esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Resilient Modulus
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multirow{2}{*}{\(\sigma_{3}\)} & \multirow{2}{*}{\(\sigma_{d}\)} & \multicolumn{4}{c}{**RM (MPa) (\%)**} \\ \cline{3-6}  & & & **0 Days** & **7 Days** & **14 Days** & **28 Days** \\ \hline
1 & 0.02 & 0.022 & 115 & 213 (+85.8\%) & 210 (\(-\)1.4\%) & 225 (+7.1\%) \\  
\hline \hline \end{tabular}
\end{table} 
Table 6: Results obtained in RM tests by curing time. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Curing Time** & \(k_{1}\) & \(k_{2}\) & \(k_{3}\) & RM Medium (MPa) & \(R^{2}\) \\ \hline
0 & 1531.6 & 0.56 & 0.08 & 281 & 0.996 \\
7 & 2752.46 & 0.56 & 0.08 & 506 (+80.1\%) & 0.992 \\
14 & 2825.28 & 0.56 & 0.04 & 561 (+10.9\%) & 0.974 \\
28 & 3743.49 & 0.60 & 0.04 & 670 (+19.4\%) & 0.935 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Composite Model Parameters. These findings suggest that a curing time of 7 days can provide satisfactory RM behavior for use in base layers, aligning well with practical design conditions. Despite the continued increase in resistance with longer curing periods, the modulus values determined in the present study are consistent with the typical range of values for granular materials reported by [PERSON] et al. [78] (100 to 400 MPa) and the range proposed by [PERSON] [79] (200 to 350 MPa) and RM values similar to the lateritegravel from the state of Acre, Brazil (566 to 585 MPa) [80]. This further supports the adequacy of a 7-day curing period for achieving appropriate resilience characteristics in base layer applications. Figure 7 presents the mixture's resilient behavior from a three-dimensional perspective. The material is more susceptible to variations due to confining stress, while the RM varies little due to deviatoric stress. Figure 6: Resilient modulus vs. stress graph. (**a**) Constant \(\sigma_{d}\) stress and variable \(\sigma_{3}\) stress; (**b**) Constant \(\sigma_{3}\) stress and variable \(\sigma_{d}\) stress. Figure 7: 3D graph of the resilient behavior of CRAM as a function of curing time conditions, deviator stress, and confining stress applied during the tests. This behavior can be compared with the results presented in the study by [PERSON] et al. [31], who observed that a mixture composed of RAP treated with crushed cement, foam asphalt, and 1% hydrated lime resembled a granular material, as its stiffness depended on the stress state. Similarly, [PERSON] et al. [42] found that mixtures without adding cement also showed dependence on confining stresses. On the other hand, other researchers who concluded that the addition of 1% to 2% cement in CRAMs stabilized with asphalt emulsion produced a mixture independent of the stress state, as a single modulus value was obtained from triaxial resilient modulus tests at different confining pressures. The lesser dependence on the stress state in these mixtures is attributed to the hydration reaction of Portland cement [40; 42; 81].
## 4 Discussion
### Clinical Workflow Integration  
Successful clinical implementation of the proposed OCT classification framework requires thoughtful integration into diverse ophthalmological workflows. In high-volume screening settings, the model could function as an initial triage mechanism, automatically identifying scans that require urgent specialist attention while providing standardized reporting for normal findings. This approach has the potential to significantly reduce the time specialists spend reviewing cases. For referral management, integrating with existing electronic referral systems would enable automated preassessment prioritization based on pathology probability scores. Implementation challenges include variable OCT hardware specifications across clinical environments, necessitating vendor-specific calibration protocols and domain adaptation techniques to maintain consistent performance. Addressing these challenges requires a phased deployment approach: (1) a shadow-mode validation phase where AI predictions are generated but not displayed to clinicians; (2) a supervised implementation phase with AI predictions displayed alongside confidence metrics but requiring expert confirmation; and (3) a selective automation phase where high-confidence normal classifications might bypass manual review in appropriate risk-stratified scenarios. This graduated approach balances the benefits of workflow efficiency against the imperative of maintaining diagnostic safety while allowing for continuous model refinement through feedback loops from clinical outcomes.
## 3 Simulation of the Number of Light Array IDs
From Formula (4), it can be seen that, when \(F_{s}>f_{Tx}\), the width of the stripe will be greater than 1 pixel, and the bright stripes and dark stripes will not overlap, so that the striped light and dark spots can be obtained. Then, the emission sequence of the light source can be obtained. Therefore, the light source transmitting frequency \(f_{Tx}\) should first satisfy \(f_{Tx}<a\times b\). (2) Ensure that the flicker frequency of the light source does not produce dazzling and does not affect the lighting. Flicker is not noticed by the eyes when the flicker frequency is greater than 200 Hz. Therefore, the light source transmitting frequency \(f_{Tx}\) should satisfy \(200<f_{Tx}<a\times b\). (3) Ensure that the 0 run in the sequence does not cause the lighting to be interrupted. The length of an m sequence of \(n_{p}(n_{p}\in N)\) levels is \(2^{n_{p}}-1\), and the sequence with a continuous \"0\" length does not exceed \(n_{p}-1\). The visual persistence of the eyes is 0.1 s to 0.4 s; to ensure that the sequence of long consecutive zero periods is not detected by the eyes, \((n_{p}-1)t_{p}<0.1\) should be satisfied. (4) Ensure that the positioning refresh is timely. The positioning refresh rate is \(G\), and the time required to obtain the emission sequence is  
\[t_{p}=\frac{G}{2^{n_{p}}-1}. \tag{5}\]  
The corresponding emission frequency is  
\[f_{T}x=\frac{1}{t_{p}}=\frac{2^{n_{p}}-1}{G}. \tag{6}\]  
The length of the sequence that satisfies all of the above requirements is shown in Formula (7):  
\[ \begin{cases}200\times G<(2^{n_{p}}-1)<a\times q,0<n_{p}\leq 20.\\ (10n_{p}-10)\times G<(2^{n_{p}}-1)<a\times q,n_{p}>20.\end{cases}  \tag{7}\]  
Once the refresh rate, receiver resolution, and frame rate are determined, the length of the light source's emission sequence can be determined by using Formula (7). In practice, smartphones have a resolution of mostly 1080 p (1920 \(\times\) 1080) and a frame rate of 30 fps. The ideal value of the refresh rate for a global positioning system (GPS) is 1 ms. The emission sequence length is \((2^{n_{p}}-1)\leq 31\) after taking Formula (7). An m sequence of length 31 generates 145 light source array IDs. See Appendix A for details of the IDs (Table A1). The longer the emission sequence, the greater the number of available light source IDs. Conversely, the shorter the emission sequence, the faster the emission sequence can be read. Therefore, in practice, the appropriate length of the emission sequence can be selected according to the specific needs.
## 3 Results
### Evaluations of Three Models under Extreme Conditions  
We assessed the performances of the three GPP models under four types of extreme environmental conditions, as we clarified in Section 2.2.6. The performance comparison of the three GPP models is shown in Figure 7. For extremely wet (High-P), extremely dry (Low-P), and extreme high-temperature (High-T\({}_{\rm a}\)) conditions, the H-LUE model showed significantly better performance, with R\({}^{2}\) (RMSE) = 0.89 (2.28 gC m\({}^{-2}\) d\({}^{-1}\)), 0.74 (1.42 gC m\({}^{-2}\) d\({}^{-1}\)), and 0.90 (2.49 gC m\({}^{-2}\) d\({}^{-1}\)), respectively, as compared to VPM and VPRM. For the extremely low-temperature (Low-T\({}_{\rm a}\)) condition, the H-LUE model yielded notably larger R\({}^{2}\) and  
Figure 6: Performance of the H-LUE model when estimating daily GPP in different PFTs. CRO-C3: C3 crops; CRO-C4: C4 crops; DBF: deciduous broadleaf forests; EBF: evergreen broadleaf forests; ENF: evergreen needleleaf forests; GRA: grasslands; MF: mixed forests; SAV: savannahs; SHR: shrubs; WET: wetlands. The color bars represent the density of points within a circle area with a radius of 1.0 gC m\({}^{-2}\) d\({}^{-1}\).  
comparable RMSE values compared to VPM and VPRM. It should be noted that under the Low-T\({}_{\rm a}\) condition, the ecosystem photosynthesis rate becomes very small, which explains the comparable RMSE between the three GPP models under such conditions. However, the H-LUE model tends to capture more variations in GPP under the Low-T\({}_{\rm a}\) condition. These results suggested notable advantages of the H-LUE model over conventional LUE models under extreme conditions.
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 2 Data Collection Setup  
The mobile device used during the measurement was comprised of a Multitech mDot module [26], which is controlled by a Raspberry pi single board computer. The Kerlink gateway was equipped with LoRa SX1301, a mobile network, and microprocessors [27]. The gateway was located on the rooftop of the George Moore Building at Glasgow Caledonian University (GCU), which is about 27 m high. The mobile device was configured to regularly transmit and receive a sequentially increasing message. This sequential increment also helped to identify any lost messages during the data analysis. Data was collected on the eighth and seventh floor of the Hamish Wood Building at GCU, which has a height of 27 m, length of 60 m, and width of 22.5 m. To cover the entire building, the mobile device was moved to several different locations inside the building. The equivalent isotropic radiation power and frequency of transmission were 14 dBm and 867.1 MHz, respectively. Other configurations were a spreading factor of nine, a bandwidth of 125 kHz, and antenna gains of 2 dBi. Nearly 10 received signal strength indicators (RSSI) were collected at each location. These RSSI values were logged both on the network server and locally on the mobile device. Figure 1 demonstrates the test environment.  
The internal structure of the eighth floor is presented in Figure 2. The mobile LoRaWAN transceiver was moved to sampling locations marked from 1 to 27 to cover the entire area, and the averaged RSSI was calculated at each location. The location numbers and averaged RSSIs are indicated in Figure 2, where they are separated by a comma. There are also a total of 43 walls on each floor; however, most of the walls along the width of the building were not blocking the LoS due to the positioning of the LoRaWAN gateway. The gateway was mounted on the rooftop of the George Moore Building, which is 55 m away from the first obstructing wall, that is indicated by a dashed-line in Figure 2.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 4 Design of Joint Positioning Algorithm
### Joint Positioning Estimation  
Assuming the coordinates of device and TRP positions are denoted as \((x_{0},y_{0},z_{0})\) and \(\mathcal{S}=\left\{(x_{1},y_{1},z_{1}),\ldots,\left(x_{N_{\text{TRP}}},y_{N_{ \text{TRP}}},z_{N_{\text{TRP}}}\right)\right\}\), the corresponding real distance and angle is \(\mathcal{D}=\left\{d_{1},\ldots,d_{N_{\text{TRP}}}\right\}\) and \(\Theta=\left\{(\theta_{1},\phi_{1}),\ldots,(\theta_{N_{\text{TRP}}},\phi_{N_{ \text{TRP}}})\right\}\), (8) and (9) can be re-expressed as  
\[\left\{\begin{array}{l}\hat{d}_{i}=d_{i}+\delta_{i}\\ \hat{\theta}_{i}=\theta_{i}+\delta_{i}\\ \hat{\phi}_{i}=\phi_{i}+\delta_{i}\end{array}\right., \tag{11}\]  
where \(d_{i}=\sqrt{\left(x_{0}-x_{i}\right)^{2}+\left(y_{0}-y_{i}\right)^{2}+\left(z _{0}-z_{i}\right)^{2}}\), \(\phi_{i}=\arctan\left(\frac{z_{0}-z_{i}}{\left(x_{0}-x_{i}\right)\cos\theta_{i }+\left(y_{0}-y_{i}\right)\sin\theta_{i}}\right)\), \(\theta_{i}=\arctan\left(\frac{y_{0}-y_{i}}{x_{0}-x_{i}}\right)\), \(\delta_{i}\) denotes the noise. Correspondingly, the estimated device position \((\hat{x}_{0},\hat{y}_{0},\hat{z}_{0})\) can be obtained as  
\[\left\{\begin{array}{l}\hat{x}_{0}=x_{i}+\hat{d}_{i}\cos\hat{\theta}_{i}\\ \hat{y}_{0}=y_{i}+\hat{d}_{i}\sin\hat{\theta}_{i}\\ \hat{z}_{0}=z_{i}+\hat{d}_{i}\sin\hat{\phi}_{i}\end{array}\right.. \tag{12}\]  
To solve (12) and obtain an accurate position of the target device, the estimated coordinates can be rewritten as  
\[\left[\begin{array}{cc}\hat{x}_{0}&\hat{y}_{0}&\hat{z}_{0}\end{array}\right]= \boldsymbol{\Lambda}\big{[}\begin{array}{cc}\boldsymbol{\Gamma}_{x}& \boldsymbol{\Gamma}_{y}&\boldsymbol{\Gamma}_{z}\end{array}\right], \tag{13}\]  
where \(\boldsymbol{\Lambda}\) is the optimization weight for positioning calculation, and \(\boldsymbol{\Gamma}_{x}\), \(\boldsymbol{\Gamma}_{y}\) and \(\boldsymbol{\Gamma}_{z}\) can be expressed as  
\[\left\{\begin{array}{l}\boldsymbol{\Gamma}_{x}=\left[x_{1}+\hat{d}_{1}\cos \hat{\theta}_{1},\ldots,x_{N_{\text{TRP}}}+\hat{d}_{N_{\text{TRP}}}\cos\hat{ \theta}_{N_{\text{TRP}}}\right]^{T}\\ \boldsymbol{\Gamma}_{y}=\left[y_{1}+\hat{d}_{1}\sin\hat{\theta}_{1},\ldots,y_{ N_{\text{TRP}}}+\hat{d}_{N_{\text{TRP}}}\sin\hat{\theta}_{N_{\text{TRP}}}\right]^{T}\\ \boldsymbol{\Gamma}_{z}=\left[z_{1}+\hat{d}_{1}\sin\hat{\phi}_{1},\ldots,z_{N_{ \text{TRP}}}+\hat{d}_{N_{\text{TRP}}}\sin\hat{\phi}_{N_{\text{TRP}}}\right]^{ T}\end{array}\right. \tag{14}\]
# LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications
## 2 Literature Review  
The task of classifying lung diseases using X-ray images has been a point of discussion for the past decade. Many works have explored the use of convolutional neural networks for the task and achieved reasonably decent results. [PERSON] et al. [16] used a Slim convolutional neural network to classify pneumonia disease from other diseases using a mobile phone, and achieved an accuracy of 94.4%. The authors show that the accuracy score drops significantly by adding a third class for classification, achieving a score of only 84.66%. Upon quantization using TensorFlow Lite, the authors noticed a drop of 2.43% in the binary classification task and a 0.83% drop in the three classes classification task, without clearly highlighting the form of quantization followed. [PERSON] et al. [17] used a CNN model compressed using TensorFlow Lite int-8 quantization to classify six classes from the Chest X-ray14 dataset [18]. The authors reported an accuracy of around 65.82% and highlighted a 6% drop after quantization. [PERSON] et al. [19] used GoogleNet to distinguish Tuberculosis disease from others. The proposed methodology achieved an accuracy of 98.39% for the binary problem described. The authors later deployed the model on an IOS device to assess its functionality, but without highlighting the methodology followed. Other works included the use of a pre-trained NASNetMobile to classify pneumonia, which achieved an accuracy score of 97.39% on the testing data [20]. Generally speaking, it can be noticed that only a subset of the lung diseases dataset is usually taken into consideration, with the maximum number of classes used being that which is carried out in [17] using 6 classes, even though the biggest version of the lung diseases dataset contains 9 different classes. Table 1 summarizes the results obtained in previous work in regard to X-ray imagery classification for mobile devices.
# The Potential of Cannabidiol in the Management of Oral Infections
## 5 CBD Delivery Methods for Oral Infections: Current Strategies and Therapeutic Potential
### Topical Delivery Systems
#### 5.1.2 Mouth Rinses  
CBD-containing mouthwashes provide a convenient method to address generalized microbial colonization and gingival inflammation. Studies have reported significant reductions in plaque accumulation and gingival bleeding, with efficacy comparable to chlorokenidine. However, short retention time due to salivary dilution remains a limitation, potentially necessitating repeated administration [45; 59].
## 3 Hybrid Visual SLAM with Deep Keypoints, Depths, Poses, and Line Features
### Virtual Stereo Visual SLAM
#### 3.3.3 The Motion Model From PoseNet  
In traditional handcrafted feature-based SLAM (i.e., ORB-SLAM3), the tracking module first uses a constant velocity motion model \(\xi^{\text{motion}}_{lc}\) to estimate the relative pose between adjacent frames and then combines the pose \(\xi^{l}_{uv}\) of the last frame to calculate an initial pose \(\xi^{e}_{uv}\) for the current frame. Subsequently, the initial pose of the current frame is used to project the 3D points in the mapping onto the image coordinates of the current frame. The 3D points in the mapping correspond to the keypoints of the last frame. Then we match the keypoints within a certain radius centered on the projected points in the current frame. Keypoints with similarity exceeding a certain threshold are considered to be matching  
Figure 2: The Scale Degradation of Monocular VSLAM In Low-texture Scene.  
keypoints with the keypoints of the last frame. Finally, as Equation (14) shows, we construct the pose-only optimization error \(e\left(\vec{\xi}_{w}^{c}\right)\) from the matching keypoints of adjacent frames. Then we use this optimization equation to find the optimal pose estimate \(\xi_{w}^{c}\) of the current frame. However, the constant velocity motion model is too ideal and does not conform to real motion, which leads to a large difference between the initial pose and the ground truth, thereby affecting the pose optimization result. In view of this, we replace the constant velocity motion model with the pose estimate of the self-supervised PoseNet. The photometric loss and 3D geometry consistency loss theoretically originate from the multi-view geometry model. In summary, compared with the constant velocity motion model, the estimation of PoseNet is more consistent with the real motion model. In our implementation, we combine the rotation estimates from PoseNet with the translation from the constant velocity motion model to form the motion model.  
\[\vec{\xi}_{w}^{c}=\xi_{lc}^{\text{motion}}\xi_{w}^{l} \tag{13}\]  
\[\xi_{w}^{c}=\arg\min_{\vec{\xi}_{w}}\frac{1}{2}\sum\left\|e\left(\vec{\xi}_{w} ^{c}\right)\right\|^{2} \tag{14}\]
## 3 Methodology
### Hazard Disaggregation: Identification of Modal Scenarios
As shown in Figure 1b, employing a structuring element with a square pattern configuration correctly identifies three modes. Then, we examined the impact of the structuring element for two different sizes, while keeping its configuration fixed (a square pattern configuration including all possible neighboring pixels within the window was considered): a 3-by-3 window, and a 5-by-5 window. The results are shown in Figure 2 for the same example PMF considered in Figure 1. While the 3-by-3 window (Figure 2a) correctly detects the second mode (\(M_{2}^{*}\), \(R_{2}^{*}\)), the 5-by-5 window is insensitive to it because of the presence of a higher-value pixel (yellow bar) within the structuring element. This results in it overlooking the contribution of the \(\left(M_{2}^{*},R_{2}^{*}\right)\) scenario (Figure 2b), which consequently is not detected as a mode. It follows that under the influence of neighboring pixels with higher values, especially those located at greater distances from the center of the structuring element, potential peaks with lower values may be masked during the filtering operation. In other words, as the size of the structuring element increases, the resolution when detecting local maxima decreases. Thus, a 3-by-3 square window was considered in the analysis (Figure 2a). Figure 1: Sensitivity of the maximum filtering operation to the configuration of the structuring element: (**a**) a cross pattern with 4 neighboring pixels and (**b**) a square pattern encompassing 8 neighboring pixels. The mean and modal scenarios are shown in yellow and red, respectively.
###### Abstract  
In this paper, a comprehensive demand response model for the residential sector in the Jordanian electricity market is introduced, considering the interaction between the power generators (PGs), grid operators (GOs), and service providers (SPs). An accurate day-ahead hourly short-term load forecasting is conducted, using deep neural networks (DNNs) trained on four-year data collected from the National Electric Power Company (NEFCO) in Jordan. The customer behavior is modeled by developing a precise price elasticity matrix of demand (PEMD) based on recent research on the short-term price elasticity of Jordan's residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys. First, the DNNs are fine-tuned with a detailed feature analysis to predict the day-ahead hourly electrical demand and achieved a mean absolute percentage error (MAPE) of 1.365% and 1.411% on the validation and test datasets receptively. Then the predictions are used as input to a detailed model of the Jordanian power grid market, where a day-ahead peak-time demand response policy for the residential sector is applied to the three distribution power companies in Jordan. Based on different PEMD analyses for the Jordanian residential sector, the results suggest a reduction potential of 5.4% in peak demand accompanied by a cost reduction of USD 154,505 per day for the Jordanian power sector.  
deep neural networks; demand response; peak reduction; electricity market; price elasticity matrix of demand; Jordan +
Footnote †: journal: Applied Sciences
# Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach
## 6 Results and Discussion
### Evolution of Internal Trust  
In this section, we have conducted a set of experiments based on one scenario where there are 50% malicious (untrustworthy) member nodes in one FCD to evaluate the effect of the internal trust levels of the member nodes of the FCD. Whenever a member node takes part in a transaction, its FC updates the member node's trust level in its \(ITT\) utilizing EWMA after discounting the received feedback. The discounting process is based on direct trust with the FC who sent the feedback. We have simulated this scenario in Figure 7.  
Figure 7 shows that the network exhibits two types of behaviors. We plot the behavior of trustworthy member nodes of \(FCD_{27}\) in Figure 7a. According to the results, the trustworthiness of these trustworthy node members is increasing as the number of transactions increases, and this increase is unaffected by the other 50% unreliable node members in the same FCD (i.e., \(FCD27\)).  
We then plot the behavior of untrustworthy member nodes of \(FCD_{27}\) in Figure 7b. The results show that as the number of transactions increase, the trust level of these untrustworthy node members is decreasing and this decrease is not affected by the trustworthy behavior of the other 50% trustworthy node members in the same FCD (i.e., \(FCD_{27}\)).  
This is an important result that shows that the individual behavior of member nodes are captured and isolated their \(FC\). Hence, an incentive (i.e., reward/punishment) mechanism can be utilized by the \(FC\) to further discourage or even prevent untrustworthy behavior in its \(FCD\).
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 3 Proposed Scheme
### Numerical Examples
#### 3.4.3 Data Extraction and Recover Phase  
Given two stego pixels  
\[x_{j}^{1}=[1\;0\;0\;1\;1\;1\;0],\quad x_{j}^{2}=[1\;0\;0\;1\;1\;1\;1],\]  
extraction and recovery are performed as follows:  
1. Virtual Pixel Reconstruction: Reconstruct the virtual pixel \[y=[1\;1\;1\;0\;1\;1\;1].\] (2) Syndrome Computation: Compute the syndrome to extract the embedded bits: \[S=yH^{T}=[1\;0\;1]=5,\] recovering the secret bits \(m=[1\;0\;1]\).
2. Cover Pixel Recovery: To restore the original cover images, the closest codeword associated with zero syndrome is selected: \[\varepsilon =\min\Bigl{(}(15-14)^{2}+(7-7)^{2}\Bigr{)}=1,\] \[h =[1\;1\;1\;1\;1\;1\;1].\] The recovered cover pixels are \[x_{j}^{1} =[1\;0\;0\;1\;1\;1\;1],\] \[x_{j}^{2} =[1\;0\;0\;1\;1\;1\;1].\] Thus, both the hidden secret data and the cover images are perfectly restored.