esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast, Lightweight, and Efficient Cybersecurity Optimization for Tactical-Operational Management
## 2 Problem Modeling and Formulation
### Stagnation Detection and Sape  
The FLECO algorithm incorporates a mechanism to detect stagnation and, if possible, escape it in order to converge towards a high-quality solution (Algorithm 1). To do so, a time threshold (2.5%) is defined as a percentage of the maximum allowed time (five minutes). At the beginning of the algorithm, the current time is recorded and updated every time the best individual fitness is improved. If there is no improvement, the time remains the same. This approach enables the computation of the consumed amount of time from the last improvement of the best chromosome's fitness. If the time reaches the defined threshold and the population has not yet converged, the stagnation warning is triggered.  
Under stagnation, the FLECO algorithm adapts dynamically to try to escape from local minimums:  
* The \"raw\" population size, which is typically 30 in our proposal or very close to it, is enlarged up to 50% more, resulting in a total of 45 individuals [33; 34]. This size adjustment aims to help the algorithm explore alternative regions of the solution space.
* Additionally, the mutation rate, usually fixed at 0.05, is dynamically increased [35] 20-fold to yield a value of 1.0, which helps the algorithm evade potential sub-optimal solutions.
* If the entrapment situation persists despite these adaptive adjustments, a secondary threshold (3.13%) is used to detect it. In this case, the top 1/2 (50%) of the best fitted individuals in the population are removed from the population and replaced by random individuals. This adjustment functions as a soft reset for the algorithm [36], preserving part of the already mature population while eliminating the most problematic individuals. This approach enables FLECO to escape from low-quality solutions in most situations and explore alternative regions of the solution space.  
This parameter adjustment process implemented in FLECO to prevent stagnation improves upon its adaptive capabilities [37], enabling it to effectively respond to the evolving problem context. The activation of the jamming alert does not inherently impact the quality of the solution identified by FLECO. It serves solely as a mechanism to detect the potential occurrence of the algorithm becoming trapped in a local minimum. If such a situation arises, the alert aids in the algorithm's escape from this state and facilitates the continued exploration of the solution space for potential alternatives. Once the local minimum is successfully bypassed, the dynamic parameters are reset to their predetermined values.
# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
###### Abstract  
As a core component of small unmanned aerial vehicles (UAVs), GPS is playing a critical role in providing localization for UAV navigation. UAVs are an important factor in the large-scale deployment of the Internet of Things (IoT) and cyber-physical systems (CPS). However, GPS is vulnerable to spoofing attacks that can mislead a UAV to fly into a sensitive area and threaten public safety and private security. The conventional spoofing detection methods need too much overhead, which stops efficient detection from working in a computation-constrained UAV and provides an efficient response to attacks. In this paper, we propose a novel approach to obtain a lightweight detection model in the UAV system so that GPS spoofing attacks can be detected from a long distance. With long-short term memory (LSTM), we propose a lightweight detection model on the ground control stations, and then we distill it into a compact size that is able to run in the control system of the UAV with knowledge distillation. The experimental results show that our lightweight detection algorithm runs in UAV systems reliably and can achieve good performance in GPS spoofing detection.  
k +
Footnote †: journal: Future Internet  
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Footnote 0: _Future Internet_ **2023**, _15_, 389. [[https://doi.org/10.3390/fi5120389](https://doi.org/10.3390/fi5120389)]([https://doi.org/10.3390/fi5120389](https://doi.org/10.3390/fi5120389))
###### Abstract  
In the United States, there are laws and standards guiding how people should be informed about the use of their private data. However, the challenge of communicating these guidelines to the naive user is still at its peak. Research has shown that the willingness to read privacy statements is influenced by attitudes toward privacy risks and privacy benefits. Many websites publish privacy policies somewhere on their web pages, and it can be difficult to navigate to them. In the healthcare field, research has found that health information websites' key information is presented poorly and inconsistently. For the policies to be legally binding, a person must be able to find them. In the healthcare industry, where sensitive data are being collected, research on how a user navigates to privacy policies for different size hospital websites is limited. Studies exist about privacy policies or website design and not both. This descriptive study involved ascertaining commonalities and differences among different-sized hospitals' website designs for supporting privacy policies. A foundation framework was created using Web Content Accessibility Guidelines (WGAC) principles and the literature review findings for evaluating practices for website publishing of privacy policies. The results demonstrated a very low variance in the website design concepts employed by hospitals to publish their privacy policy.  
privacy; website design; healthcare +
Footnote †: journal: _Future Internet_  
The main contributions of this study are as follows: (1) The literature review revealed the gap in research on website design in healthcare to attract users to locate privacy policies. To date, published research has separately investigated website designs or privacy policies. Studies have not explored how to ensure that a user locates the privacy policy online and what techniques can improve their ability to find it. (2) Based upon select WGAC principles, a framework for assessing website design to support privacy policy posting was implemented to compare the website design of different-sized hospitals. This structure provides a foundational standard methodology to evaluate design practices for online privacy policy publication. For future research, this provides a baseline to refine and enhance the development of a tool that could be used to appraise website designs that improve the user's experience in locating privacy policies. (3) It reveals future research opportunities to expand the use of a privacy policy posting standardized website design to other industries. Further, the homogeneous website design methods that were widely used by hospitals can be researched to find which are more beneficial to use to obtain improved user reviews of their privacy policies over others.  
The paper is organized as follows--(1) Introduction to the problem space; (2) background on healthcare industry website design for privacy protection, (3) proposed foundational framework assessment of website posting of privacy policies; (4) methodology; (5) results; (6) discussion; (7) limitations; (8) future research; and (9) conclusion.
# Self-Adaptive Priority Correction for Prioritized Experience Replay
## 3 Proposed Approach
### Overview  
The core idea of the proposed algorithm is to use importance weights to correct the update direction of DQN or DDPG based on the priority of data. The key challenges are how to accurately predict the real priorities and correct priorities at the lowest possible computational cost. In this section, we only discuss the DQN algorithm. The DDPG extends DQN and applies it to the continuous state-action space. They have a similar training framework. Figure 3 shows the architecture of the Imp-PER algorithm on DDQN. First, the proposed algorithm needs to get the stored sampling probability \(p(x)\) and predict the real sampling probability \(q(x)\) of each replayed samples, which is handled by the TD sum model. Then, it calculates the importance weights \(w_{i}\) based on \(p(x)\) and \(q(x)\). Last, the loss function of DQL is corrected by the importance weight and trained as original PER, as shown in the DDQN's definition. The other modules are the same as the original PER. Therefore, the Imp-PER is general and can be used in any DRL algorithm based on PER.  
Figure 3: The architecture of the Imp-PER on DDQN.  
Figure 2: (**a**) Shows the screen of task cartpole. (**b**) Shows the screen of task mountaincar. (**c**) Shows the performance of DQN based on real, store and uniform priorities on task cartpole. (**d**) Shows the performance of DQN based on real, store and uniform priorities on task mountaincar. The orange, green, and blue curves represent real, store, and uniform priority, respectively.
# Experimental Investigation on the Dynamic Characteristics of Bubble-in-Chain Near a Vertical Wall
## 3 Results
### Dynamic Characteristics of the Bubble-in-Chain
For bubbles generated at the frequency of \(F3\), their strong inducing ability leads to a closer proximity of the mainstream flow field induced by the bubbles to the wall, resulting in an initial decrease followed by an increase in the drag coefficient \(C_{D}\) with increasing \(H^{*}\).
## 4 Experimental Scenario for CO2 Emission Prediction Through the GreenNav Application
### Scenario 2: Scenario for a Future Date  
In this second scenario, [PERSON], a user of the GreenNav application, wants to estimate future CO\({}_{2}\) emissions for Boulevard Saint-Germain at a specific time. He selects the reference date and time on Wednesday, 10 July 2024, at 01:00 p.m. (see Figure 17). However, he is particularly interested in knowing the expected CO\({}_{2}\) emissions for 02:00 p.m. on the same day.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Hour** & **Flow (q)** & **Occupancy (k)** & **Actual CO\({}_{2}\) (g/CO\({}_{2}\))** & **Predicted CO\({}_{2}\) (g/CO\({}_{2}\))** \\ \hline
00:00 & 517.0 & 3.9 & 15,932.5 & 15,895.2 \\
01:00 & 323.0 & 2.8 & 10,143.7 & 10,120.5 \\
02:00 & 212.0 & 1.4 & 6434.6 & 6  
By entering the future date and time, GreenNav queries the hybrid CNN-LSTM prediction model to estimate CO\({}_{2}\) emissions based on projected traffic conditions. The model considers key traffic features such as traffic flow (q), occupancy rate (k), and traffic state, leveraging its spatio-temporal learning capabilities to produce an accurate prediction. The application returns the following result:  
The model predicts that Boulevard Saint-Germain's CO\({}_{2}\) emissions at 2:00 p.m. on 10 July 2024 will be 41,053.21 g CO\({}_{2}\)e/h
## 3 Results and Discussion
### The Effect of CySH Addition  
As in the case of AsA addition, the sigmoidal course of Qt decomposition was also pronounced by the addition of increasing amounts of CySH (Figure 6), compared to control, although less so compared to AsA. The addition of increasing amounts of CySH resulted in a consistently decreasing tendency in \(k\), and when CySH was added at a level of 200 umol L-1, Qt displayed the least \(k\), which was by almost 43% lower compared to control (no CySH addition). On the other hand, \(t_{1/2}\) increased up to 50 umol L-1 CySH, but then it exhibited a rather stabilizing trend (Table 3). Based on the hypothesis set out for AsA, it would appear that CySH stabilized Qt quinone by preventing quinone reaction with oxygen, or by acting as radical scavenger, up to 50 umol L-1; additional increase in CySH had no or little effect. Stabilizing effect of CySH against thermally-induced, copper-catalyzed oxidative Qt degradation has been previously reported to occur at 1:1 molar ratio [34]. The authors attributed this protective action to the ability of CySH to react with oxygen, giving stable products, such as L-cystine, L-cysteine sulphinic acid, and L-cysteine sulphonic acid. On the other hand, under different conditions, CySH might accelerate Qt degradation through the involvement of cysteinyl radicals [18]. However, given the stabilizing effect observed, the latter case would be rather unlikely.  
The chromatographic profile of a Qt mixture with 200 umol L-1 CySH, after 50 min of treatment at 90 \({}^{\circ}\)C, did not show any difference compared to that of Qt alone, and the major products detected were PCA, PGCA, and Qt quinone (Table 2). Unlike previous examinations [18; 34], the appearance of peaks that would manifest Qt reaction with CySH was not evidenced. Such a finding would strongly point to a non-radical degradation mechanism, as also supported for Qt/AsA mixtures.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 2 Background and Related Work
### Cognitive Packet Network  
The Cognitive Packet Network (CPN) is a Quality of Service (QoS)-driven routing protocol, in which each flow specifies the QoS metric (e.g., delay, loss, jitter, or other composite metrics) that it wishes to optimize [27]. The work describes an experimental system that implements multi-path routing. In Reference [28], the authors show how such multi-path routing schemes can be used to mitigate and block network attacks, and in Reference [29] the dynamic recovery and QoS robustness of multi-path routing schemes is demonstrated in the presence of attacks. Reference [30] addresses one of the challenges of multi-path policies in that they create transients in multi-hop systems. In the recent papers by Reference [31; 32], the use of multi-path routing to mitigate and recover from attacks with the help of software defined networking (SDN) controllers is discussed, and demonstrated with experimental results. The work in Reference [33] details how SDN controllers can be exploited together with their dynamic ability to vary paths for given connections, to optimize the access to Fog services. The SDN system in the test-bed was extended using the \"cognitive packet routing algorithm\" [34]. In our paper, we consider and discuss SD-WAN as WAN of SDN on multi-path for the global network.
# Life Cycle Assessment of Medical Waste Management: Case Study for Istanbul
## 2 Materials and Methods
### Life Cycle Assessment
#### 2.2.2 Inventory Analysis  
Inventory analysis consists of data collection and data calculation procedures. Data collection should be conducted for each process within the system boundaries. The inputs and outputs consist of the data collected and calculated. The inputs in this study include waste, raw material, and energy, which are obtained using the GaBi software database and a literature review. The outputs are emissions and energy produced. Figure 2 presents a screenshot of the GaBi software demonstrating the inputs and outputs of all scenarios as main categories. The assumptions made in the scope of this study are listed below.  
* Of the waste collected in Istanbul, 63% is from the European side, whereas the ratio of waste collected on the Asian side is 37%.
* Waste collected from healthcare facilities in Istanbul is mainly classified into two categories: (i) municipal solid waste (non-hazardous) and (ii) medical waste (hazardous).
* Waste generated is collected by ISTAC's licensed trucks and disposed of at the Odayeri and Komircioda Plants on the European side and Asian side, respectively.
* Non-hazardous municipal solid wastes are directly sent to the landfill sites.
* Medical waste is defined as infectious, pathological, and sharp waste from healthcare facilities in Annex 2 to the Regulation on the Control of Medical Wastes. Waste collected from healthcare facilities is classified into 4 main categories: (i) non-hazardous municipal solid waste, (ii) medical waste, (iii) hazardous waste, and (iv) radioactive waste [14]. Each waste category is collected in a different-colored waste bag as determined by the relevant regulation. Furthermore, it is forbidden to open waste bags collected from healthcare facilities according to the Regulation on the Control of Medical Wastes. Therefore, waste characterization could not be conducted in this study. Furthermore, 38% of medical waste is incinerated while the remaining, non-hazardous municipal solid waste is disposed of through sterilization in the current situation.  
Figure 1: The system boundaries used for LCA.  
* Scenario 0, the baseline scenario, represents the current medical waste management system in Istanbul.
* Waste transportation was assumed to operate in a one-way direction, and the return was not considered. Average distances between the waste generation points and waste disposal sites were determined as follows: (i) the transportation distance of waste generated on the Asian side to the Komurcuoda landfill site is 25.2 km; (ii) the transportation distance of waste collected from the European side to the Odayeri landfill site is 38.2 km; and (iii) the transportation distance of waste generated on both the Asian side and European side to the Odayeri incineration and sterilization facilities is 44.2 km.
* \"Truck, diesel driven, Euro 0-5 mix, 7.5 t gross weight/3.3 t payload capacity\" data in the GaBi database were used to calculate the fuel consumption of trucks during waste transportation and the emissions.
## 3 Materials and Methods
### Planning the SLR Process
#### 3.1.3 Setting the Keyword Plan
It leads to focus on addressing the well-developed technologies in SCM. After analyzing the relevant papers based on their publication date, two groups of digital technologies are identified. The first one includes digital technologies that have recently acquired a growing interest, as witnessed by the increasing number of publications about SCM (Most debated technologies). The second group comprehends those digital technologies that either are debated in a decreasing number of publications in the last few years or do not have a steady increasing trend in the number of related SCM publications (Least debated technologies). Hence, based on the results from Figure 2, the most debated Industry 4.0 technologies in recent years are: BC, CC, BDA, AM, and IoT. Therefore, this group is considered as the main technology to search as the second level keywords. The group of least debated technologies in SCM (Figure 3) includes UAV, AR, SDV, and AI. Finally, to provide a comprehensive picture of contributions on digital technologies and Lean DSC, \"Industry 4.0\" and \"digital\" were added to the list of second level keywords. _Third-level keyword selection (Lean principles)_  
The previous SLRs about the relationship between Lean principles and digital technologies mostly address that by making comparison among each specific Lean tool (e.g., 5 s, Kanban, JIT, etc.) and single digital technologies at the production level [13; 31]. However, the missing point is the impact of Lean principles and digital technologies at the SC level. To this end, the present work considers \"Lean\" as a general keyword in combination with the keywords about digital technologies and SC that were previously identified, in order to discover all the possible related works.
## 4 Experiments and Discussion
### Database Generation  
For evaluating the presented crack defect detecting method, in this work, 400 concrete images with a resolution of \(4608\times 3456\) pixels were practically collected using a Canon HS125 camera. These concrete images were obtained from several concrete structures (e.g., deck slab, beams, and so on) at Shijiazhuang Tiedao University, China.  
In addition, to achieve the best performance of our proposed model, some guidelines for image acquisition are suggested here (but are not required): (1) Images should have sufficient resolution so that objects and cracks can be seen. (2) Perspective angle between the camera and concrete structure should not be large. With a larger perspective angle, the crack regions may be occluded by other objects. (3) Distance between the camera and concrete structure should keep roughly constant. If the distance becomes larger, the crack object will be very tiny, and may be omitted by the complicated surroundings. If the distance is very small, the width of cracks will be very large, and only the edge of cracks may be detected by the presented method.  
As for the training and cross-validation of the crack damage detector, we randomly choose 300 images from the total 400 concrete images. In order to ensure the complexity of training data, these images should contain various conditions (e.g., non-uniform illumination, shadows, blurring, pockmark, attachment, crack-like, and so on). The effectiveness of the proposed approach was tested on the remaining 100 images, which were not used for the training and validation processes.  
In this work, for training the ELM-based crack detector, large amounts of small image patches of \(n\times n\) pixels are required. By dividing these 300 training images in an overlapped manner, plenty of candidate training samples were generated. With help of manual operation, the typical image regions, including crack or non-crack samples, were collected. As shown in Figure 4, for obtaining more patterns of crack or non-crack images, the partitioned image patches were rotated by \(-90\) degrees and 90 degrees. Finally, the total number of prepared training image samples was 50,000, which contained 25,000 non-crack samples and 25,000 crack ones.  
For training process, we randomly selected ninety percent of the crack samples and ninety percent of the non-crack ones. The rest of the training image samples were utilized for cross-validation testing process. In this paper, for one well-trained ELM-based crack detector, the training accuracy and cross-validation testing accuracy should be larger than 0.9.  
As for the testing process, each one of these remaining 100 images was divided into non-overlapped image patches of \(n\times n\) pixels. Then, the advocated ELM-based crack detection model was employed for finding the crack regions among all the separated candidate ones. By artificially labeling these divided candidates, the testing accuracy (i.e., false negative rate or false positive rate) could be computed, which can be used to evaluate the performance of presented method quantitatively.
## 3 Problem Definition and Measurements
### ICT Fluency Determinants (IFE)  
The goal was to estimate the IFE and to realize the interpretation in order to dress useful conclusions related to its determinants among different groups of students. To make the interpretation easier, we proposed a formalism allowing the estimation of their probabilities. Several methods could be used including Logistic Regression (LR) and Support Vector Machine (SVM) [68]. In this paper, LR was used since it outperforms SVM [59]. The Logistic Regression (LR) was used for the estimation of IFE for  
Figure 1: Concentration curves with \(L_{N}\) and \(L\) as a function of the capacity \(s\in\) [0,1]. Only s the diagonal line represents the cases where \(L(s)=L_{N}(s)\).  
each student. To make the interpretation easier, we also estimated the probability for each student. In order to formulate the LR, we assumed that \(x_{i1},\ x_{i2},\ldots,x_{il}\). Under this assumption, similar to Equation (2) the regression model linking the variables can be written as Equation (7):  
\[y_{i}=\alpha+\sum_{q=1}^{l}\phi_{q}x_{iq}+\varepsilon_{i} \tag{7}\]  
where \(y_{i}=IFE_{i}+\varepsilon_{i}\), \(i=1,\ldots,n\), with \(n\) the total number of the participants, and \(l\) the total number of all considered variables.  
Let us now deal with the probability. The \(IFE_{i}\) of a given student is associated with a binary variable \(t\) where it is one (1) if the \(IFE_{i}\) value is high and 0 otherwise. It follows that conditioned probability is given by Equation (8):  
\[P_{i}=P(t=1|y_{i})=\frac{e^{y_{i}}}{1+e^{y_{i}}}=\frac{e^{IFE_{i}}}{1+e^{IFE_{i}}} \tag{8}\]  
The probabilities \(P_{i}\) are used to categorize students' technology skills.