leaf with diseases. Leaf diseases solely exist in the leaves, whereas the background has no information about them. Therefore, it is essential to find the spot area (i.e., the diseased area), and extract spot features to identify leaf diseases. These spot features are informative and discriminative and play a crucial role in leaf disease identification and detection. Based on this observation, this study reveals how to extract informative and discriminative spot features from input leaf images. A novel leaf spot attention network (LSA-Net), and attention-enhanced YOLO (AE-YOLO) network equipped with an ROI-aware feature extractor, are proposed for leaf disease identification and detection, respectively. The proposed networks incorporate a leaf spot attention mechanism to find spot areas and encode spot information.
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
## 3 Framework Design
### Use Case Scenario  
In this section, we present a brief use case reflecting the typical workflow of a new user.  
* Registration stage: The user will provide some details, including preferences and calendar schedule, and choose a basic credential username and password. Considering that predefined security questions are just like other passwords, our framework requires the user to provide some preferences that are not shared with relatives, and are not available on social media websites. These preferences, in the form of questions, will be used in a situation where the minimum confidence level has not been achieved: for example, when the system is not able to retrieve all the necessary contextual information.
* Verification stage: After completion of the registration stage, the homeowner reviews the user registration and activates the account.
* Login stage: For the first login process, the user inputs credentials to obtain access to the required service. In this access, the system achieves contextual information that is related to the user, which will be later used with the predefined information at the registration time for calculating the confidence level for future access requests. For obtaining the highest level of authentication for subsequent access requests, the system associates the device that the users are using with their profile and available contextual information in the database. The Gateway will verify the context data related to the user, such as location, calendar, preferences, and log history. Based on the result, the user will or will not be granted access. The user's level of access is determined by calculating the combined confidence level based on the available contextual information. The weights of these contexts can be easily set by the homeowner based on their preferred view of priority regarding the availability of the contextual information. For example, if a user does not have a calendar, the homeowner would replace the weights according to the available contextual attributes.
* In the case of access from outside the home environment to a specific device, access will be restricted based on the predefined policies and roles by the homeowner.
* Usage stage: The user now has access to smart devices through the web GUI, with the Gateway continually confirming access using the other contextual information and history logs.
# Machine Vision System for Counting Small Metal Parts in Electro-Deposition Industry
## 1 Introduction
This allows a set of simple, yet effective image processing algorithms to correctly determine the number of items to be coated in the galvanic bath. Finally, the knowledge of the number of items will allow companies to define a suitable set or working parameters (such as the current, voltage and deposition time) for the electroplating machine and, thereby, assure the desired plate thickness from one side and avoid material waste on the other.
###### Abstract  
Group management is practiced to deploy access control and to ease multicast and broadcast communication. However, the devices that constitute the Internet of Things (IoT) are resource-constrained, and the network of IoT is heterogeneous with variable topologies interconnected. Hence, to tackle heterogeneity, SDN-aided centralized group management as a service framework is proposed to provide a global network perspective and administration. Group management as a service includes a group key management function, which can be either centralized or decentralized. Decentralized approaches use complex cryptographic primitives, making centralized techniques the optimal option for the IoT ecosystem. It is also necessary to use a safe, scalable approach that addresses dynamic membership changes with minimal overhead to provide a centralized group key management service. A group key management strategy called a one-way Function Tree (OFT) was put forth to lower communication costs in sizable dynamic groups. The technique, however, is vulnerable to collusion attacks in which an appending and withdrawing device colludes and conspires to obtain unauthorized keys for an unauthorized timeline. Several collusion-deprived improvements to the OFT method are suggested; however, they come at an increased cost for both communication and computation. The Modified One-Way Function Tree (MOFT), a novel technique, is suggested in this proposed work. The collusion resistance of the proposed MOFT system was demonstrated via security analysis. According to performance studies, MOFT lowers communication costs when compared to the original OFT scheme. In comparison to the OFT's collusion-deprived upgrades, the computation cost is smaller.  
Internet of Things; key management; group management; security; software-defined networks +
Footnote †: journal: Applied Sciences
# Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules
## 3 Multi-Criteria Optimization in Scheduling  
Multiple conflicting objectives are a natural feature of most production scheduling environments. Therefore, at first, let us briefly introduce the multi-criteria optimization problem (MOP) and a concept of identification of MOP solutions based on the notion of Pareto-optimality and Pareto dominance.
## 3 Results
### Rice Plant Growth and Nutrient Uptake Influenced by Rice Straw and Rice Husk Biochar  
The rice plant dry weight, height, filler number, panicle number, and greenness are summarized in Table 10. Treatments T2 and T3 showed a significant increase in plant dry weight, height, filler number, panelic number, and greenness compared to other treatments. Similarly, the total N and available P contents in T2 and T3 were significantly higher than other treatments without biochar and treatments with potting media biochar (Table 11). Available K in T2 was recorded to be the highest among all the treatments. The concentrations of available Ca and Mg in treatments amended with RSRH biochar (T2 and T3) did not show any significant increase over T1. The rice plant N uptake in treatment amended with RSRH biochar at 5-10 t ha (T2 and T3) was significantly higher than other treatments (Figure 4). Further, in T2 and T3, the plant P and K uptake showed a significant increase in comparison to T0, T1, T4, and T5 (Figures 5 and 6). Treatment T2 and T3 significantly increased total Ca uptake of rice plant compared to other treatments (Figure 7). The total Mg uptake was significantly higher in T2 compared to other treatments (Figure 8). Further, the treatments with RSRH biochars (T2 and T3) significantly improved nutrient use efficiency in rice plant compared with T1 (Table 12). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatments** & **Dry Weight (\(g\))** & **Height (cm)** & **Tiller Number** & **Panicle Number (\%)** & **Greenness (\%)** \\ \hline T0 & 7.64 \(\pm\) 0.84 \({}^{\ast}\) & 41.94 \(\pm\) 0.19 \({}^{\ast}\) & 2.00 \(\pm\) 0.33 \({}^{\ast}\) & 1.00 \(\pm\) 0.02 \({}^{\ast}\) & 100.00 \(\pm\) 0.97 \({}^{\ast}\) \\ T1 & 22.97 \(\pm\) 2.99 \({}^{\ast}\) & 76.18 \(\pm\) 2.92 \({}^{\ast}\) & 3.00 \(\pm\) 0.33 \({}^{\ast}\) & 2.00 \(\pm\) 0.33 \({}^{\ast}\) & 106.31 \(\pm\) 3.47 \({}^{\ast}\) \\ T2 & 39.17 \(\pm\) 1.58 \({}^{\ast}\) & 101.57 \(\pm\) 1.95 \({}^{\ast}\) & 8.00 \(\pm\) 0.88 \({}^{\ast}\) & 8.00 \(\pm\) 0.88 \({}^{\ast}\) & 158.11 \(\pm\) 3.88 \({}^{\ast}\) \\ T3 & 38.57 \(\pm\) 0.97 \({}^{\ast}\) & 96.83 \(\pm\) 1.84 \({}^{\ast}\) & 9.00 \(\pm\) 0.89 \({}^{\ast}\) & 7.00 \(\pm\) 0.89 \({}^{\ast}\) & 154.39 \(\pm\) 6.20 \({}^{\ast}\) \\ T4 & 17.54 \(\pm\) 1.14 \({}^{\ast}\) & 73.20 \(\pm\) 3.07 \({}^{\ast}\) & 3.00 \(\pm\) 0.33 \({}^{\ast}\) & 3.00 \(\pm\) 0.34 \({}^{\ast}\) & 125.23 \(\pm\) 2.84 \({}^{\ast}\) \\ T5 & 14.62 \(\pm\) 1.37 \({}^{\ast}\) & 67.67 \(\pm\) 0.98 \({}^{\ast}\) & 2.00 \(\pm\) 0.34 \({}^{\ast}\) & 1.00 \(\pm\) 0.33 \({}^{\ast}\) & 123.31 \(\pm\) 3.58 \({}^{\ast}\) \\ \hline \hline \end{tabular} Mean values within column with different letter(s) indicate significant difference between treatments by [PERSON]’s test at \(p\leq 0.05\). Columns represent the mean values \(\pm\) SE.  
\end{table} 
Table 10: Effects of RSRH biochar on dry weight, height, filler number, panicle number, and greenness at harvest (70 DAT).
# Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis
## 4 Weight Construction Based on L1-Norm
### Weight Construction Based on L1-Norm  
Traditional LLE algorithm using L2-norm to calculate weight matrix, so it will enlarge the noise factor and cause the output results being difficult to recover the intrinsic structure. In order to reduce the error in local linear reconstructed weights, and enhance the robustness of LLE algorithm in the noisy environment, when dealing with noisy data, [PERSON] et al. [22] derived that the method based on L1-norm is more robust than based on L2-norm. In addition, when calculating the weight matrix, traditional algorithm only considers distances among neighborhood points, and it does not consider the manifold structure in the neighborhood. Therefore, the calculated weight matrix is not stable in noisy data. Inspired by [23; 24; 25], L1-norm is introduced to minimize the local error. At the same time, we take distance factor and the structure factor into account, so that the M-LLE algorithm can achieve a better noise immunity.  
In traditional LLE algorithm, each sample point can get a weighted linear representation by its neighborhood points, and the residual of local linear reconstruction is as following [23]:  
\[\begin{array}{l}\xi(x)=\sum\limits_{i=1}^{n}\left|\left|x_{i}-\sum\limits_{j =1}^{k}w_{ij}x_{j}\right|\right|^{2}\\ s.t.\,\,\sum_{j}w_{ij}=1\end{array} \tag{11}\]  
In order to minimize the local linear reconstructed residual, we can adopt Lagrange multiplier method to obtain the optimal reconstruction weight \(W_{i}^{S}\). Since the distance weight matrix \(W_{i}^{S}\) depicts the distance relationship among the center point and each neighborhood point, it does not take structural factors into consideration. Thus, in the local weight construction, M-LLE algorithm combines the distance relation with the structural relation in neighborhood and redefines the local weight matrix \(W_{i}\):  
\[W_{i}=W_{i}^{S}\times W_{i}^{D} \tag{12}\]\(W_{i}^{S}\) represents the distance matrix calculated by LLE algorithm, and \(W_{i}^{D}\) represents the structure weight matrix considered the local manifold structure. In the process of calculating the structure weight matrix, L1-norm is used to regulate, and the description is listed as following:  
\[W_{i}^{D}=exp(-\frac{\left|\left|x_{i}-x_{j}\right|\right|_{1}^{2}}{D_{m}}) \tag{13}\]  
And,  
\[D_{m}=\frac{1}{k}\sum_{j=1}^{\text{k}}\left|\left|x_{i}-x_{j}\right|\right| \tag{14}\]
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 5 Security Analysis
### Proxy Forgery Attack  
A proxy forgery attack is a type of security breach in which an attacker uses a forged proxy server to intercept or manipulate data on a specific network. Proxies are responsible for relaying or filtering communications between clients and servers, and attackers can forge such proxies to intercept and manipulate communications in the middle. The main characteristics and process of proxy forgery attacks are as follows:  
1. Proxy server forgery: Attackers either build proxy servers under their control or exploit and use proxy servers that already exist.
2. Man-in-the-middle attack: An attacker intercepts communications between the victim (client) and the target server. We assume that the proxy server is controlled by an attacker.
3. Data manipulation or interception: An attacker can intercept communications in the middle and view or manipulate data. This can lead to attacks such as leaking confidential information, stealing session tokens, or injecting malicious code.  
**Defense mechanism**:  
1. To successfully carry out a forgery attack, instead of CC (Control Center), the CS (Cloud Server) needs to compute \(S_{1}=x_{H}\alpha\), \(S_{2}=x_{E}\alpha\) \(\gamma\), \(S_{3}=x_{E}\alpha\), \(S_{4}=x_{E}\gamma\), then transmit \(RE\)= \((R_{1},\ R_{2})=\left(C_{1}^{\ S_{1}},\ C_{2}^{\ S_{2}}\right)\) and \(f_{k_{E}}(S_{3},\ S_{4})\) to E. CS can calculate \(S_{1}S_{2},S_{3},S_{4}\) by generating random values for private keys \(x_{H}\), \(x_{E}\) and random numbers \(\alpha\), \(\gamma\). The re-encryption values \((R_{1},\ R_{2})=\left(C_{1}^{\ S_{1}},\ C_{2}^{\ S_{2}}\right)\) are computed as \(\left((g^{\prime})^{x_{H}\alpha},\ (y^{\prime}m)^{x_{E}\alpha}\ \gamma\right)\). CS then sends \(f_{k_{E}}(S_{3},\ S_{4})\) and \((R_{1},\ R_{2})\) to E for decryption. However, a challenge arises in transmitting \(f_{k_{E}}(S_{3},\ S_{4})\) to allow E to decrypt \(S_{3},\ S_{4}\), the value of \(k_{E}\) must be known. Since \(k_{E}\) is a secret key pre-registered for secure communication between E and CC, an attacker cannot determine its value, preventing the creation of a valid \(f_{k_{E}}(S_{3},\ S_{4})\). Consequently, E cannot decrypt it, rendering \(RE\) undercrypting.
2. In order for the CS to intercept and succeed in MitMs (man-in-the-middle attacks) and data manipulation, the attacker must first intercept and decrypt \(f_{k_{C5}}(S_{1},\ S_{2})\) and \(f_{k_{E}}(S_{3},\ S_{4})\) transmitted by CC to CS and E. However, as the attacker cannot know the secret keys \(k_{C5}\) and \(k_{E}\) of CS and E, respectively, eavesdropping becomes impossible, and as a result, MitMs and data manipulation also become impossible.
## 1 Introduction
[27], the Prony method was successfully used in the AE analysis for defect detection in rolling bearings. According to [22], this method can be applied for problems associated with seismological and hydrocarbon deposits because it increases the resolution of short seismic signals and identifies zones with anomalous seismic energy scattering (dispersion). The disadvantage of this method is the strong dependence of the decomposition process on chosen parameters. A fundamentally different approach suggests filtering the noise content in the recorded initial AE signal (the signal remains unconverted). The Kalman filter-based methods are the effective signal processing techniques, which are widely used in radio navigation, guidance and trajectory tracking applications [28; 29]. In the framework of the Kalman theory, a wide variety of recursive data processing algorithms have been developed to restore the state of an a priori given dynamic system based on noisy and incomplete measurements. Therefore, the physical features of AE signal propagation are taken into account. In regards to the AE source location, these algorithms require less computational resources and show better accuracy compared to the wavelet transform [28; 29]. This approach also includes another research direction that has recently become increasingly popular. It implies the use of artificial neural networks and the concept of deep learning for signal filtering. In [30], a supervised learning neural network was used to develop an algorithm for recovering the AE signal with a signal-to-noise ratio less than one. At the same time, auto-associators (unsupervised learning) have found wide application in speech and image processing technologies [31; 32; 33; 34]. It is known that these algorithms are able to provide more accurate estimates of the signal-to-noise ratios compared to standard filtering methods [32; 33; 34]. The advantage of these auto-associators is noise separation with maintaining true signal features determined by a training set. Hence, it can be concluded that, this signal processing principle takes into account characteristics of the process under study as Kalman filtration technique. Choice of a specific method for increasing the signal-to-noise ratio depends in each case on the character of the recorded signal and on the spatio-temporal patterns of its development. This paper pursues two goals. The first is to investigate generation of acoustic emission signals induced by the fracture in carbon fibre reinforced plastic (CFRP) samples. The second is to develop an algorithm for isolating a useful signal (corresponding to fracture) against the background of a signal that is used to model the performance of an industrial rotary equipment. Therefore, we propose two algorithms designed to reduce various types of background noise.
## 3 Results and Discussion  
An explicative workflow reporting the complete scheme of validation of both approaches, together with related time of execution, is shown in Figure 1. The workflow was optimized in terms of time required for the completion of each step of validation. Obviously, due to the longer times required for the sample-by-sample UPLC injections, the confirmatory technique required more time to complete the validation (\(\sim\)44 days) than the ELISA (\(\sim\)21 days).  
Figure 1: Validation procedure of ELISA and ultra performance liquid chromatography with fluorescence detection (UPLC/FLD) methods: step-by-step optimized workflow.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Multitask Learning  
Multitask learning, also known as joint learning, refers to the process where multiple deep models are trained and then combined to compute the result. Each model has its complexity, architecture, and loss function. This approach helps to learn different parameters from limited available video data. Multitask learning is being successfully used in several fields, such as natural language processing, computer vision, and pattern recognition [88].