esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Pathophysiological Mechanisms Linking OSA and Periodantal Disease  
### Systemic Inflammation and Immune Response  
The low-grade systemic inflammation characteristic of OSA plays a pivotal role in its association with PD. OSA-induced inflammation results in elevated levels of proinflammatory cytokines, including interleukin-1 beta (IL-1B), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-\(\alpha\)), as well as markers of oxidative stress. These have been detected in both serum and gingival crevicular fluid (GCF) of OSA patients and are strongly correlated with PD severity [27; 31; 36]. [PERSON] et al. observed significantly higher plasma concentrations of lipopolysaccharide-induced IL-6 and TNF-\(\alpha\) in OSA patients compared to healthy controls [50].  
Further investigations by [PERSON] et al. and [PERSON] et al. demonstrated increased levels of IL-1B, IL-6, IL-21, IL-33, and IL-17A in OSA patients, all of which were associated with worsened periodontal parameters. These findings emphasize the role of inflammatory mediators in linking OSA and PD [26; 51]. [PERSON] et al. also reported elevated levels of IL-6 and apelin in serum and GCF of OSA patients, with IL-6 concentrations positively correlating with the AHI and oxygen desaturation index (ODI) [52]. Similarly, [PERSON] et al. found a higher prevalence of PD in OSA patients (96.4%) compared to controls (75%), along with elevated levels of IL-1B and high-sensitivity C-reactive protein (hs-CRP). These markers were significantly associated with periodontal indices, including plaque index (PI), gingival index (GI), clinical attachment loss (CAL), and bleeding on probing (BOP),further suggesting a direct relationship between OSA-related inflammation and periodontal degradation [30].  
[PERSON] et al. also identified a significant association between AHI and PI, providing additional evidence for the inflammatory interplay between OSA and PD [49]. However, some studies found inconsistent differences in TNF-\(\alpha\) and hs-CRP levels across groups, suggesting that while these markers are relevant, they may not serve as central mediators in the OSA-PD relationship [16]. These findings collectively highlight the complexity of the inflammatory pathways linking OSA and PD, underscoring the need for further research to elucidate their precise mechanisms.
# Learning-Based Dissimilarity for Clustering Categorical Data
## 3 Learning-Based Dissimilarity
### Learning-Based Dissimilarity, the Algorithm  
For computing per-attribute Learning-Based (Dis)similarity, we follow a two-step approach. Given **X** and a target attribute, \(a_{k}\), in the first step, our algorithm extractsand then trains a classifier for \(a_{k}\) so as to compute a confusion matrix for it. Then, in the second step, our algorithm transforms the confusion matrix into a (dis)similarity measure. This procedure is applied repeatedly to \(\mathbf{X}\), according to \(m\), the number of attributes of each object in \(\mathbf{X}\).  
Figure 1 depicts a flow diagram of Learning-Based Dissimilarity. It is worth noting that, to avoid any kind of bias, in the first step (cf. inner loop), instead of using a single classifier, we rather use an ensemble of different classifiers. Then, for the attribute under consideration, we set the confusion matrix to be the one output by the classifier with the highest area under the receiver operational characteristic curve (AUC, for short). The rationale behind using a classification ensemble is as follows. Supervised classifiers make assumptions about the structure of data for a 'correct' generalization. We address this issue following [13], using an ensemble of distinct supervised classifiers and then applying a max combination rule. In our experimentation, we use Random Forest, Naive Bayes, Bayesian Networks, Bagging, Simple Logistic, and KStar, as implemented in the Weka data mining tool [14].  
Next, to transform the confusion matrix into a per-attribute (dis)similarity measure, we proceed as follows. Take an attribute, \(a_{k}\), with \(v_{k}\) different values. Then:  
Figure 1: Flow diagram for the procedure of computing Learning-Based Dissimilarity.  
1. Normalize each row of the confusion matrix, i.e., \[a_{ki}\longleftarrow\frac{a_{ki}}{\sum_{i=1}^{n_{k}}a_{ki}},\ (i\in\{1,\dots,v_{k}\});\]
2. Set the element of the diagonal to be equal to one \(a_{kk}\longleftarrow 1\); and
3. Normalize the rest of the row elements again as follows: \[a_{ki}\longleftarrow\frac{a_{ki}}{3},\ (i\
eq k,i\in\{1,\dots,v_{k}\});\]  
With this, we obtain a similarity matrix for \(a_{k}\); to transform it into a dissimilarity matrix, we simply let \(a_{ki}\longleftarrow 1-a_{ki},\ (i,k\in\{1,\dots,v_{k}\})\). We apply this procedure, repeatedly, for each attribute in \(\{a_{1},\dots,a_{m}\}\).  
As expected, to compute Learning-Based Similarity, we apply Equation (1), accordingly:  
\[\mathbf{x}_{i}\sim^{\text{LBS}}\mathbf{x}_{j}=\sum_{k=1}^{m}\omega_{k}( \mathbf{x}_{ik}\sim^{\text{LBS}}_{k}\mathbf{x}_{jk})\]  
where, for now, we have set \(\omega_{k}=1\), for \(k\in\{1,\dots,m\}\). In the remainder of this section, we show an example application of Learning-Based Similarity.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)  
The dual-parallel edge DBF architecture [40], employing five pipeline stages (V-DPEDBF) used to assess the quality, is shown in Figure 3. The (i) control unit, (ii) boundary Strength (BS) calculation unit, and (iii) filter unit are the three main modules in this architecture. The various operations of the filter architecture are administered by the control unit. An enable signal is used as a primary input from the external world to enable the control unit. The control unit oversees and coordinates a number of processes, including data fetch from the external storage, data fetch from the memory within PEDBF, data write to the PEDBF memory, data write to the external storage device, activation of the boundary strength calculator, and activation of the filter unit. The BS unit determines the values of the BS, which range from 0 to 2. The filter unit performs the filtering procedure in accordance with the computed BS value.
## 4 Numerical Calculation of Slopes Based on Weibull Parameter Random Distribution Model
### Field Verification of Anchor Cable Axial Force and Slope Displacement Monitoring  
After reinforcement, the stress concentration in localized areas of the slope was effectively alleviated. The anchor framework beam integrated various sections of the slope into a cohesive structural system, significantly enhancing the overall stability of the slope and ensuring its stability under supported conditions.  
Slope displacement monitoring provides critical data on slope movement and deformation, which is essential for predicting and preventing potential landslides and collapses [46]. Simultaneously, monitoring the axial force of anchor bolts reflects their stress state and anchoring effectiveness, which is equally vital for ensuring slope stability and preventing anchor bolt failure.  
As shown in Figure 15a, the axial force changes in Cable 1 and Cable 2 are relatively small due to their location in the break angle area, while Cable 3, located on the 1240 m platform, exhibits significant axial force changes during production. According to Figure 15b, the blasting dynamic load during production causes some deformation. A comprehensive  
Figure 14: Energy damage variable contour map of slope elements before support. (**a**) Energy consumption and damage variable contour map of the slope excavation at the 4 th step under unsupported condition; (**b**) energy consumption and damage variable contour map of the slope excavation at the 13 th step under unsupported condition; (**c**) energy consumption and damage variable contour map of the slope excavation at the 4 th step under supported condition; (**d**) energy consumption and damage variable contour map of the slope excavation at the 13 th step under supported condition.  
analysis of the displacement monitoring data reveals that although slope displacement increased during production, the overall magnitude of change was relatively small, indicating that the slope structure remained stable under the implemented support measures.
# Dynamic Round Robin CPU Scheduling Algorithm Based on K-Means Clustering Technique
## 4 Experimental Implementation
### Performance Evaluation  
The proposed algorithm was compared with five common algorithms; PWRR, VTRR, BRR, SRR, and ADRR on different nine combinations of number of processes and burst times. The authors implemented all these algorithms using the benchmark datasets. The average waiting and turnaround times depend on the number of processes in the ready queue; as number of processes increases, time cost increases. In addition, long burst times of the processes increase the time cost. To emphasize the efficiency of the proposed algorithm, benchmark datasets varying in number and burst times of processes are used. The time taken in clustering the dataset is trivial. Comparing with waiting and turnaround times, it can be said that the clustering time has no effect and can be ignored. Table 1 compares between the running times of the proposed algorithm (including the times consumed in the clustering) against other methods.  
Table 2 shows a comparison of the time cost between the proposed algorithm and other algorithms in terms of average waiting time, turnaround time, and NCS. Table 3, Figure 4, and Figure 5 show the superiority of the proposed algorithm over the compared algorithms in all the datasets where the time cost of the proposed algorithm is the smallest (average waiting time and average turnaround time are 979.14 tu, 1061.36 tu respectively). Figure 6 shows how much improvement is achieved by the proposed algorithm. Unlike PWRR, BRR, and SRR which their time slices are less than or equal to the time slice defined by SRR, VTRR may behave somewhat similar to FCFS as it may give a process very long time slice. Therefore, the time slices calculated in the VTRR algorithm are restricted to be less than or equal to the time slice defined by SRR (i.e., 10 tu).  
Figure 4: Comparing algorithms’ time cost.  
Figure 5: Comparing algorithms’ NCS.
## 3 Results
### Comparison with Existing Gesture-Controlled Drone Systems  
To contextualize the novelty and performance of the proposed system, we compared it with two recent state-of-the-art gesture-based drone control systems from [PERSON] et al. [28] and [PERSON] and [PERSON] [29]. Each system focused on hand gesture recognition for drone navigation but differed significantly in terms of autonomy, user feedback, and mapping capabilities.  
The system proposed by [PERSON] et al. [28] utilized an RGB camera to recognize multiple different single-handed gestures for drone control. While their system achieved a high accuracy of +90% using both motion- and posture-based gestures and introduces a lightweight architecture for mobile and edge devices, it lacks a built-in obstacle avoidance system. In contrast, our system not only offers gesture-based navigation with similarly high recognition accuracy but also integrates autonomous obstacle avoidance. This feature is valuable in gesture-controlled drone systems because it enables novice users to operate the drone intuitively while minimizing the risk of collision.  
[PERSON] and [PERSON]'s wearable drone controller [29] uses an inertial measurement unit (IMU) on the hand to classify gestures via machine learning and includes vibrotactile feedback to alert users of nearby obstacles. Although this provides a helpful sensory cue, the user must interpret the vibration and manually respond by steering the drone to avoid collisions. In contrast, our system eliminates this burden: the drone's obstacle avoidance is entirely autonomous, requiring no user input to prevent crashes. This makes our platform more accessible and safer, especially in obstacle-dense environments.  
Also, neither of the two compared systems offers a mapping system. Our solution includes 3D mapping that visualizes the drone's flight trajectory and marks obstacles as red prisms in a virtual space. This capability provides insights into the environment and drone behavior over time and is especially useful for post-flight analysis.  
In summary, unlike existing systems that rely heavily on manual control or external feedback, the proposed system is distinguished by its tight integration of gesture control, autonomous obstacle avoidance, real-time 3D mapping, and FPV video streaming, offering a unique and user-friendly platform for advanced drone interaction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Action** & **Latency (ms)** & **Std Dev (ms)** \\ \hline AI-Deck to Host PC & 100 & \(\pm\)50 \\ Gesture Detection & 200 & \(\pm\)50 \\ Command Transmission & 4 & \(\pm\)1 \\ Command Execution & 5 & \(\pm\)2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Latency measurements for key phases in the gesture-to-command control loop.
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 5 Conclusions  
In summary, doping represents a valuable tool to extend the use of ZnO NPs in nanomedicine. Wet chemistry approaches based on sol-gel, hydrothermal routes and combustion methods have been largely used to dope ZnO NPs with various elements including rare earth (REs) elements and transition metals (TMs). It is found that RE elements have been more extensively considered to improve the photocatalytic and optical properties, but also to confer on ZnO NPs magnetic and improved electromechanical behaviors. Overall, doping ZnO NPs with TMs was mainly considered to give ZnO unprecedented ferromagnetic properties.  
Doped ZnO NPs have been successfully proposed as therapeutic agents against cancer and showed enhanced antibacterial properties against both Gram-positive and Gram-negative bacteria. The mechanism through which doping can increase ZnO NPs toxicity against cancer cells and bacteria is still controversial and not univocally stated. In some cases, it was ascribed to the improved photocatalytic activity and increased generation of harmful ROS. In other cases, the increased bactericidal action was due to a more efficient attachment of the nanoparticles to the cell membrane. Even if further studies are required, the results described herein clearly highlight that doped ZnO NPs can be a powerful weapon to fight bacterial infections and treat cancer. Doping ZnO NPs with TMs also represents a powerful approach to acquire new bioimaging properties which have been successfully applied to magnetic resonance imaging applications. Promising results were also found in terms of an increased cytocompatibility by using Fe-doped ZnO nanoparticles due to the reduced and even more controllable release of cytotoxic zinc cations.  
Conceptualization, M.C. and M.L.; writing--original draft preparation, M.C. and M.L.; writing--review and editing, M.C., S.B., V.C. and M.L.; supervision, V.C. and M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
## 4 Results and Discussion
### Subjective Evaluation
The averaged results obtained are provided in Table 6. From Table 6, the proposed model shows improvement over the methods in [31; 32; 42]. In the second multistage model shown in Figure 2, previous parameter values are used for RT, window length, and FFT frame length. The SNR parameter value was varied from \(-2\) to 10 dBs. The averaged MOS results are presented in Table 7, showing the performance improvement of the proposed model compared to super-Gaussian [31], Student's T [42], and generalized Gaussian [32] source priors. Next, the RT parameter wa varied from 40 to 200 msec with a window length = 512, FFT = 1024, and SNR = 4 dB. The proposed model-averaged MOS results in Table 8 reflect an improvement from [31; 32; 42].
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods
### Methodological Framework  
The FP-Growth algorithm was applied to discover frequent behavioral patterns and assess their relationship with traffic conflicts. A predictive LSTM neural network model was created using driver, vehicle, road, and environmental factors.
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
## 2 Antenna Design
### The Proposed Single Microstrip Monopole Antenna
#### 2.1.1 The Proposed Patch Antenna Structure  
The proposed antenna design was based on the idea, which is presented in [24], using a full ground plane. Figure 1 shows the different stages in the evolution of the proposed monopole antenna. Each of the iterated structures was tested via simulating the return loss for each. The initial structure illustrated in Figure 1a was the basic shape, which consisted of a rectangular patch antenna and two parasitic strips; each one had a circular slot for enhancing the overall antenna performance. Afterward, an additional L-notch was embedded in the patch to introduce the second case, as depicted in Figure 1b. Afterward, a rectangular slot was added in the patch, as shown in Figure 1c, to improve the bandwidth by 2 GHz and the gain by 1.3 dB, as shown in Figure 2 and declared in Table 1, respectively. A novel technique consisting of double T-shaped structures was added in the rectangular slot of the patch with an optimized separation distance between them, as demonstrated in Figure 1d. Finally, the proposed antenna was obtained by adding stubs at the left and right sides, as illustrated in Figure 1e.  
Figure 1: Evolution of the patch structure: (**a**) Rectangular Patch Antenna applied parasitic element with circular slot, (**b**) converted patch to monopole, (**c**) Install rectangular slot at the center, (**d**) add symmetrical T shapes in the rectangular slot, (**e**) add stubs at left & right sides of the patch.  
Figure 2: Simulated return loss characteristics of the different iteration of antenna structures with the full ground structure in the bottom plane of the proposed antenna.  
Figure 2 and Table 1 show the return loss, bandwidth, and gain of each iteration of the antenna separately. It could be seen that the impedance bandwidth improved from 1.15 GHz to 1.5 GHz by varying the antenna structures from \"iteration 1\" to \"iteration 5\", meaning an increase in the achieved return loss by 30.4%. Furthermore, the gain was increased from 6.87 dBi to 8.4 dBi, causing an improvement in the achieved gain by 22.2%. Consequently, iteration 5 proved to be superior to its predecessor iterations in both return loss and gain while giving an adequate bandwidth, which was satisfactory for the desired application.
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 4 Experimental Analysis
### UWB Anchors  
During the project, a demonstrator of the system was developed in one of the Sofidel S.p.a. large warehouses, by selecting a target area of size 4560 m\({}^{2}\), divided in two corridors. Thirty UWB Decawave anchors were installed at the ceiling of the warehouse designed area, by taking advantage of the presence of six metallic bars at a height of around 6 m oriented along the \(y\)-direction of the warehouse and spaced along the \(x\)-direction of around 10 m. A total of 30 UWB Decawave (Figure 10) DWM1001 modules configured to work  
Figure 9: The RFID Smart Forklift with all the equipped sensors. The body local reference frame is also depicted centered on the box containing IMU and OFS.  
as UWB anchors were installed on the bars (Figure 10a). The map of their deployment is shown in Figure 10b, where each UWB anchor is denoted as a blue asterisk. The location of UWB anchors was measured through the Leica Flexline TS03 manual total station [69]. It is a device equipped with lasers and a high-precision orientation meter that can provide a measurement of three-dimensional coordinates of a point within the space, with millimeter accuracy.
# Fitness Landscape Analysis of Product Unit Neural Networks
## 5 Training Algorithms for Product Unit Neural Networks
### Stochastic Gradient Descent  
Gradient descent (GD) [38] is possibly the most popular approach used to train NNs. GD requires an error function to measure the NN's error at approximating the target. GD calculates the gradient of the error function with respect to the NN weights to determine the direction the algorithm must move towards in the weight space in order to locate a local optimum. The \"stochastic\" component in SGD is introduced by adjusting the weights after a single pattern that is randomly selected from the training set. Random selection of training examples also prevents any bias that may occur due to the order in which patterns occur in the training set [14]. SGD has the disadvantage of fluctuating changes in the sign of the error derivatives as a result of weight adjustment after each pattern. This causes the NN to occasionally unlearn what the previous steps have learned. Therefore, SGD makes use of momentum to average the weight changes in order to ensure that the search path continues in the average downhill direction. Refer to [14] for the PUNN training rule using SGD, and find the pseudocode in Algorithm 1. In this algorithm, \(\alpha\) is the momentum, \(\eta\) is the learning rate, \(t\) is the number of epochs, \(\mathcal{E}_{T}\) is the training error, \(t_{k,p}\) and \(\alpha_{k,p}\) are the target and actual output values for the \(k\)'th output unit, respectively, for pattern \(p\), and \(K\) is the number of units in the output layer. Refer to [14] for the derivations of \(\Delta w_{kj}(t)\) and \(\Delta v_{kj}(t)\) in the context of PUNNs.  
```
Initialize weights (\(\mathbf{w}\), \(\mathbf{v}\)), \(\eta\), \(\alpha\), and the number of epochs whilestopping condition(s)  not truedo  Let \(\mathcal{E}_{T}=0\); foreach training pattern \(p\)do  Do the feedforward phase to calculate \(y_{j,p}(\forall j=1,...,J)\) and \(o_{k,p}(\forall k=1,...,K)\);  Compute output error signals \(\delta_{o_{k,p}}\) and hidden layer error signals \(\delta_{y_{j,p}}\);  Adjust weights \(w_{kj}\) and \(v_{ji}\) (backpropagation of errors); \(w_{kj}(t)+=\Delta w_{kj}(t)+\alpha\Delta w_{kj}(t-1)\); \(v_{kj}(t)+=\Delta v_{kj}(t)+\alpha\Delta v_{kj}(t-1)\); \(\mathcal{E}_{T}+=\sum_{k=1}^{K}(1_{k,p}-o_{k,p})^{2}\);  end fore  t = t + 1  end fore
```  
**Algorithm 1**Stochastic gradient descent learning algorithm.