esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Performance Evaluation Measures  
To evaluate the performance of the proposed approach, we considered the standard evaluation metrics such as accuracy, precision, F1-score, and recall, as has also been adopted via the comparative approaches. Moreover, we have also used the confusion matrix analysis to report our method's wound classification performance.  
**Accuracy** is calculated as the sum of the true positives (_TP_s) and true negatives (_TN_s) instances of each wound class, divided by the total number of cases. Overall accuracy is computed as:  
\[Accuracy=\frac{\sum_{j=1}^{n}(TP_{j}+TN_{j})}{\text{Total number of instances}} \tag{5}\]  
**Balanced Accuracy** gives equal weight to each wound class while providing the model accuracy in case of an imbalanced dataset. It is computed as the average of the recall of each wound class. Balanced accuracy can be calculated as follows:  
\[Balanced\ Accuracy=\frac{\sum_{j=1}^{n}(Recall_{j})}{\text{n}} \tag{6}\]  
**Precision** measures how accurately the proposed framework predicted each type of wound. Mean precision is calculated as:  
\[Precision=\frac{\sum_{j=1}^{n}(TP_{j})}{\sum_{j=1}^{n}(TP_{j}+FP_{j})} \tag{7}\]  
**Recall** represents the number of accurate optimistic predictions made of all the positive instances in the case of each type of wound in the dataset. Mean recall can be computed as follows:  
\[Recall=\frac{\sum_{j=1}^{n}(TP_{j})}{\sum_{j=1}^{n}(TP_{j}+FN_{j})} \tag{8}\]  
**F1-Score** represents the harmonic mean of the precision and recall of a model. For multi-class wound classification, it is computed as the arithmetic mean of the F1-Score of each wound class. The mean F1-Score can be calculated as follows:  
\[F1-Score=\frac{\sum_{j=1}^{n}\frac{2\times Precision_{j}\times Recall_{j}}{Precision_{j }+Recall_{j}}}{n} \tag{9}\]  
where, in Equations (5)-(9), \(n\) represents the total number of wound classes.  
**Confusion Matrix** is considered as a visual evaluation tool for wound classification that summarizes the performance of the proposed model via enumerating true positives, false positives (_FPs_), true negatives, and false negatives (_FN_s). It aids in highlighting the wound classes that the proposed model misclassifies as other wound classes. This can help to interpret the model results in a better way, by analyzing the false rejection and acceptance scenarios.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 4 Proposed Intrusion Detection Algorithms for Smart Cities Network
### Threshold-Based IDS (TBIDS)  
Here is another method in detecting the sinkhole intrusion attack. The idea behind the TBIDS is its simplicity, and it works only on the sink node, which saves sensors' energy, and it does not affect the network packet loss. In this algorithm, the sink node plays the major rule in detecting the attack. It is assumed that the sink node is aware of all of the messages' routes. Therefore, it can determine the path of any of the messages and recognize the cluster heads that forwarded them. At the same time, the sink node is aware of the cluster head nodes' positions. Thus, the sink node would exactly know the number of messages transmitted to certain \(CH\), as well as the number of messages transmitted to the sink node from that particular \(CH\). A sinkhole node attracts other \(CH\)s to forward their messages to it. It either drops the received messages or sends them to the nearest \(CH\) or to the sink node, based on the sinkhole node position in the network. In both cases, the sink node is now aware of the situation and can determine if there is a sinkhole or not. The sink node sets threshold values for each \(CH\), \(N_{i}\), \(\alpha_{1}\), and \(\alpha_{2}\), based on the number of received and transmitted messages compared to other CHs. Those values need to be learned by the sink node; however, for the purpose of the performance measure to the TBIDS, those values are manually set based on trial and error experiments, where the learning process is out of the scope of this paper, and it is part of my future work.  
The sink node computes certain ratios for each \(CH\) named sinkhole ratios, \(R\)_'and_\(SR\)_', in which \(SR\)_' is the transmission sinkhole ratio, and \(SR\)_' is the blocked sinkhole ratio for \(CH_{j}\). Those ratios are compared to \(\alpha_{1}\) and \(\alpha_{2}\), respectively, to determine if the \(CH\) is a sinkhole or not. The detailed algorithm is shown in Algorithm 2. As can be seen, the sink node computes \(SR\)_' and_\(SR\)_' for each cluster head \(CH_{j}\) and compares them to the thresholds \(\alpha_{1}\) and \(\alpha_{2}\), respectively, to determine if \(CH_{j}\) is a sinkhole node or not. If it is a sinkhole, it will be isolated, and CHs, as well as nodes, will be notified; otherwise, no action is determined.  
```
1:\(s_{1}\dots s_{n}\in S\)
2:\(CH_{1}\dots CH_{h}\in H\)
3:\(sn_{1}\dots s_{n}\in SN\)
4:\(SR\)_' is the Sinkhole transmission ratio for \(CH_{j}\)
5:\(SR\)_' is the Sinkhole transmission ratio for \(CH_{j}\)
6:\(RM_{j}\) Received messages at \(CH_{j}\)
7:\(TM_{j}\) Transmitted messages at \(CH_{j}\)
8:Repeat
9:\(\forall\;sn_{i}\;\in\;SN\)
```  
**Algorithm 2**Threshold-Based IDS (TBIDS)  
The sink node does not base its computation on the \(CH\) self-report, but it is based on its neighbors' report, as well as on the received messages routing information. Therefore, a \(CH\) cannot deceive the sink node.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 7 Conclusions & Future Research  
This survey is devoted to cryptographic key management mechanisms in DTNs. Though in its early stages, various methods have been proposed to address the challenging task of security and cryptographic key management in such restricted and challenging networks. From the analysis conducted, it emerges that the proposed methods are still far away from being real effective and further research attention is required [49]. Putting it another way, research on cryptographic key management, in such challenged networks is still in its infancy as the field is relatively new.  
To spur and fuel further research efforts in this area, we have made an in-depth study of the solutions proposed so far in the literature and classified them into three major classes depending on which phase of key management each survey work addresses. As a future work, we intend to make an objective performance evaluation of the proposed methods considering characteristics and limitations of diverse DTN environments.  
All authors have written and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Current Gaps and Needs in Heart Failure Management  
This paragraph reviews current gaps in the management of HF patients, to define the most useful application of AI tools. What are the unmet needs of HF management at the current stage?  
* Tailored medicine: despite advances in our understanding of the underlying causes of HF, there is still a lack of a truly personalised approach to treatment, taking into account individual factors such as genetics, lifestyle, and disease history [18]. It is essential to define the most appropriate therapeutic strategies depending on patients' comorbidities, the specific etiology of CHF, the patient's lifestyle, and specific disease subgroups (elderly individuals, women, patients with congenital heart disease) [19].
* Early detection: CHF is a chronic, progressive, and irreversible disease. In this context, improving our process for the early detection of HF is of key relevance to improve patient outcomes, especially at early stages [20].
* Remote monitoring: the development of effective and scalable remote monitoring solutions is paramount to improving HF management and reducing hospitalisation rates, with a relevant impact on the control of management costs for healthcare systems and to protect patients' autonomy [21].
* Predictive modelling: to support decision-making and improve patient management, it is advisable to improve the prediction of HF progression and to define the underlying etiologies [22].
* Integration of data: methods for integrating and analyzing large amounts of data from various sources, including electronic health records, imaging, lab results, and wearable devices, to support the diagnosis and management of heart failure are paramount in the current context, where a growing number of clinical data are recorded and stored but often left unused [23].
* Research reorganisation: AI tools could help policy-makers and public payers to improve the prioritisation of research, to better focus on under-investigated and/or most promising topics. As an example, although it is recognised that the microbiome plays an essential role in the pathogenesis of HF, the exact mechanism of action in the development and progression of heart failure is still unknown. Similarly, there is a need to increase research resources on regenerative approaches to HF, including cell-based therapies, gene editing, and tissue engineering, to support the development of new treatments [24].
## 5 Discussions
### Emerging Directions and Practical Considerations for IoE Security
#### 5.5.2 Non-Terrestrial Networks (NTNs) for Secure IoE Connectivity  
Non-Terrestrial Networks (NTNs), including satellite and aerial platforms, are emerging as critical enablers for IoE connectivity, particularly in remote or infrastructure-limited regions. Their integration into IoE security architectures can ensure seamless coverage, improve resilience against local network failures, and facilitate secure data exchange across heterogeneous domains. Recent research focusing on the use of distributed learning techniques for processing Earth observation data within complex, multi-layered non-terrestrial network architectures has demonstrated that NTNs can be combined with blockchain-based trust mechanisms and federated intelligence to support secure, low-latency communication in geographically dispersed IoE deployments [184]. As IoE adoption expands into rural, maritime, and disaster-response scenarios, NTN-enabled security frameworks will likely play a decisive role.
# Stochastic Analysis of an Industrial System with Preparation Time Repair under Warranty Policy
## 1 Introduction
[25] in 2015, introduced the concept of preventive maintenance beyond warranty in two reliability models for a single-unit system. Providing a free system warranty for a given operating period is one of the most effective means to ensure the reliability of the system. In 2016, the same authors [26] calculated the mean time to system failure and profit function of a single-unit system with inspection of unit beyond warranty. [PERSON] and [PERSON] (2015) [27] developed the previously proposed system in 2013 in which they examined the concept of preventive maintenance during the warranty period and assumed that there is a repair force that repairs and replaces the defective device within the warranty period. In 2018, [PERSON] and [PERSON] [28] presented a single-unit industrial system operating under the cost-free guarantee policy. Here, the system goes to rest after a random period of time. After a complete break, the system resumes its work with full efficiency. In the same year, more precisely in September, [PERSON] [29] presented a model for maintenance scheduling based on the concepts of the failure-free warranty policy. [PERSON] and [PERSON] [30] emphasized that systems that rest after a certain period of operation have a longer life and fewer failures. Finally, in 2022, [PERSON] and [PERSON] [31] introduced the idea of linking the administrative delay time for repairing a failed unit with the free warranty policy for a single-unit repairable system. Many of the authors who have addressed the free warranty policy mentioned above have assumed that a completely failed unit is immediately taken to a repair shop for maintenance. However, we think this is not realistic in many cases, as the failed unit requires some preparation time before repair can begin. Example: When a car breaks down, the mechanic gathers some materials, including the defective parts, before starting to repair the fault that caused the car to fail. It is clear that the above studies have only dealt with the analysis of systems without considering the product warranty strategy with the need for preparation before repairing the failed device. Therefore, this article focuses on presenting the product warranty policy with the product repair plan in terms of pre-repair preparation and post-repair inspection to ensure that they obtain a product with high operating efficiency. The residuum of this article is organized as follows. Section 2 contains the system assumptions, state-specification and notations. Section 3 presents the mathematical model of this system to derive metrics for the reliability of the system, such as the reliability function, mean time to system failure, and steady state availability, using the Markov process. In Section 4, sensitivity and relative sensitivity analyses of the system parameters are obtained. Numerical examples illustrating the behavior of the system are shown in Section 5. Conclusions are summarized in the final section.
# Synthesis and Features of Luminescent Bromo- and Iodohectorite Nanolay Materials
## 1 Introduction
Synthetic hectorite Na\({}_{0.7}\)(Mg\({}_{5.5}\)Li\({}_{0.3}\))[Si\({}_{5}\)O\({}_{20}\)](OH)\({}_{4}\) is easily obtained at low temperatures and pressures [11; 20], which enables its production in high quantities, at low prices and with controllable composition, purity and crystal dimensions [1]. Hectorite is a T:O:T (or 2:1) type of trioctahedral smectite, meaning it is composed of two sheets of corner-sharing SiO\({}_{4}\) tetrahedra (T) intercalated with one sheet of edge-sharing M(O,OH)\({}_{6}\) octahedra (O), where M is Mg\({}^{2+}\) or Li\({}^{+}\). These T:O:T units are negatively charged, and they are electrostatically connected to each other across the interlayer space composed of hydrated Na\({}^{+}\) species [2; 11]. This composition may vary, for instance when OH\({}^{-}\) groups are partially replaced by F\({}^{-}\), creating fluorohectorite [11; 21]. When aiming for luminescence applications, the OH\({}^{-}\) groups of the hectorite structure must be avoided since the high energy phonons of these ions (3500 cm\({}^{-1}\)) can cause luminescence quenching through multiphonon de-excitation [22]. The primary goals of the current work were to design and synthesize bromohectorite (Br-Hec) and iodohectorite (I-Hec) nanoclay materials based on the hectorite structure, but with the hydroxyl groups replaced by Br\({}^{-}\) and I\({}^{-}\). The second intention was to maintain the essential features characteristic of hectorites that are needed for future applications, i.e., high surface area and good thermal stability. Finally, it was intended that the materials would show the inherent broadband luminescence emission observed earlier for fluoro- [23] and chlorohectorite [24]. To the best of the authors' knowledge,the synthesis and properties of bromohectorite and iodohectorite are now being reported for the first time.
# Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables
## 3 Stay Cable Experiments
### Full-Scale Model Experiment  
The feasibility of the proposed time-varying cable force identification method was preliminarily explained on the scale model. A full-scale test was also performed to further prove the feasibility of the proposed method in this paper. A single full-scale stay cable was produced to obtain the vibration acceleration data of the cable under different tensile conditions, and the VMD algorithm was used to identify the time-varying cable force. The full-scale model of the tested stayed cable is shown in Figure 7. Similar to the scaled experiment, the left part in this Figure the also the enlarged sketch of the tension and anchor. The length of this stayed cable was 167.85 m, the mass per unit length was 28.0 kg/m, the diameter was 87 mm, the elastic modulus \(\text{EI}=1.97\times 10^{11}\) Pa, the tensile strength of the cable was 1670 MPa, and the damping ratio and fundamental frequency were 0.012 and 0.84 Hz respectively. As mentioned before, the proposed method only needs the acceleration information obtained by an acceleration sensor, and an additional sensor was also added for accurate data. Two acceleration sensors were installed at 38 m and 48 m, as shown in Figure 7. The sampling frequency of the sensors was 100 Hz. The whole layout is shown in Figure 8. In actual situations, sudden changes may occur in cable force, such as the damper falling. Therefore, three operating conditions were designed in this full-scale experiment to consider all situations, as shown in Table 2.  
Figure 6: Time-varying cable forces of scale model experiment: (**a**) Condition 1, (**b**) Condition 2, (**c**) Condition 3, (**d**) Condition 4.  
Figure 7: Design drawing of the stay cable in the full-scale model experiment.
## 5 Simulation Results
### Ergodic Sum Rate  
The signal power of the HS is assumed to be \(P_{0}=1\) W. Letting \(M=5\), \(\tau=0.8\), and \(\alpha=\{0.02,0.04\}\), simulation results of the ergodic sum rate versus \(\beta\) are shown in Figure 5. As expected, the curves of theoretical expression perfectly match with those of numerical results, which validates the effectiveness of Theorems 1 and 2. By deeply analyzing the curves, we can clearly find that the spectrum-saving scheme outperforms the conventional method in the high-SNR region. This can be explained as the benefit of extended bandwidth allocated for UTs is much more noticeable than the interference incurred by the new method.  
Figure 6 illustrates the ergodic sum-rate with fewer accessed UTs. Here, we set \(M=3\). It can be observed that the ergodic sum rate decreases compared with the results in Figure 5. In addition, the spectrum-saving approach still outperforms the conventional method.  
To learn more about the effect of shared bandwidth on the ergodic sum rate, we ran simulations with different values of \(\tau\), where the result is presented in Figure 7. Specifically, \(\beta\) is set to \(145\) dB and \(\alpha=\{0.02,0.04\}\) in this scenario. Obviously, more competitive results can be obtained by increasing the power factor \(\alpha\) for UTs' signals. It is noteworthy that the proposed method is not satisfactory when \(\tau\) is relatively small. However, the performance gain becomes noticeable as the bandwidth grows, which can be learned from the 'Gap' curve. In this sense, we can expect promising performances by designing a proper value of \(\tau\).  
Figure 5: Ergodic sum rate with variable \(\beta\) when \(M=5\).  
Figure 6: Ergodic sum rate with variable \(\beta\) when \(M=3\).
## 3 Simultaneous Estimation Scheme for Parameter and State  
### Observability Of System  
Is a reliable observer available for the system (1) based on the past values of \(y(t)\) and \(u(t)\)? This problem leads to the concept of observability [22; 23; 24; 25].  
In the system (1), inputs \(u(t)\) are also called excitation of the system. Admissible inputs \(u(.)\) are assumed to be taken in some measurable and bounded functions set \(\mathcal{U}(t)\). Let \(\chi_{u}(t,x_{0})\) denotes the system solution at time t, with initial condition \(x_{0}\) at time \(t=0\), and control \(u(t)\).  
**Definition 1**.: _Indistinguishability_  
_A pair \((x_{0},\ \tilde{x}_{0})\) will be said to be indistinguishable by \(u\) if \(\forall t\geq 0\), \(h(\chi_{u}(t,x_{0}))\equiv h(\chi_{u}(t,\tilde{x}_{0}))\). The pair is just said to be indistinguishable, if it is so for any \(u\)._  
**Definition 2**.: _Observability_  
_The system (1) is observable if it does not have any indistinguishable pair of states [26]. Observability is the necessary condition of a system to obtain an available observer. If a system is not observable, one can not obtain the reliable estimate of the initial state \(x(0)\). While depending the initial state \(x(0)\) the system solution \(\chi_{u}(t,x_{0})\) under the input \(u(t)\) can be estimated. It means the availability of the observer._  
The observability defined here does not exclude the possible existence of inputs for which some states are indistinguishable. In other word this defined observability depends on the excitation and it is not an uniform observability, it is not enough to design an reliable observer unless the action of inputs is taken into account. The observability independent of the input, that is, the uniform observability requires strong conditions on the system structure. However, in this paper, we assume that the considered systems are observable with their normal working excitation and the observability under the normal working excitation ensures the available of the observer. We will not further discuss the problem of observability of nonlinear systems for which the interested reader is referred to Reference [27].
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis
### Comparison Experiments with Similar Algorithms
#### 4.6.1 Invisibility Comparison  
The initial QP from the literature [24] is 20, and the initial QP from the literature [25] is 28. In this paper, when performing the invisibility comparison experiment, the initial QP was consistent with the comparison literature. Tables 4 and 5 show the invisibility experimental results of the three algorithms, and it can be seen that the SSIM value of this  
Figure 8: Experimental results of bit increase rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Experiment** & **Original** & **Watermarking** & **Difference** & **Original** & **Watermarking** & **Difference** \\
**Video** & **Encoding** & **Encoding** & **Decoding** & **Decoding** & **Decoding** & **Difference** \\ \hline Foreman & 340.237 & 345.499 & 5.262 & 123.944 & 125.172 & 1.228 \\ News & 299.304 & 302.826 & 3.522 & 118.118 & 120.398 & 2.28 \\ Mobile & 359.185 & 361.152 & 1.967 & 126.979 & 121.886 & 5.093 \\ Hall & 315.482 & 317.878 & 2.396 & 135.158 & 132.640 & 2.518 \\ \hline \end{tabular}
\end{table}
Table 3: Time complexity experiment results.  
algorithm is higher than that of the literature [24; 25], which indicates that this algorithm has better invisibility.