esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 5 Experimental Results
### Datasets
#### 5.1.2 CoMoFoD  
This dataset contains 200 original images with a size of \(512\times 512\) pixels that have undergone various geometric transformations, which are divided into five categories: scaling, rotation, translation, deformation, and combination. Each category contains 40 examples of tampered images, with different tampering methods applied to each category, including noise, blur, JPEG compression, rotation, and scaling. The CoMoFoD dataset contains a total of 10,400 images, in which the area of the replicated region ranges from 0.14 to 14.3% of the image area.  
Figure 4 displays original images, tampered images, and corresponding binary ground truth maps from both datasets.  
Figure 4: Examples of image copy-move forgery in the dataset. First row: original images; second row: forged images; third row: binarized truth images.
## 2 Four Channels Generator
### Solid State Marx Modulator  
The simplified topology, of each positive Marx modulator, MM, with 8 stages, capable of delivering repetitive positive high voltage output pulses to a load, between HV+ and HV\(-\), using MOSFETs as on-off switches, is presented in Figure 2, where each stage comprises a diode D\({}_{\text{i}}\), a capacitor C\({}_{\text{i}}\), and two switches, S\({}_{\text{ci}}\) and S\({}_{\text{pi}}\).  
Figure 1: General schematic layout of the four channels generator, including: capacitor charging power supply, CCPS; four Marx modulators, MM; main control board and trigger signals generation board; PC user interface.  
The basic circuit operation, of Figure 2 circuit, can be understood considering two main operating modes. First the charging mode of the main capacitors \(\mathrm{C_{i}}\), for \(\mathrm{i}\in\)[1; 2; 3; 4; 5; 6; 7; 8], from the capacitor charging power supply, CCPS, \(\mathrm{U_{dc}}\), connected to terminals PS+ and PS-, where \(\mathrm{S_{ci}}\) and \(\mathrm{S_{pi}}\) switches are, respectively, on and off. In this operating mode, the HV+ terminal is grounded, resetting the load. Then, the positive pulse mode, where switches \(\mathrm{S_{ci}}\) and \(\mathrm{S_{pi}}\) are, respectively, off and on, if applying full voltage, approximately, \(\mathrm{V_{out}}\approx 8\mathrm{U_{dc}}\) into a load, connected to terminals HV+ and HV-. A more detailed description of the operation of the circuit is Figure 2 is given [27].  
It is possible to apply a lower voltage into the load, also, different from \(8\mathrm{U_{dc}}\), by choosing the number of \(\mathrm{S_{pi}}\) switches to set on. For example, if \(\mathrm{S_{p1}}\) is not set on, during pulse mode, the load current flows through the \(\mathrm{S_{c1}}\) anti-parallel diode and bypass stage one, then the output voltage is \(7\mathrm{U_{dc}}\) in this situation.  
Considering the operating modes described, one can produce at the output a sequence of pulses with different amplitudes, up to 8, and various pulse widths, depending on the time \(\mathrm{S_{pi}}\) switches are set on. Clearly, in order to do so, each stage much be triggered independently, which increases the control circuits complexity. The control circuit is described in the next sections.
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 4 System Architecture and Design
### Software-Defined Semantic Orchestration  
A key innovation of our system is the semantic orchestration layer, which functions as the intelligent controller coordinating task execution among distributed agents. Situated either centrally in the cloud or dispersed across edge nodes, this software-defined orchestration layer maintains task definitions and policy instructions in a unified, human-readable, and machine-interpretable format. To facilitate precise task specification, we developed a Domain-Specific Language (DSL) grounded in semantic programming concepts. The DSL explicitly defines conditions of interest (patterns of sensor data), actions triggered by these conditions, and the contextual goals or purposes influencing execution. For example, a defined task (\"FallAlert\") might state: if a fall is detected by a motion sensor and the patient's heart rate exceeds 100 bpm, an alert (\"Possible fall with tachycardia\") is triggered under an \"emergency_response\" context.  
These task definitions are loaded into the orchestration engine and dynamically decomposed into subtasks allocated between edge devices and the cloud, based on local data availability and computational requirements. Tasks such as fall detection, utilizing local sensors and immediate physiological readings, can be autonomously managed at the edge without cloud involvement, thus optimizing latency and resource use. Conversely, tasks needing broader contextual or historical data analysis are designated for cloud execution.  
A significant advantage of this semantic, software-defined architecture is the ease of real-time adaptability. High-level task rules--rather than hard-coded behaviors--are centrally defined and disseminated across agents, enabling instant, network-wide implementation of new medical guidelines without device firmware updates or manual reconfiguration [44]. Furthermore, our orchestration framework includes a feedback loop, capturing alerts and performance metrics from edge agents to the central controller, facilitating continuous refinement of task parameters and thresholds.  
Simulation experiments validated the robustness and responsiveness of this architecture. For instance, modifying the nighttime heart-rate monitoring sensitivity remotely (from low to high) prompted immediate adaptation by edge agents, adjusting their alert threshold without downtime. Overall, the semantic orchestration layer demonstrates effective integration of the DIKWP cognitive model into an adaptive, intelligent IoT health  
Figure 5: Continuous edge–cloud collaborative learning loop.  
care infrastructure, dynamically translating high-level medical protocols into coordinated, real-time sensor and agent behaviors.
# Sigma Routing Metric for RPL Protocol
## 2 RPL Protocol
In this paper, the performance of RPL in various traffic scenarios was investigated through experimental measurements [14; 15]. In addition, an adaptation of a specific metric for the RPL protocol in the objective function MRHOF is presented.
## 3 Results and Discussion
In this study, LDMC and LTD gradually increased with the increase of parasitic intensity, and showed a very significant positive correlation. Figure 8: Test of chlorophyll inversion model based on red valley reflectance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **LT** & **LA** & **SLA** & **LDMC** & **LTD** & **CCI** \\ \hline LT & 1 & & & & & \\ LA & \(-\)0.1696 & 1 & & & & \\ SLA & \(-\)0.1502 & 0.3581 * & 1 & & & \\ LDMC & \(-\)0.1293 & \(-\)0.4246 * & \(-\)0.6991 ** & 1 & & \\ LTD & \(-\)0.5436 * & \(-\)0.4218 * & \(-\)0.5950 ** & 0.7517 ** & 1 & \\ CCI & 0.2566 & 0.2623 & 0.4993 * & \(-\)0.2201 & \(-\)0.4456 * & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation between plant functional traits indicators. * indicates a significant correlation between functional traits at the level of \(p<0.05\), and ** indicates a significant correlation between functional traits at the level of \(p<0.01\). This indicated that the host plant can improve the nutrient retention ability of leaves under the adverse environment of parasitic stress, and thus making more effective use of limited resources [58; 60]. The increase of LTD was beneficial to strengthen the defense ability of plant against biological factors [61]. To sum up, after being parasitized, the leaf functional traits of the host plant were generally characterized by large leaf thickness, small leaf area, small specific leaf area, low chlorophyll content index, high dry matter content and high leaf tissue density. Therefore, we suspect that the leaf economics spectrum may also exist in the parasitic environment, and there was a general trend toward \"slow investment-return\" type in the global leaf economics spectrum (Figure 9).
# Use of Total Least Squares Adjustment in Geodetic Applications
## 2 Solution of the Predetermined Model
### Method of Taking into Account the Presence of Errors in Variables (EIV)  
The method is called total least squares (TLS) [6; 7; 11]. In this case, we consider the predetermined system (1), which results in \(\mathbf{A}\mathbf{x}\cong\mathbf{b}\) and \(\{\text{rank}(\mathbf{A})=u\}\
eq\{\text{rank}([\mathbf{A}|\mathbf{b}])=u+1\}\) due to random errors in measurements. Classical treatment or searching for a solution for the vector of unknowns in the LS method only considers the presence of random errors in measured values (in vector \(\mathbf{b}\)). If we assume, in addition to measurement errors, there is also the presence of errors in variables or unknowns [6; 7], it can be written as:  
\[\mathbf{b}+\mathbf{v}=(\mathbf{A}-\mathbf{E}_{\text{A}})\mathbf{x}, \tag{6}\]  
with the stochastic properties:  
\[\begin{bmatrix}\mathbf{v}\\ \mathbf{e}_{\text{A}}\end{bmatrix}:=\begin{bmatrix}\mathbf{v}\\ \text{vec}(\mathbf{E}_{\text{A}})\end{bmatrix}\sim\left(\begin{bmatrix}\mathbf{0 }\\ \mathbf{0}\end{bmatrix},\sigma_{0}^{2}\begin{bmatrix}\mathbf{Q}_{\text{b}}& \mathbf{0}\\ \mathbf{0}&\mathbf{Q}_{\text{A}}\end{bmatrix}\right), \tag{7}\]  
where \(\mathbf{e}_{\text{A}}=\text{vec}(\mathbf{E}_{\text{A}})\) is a vector, which consists of the columns of matrix \(\mathbf{E}_{\text{A}}\) of the dimensions \((n\cdot u\times 1)\). \(\mathbf{Q}_{\text{b}}\) represents the cofactor matrix of observations and \(\mathbf{Q}_{\text{A}}\) represents the cofactor matrix of unknowns.  
The solution for \(\mathbf{x}\) in Equation (6) can be derived with the help of Lagrange multiplication factors \(\mathbf{k}\) of the dimensions \((n\times 1)\). The condition of the minimum sum of squares is written with a weighting function \(\Phi\)[6]:  
\[\Phi=\mathbf{v}^{\text{T}}\mathbf{Q}_{\text{b}}^{-1}\mathbf{v}+\mathbf{e}_{ \text{A}}^{\text{T}}\mathbf{Q}_{\text{A}}^{-1}\mathbf{e}_{\text{A}}+2\mathbf{ k}^{\text{T}}(\mathbf{b}+\mathbf{v}-\mathbf{A}\mathbf{x}+\mathbf{E}_{\text{A}} \mathbf{x})=\text{min}. \tag{8}\]  
If we unify \(\mathbf{e}_{\text{A}}\) and \(\mathbf{E}_{\text{A}}\) in Equation (8) with the use of the Kronecker product (\(\otimes\)), we obtain:  
\[\Phi=\mathbf{v}^{\text{T}}\mathbf{Q}_{\text{b}}^{-1}\mathbf{v}+\mathbf{e}_{ \text{A}}^{\text{T}}\mathbf{Q}_{\text{A}}^{-1}\mathbf{e}_{\text{A}}+2\mathbf{ k}^{\text{T}}(\mathbf{b}+\mathbf{v}-\mathbf{A}\mathbf{x}+(\mathbf{x}^{\text{T}} \bigotimes\mathbf{I}_{n})\mathbf{e}_{\text{A}})=\text{min}. \tag{9}\]  
For the determination of the minimum  
\[\mathbf{Q}_{\mathrm{g}}=(\overset{\sim}{\mathbf{A}}\mathbf{Q}_{\mathrm{b}}^{-1} \overset{\sim}{\mathbf{A}})^{-1}, \tag{27}\]  
with the corresponding a posteriori reference variance as:  
\[\hat{\sigma}_{0}^{2}=\frac{\hat{\mathbf{v}}^{\mathrm{T}}\mathbf{Q}_{\mathrm{b}} ^{-1}\hat{\mathbf{v}}}{n-u}=\frac{\mathbf{k}^{\mathrm{T}}\mathbf{Q}_{\mathrm{b} }^{-1}\mathbf{k}}{n-u}. \tag{28}\]
## 3 Results and Discussions
### Final Products Characterization  
#### 3.2.1 Physico-Chemical Parameters of Buns  
Table 6 presents the following physico-chemical buns parameters: moisture, ash, total fat, protein, fibers and TTA. The analyzes performed on the final baked products showed that buns moisture increases with the augmentation amount of buckwheat added compared to the control buns manufactured only with wheat flour. This could be explained due to the higher fiber content of BW compared to WF, increasing the water absorption capacity of buckwheat flour compared to the wheat. Several authors also reported higher moisture in various bakery products in which buckwheat was added compared to the control sample, attributing this to BW increased water absorption capacity [40; 41; 42; 43]. On the other side, the addition of sprouted buckwheat flour did not bring any modification of this parameter, the absorption capacity being the same: either 10% or 30% of this flour was added. This could be explained due to the higher fat content of SF compared to BW. The fat content of the raw material could have a directly influence on lowering the water absorption capacity, as previously demonstrated by [57]. The highest values for ash and total fat content were registered by buns supplemented with buckwheat sprouts flour followed by the products with addition of buckwheat flour. As we expected, wheat-based products had the lowest levels concerning the ash and total fat content. The highest fiber content was identified in samples with the addition of SF, followed by samples manufactured with BW. The samples made with WF registered the lower fiber values content. This could be explained by the richest chemical composition of SF fiber (4.67%) compared to the BW content (4.08%), and WF content (1.14%), respectively Table 6. The use of improver and guar gum did not change in a statistically significant way the final fiber content. [PERSON] et al. [58] reported for wheat bread 1.98% fiber content, 2.58% fiber content for the bread with 10% buckwheat flour and a value of 3.16% for the product with 20% buckwheat flour. These results are higher than those obtained in the present study, probably due to the fiber initial raw materials
# A Survey of Internet of Things (IoT) Authentication Schemes +
## 5 Analysis of IoT Authentication Schemes
### Wireless Sensor Networks
This protocol consists of a number of features: a two-factor authentication (smart card and password), mutual authentication between sensors, symmetric cryptography to ensure message confidentiality and the ability to change passwords. In [123], the authors provided a two-factor-based user authentication scheme for WSN using both passwords and smart cards. The approach is resistant to stolen-verifier and impersonation attacks, but it does not take into consideration node impersonation attack. The authors of [124] proposed a new anonymous authentication scheme for IoT based on the lightweight ECC. The protocol is composed of two main steps: the registration step and the authentication step. A comparison is held between the ECC and RSA with respect to the time spent for encryption and decryption using different sizes of key. The authors of [125] presented a secured way for the authentication of devices in Internet of Medical Things (IMT) using the physical characteristics of people for identification. The authors provided a survey of all the biometric techniques used in the literature especially in the smart health environments with an analysis regarding their compatibility with the IMT, a security analysis is also provided. Finally, the authors provided requirements and open issues for researchers and developers while using biometric properties for authentication. The authors of [126] proposed a novel mutual authentication and key management for WSN systems based on biometric and symmetric crypto-system. They compared their scheme with the schemes provided in the literature regarding computation and communication cost and security threats using both BAN-logic and AVISPA [127] tools.
## 1 Introduction
### The Wear Method Selection  
Each of the methods for measuring or evaluating the wear of disc cutters has some advantages and disadvantages. It would be pointless to determine which method is better than the others as they are all suitable for a specific purpose. The wear methods are generally differentiated by four influencing factors which are listed in Table 1:  
- Measuring and/or processing equipment;  
- Execution time;  
- Cost of equipment and personnel;  
- Location of measurement or evaluation.  
The accuracy of each method depends on the accuracy of the equipment used and the operator performing the measurements or evaluation. The choice of wear assessment method depends on the purpose. Experience shows that in general, a combination of several methods simultaneously provides the best results and a comprehensive insight into the wear of disc cutters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Equipment**} & **Time Expenditure** & **Estimated Cost** \\  & & **(Fast/Medium/Slow)** & **(Low/Medium/High)** \\ \hline \multirow{3}{*}{Close-range} & digital camera & \multirow{3}{*}{slow} & \multirow{3}{*}{medium} \\  & accessories for & & \\ \cline{1-1} photogrammetry & photography & & \\  & computer software & & \\ \hline \multirow{3}{*}{Laser scanning} & laser scanner & \multirow{3}{*}{slow} & \multirow{3}{*}{high} \\  & accessories for scanning & & \\  & computer software & & \\ \hline \multirow{2}{*}{Weighing} & weighing scale & fast & low \\ \hline Profile gauge & wear ruler & fast & low \\ \hline \multirow{2}{*}{Caliper} & digital or mechanical & \multirow{2}{*}{medium} & \multirow{2}{*}{low} \\  & caliper & & \\ \hline \multirow{3}{*}{Numerical modeling} & equipment for laboratory & \multirow{3}{*}{slow} & \multirow{3}{*}{high} \\  & testing of material (steel & & \\ \cline{1-1} \cline{4-4}  & and rock) & & \\ \cline{1-1}  & computer software & & \\ \hline \multirow{3}{*}{Microscopic inspection} & microscope & \multirow{3}{*}{slow} & \multirow{3}{*}{high} \\  & equipment for obtaining & & \\ \cline{1-1} \cline{4-4}  & metallographic & & \\ \cline{1-1}  & cross-sections & & \\ \hline \multirow{3}{*}{Macroscopic inspection} & \multirow{3}{*}{/} & fast & low \\ \cline{1-1}  & & & \\ \cline{1-1} \cline{4-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of frequently used methods for the high-precision measurement and evaluation of the wear of disc cutters with a comparison according to the most important influencing factors.
## 5 Numerical Results and Discussion
### System Performance Comparison  
Regarding the two performance indexes of PU1 packets' throughput \(T_{P1}\) and blocking rate \(B_{P1}\), we designed numerical experiments to make curve comparisons between the proposed failure and repair mechanism with classified PUs and the traditional failure and repair mechanism with unclassified PUs. In the traditional mechanism, unclassified PUs mean that PU packets generated by PUs with different demands are transmitted equally.  
Figure 3: The performance change trends of SU packets: (**a**) the change trend in \(B_{S}\); (**b**) the change trend in \(L_{S}\); (**c**) the change trend in \(T_{S}\).  
There is no mutual preemption in different system states, and they have the same rights. The parameters of each numerical experiment are displayed in Table 2.  
Figure 4 reflects the comparison results of the changes in PU1 packets' throughput and blocking rate with the communication failure rate under the two mechanisms.  
By observing Figure 4, we can find that under both of the two mechanisms, for PU1 packets, the throughput decreases and the blocking rate increases while the communication failure rate increases, but our proposed mechanism can enhance the throughput of PU1 packets and reduce the blocking rate of PU1 packets compared to the mechanism with unclassified PUs. So, prioritized PU packets and different preemption principles make PU1 packets enjoy certain privileges and meet higher transmission requirements.  
According to Figure 4, when the normal transmission rate of PU1 packets \(\beta_{11}\) increases, PU1 packets' throughput \(T_{P1}\) increases and blocking rate \(B_{P1}\) decreases. With the increase in \(\beta_{11}\), the transmission time of newly arrived PU1 packets decreases, the number of PU1 packets with completed transmission increases, and the number of blocked PU1 packets decreases. When the normal transmission rate of PU2 packets \(\beta_{12}\) increases, \(T_{P1}\) also increases and \(B_{P1}\) decreases. When \(\beta_{12}\) increases, PU2 packets take less time to occupy the channel and PU1 packets have more chances to enter the channel. Therefore, the number of PU1 packets with completed transmission increases and the number of blocked PU1 packets decreases.
## 3 Establishment of Water Rut Safety Model and Analysis of Braking and Side Deflection
### Driving Safety Evaluation Model for Water Rut
#### 3.3.2 Side Deflection Safety Evaluation Model  
Similarly, multiple linear regression models of side deflection time thresholds and rut width, left and right rut depth difference, rainfall intensity, and driving speed can be established based on the simulation results of 13 operating conditions (3, 5, 6, 7, 11, 12, 13, 14, 15, 16, 17, 18, and 19). The obtained vehicle safety evaluation model based on side deflection time thresholds is a regression equation as shown in Equation (15):  
\[T=25.644+2.423W_{R}-1.165D_{d}-0.103\beta_{3}I-0.097V\quad(R^{2}=0.97) \tag{15}\]  
where \(T\) is critical side deflection time (m), \(W_{R}\) is the rut width (m), \(D_{d}\) is the left and right rut depth difference (mm), \(I\) is the rainfall intensity (mm/h), and \(V\) is the driving speed (km/h).  
An analysis of variance was performed on the model and the results are shown in Table 2.  
Figure 13: Evaluation of the safety of the side deflection of a waterlogged rut.  
The \(p\)-value of the F-statistic of the regression equation of this model is \(2.16\times 10^{-5}\), which is much smaller than the significance level of 0.05. Therefore, the evaluation model of the waterlogged rut traveling side deviation (Equation (15)) constructed in this paper has a significant regression effect at a confidence level of 95%.
## 3 The Proposed MPPT Algorithm
### Filter Sizing  
In order to ensure that inductor voltage drop and the capacitance current absorption are negligible with respect to their rated values, the maximum values of \(L\) and \(C\) can be found by Equation (6):  
\[\left\{\begin{array}{l}\frac{\mathrm{d}i}{\mathrm{d}t}=\frac{\mathrm{d}t}{( +i\frac{\mathrm{d}t}{\mathrm{d}t})}\\ \frac{\mathrm{d}v}{\mathrm{d}t}=\frac{\mathrm{d}t}{(+i\frac{\mathrm{d}t}{ \mathrm{d}t})}\end{array}\right.\Rightarrow\left\{\begin{array}{l}C\frac{ \mathrm{d}i}{\mathrm{d}t}\Big{|}_{\mathrm{max}}=C\frac{p_{slope}}{(v_{mpp}-i_{ nnp}R_{2})}<\delta_{i}\cdot i_{mpp}\\ L\frac{\mathrm{d}v}{\mathrm{d}t}\Big{|}_{\mathrm{max}}=L\frac{p_{slope}}{(v_{mpp}-i_{ nnp}G_{2})}<\delta_{v}\cdot v_{mpp}\end{array}\right.\Rightarrow\left\{ \begin{array}{l}C<\delta_{i}\cdot\frac{(v_{mpp}-i_{nnp}R_{2})i_{mpp}}{P_{slope }}\\ L<\delta_{v}\cdot\frac{v_{mpp}\big{(}i_{nnp}-v_{np}G_{2})}{P_{slope}}\end{array} \right., \tag{6}\]  
In the series \(R\), \(L\), \(C\) of Figure 2a, it is easy to verify that the PV current is characterized by a damping factor whose expression is:  
\[\zeta=\frac{RC}{2\sqrt{LC}}, \tag{7}\]  
In order to ensure that the variation of the PV current is low, the damping factor must assume a high value (\(\zeta>>1\)). On the other hand, it has to be remembered that, in contrast to the ideal examined case, the real converter will introduce a high harmonic content linked to the chosen switching frequency. This harmonic content will reflect on the PV current via a reduction factor \(\xi_{r}\) depending on the chosen \(L\) and \(C\) values.  
\[\xi_{r}=\frac{j\omega L-j\frac{1}{\omega\mathrm{C}}}{j\frac{1}{\omega \mathrm{C}}}\cong LC\omega^{2}, \tag{8}\]  
In order to ensure that the PV operating point is kept very close to the MPP, the reduction factor must be fixed at a high value. Setting minimum values for the damping factor and the reduction factor, the following conditions have to be satisfied:  
\[\left\{\begin{array}{l}\zeta=\frac{RC}{2\sqrt{LC}}>\zeta_{\mathrm{min}}\\ \xi_{r}=LC\omega^{2}>\xi_{r,\mathrm{min}}\end{array}\right.\Rightarrow\left\{ \begin{array}{l}\frac{R^{2}C}{4L}>\zeta_{\mathrm{min}}\\ \frac{\xi_{r,\mathrm{min}}}{LC}>\frac{\xi_{r,\mathrm{min}}}{LC}\end{array} \right., \tag{9}\]