esa-sceva/satcom-chunk-collection huggingface dataset:
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 4 Evaluation and Comparison
### Replay and Injection of Messages  
If an adversary replays a Hello or an Ack1 message, the receiver will recognize the sender as authentic. However, the opponent will not be able to decrypt the random number sent by the node, so he/she will not be able to establish a pairwise key. By replaying an Ack2 or an Ack3, the adversary cannot achieve additional information. The only way to successfully execute a replay attack would be to break the public cryptosystem.  
If an adversary sends a new Hello or a new Ack1 message, he/she must pass the authentication check. In order to pass the authentication check, the adversary should generate a couple of public and secret keys. The bits of the public key pointed out by the secret mask of the node that will do the check must be equal to the original ones. Moreover, the rest of the bits of the public key must produce the same hash values. However, the adversary does not know the mask, which is different for every node.  
The adversary could try to pass the authentication check with a brute force attack, by generating many couples of public and private keys. However, in order to find an eligible public key to pass an authentication check, the adversary should execute a handshake directly with a original node. Therefore, the time per attempt is long and the attack is not possible.
# An Updated Review: _Opuntia focus indica_ (OFI) Chemistry and Its Diverse Applications
## 3 Applications
### Pharmacological Ability
#### 3.4.1 Antioxidant Capacity  
The total polyphenic compounds, obtained from the aqueous extract of dehydrated flowers and peels from OFI, have shown activity in controlling radical scavenging against hydroxyl anions and superoxide. When the antioxidant activity of dehydrated flower and peel extract was observed, the _2,2-diphenyl-1-picrylhydrazyl_ (DPPH) scavenging action was lower in the peel (5.17 g G.A.E. kg/D.W.) (Gallic acid equivalent per kg of dry weight) than in the flower (30.40 g G.A.E. kg/D.W.). These results showed that the antioxidant action of OFI peels was six times lower than that of the flower [90]. The antioxidant action of seed oil from OFI extraction was found in three different solvents, C\({}_{6}\)H\({}_{14}\), C\({}_{2}\)H\({}_{5}\)OH, and C\({}_{2}\)H\({}_{5}\)-acetate, exhibiting noteworthy scavenging motion towards free radicals (DPPH). The oil extract in C\({}_{2}\)H\({}_{5}\)-acetate has a maximum antioxidant action of 274 (\(\mu\)mole TE/20 mg), followed by C\({}_{2}\)H\({}_{5}\)OH 247.00 (\(\mu\)mole TE/20 mg) and C\({}_{6}\)H\({}_{14}\), which have the lowest values. The antioxidant action of oil is greatly influenced by the solvent for extraction [91]. In addition, several typical models showed that Betalain dyes have notable antioxidant action. The UV-Visible spectroscopy technique was used to analyze the antioxidant action of different samples simply and quickly by examining the change in the color of the DPPH radical scavenging activity assay from a dark purple hue to a bright yellow. The highest contents of polyphenic compounds in the purple peel of _Opuntia_ spp. exhibited the maximum scavenging activity as compared to yellow and others. Furthermore, the antioxidant activity increased when the number of polyphenic compounds increased [92]. According to [PERSON] [93], the use of _Opuntia_ polyphenic compounds causes a decrease in rat liver damage. Moreover, the oven drying peel is used in baking goods as an additive due to its antioxidant ability [94].
## 2 Materials and Methods
### Cell Viability and Proliferation Assays  
Human embryonic kidney cell line (HEK 293T), human brain cancer cell line (U251) and human gastric epithelial cell line (GES-1) were seeded at a density of 1 \(\times\) 10\({}^{4}\) cells per well in the 96-well plates, and then added with 50 \(\upmu\)L of PBS and various concentrations (0.5 nM, 5 nM and 50 nM) of GSH-CdTe/CdSe QDs. After the additional 24 h and 48 h incubation, cell survivals were measured using a tetrazolium salt MTT assay. Fresh growth medium (180 \(\upmu\)L) and MTT (20 \(\upmu\)L, 5 mg\(\cdot\)mL\({}^{-1}\)) solution were added to each well. The plate was incubated for 4 h, and then 200 \(\upmu\)L of dimethyl sulphoxide (DMSO) was added to each well to dissolve the purple formazan crystals. Finally, the absorbance at 492 nm of each well was measure.
## 3 Experimental Set-Up and Procedure
### Electro-Dynamic Vibrator and Instruments  
An electro-dynamic exciter (Uttrakhand, India) (MEV-0020) was used to produce vibration. The system generated the vibrations with the drive coil connected rigidly to the moving platform and positioned. When AC flows in the drive coil, it gives rise to force by converting an electric current into mechanical vibrations that move the platform. The exciter was connected to the power amplifier cum signal generator (MPA-0500) to control the mechanical force. The function of a power amplifier cum signal generator was used to generate and amplify the signal sufficiently to drive the exciter to the desired frequency.  
The dynamic response of the confined granular soils was monitored using two accelerometers, one PCB piezoelectric accelerometer (353B03) with low noise cable (M003), and one accelerometer (NP-3412) with signal cable (S381) (Haryana, India) (Figure 2). The PCB (353B03) has a sensitivity of 9.98 mV/g, transverse sensitivity of 0.9%, and resonant frequency of 56.1 kHz. The accelerometer NP-3331 has a sensitivity of 5.038 mV/g. Both sensors were connected to an _FFT_ analyser (CF-9200). The _FFT_ analyser has a bandwidth of 100 mHz to 100 kHz and a frequency tolerance of \(\pm\)0.005%. The PCB accelerometers were attached to the centre of the granular fill to measure the vertical vibration. An accelerometer (NP-3412) was employed on one side of the tank to monitor the horizontal vibration. The data collected were the _FFT_ spectrum and _FRF_ (frequency response function) of vibration for various exciting frequencies.  
To measure the effect of vibration on the voltage output of piezoelectric patches, the positive and negative node of the _PZT_ is connected to an oscilloscope (DS110 ZZ-E) using the probes (PVP 3150). The output data are obtained in the form of voltage vs. time.
## 2 Automatic Optimization System Framework
### Automatic Optimization Module Based on NSGA-II Algorithm  
#### 2.2.1 Fast Non-Dominated Sorting  
The fast non-dominated sorting strategy is an optimization strategy chosen in this study to address the heat source layout optimization problem. This strategy was initially proposed by [PERSON] et al. to reduce the complexity of multi-objective evolutionary algorithms [18]. Due to its significant low computational cost and stable convergence, this study applied this strategy to the NSGA-II optimization algorithm, and the algorithm process is illustrated in Figure 2:  
(1) Population solution set: find population solution set by the MCM thermal model, and use it as input for the next step.  
(2) PARETO-dominated sorting: first, determine the non-domination rank \(n\) (\(n=0\), 1, 2,...) and compare each individual with every other individual to find the PARETO front [19]. The group of individuals with the non-domination rank \(n\) will be used as input for the next step.  
(3) Rank sorting: put the input solution into the solution set at domination rank \(n\), then update the domination rank to \(n+1\) and the remaining individuals not in the \(n\) rank solution set in the input for the previous step, forming a loop with the previous step until there are no more remaining individuals.  
(4) Output sorting: output all domination ranks.  
Fast non-dominated sorting is a strategy that quickly filters out the PARETO optimal population of this generation. However, relying solely on this strategy can lead to results getting stuck in local optimum. To achieve global optimum, another strategy will be introduced below.  
Figure 2: Fast non-dominated sorting flow chart.
# Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles
## 2 Proposed Method
### Segmentation  
The segmentation stage splits the file recordings into as many syllables as possible to yield useful information for the taxonomy identification. The procedure has been developed based on the Harma segmentation algorithm [20]. It uses short-time analysis to divide the acoustic signal into a set of frequency and amplitude modulated pulses, where each pulse corresponds to one detected call. For this purpose, the acoustic signal spectrogram was calculated utilizing Short Time Fourier Transform (STFT) with a Hamming window size of 5.8 milliseconds and a 33% overlap, with which they have been heuristically computed. The spectrogram is represented by a matrix \(S(f,t)\), where \(f\) is the frequency and \(t\) denotes the time. Then, the algorithm explores the matrix using the following strategy:  
1. Find \(t_{n}\) and \(f_{n}\) such that \(|S(f_{n},t_{n})|\geq|S(f,t)|\forall(f,t)\), placing the \(n\)th syllable in \(t_{n}\). The amplitude of this point is calculated as Equation (1): \[Y_{n}(0)=20\text{log}_{10}(|S(f_{n},t_{n})|)\] (1)
2. If \(Y_{n}(0)<Y_{0}(0)-\beta dB\), the segmentation process is stopped, as the signal amplitude is inferior to the stopping criteria \(\beta\). For reptile sounds, \(\beta\) has been set to 25 dB.
3. From \(t_{n}\), seek the highest peak of \(|S(f,t)|\) for \(t>t_{n}\) and \(t<t_{n}\), until \(Y_{n}(t)<Y_{n}(0)-\beta dB\) for both sides. Thus, the starting and ending times of the \(n\)th syllable are denoted as \(,t_{n}-t_{s}\) and \(t_{n}+t_{e}\).
4. Save the amplitude trajectories as the \(n\)th syllable.
5. Delete the \(n\)th syllable from the matrix \(S(f,t_{n}-t_{s},\cdots,t_{n}+t_{e})=0\) and set \(n=n+1\).
6. Repeat from Step 1 until the end of the spectrogram.  
Figure 2 shows four spectrograms belonging to several kinds of reptiles: _Crotalus atrox_ (Diamondback rattlesnake), _Gecko_, _Alligator mississippiensis_ (American alligator), and _Chelonoides nigra_ (Galapagos giant tortoise). The alligator spectrogram presents a complex and most rich vocalization similar to birds, while the other species present a simpler sound production of hissing and groan calls.  
Figure 1: Automatic acoustic classification system diagram.
# Comparison of the Usefulness of CBCT and MRI in TMD Patients According to Clinical Symptoms and Age
## 2 Materials and Methods  
### Subjects  
This study included 325 patients (age range: 12-74 years; mean age: \(33.3\pm 16.23\) years) with clinical symptoms of TMD who underwent both CBCT and MRI examinations within a 2-month interval, from January 2019 to December 2019, at Yonsei University Dental Hospital. Patients with a TMJ fracture, cyst, tumor and inflammation affecting the TMJ were excluded. Patients who received more than 2 months of treatment, including medication, physical therapy and splint therapy were excluded. The right and left joints were reviewed separately in each patient and the joints that did not cause symptoms were excluded. Finally, 473 joints associated with TMD symptoms were included in this study. The study was approved by the Institutional Review Board of Yonsei University Dental Hospital.  
Clinical symptoms were classified as sound, pain and limited mouth opening, corresponding to the main symptoms of TMD. Patients with multiple clinical symptoms were recorded multiple times, since the analysis was conducted on the symptom level, not on the patient level.
# Topology Optimization and Testing of Connecting Rod Based on Static and Dynamic Analyses
## 3 Computational Analysis
### Static Structural Analysis  
Static structural analysis was performed in the ANSYS workbench, and engineering data were added to the material library for the material 36 MnVS4. Then, the initial connecting rod's CAD model was imported into the ANSYS environment. Figure 4 shows the boundary conditions and mesh properties of the static structural analysis used in the initial design of the connecting rod. Mesh properties are important in structural analysis, as higher mesh resolution leads to more accurate simulation results [18]. As a boundary condition, the big end of the connecting rod was fixed, and a load of 64,500 N was applied to the small end as shown in Figure 4a. The mesh was then generated, as shown in Figure 4b.  
Calculation of force acting on the piston due to combustion (\(f_{C}\)).  
For gasoline engines, their maximum peak pressure is between 70 bar and 130 bar [19].  
Figure 3: Topology optimization and validation process workflow.  
Assuming the peak pressure (\(P\)) as 125 bar for the gasoline engine, the area of the piston (\(A\)) was determined using a piston diameter (\(d_{p}\)) of 81 mm in the following equation:  
\[A=\pi\times\left(\frac{d_{p}}{2}\right)^{2} \tag{3}\]  
The area of the piston (\(A\)) was therefore 0.005153 m\({}^{2}\).  
\[f_{C}=P\times A \tag{4}\]  
\[f_{C}=64,412.5\approx 64,500\,\mathrm{N} \tag{5}\]  
The force acting on the piston due to combustion (\(f_{C}\)) was 64,500 N.  
As a boundary condition, the big end of the connecting rod was set as fixed, and the combustion force of 64,500 N was applied directly on the piston and carried to the small end of the connecting rod. When the combustion force was applied on the small end of the connecting rod, a buckling load acted on the shank part of the connecting rod as a result, causing it to experience compressive stress, which may have led to bending or deformation. Later, meshing was performed and 302,691 nodes with 209,641 elements were generated for a resolution of 5, slow transition, and fine span angle center as shown in Figure 4. Once the mesh was generated, the loading conditions were applied based on the peak pressure.  
Based on the boundary conditions, the obtained maximum von Mises stress was 5.8556 \(\times\) 10\({}^{8}\) Pa and the maximum von Mises strain was 0.0028.
## 5 Conclusions  
This study compared absorbed dose to water conversion coefficients among the three most common dosimetry protocols--TRS-398, TG-51, and DIN--and showed a high degree of consistency in measurements. Chamber-to-chamber variations in absorbed dose measurements were insignificant, although minor uncertainties in the results were seen when using cylindrical chambers instead of plane-parallel chambers. Estimated uncertainties for cylindrical ion chambers were slightly lower than those measured using plane-parallel chambers. Furthermore, significant uncertainties arose from the beam quality correction factor. We concluded that equipping standard laboratories with medical linear accelerators may reduce uncertainties.  
Conceptualization, I.I.S., F.E.M.E. and F.H.; methodology, I.I.S., F.E.M.E. and W.K.; validation, M.H.E. and A.M.E.-K.; formal analysis, F.H., A.M.E.-K. and M.H.E.; investigation, F.E.M.E. and W.K.; resources, W.K. and F.H.; data curation, F.E.M.E.; writing--original draft preparation, F.E.M.E., W.K. and F.H.; writing--review and editing, I.I.S., A.M.E.-K. and M.H.E.; visualization, A.M.E.-K. and F.H.; supervision, I.I.S.; funding acquisition, I.I.S. and M.H.E. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding this work through Research Group No. RG-21-09-45.  
Not applicable.  
Data Availability Statement: Data available upon request.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Influence Quantities**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Evaluation Type**} & \multicolumn{2}{c}{**Cylindrical Chamber**} & \multicolumn{2}{c}{**Plane-Parallel Chamber**} \\ \cline{3-6}  & & & **TRS-398** & **DIN 6800-2** & **TRS-398** & **DIN 6800-2** \\ \hline N\({}_{\text{D,W}}\) & Chamber certificate & B & 0.55 & 0.55 & 0.55 & 0.55 \\ Depth of measurement & Calculated & B & 0.33 & 0.33 & 0.33 & 0.33 \\ \(k_{pd}\) & Calculated & A/B & 0.04 & 0.04 & 0.12 & 0.12 \\ \(k_{TP}\) & Calculated & A/B & 0.01 & 0.01 & 0.01 & 0.01 \\ \(k_{S}\) & Calculated & B & 0.04 & 0.04 & 0.12 & 0.12 \\ \(k_{Q}\) & IAEA TRS-398 & B & 1 & 1 & 1 \\ \(k_{E}\) & DIN 6800-2 & B & 1 & 1 & 1 \\ Interaction coefficient & IAEA TRS 277 & B & & & & \\ Dosimeter stability & Dosimeter manual & B & 0.28 & 0.28 & 0.28 & 0.28 \\ Dosimeter reading & Calculated & A & 0.20 & 0.20 & 0.20 & 0.20 \\ \multicolumn{2}{c}{Combined uncertainty (k = 1)} & 1.24 & 1.24 & 1.25 & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relative uncertainty (%) associated with absorbed dose to water, N\({}_{\text{D,W}}\), measurements using the IAEA TRS-398 and DIN 6800-2 dosimetry protocols.
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 6 Discussion  
After applying the optimization methods to both relational MySQL and document-based MySQL and running performance tests on CRUD operations before and after these optimizations, the impact of these optimization methods on each database is summarized in Table 15.  
In the case of an insert operation, optimizations do not have an important impact, neither in relational MySQL nor in document-based MySQL, mainly because an index is already used in relational MySQL.  
For the update operations, the optimizations improve the database performance, thus reducing the execution time when queries are more complex, more tables are used, more fields are updated, and more fields were used in the filter condition. For the simple update operation, the differences are not so great.  
However, the proposed optimization methods have a great impact on relational MySQL in the case of more complex selections, such as selections that do not use primary keys, in which different functions were applied on different fields, and that have more fields in the search condition and use more tables; for those, response times before optimization are very long, but after optimization, these response times decrease greatly, in some cases by 80%, being much faster or as fast as document-based MySQL before optimizations.  
Figure 14: Performance time (ms) for the soft delete operation.  
For the soft delete operation, the optimization also brings an important impact on relational MySQL, reducing the execution times, but in the case of the hard delete operation, the impact is much lower.  
Both databases were easy to configure and to integrate in the application, the same MySQL 8.0.25 version being used in both cases. For relational MySQL, queries could be written manually in repositories or various frameworks could be used where certain general operations are predefined, while for document-based MySQL, the library offers parametrized predefined methods for all operations.
## 3 Detailed Description of the Existing Protocols
### Letr  
A homogeneous model is proposed in LETR as shown in Figure 6. It is clear from the figure that sensor nodes are randomly deployed in the network [14] and multiple sinks (housed with both acoustic and radio modems) are placed at the sea surface. These nodes are provided with GPS facility to determine their location. Initially, all sensor nodes broadcast the beacon message, while the sink nodes only once. The size of this beacon message is kept small (to minimize the EC). With the passage of time, the location of the nodes get updated and become ineffective. Therefore, sensor nodes broadcast periodic beacon (to be up to date). LETR prioritizes adaptive transmission range adjustment over DA to save the extra EC. It performs geographic and opportunistic routing by incorporating the location  
Figure 4: System model of co-improved hydrocast protocol.  
Figure 5: Back transmission and low pressure level nodes selection in co-improved hydrocast protocol.  
error with DA and transmission range adjustment. In the end, data reaches successfully at the sink node. We assumed that, if a DP is successfully received at one sink, it means that data is successfully delivered to the control station. The working of the protocol is as follows:  
1. In LETR, every node performs periodic beaconing (which is basically broadcasted). However, each sink node broadcasts a single beacon message.
2. This message is broadcasted to remain up to date with neighbor node information (the neighbor nodes are selected using angle based neighbor selection [14]) lies in its vicinity.
3. Transmission power of the nodes is divided into \(k\) different levels.
4. This division helps the nodes during void hole recovery.
5. When a node does not receive a beacon, it means that it is declared as a void node. Therefore, it adaptively adjusts their transmission range (step I).
6. After DA, each sensor node broadcasts the beacon message once again. This beacon message includes relative information of their neighbor nodes existing in its vicinity, i.e., location information and current clock time in beacon message (to identify the recent beacon from a node).
7. If a node does not find the eligible forwarder node in its first stage (after step I), then node adjust their transmission power and continue the process to eliminate the void node (step II).
8. Then, the current source node finds the potential node existing at that stage (after step II) and sends a packet of acknowledgment including coordinates of the node.
9. The DA is rare in LETR. Therefore, the beaconing mechanism is used rarely.
10. This process remains continuous until all the data reaches the destination.  
The detailed description of this protocol with the algorithm is given in [14].
## 4 Experiments and Result Analysis
### Model Evaluation Metrics  
The performance of the proposed birdsong classification model was evaluated using four metrics such as accuracy, precision, recall, and F1-score, which are calculated as shown in Formulas (10)-(13).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Test Environment** & **Type** \\ \hline System & Windows 10 64-bit \\ Graphics card & NVIDIA GeForce RTX A6000 \\ CPU & AMD Ryzen 95,920\(\times\)@4.8 Ghz \\ RAM & 128G \\ Framework & PyTorch2.3.1 \\ Programming language & Python 3.10 and Matlab 2020a \\ \hline \hline \end{tabular}
\end{table}
Table 2: The settings of experiments.  
Figure 6: The architecture of the proposed DBS-NET for birdsong classification. It consists of an unsupervised branch based on SimCLR and a supervised branch built upon Res-iDAFF. The unsupervised branch uses SimCLR to extract features from unlabeled data, while the supervised branch enhances feature extraction from birdsong spectrograms by modifications to the residual feature fusion module and the integration of a more complex linear classifier. For branch integration, we employ a weight assignment strategy to balance the contributions of both branches.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\] \[\text{Precision}=\frac{TP}{TP+FP}\] (11) \[\text{Recall}=\frac{TP}{TP+FN}\] (12) \[\text{F1-Score}=\frac{2 TP}{2 TP+FP+FN} \tag{13}\]  
TP (True Positive) is the number of instances in which the model correctly predicts positive results. TN (True Negative) is the number of instances that the model correctly predicts as negative. FP (False Positive) is the number of instances that the model incorrectly predicts as positive. FN (False Negative) is the number of instances that the model incorrectly predicts negative.