esa-sceva/satcom-chunk-collection huggingface dataset:
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 2 Related Work
### Transitory Master Key  
The group of schemes based on a transitory master key employs MK to generate the pairwise keys among the nodes. MK is deleted by every node after a timeout. The security in this approach is founded on the assumption that a node cannot be compromised before a lower bound of time. Therefore, before this time each node should delete MK.  
The first period of activity of a node after its deployment is called the initialization phase. After a timeout, a node moves to the working phase. In a basic version, the nodes in the working phase cannot establish new keys. However, some approaches allow nodes in the initialization phase to establish keys with nodes in the working phase. This mechanism allows new nodes to be added into the network.  
If the initialization phase is particularly long, the probability that an opponent can compromise the MK grows. However, by reducing the timeout, even the quantity of keys that a node can establish becomes lower. Therefore, a shorter timeout reduces the connectivity of the WSN. If an adversary compromises MK, he/she can decrypt all the messages eavesdropped during the initialization phase, potentially compromising all the pairwise keys in the network. Moreover, if the key management scheme allows the addition of nodes to the networks after the first deployment, the opponent can establish a pairwise key with every new node.  
Localized encryption and authentication protocol plus (LEAP+) [25] is a well-known transitory master key scheme. During the initialization, all the nodes know a pseudo-random function and MK. All nodes have their own private master key (PMK), computed with the pseudo-random function by using the identifier of the node as a seed and MK as a key. Within the initialization phase, each pair of nodes generate the pairwise keys with the pseudo-random function by using the identifier of the second node as a seed and PMK of the first node as a key. The nodes added after the initial deployment use MK to compute the PMKs of the nodes in its communication range. With this secret they can generate a common keys. After a timeout period, each node erases all keys used to compute the final pairwise keys with the exception of their PMK, that will be used in case of possible future added nodes.  
LEAP+ can be applied only to static wireless networks.  
In [26], the authors propose Fast LEAP+ (FLEAP) a new transitory master key scheme with a faster key establishment phase. The handshake is split in separated sub-phases in order to anticipate the deletion of the master secrets.  
In[27], Random seed distribution with transitory master key (RSDTMK) is proposed. This protocol distributes transitory master secrets before deployment. The nodes need shared secrets in order to establish pairwise keys and the secrets are deleted after a timeout.
## 3 Results
### Physical-Chemical Characterization
(**a**) section at 3–6 mm (green upper diffractogram); section at 0–3 mm (red middle diffractogram); reference FEBEX-Mg (black bottom diffractogram); (**b**) zoom of the 29–44 20 range. After 22 months, more clear and intense reflections were observed regarding the altered products. The crust analyzed in contact with steel showed a main composition of siderite (2.79 A). Smectite, ferri-sudoite and albite were detected as minor minerals. On the bentonite side, the main reflections were associated with ferri-sudoite (7.05 A, 4.80 A, 3.53 A, 2.44 A, 2.14 A and 1.55 A) and green rust (7.5 A and 3.76 A), but low-intensity reflections at 2.90 A and 2.51 A were attributed to ankerite and maghemite, respectively. Aqueous extracts were analyzed to study the redistribution of aqueous species in the bentonite porewater (Figure 9). After 1 month Cl\({}^{-}\) varied within a range between 395 and 483 mM, similar to the reference FEBEX-Mg (455 \(\pm\) 2 mM), concentrating at the middle of the bentonite column (6-15 mm). The same tendency was observed after 6 months but within a lower concentration range (220-280 mM). After 22 months, Cl\({}^{-}\) decreased to lower concentrations, within a range of 54-58 mM. Mg\({}^{2+}\) maintained concentrations close to the reference FEBEX-Mg (130 \(\pm\) 6 mM), within a range between 100 and 166 mM after 1 and 6 months, but after 22 months Mg\({}^{2+}\) concentrations decreased within a narrow range of 28-36 mM along the bentonite column. The rest of the ions analyzed (Na\({}^{+}\), Ca\({}^{2+}\), SO\({}_{4}\)\({}^{2+}\) and K\({}^{+}\)) were in a lower concentration range (3-58 mM). A general decrease of concentrations was observed with the increasing reaction time. After 22 months, Na\({}^{+}\) was determined to be within a range between 36 and 40 mM, while the reference Na\({}^{+}\) concentration was 15 \(\pm\) 9 mM; Ca\({}^{2+}\) increased up to 11-16 mM from the initial reference of 9 \(\pm\) 2 mM; SO\({}_{4}\)\({}^{2-}\) increased to 9-12 mM from the reference of 5 \(\pm\) 1 mM, and K\({}^{+}\) decreased from 10 \(\pm\) 1 mM to 4 mM. Figure 8: XRD of corrosion crusts examined from the contact with steel (fuchsia) and with bentonite (orange) after (**a**) 1 month; (**b**) 6 months; and (**c**) 22 months. Ab: albite; Ank: ankerite; Crs: cristobalite; GR: green rust; Hem: hematite; Mag: magnetite; Mgh: maghemite; Qz: quartz; Sd: siderite; Sme: smectite; Sud: sudoite.
## 7 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Training Dataset** & **Exp-1 Response** & \(TP\) & \(FP\) & \(FN\) & \(TN\) \\ \hline AK & 867 & 802 & 662 & 140 & 205 & 24,324 \\ BCC & 3323 & 3956 & 3203 & 753 & 120 & 21,255 \\ SCC & 628 & 295 & 276 & 19 & 352 & 24,684 \\ BKL & 2624 & 2285 & 2105 & 180 & 519 & 22,527 \\ MEL & 4522 & 3746 & 3597 & 149 & 925 & 20,660 \\ NEV & 12,875 & 14,105 & 12,659 & 1446 & 216 & 11,010 \\ DER & 239 & 48 & 47 & 1 & 192 & 25,091 \\ VASC & 253 & 94 & 94 & 0 & 159 & 25,078 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the Exp-1 based on the confusion matrix in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & _Accuracy (\%)_ & _Precision (\%)_ & _Sensitivity (\%)_ & _Specificity (\%)_ \\ \hline AK & 98.64 & 82.54 & 76.36 & 99.43 \\ BCC & 96.55 & 80.97 & 96.39 & 96.58 \\ SCC & 98.54 & 93.56 & 43.95 & 99.92 \\ BKL & 97.24 & 92.12 & 80.22 & 99.21 \\ MEL & 95.76 & 96.02 & 79.54 & 99.28 \\ NEV & 93.44 & 89.75 & 98.32 & 88.39 \\ DER & 99.24 & 97.92 & 19.67 & 100.00 \\ VASC & 99.37 & 100.00 & 37.15 & 100.0 \\
**Mean \(\pm\) SD** & \(\mathbf{97.35\pm 2.04}\) & \(\mathbf{91.61\pm 6.89}\) & \(\mathbf{66.45\pm 29.09}\) & \(\mathbf{97.85\pm 3.98}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance metrics obtained from the values in Table 3 for the Exp-1 with \(\alpha=-2.0\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & _Accuracy (\%)_ & _Precision (\%)_ & _Sensitivity (\%)_ & _Specificity (\%)_ \\ \hline AK & 98.62 & 82.68 & 76.47 & 99.42 \\ BCC & 96.78 & 82.63 & 96.21 & 96.88 \\ SCC & 98.55 & 94.98 & 45.22 & 99.94 \\ BKL & 97.22 & 92.78 & 79.88 & 99.27 \\ MEL & 95.73 & 96.53 & 79.39 & 99.27 \\ NEV & 94.16 & 91.12 & 98.31 & 89.69 \\
**Mean \(\pm\) SD** & \(\mathbf{96.85\pm 1.71}\) & \(\mathbf{90.12\pm 6.07}\) & \(\mathbf{79.25\pm 19.06}\) & \(\mathbf{97.42\pm 3.93}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance metrics obtained for Exp-2 with \(\alpha=-1.9\). The dataset has 24,839 images.
# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
###### Abstract
Machine learning (ML)-based approaches have been widely utilized to help network administrators deal with a wide range of harmful attacks in preventing these attacks [10]. Ensemble learning (EL) enhances ML outcomes by combining many models into one. These algorithms evaluate the state of the network by classifying the processed data into normal and abnormal categories. These algorithms exercise and simulate attacks with precision to evaluate capabilities with diverse datasets. Nevertheless, most of these datasets are extremely imbalanced. A total of 98% of these datasets are regarded as normal, whereas the remaining 2% are categorized as assaults [11]. [PERSON] et al. [12] suggested a novel deep-learning model based on ensemble learning for interpreting non-stationary datasets such as IDS logs. It is desirable to be able to construct a better detection system, especially when utilizing ensemble classifiers. When creating an ensemble, selecting suitable classifiers and deciding on combiners are two important issues. In [13], a study of ensemble learning for IDSs was provided by [PERSON]. However, most traditional ML techniques fall within the area of superficial learning and place a low emphasis on the design and selection of features; they are incapable of addressing the large classification task imposed by attack data in a real-world network application. As the number of datasets continues to grow, the accuracy of multiclassification attack detection will decrease. Consequently, intelligent assessment is inconsistent with ML and the projection requirements of higher-dimensional learning with vast amounts of data [14]. This study aimed to develop a network intrusion detection system that is based on flow-based statistics utilizing the benchmark Canadian Institute for Cybersecurity intrusion detection system (CICIDS) 2018 dataset, which accurately identifies and categorizes every type of attack using a multi-categorization scheme. To identify network traffic, we developed an improved 1D CNN-based deep neural network model. The main contributions of this paper are as follows. * Proposal of a deep-layered architecture using the recurrent neural network (RNN) and convolutional neural network (CNN) to detect and classify malicious traffic. * Detailed analysis of existing machine learning and deep learning techniques. * In-depth analysis of the CICIDS 2018 dataset. The remaining sections of this work are structured as follows: Section 2 presents the background while Section 3 explains the related work. Section 4 discusses the proposed HDLNIDS model. The dataset explanation is presented in Section 5. The experimental details and discussion are presented in Section 6, followed by a comparison with existing approaches in Section 7, and finally the summary and the conclusion in Section 8.
## 4 Conclusions  
In this work, MgAl-LDH intercalated with nitrates was prepared by coprecipitation. The influence of the reactant concentration on the formation of LDH was investigated. The following points of conclusions can be obtained:  
1. The change of reactant concentration hardly had an influence on the morphology and size of the LDH platelets, while the pH difference caused by different metal ion concentrations may have a remarkable effect on the intercalated anions. The difference in reactant concentration resulted in the change of pH value of the reactant solution. An obviously higher pH with a value of 12.7 in the case of C-low leads to the intercalation of a larger number of carbonates, resulting in a lower chloride adsorption capability due to the difficulty of anion exchange reaction. This finding emphasizes the importance of pH value in LDH synthesis, which can be easily ignored when other conditions were changed. pH should be controlled well during the LDH synthesis process to guarantee the quality of the synthesized LDH. This result could provide meaningful guidance into further preparation of MgAl-LDH.
2. The sample of C-high presents superior chloride adsorption ability with a \(Q_{\text{m}}\) value of 155.88 mg g\({}^{-1}\), which was much better than that reported in the literature. This work is able to provide a solid foundation for the production of LDH with a strong chloride adsorbing property and further industrial application in the field of corrosion protection in the future.  
Conceptualization, Y.C. and K.C.; Methodology, H.Q.; Software, X.Z.; Validation, Y.C., H.Q. and S.F.; Formal Analysis, S.F.; Investigation, Y.C.; Resources, K.C.; Data Curation, S.F.; Writing--Original Draft Preparation, Y.C.; Writing--Review and Editing, J.W.; Visualization, J.L.; Supervision, J.W.; Project Administration, C.H.; Funding Acquisition, H.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Key Laboratory for Marine Corrosion and Protection Foundation (JS20902).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
[PERSON] is acknowledged for his help in TEM characterization.  
The authors declare no conflict of interest.
## 2 Material and Methods
### Western Immunoblots  
Samples were electrophoresed through 12%, 10%, or 7.5% SDS-PAGE gels, followed by blotting of the proteins onto nitrocellulose membranes. After blocking with either skim milk or 3% BSA, the blots were incubated overnight with the antibodies listed below, washed and incubated with the appropriate horseradish peroxidase (HRP)-conjugated secondary antibody. Antibodies against BRCA1 (C-20), TOPOII (C-15), MET (C-28), p120 (H-90), androgen receptor [(AR), N-20], IGF-IR \(\upbeta\)-subunit (C-20), Sp1 (PEP2), TCF3A (M-20), c-fos (4), ER \(\upalpha\) (MC-20), WT1 (C-19), KLF6 (R-173), lamin A/C (636), Smad2/3 (FL-425), c-Myc (9E10), p63 (4A4), E2F1 (KH95), LEF1 (C-19), TCF1A (C-21), p53 (mixture of DO-1 and Pab 1801), GSK3\(\upbeta\) (11B7), phospho c-jun (KM-1), cyclin D (H295), cyclin G (F-5), histone1 (FL-219), \(\upbeta\)-catenin (E-5), p300 (C-20), BRCA2 (H-300), TLE-1 (M-101), p21 (C-19), normal mouse IgG [NMS (SC-2025)], and normal rabbit IgG [NRS (SC-2027)] were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). An antibody against tubulin (Clone: B-5-1-2) was purchased from Sigma-Aldrich Co. Anti IRS-1 was kindly provided by Dr. [PERSON] (Tel Aviv University). Antibodies against poly ADP ribose polymerase [(PARP); 9542 and 9532], phospho-IGF-IR (3024), Foxo1 (9462), and phospho-Smad2 (3101) were obtained from Cell Signaling Technology (Beverly, MA, USA). In addition, the following antibodies were used: p73 (Ab-1; Oncogene Research Products, San Diego, CA, USA), c-jun (610326; BD Transduction Laboratories, Franklin Lakes, NJ, USA), APC (OP47L; Calbiochem, San Diego, CA, USA) and hemagglutinin (HA-11; Berkeley Antibody Company, Richmond, CA, USA). The secondary antibodies were HRP-conjugated bovine (1:500) or donkey (1:500) anti-goat IgG (both from Santa Cruz Biotechnology), goat anti-rabbit IgG (1:50,000) and donkey anti-mouse IgG (1:25,000; Jackson ImmunoResearch Laboratories, West Grove, PA, USA). Proteins were detected using the SuperSignal West PicoChemiluminescent Substrate (Pierce, Rockford, IL, USA).
## 5 The Routing Algorithm
### BNH Selection and Packet Forwarding  
The second stage of the routing process occurs when a link is established between the current host and some of its neighbor nodes. At that moment, the router will attempt to exchange deliverable messages (i.e., messages whose final destination is among the current connections), if any. Then, the host router asks the connected nodes for their fitness values (which, thanks to their fitness center, are always up to date). After that, before entering the final selection, the router directly discards those connections whose \(f\) value is not at least the fitness threshold \(\alpha\), and orders in descending order the remaining connections, according to their fitness. With a complete list of fit candidates, the selection process is straightforward: The best next hop will be the fittest node (the one with the higher \(f\) value), so the router will attempt to replicate a data package to the nodes in that order. Algorithm 1 summarizes the routing protocol, as explained in the previous subsections.  
```
Message received event--msg fitness update Inputs: \(m_{i}\): incoming message \(M\): \(c\)'s message queue Outputs: \(M_{o}\): \(c\)'s message queue, ordered by fitness value Steps:
1. Insert \(m_{i}\) in \(M\), in descending order
2. Return \(M\) Connection down event--host fitness update Inputs: \(X\): the set of features \(x_{i}\) of \(c\) Outputs: \(f_{x}\): the updated fitness value of \(c\) Steps:
1.\(X\leftarrow\) current features \(x_{i}\) of \(c\)
2. Normalize \(X\) according to Equation (5).
3. Compute the value \(f_{x}\) of \(c\) according to Equations (3) and (4). Connection up event--Selection of BNH and BNM dispatch Inputs: \(C\): the set of nodes connected to \(c\) at that moment \(M\): \(c\)'s message queue Outputs: \(C_{o}\): the set of connection tuples ordered by fitness Steps:
1. Exchange messages whose final destination is in \(C\)
2. Do: for each \(c_{i}\in C\): \(\textbf{get}f_{i}\) if\(f_{i}\geq\textbf{\alpha}\): store tuple (\(c_{i},f_{i}\)) in \(C_{o}\)
3. Sort \(C_{o}\) in descending order
4. Do: for each \(m_{i}\in M\): \(\textbf{get}f_{i}\) if\(f_{i}\geq\beta\): for each \(c_{i}\in C_{o}\): \(\textbf{replicate }m_{i}\) to \(c_{i}\)
```  
**Algorithm 1** DLR+ algorithm. Actions in node \(c\) connected to a set of nodes \(C\) and having a queue of messages \(M\).
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Physical Properties and Their Characterization
#### 3.1.5 Magnetometry  
Comprehending and enhancing the magnetic properties of MNPs is essential for their effective application in diverse domains, particularly in biomedicine. The magnetic characteristics, including magnetic moment, coercivity, and responsiveness to external magnetic fields, determine the operational capabilities of MNPs in many applications such as MRI, targeted drug administration, hyperthermia, and magnetic data storage [66]. The optimization of these attributes guarantees the effectiveness, precision, and security of MNPs, rendering their evaluation through state-of-the-art methods such as Vibrating Sample Magnetometry (VSM) and/or Superconducting Quantum Interference Device (SQUID) Magnetometry essential [67; 68].  
**VSM** is a cost-effective and straightforward technique that offers accurate information about the value of the magnetic moment of the analyzed substance. The sample is positioned within a copper coil and subjected to an applied magnetic field. Subsequently, it is oscillated in a direction perpendicular to the field. The magnetic moment is quantified by detecting a variation in the electrical potential. This method is valuable for evaluating the magnetic moment in relation to temperature, magnetic field, and crystal orientation [101]. Nevertheless, VSM has a relatively lower sensitivity in comparison to more sophisticated techniques such as SQUID magnetometry [102].  
**SQUID magnetometry** is renowned for its exceptional sensitivity and is employed to analyze superparamagnetic substances, which can be recognized by the absence of a hysteresis loop. Hysteresis loops, which depict the magnetization characteristics of a material, play a vital role in comprehending magnetic features such as remnant magnetization and coercive field. Superparamagnetic nanoparticles, which have a single magnetic domain, do not display a hysteresis loop like ferromagnetic ones [101; 103]. This method also aids in determining intrinsic characteristics, such as the Curie temperature for ferromagnetic substances and the Neel temperature for antiferromagnetic materials [102; 103].
###### Abstract  
(1) Background: Preservation surgery of the diabetic foot aims at tissue sparing to avoid further pathological changes from developing, while ultimately preserving the limb. For this purpose, osteoreggenerative, antimicrobial, and osteostimulatory materials are now available. The aim of this case series is to demonstrate several clinical situations in which the use of bioactive glass, a third-generation biomaterial, can potentially be of benefit. (2) Methods: In this retrospective case series, we present patients with a diabetic foot complication with septic osteoarthritis, treated at the University Medical Centre Ljubljana, in the years 2020-2022. Patients received surgical care with resection of the affected joint and bone and placement of bioactive glass material. We observed patient characteristics, recurrence of septic osteoarthritis or osteomyelitis, and the need for amputation in the follow-up period. (3) Results: Of the 16 included patients, 8 received treatment of the metatarsophalangeal joints, 4 of the midfoot joints, and 4 of the ankle joints. Ten suffered from Charcot neuroarthropathy. None required readmission within 30 days or suffered early complications. One complication with recidivant septic osteoarthritis and osteomyelitis was observed. (4) Conclusions: Our case series presents possible applications for treatment with bioactive glass in various joints and bones affected by septic osteoarthritis of the diabetic foot.  
diabetic foot; osteomyelitisitis; bioglass; Charcot arthropathy +
Footnote †: journal: Medical Centre Ljubljana, Vrazov Treg 2, 1000 Ljubljana, Slovenia 1
These studies are essentially based on atmospheric correction using the R-D model and have not yet directly performed atmospheric correction on the RPC model. Furthermore, obtaining ground control points is costly, especially in the western regions of China, where harsh weather conditions often make acquiring control points difficult. In the absence of ephemeris data and ground control points, atmospheric correction for spaceborne SAR images using RPC models becomes a challenge. The key technologies lie in satellite position determination and RPC parameter optimization. This study presents a novel and effective approach for atmospheric correction in the RPC model of spaceborne SAR imagery. A radar line-of-sight (LOS) autonomous intersection method is introduced to invert the satellite position. The study also investigates the impact of stratified calculations on atmospheric delay errors, transforming these errors into parameters within the RPC model. A reconstruction method for the RPC model, considering atmospheric delays, is proposed, which enables the calculation of optimized RPC parameters and thus achieves atmospheric correction for spaceborne SAR imagery. An uncontrolled positioning comparative experiment was conducted using GF-3 imagery, with ephemeris data serving as reference data to evaluate the accuracy of satellite position inversion. Additionally, the atmospheric delay results derived from this study's method were compared with those calculated using ephemeris data, validating the effectiveness of the proposed approach.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 4 Results and Discussion
### Moderator Analysis for VC Platform-Supported Online Learning
#### 4.3.1 Research Context in VC Studies
At the same time, this study found that grade level had a significant impact on the learning outcome (\(Q_{B}\) = 9.288, \(p\) < 0.01), and the effect size for K-12 (\(g\) = 0.801) was larger than for higher education (\(g\) = 0.208). This may be because K-12 students tend to require more supervision due to their lower level of self-discipline. VC platforms offer a centralized online learning environment where young students can be supervised effectively. College students may have already adapted to this approach to online learning, which could result in less novelty and improvement in learning outcomes than among K-12 students.