esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Clinical Implications of PD and OSA Co-Management  
### Importance of Screening and Multidisciplinary Management  
Early detection and management of OSA and PD are critical due to their interconnection and systemic implications. Dentists, who routinely evaluate patients for periodontal inflammation, are uniquely positioned to identify OSA risk factors, particularly when symptoms such as snoring, daytime fatigue, or dry mouth overlap with PD [79]. Commonly used screening tools for OSA include the Berlin Questionnaire, STOP-BANG Questionnaire, and Epworth Sleepiness Scale, which can assist dentists in preliminary identification of at-risk patients [80; 81; 82]. Polysomnography remains the gold standard for OSA diagnosis,but these questionnaires provide a practical initial approach for dental professionals. The Berlin Questionnaire is a widely used tool designed to categorize patients into high or low risk for OSA by evaluating their responses to questions about snoring, fatigue, and the presence of hypertension. Similarly, the STOP-BANG Questionnaire, valued for its simplicity, assesses OSA risk through eight key factors: snoring, tiredness, observed apnea, high blood pressure, body mass index, age, neck circumference, and gender. A higher total score on this questionnaire indicates an increased likelihood of OSA. In addition to these tools, the Epworth Sleepiness Scale provides a self-reported measure of daytime sleepiness, a hallmark symptom of OSA, by asking patients to rate their propensity to doze off in various everyday situations. Together, these instruments play a critical role in identifying individuals at risk for OSA, facilitating timely diagnosis and intervention. For patients presenting with PD and OSA-related symptoms, referral to a sleep specialist for further evaluation may be beneficial [83]. Dentists must also be aware that patients with OSA are more likely to have PDs, emphasizing the need for an interdisciplinary approach to management.  
Similarly, sleep specialists managing OSA should consider the oral health impacts of treatments like CPAP, which can exacerbate periodontal issues through side effects such as dry mouth or plaque accumulation. In such cases, referrals to dental professionals for periodontal evaluation and management are warranted, as untreated PD can lead to further systemic complications [16].  
A collaborative approach between dentists and sleep specialists ensures a comprehensive and multidisciplinary strategy for managing both conditions. This integrated care model not only facilitates early detection and treatment but also enhances overall patient outcomes by addressing the shared risk factors and systemic effects of OSA and PD effectively.
## 4 Potential Application Scope  
The existing OIN has a more complex network structure and network equipment, which generally complete the scheduling and management of network resources by the software defined network (SDN). The proposed scheme provides a more efficient and adaptive modulation and demodulation algorithm for SDN. SDN controller dynamically adjusts network rate, modulation order and other parameters according to the current network link characteristics and quality of service (QoS), controls the data exchange between switches, and runs network services more effectively and meets dynamic business needs. Meanwhile, the implementation does not need the corresponding demodulation and DSP module, which has lower hardware complexity and can effectively save the cost in OIN deployment. The K-means algorithm used by the receiver is also apt to integrate with SDN, which provides theoretical support for two-tier OIN.  
In addition, we all know that the average communication distance between data centers in the same city, especially disaster-tolerant backup data center, is about 60 km, and the longest distance does not exceed 200 km. However, we simulated the MI of each scheme under different loops in Section 3. The maximum loop is equal to 10, reaching 1000 km. At the same time, the proposed scheme shows better BER performance when the modulation formats are 16-IPM and 64-IPM and the simulated transmission distance is 500 km. Since the simulated transmission distance exceeds the actual distance between data centers, and the proposed scheme has better performance, which jointly decide that it can be used not only for OINs, but also for any high-speed and long-distance optical fiber communication system.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Modulation Order** & **IPM Using K-means** & **IPM Using BPS** & **QAM Using BPS** \\ \hline
16 & 16.0 dB & 16.9 dB & 17.7 dB \\ \hline
64 & 23.7 dB & 24 dB & 24.8 dB \\ \hline
256 & 28.6 dB & 29 dB & 29.4 dB \\ \hline \end{tabular}
\end{table}
Table 2: SNR of different approaches at HD-FEC threshold.
# Boosting the Learning for Ranking Patterns
## 4 AHP-Based Learning Approach for Ranking Patterns
### Learning Weights Process  
Algorithm 1 implements the learning process that computes a weight vector \(w\) (a weight for each measure) by exploiting the pair-wise comparisons of measures. It takes as inputs a vector of pair-wise comparisons \(\Delta\) and its corresponding index \(l\), a user ranking \(S\), and a set of measures \(\mathcal{M}\). The learning step is performed at line 2 where for each pair of measures, we first compute the Kendall W coefficient for each measure \(i\) with respect to the user ranking \(S\) (\(K_{i}(S)\)). Then, we compute the gap between the pairs.  
```
1:Set of measures \(\mathcal{M}=\{M_{1},\ldots,M_{m}\}\); user ranking \(S=\langle P_{1},\ldots,P_{n}\rangle\);
2:InOut:Pair comparisons and index parameter \(\langle\Delta,l\rangle\);
3:Out:Weight vector \(w\);
4:\(\mathbf{A}[i,j]\gets 1\), \(\forall i,j\in\{1,\ldots,m\}\);
5:foreach\(M_{i},M_{j}\in\mathcal{M}:i<j\)do
6:\(\Delta_{i,j}\leftarrow\tfrac{l}{l+1}\Delta_{i,j}+\tfrac{1}{l+1}(K_{i}(S)-K_{j}( S))\);
7:end
8:foreach\(\Delta_{i,j}\in\Delta\)do
9:if\(\Delta_{i,j}<0\)then
10: scale \(\Delta_{i,j}\) to \((-9..-1)\); \(\mathbf{A}[j,i]\leftarrow|\Delta_{i,j}|\); \(\mathbf{A}[i,j]\gets 1/|\Delta_{i,j}|\);
11:else
12: scale \(\Delta_{i,j}\) to \((1..9)\); \(\mathbf{A}[i,j]\leftarrow\Delta_{i,j}\); \(\mathbf{A}[j,i]\gets 1/\Delta_{i,j}\);
13:end
14:\(w\leftarrow\mathit{solve}\left(\begin{array}{c}\mathbf{A}\cdot w=\lambda_{ max}\cdot w\\ \text{subject to }\sum_{j=1}^{m}w_{j}=1,\quad w_{j}>0,\forall j=1\ldots m.\end{array}\right)\)
15:return\(w\);
```  
**Algorithm 1**\(\mathtt{learnWeights}(\langle\Delta,l\rangle,S,\mathcal{M})\).  
Algorithm 1 implements the learning process that computes a weight vector \(w\) (a weight for each measure) by exploiting the pair-wise comparisons of measures. It takes as inputs a vector of pair comparisons \(\Delta\) and the corresponding index \(l\), a user ranking \(S\), and a set of measures \(\mathcal{M}\). The function incrementally updates \(\Delta\) by taking into account the previous \(l\) user rankings at line 3. At line 5, the AHP matrix is constructed by scaling the \(\Delta_{i,j}\) comparisons to the AHP values between \(1/9\) and \(9\). Once constructed, the criteria weight vector \(w\) is returned in line 12 by solving the minimization problem described in line 11, using the eigenvector method (EVM) [30].  
After learning the weight vector \(w\), we use it to compute the score of a given pattern \(P_{i}\) by applying a weighted aggregation function:  
\[g_{w}(P_{i})=\sum_{M_{i}\in\mathcal{M}}w_{i}\;scale(M_{i}(P)) \tag{6}\]  
The _scale_ function is used to normalize the interestingness values of the different measures to a \([0,1]\) scale so that they can be combined using the weighted aggregation function.
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 6 Background  
### Radar Operating Principles  
The two main operating principles used in radar level measurement consist of pulse and FMCW (broadband) radars. To calculate the distance of the medium away from the radar device, a \"time of flight\" method is applied. This method measures how long a transmitted electromagnetic (EM) signal takes to reflect off the medium, and to be received again by the device.  
Since the EM signal travels at the speed of light, C, the medium range, D, is known to be twice the time delay, \(\Delta\)t, multiplied by the speed of light:  
\[2\text{D}=C\Delta\text{t}, \tag{1}\]  
From (1), the medium range can then be calculated by:  
\[\text{D}=(C\Delta\text{t})/2, \tag{2}\]  
Both principles utilise the same method of measuring range, with only a varied means of measuring the time delay, \(\Delta\)t.
## 3 Vulnerability Detection Methods
### Dynamic Application Security Testing (DAST)  
Unlike SAST methods, DAST methods evaluate behaviors and program responses with different usage scenarios to detect potential vulnerabilities without providing access to the source code. Thus, DAST methods help to identify vulnerabilities arising from the application runtime environment. The first-use cases of DAST were mostly for online applications such as SQL injection, cross-site scripting and weak authentication [28]. With the inclusion of modern software in comprehensive development processes such as APIs and mobile applications, DAST methods have expanded to include this development. At the same time, firewalls, intrusion detection, configuration errors, interactions of application components and access control problems are among the tools to find risks and threats that can be overlooked in static analysis. Recently developed DAST tools are supported by artificial intelligence models to reduce false positives and anomalies suggestive of vulnerabilities or attacks.
## 3 Energy and Information Beamforming
### Statistical Max-SINR Scheme
#### 3.1.1 Energy Beamforming Design
It is interesting that the energy beamformer (20) under the statistical SLNR criterion gives rise to a similar interpretation in the sense of maximizing the average harvested energy at the intended sensor. Moreover, it is seen that the solution of the energy beamformer is decoupled with the information beamformer. This makes the closed-form solution for \(\mathbf{w}_{DJ}\) possible.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 4 Design of the Space-Based Time System for Navigation Constellations
### Centralized Two-Way Time Comparison Method Based on Inter-Satellite Link
 \tag{23}\]  
For 1-min uniform time intervals, redundant two-way observations can be obtained, facilitating \"one satellite-multiple links\" at a uniform moment. The simplified form of Equation (23) is  
\[\frac{\rho_{AB}^{{}^{\prime}}(t_{0})-\rho_{BA}^{{}^{\prime}}(t_{0})}{2}=c \times(CLK_{BA}), \tag{24}\]where \(\rho^{{}^{\prime}}_{AB}(t_{0})\) and \(\rho^{{}^{\prime}}_{BA}(t_{0})\) are defined as  
\[ \begin{split}\rho^{{}^{\prime}}_{AB}(t_{0})&=\rho_{AB} (t_{0})-\Delta\rho^{AB}_{cor},\\ \rho^{{}^{\prime}}_{BA}(t_{0})&=\rho_{BA}(t_{0})- \Delta\rho^{BA}_{cor}.\end{split}  \tag{25}\]  
The right side of the equation is given by  
\[CLK_{BA}(t_{0})=CLK_{BA}^{0}(t_{0})-[\frac{\tau_{B}^{Send}-\tau_{B}^{Rcv}}{2} -\frac{\tau_{A}^{Send}-\tau_{A}^{Rcv}}{2}]. \tag{26}\]  
The first term of Equation (26) is the relative clock offset of the two satellites:  
\[CLK_{BA}^{0}(t_{0})=clk_{B}(t_{0})-clk_{A}(t_{0}). \tag{27}\]  
The second term of Equation (26) is the delay difference of the Ka combination, which can be considered as a constant within a certain period and calibrated in advance. If a star is selected as the reference star (\(q\) subscript), such that \(clk_{q}(t)-\frac{\tau_{A}^{Send}-\tau_{A}^{Rcv}}{2}=0\), then \(CLK_{qq}=0\). The relative clock offset observation equation of each star relative to the reference star can be obtained:  
\[\frac{\rho^{{}^{\prime}}_{AB}(t_{0})-\rho^{{}^{\prime}}_{BA}(t_{0})}{2c}=CLK _{Bq}-CLK_{Aq}. \tag{28}\]  
For the Ka inter-satellite clock offset observations of the \(i\)-th and \(j\)-th satellites in a constellation of \(n\) stars,  
\[clk_{ij}(t)=(A_{0}^{I}+A_{1}^{i}(t-t_{0})+A_{2}^{i}(t-t_{0})^{2})-(A_{0}^{J}+A_ {1}^{j}(t-t_{0})+A_{2}^{j}(t-t_{0})^{2})+\varepsilon_{ij}. \tag{29}\]  
In the above formula, \(A_{0}\) represents the clock error, \(A_{1}\) represents the clock drift, and \(A_{2}\) represents the clock drift rate. By taking the relative clock offset observations of all links between satellites for a certain period, the clock offset, clock velocity, and clock drift parameters of \(n-1\) stars in a constellation of \(n\) stars can be estimated. The transmission and receiving delay on satellites only affect the clock offset parameters, not the clock velocity and clock drift parameters. If the constellation contains only satellites, time synchronization among all satellites is achieved. The reference for the broadcast clock offset parameters from the navigation satellite system is the system time. By adding the relative clock offset observations between the anchor station and the satellite based on the \(n\)-satellite constellation, and selecting the anchor station as the reference, the clock offset, clock velocity, and clock drift parameters can be directly broadcast.
# Evaluation of the Distal Periodontal Bone Defect Associated with Lower Third Molar Extraction
## 4 Discussion
These anatomical features, particularly the proximity between the second and third molars, are associated with greater bone loss distal to the second molar [33]. In our study, distal bone peaks (POD) were significantly lower in horizontal and mesioangular impactions than in vertical ones. This finding aligns with our study, where the distal bone peak (POD) to the second molar was significantly lower in horizontal and mesioangular third molars compared to vertical ones. Clinically, this translates into greater probing-depth distal (PPD) and reduced clinical attachment level (CAL) to the second molar in horizontal and mesioangular cases. This is consistent with findings by [PERSON] et al. [34], who suggested that the preventive extraction of asymptomatic third molars with PPD \(\geq\) 4 mm may improve periodontal health in young patients. Plaque control is another essential factor influencing healing. Although the plaque index values recorded at follow-up did not differ significantly between groups, effective plaque management is necessary to support optimal healing and prevent infections or bone loss [35]. Our results also indicated a trend toward less favorable healing in horizontal third molars, with higher residual PPD and reduced bone healing compared to other groups. While these differences were not statistically significant--likely due to the limited sample size--they suggest an area for further investigation. Despite this trend, the maximum observed PPD distal to second molars did not exceed 6 mm, which, although pathological according to guidelines, represented a significant improvement compared to preoperative levels. Overall, the alveolar bone height improved postoperatively, though sites with deeper mesioangular and horizontal impactions remained at greater risk of developing residual bone defects, as supported by [PERSON] et al. [36]. Postoperative complications were not significantly associated with changes in PPD or CAL values across the three groups. However, the slight increase in complications in vertical impactions warrants further exploration. Additionally, the role of meticulous postoperative plaque control in mitigating tissue susceptibility to infections and bone loss should not be underestimated. These findings underscore the importance of individualized treatment planning for third molar management. Tailoring the surgical approach to account for patient-specific factors--such as age, impaction type, and preoperative periodontal conditions--can enhance outcomes and reduce the risk of long-term complications.
# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
## 4 Experiment  
### Experiment Settings  
The experiment was carried out on a cluster system consisting of four high-performance computers as computing nodes. The four computing nodes were connected via a 10-gigabit Ethernet network. Each node had 32 CPUs and 512 GB of memory. Experimental data were from the open-source projects OpenStreetMap [3] and OpencellID [27]. Some data with more than 100 million geographical vector features were selected to verify the efficiency of the proposed method. The experimental data configuration is shown in Table 1.  
The algorithm code in this paper is based on C++11, and the external function libraries that were called include Proj 4.9.2, Mpi 3.3.2, Redis 3.2.12, Libpng 1.2.59, Crow 0.3, etc. The software used for performance comparison relies on libraries such as Boost C++ 1.72, Hadoop 2.9.2, Spark 2.3.3, SpatialHadoop 2.4.2, GeoSpark 1.2.0, and Mapnik 3.0.22. In the  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Type** & **Records** & **Size** \\ \hline P\({}_{1}\): OpenCeilliD cell tower locations & point & 40,719,478 & 40,719,478 points \\ P\({}_{2}\): OSM all points on the planet & point & 2,682,401,763 & 2,682,401,763 points \\ L\({}_{1}\): OSM water areas boundaries & linesstring & 9,211,000 & 376,208,235 segments \\ L\({}_{2}\): OSM roads & linesstring & 72,336,396 & 717,048,198 segments \\ L\({}_{3}\): OSM all linestrings on the planet & linesstring & 106,268,554 & 1,573,469,984 segments \\ A\({}_{1}\): OSM buildings & polygon & 114,839,692 & 804,028,282 edges \\ A\({}_{2}\): OSM all polygons on the planet & polygon & 181,772,692 & 2,077,524,465 edges \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of experimental data.  
experiment, VPQ indexes were established for each dataset, which were saved as index files in the file system and deployed in each computing node in the cluster environment.
# Surface Quality Evolution Model and Consistency Control Method of Large Shaft Multi-Pass Grinding
## 5 Case Study  
### Experimental Set Up  
A case study is conducted in this section to verify the proposed methods, i.e., the surface quality evolution model and a consistency control method of surface quality. The grinding machine, grinding wheel, workpiece, and testing instruments used in the experiment are the same as those described in Section 2.  
In case study, the complete grinding process is carried out, including rough grinding, semi-finish grinding, finish grinding, and spark-out grinding. The parameters of these four grinding stages are shown in Table 4.  
Six measuring points are selected on the axis with \(z\)-coordinates of 200, 400, 600, 800, 1000, and 1200 mm, respectively. Before each grinding pass in the rough grinding, the profile of the shaft along the \(z\)-direction is measured and the actual grinding depth is adjusted by the consistency control method. The dimensional error is predicted according to the process parameters, and the grinding wheel pass is adjusted by compensation method to guarantee the actual grinding depth. The iteration step length \(l\) is 0.02 \(\upmu\)m, and the tolerance range of machining error is 1 \(\upmu\)m in rough grinding. In the semi-finish grinding and finish grinding, only the dimensional error is compensated, while the consistency of surface quality is not controlled. In spark-out grinding, the grinding wheel path is not adjusted, since the grinding depth is 0. After each grinding pass, the surface roughness and glossiness of the measuring points are tested.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ginding Stage** & \(\mathbf{a_{p}}\) (\(\upmu\)m) & \(\mathbf{N_{\text{wheel (r/min)}}}\) & \(\mathbf{N_{\text{shaft (r/min)}}}\) & \(\mathbf{f}\) (mm/min) & **Ginding Pass** \\ \hline Rough & 5 & 1000 & 80 & 1200 & 3 \\ Semi-finish & 3 & 900 & 70 & 1050 & 2 \\ Finish & 2 & 800 & 70 & 1000 & 2 \\ Spark-out & 0 & 800 & 60 & 1000 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters of grinding process.  
Figure 13: Actual grinding depth error after compensation.
# Material Removal Model for Lapping Process Based on Spiral Groove Density
## 2 Materials and Methods  
### Definition of Groove Density  
In the lapping process, a wafer is polished by transferring pressure from the platen to the abrasive particles present between the platen and the wafer. The grooves serve as a flow path that supplies the slurry to the entire surface of the workpiece and removes the debris generated during the process to prevent scratches. The grooves are closely related to the lapping characteristics because they increase the surface area of the platen to help discharge the heat energy generated during the process and change the lubrication condition of the slurry between the wafer and platen by forming a bumpy platen surface [18].  
The types of grooves can be largely classified by their groove pattern and cross-sectional shape. Groove patterns are divided into grid, concentric, and radial types; however, the spiral type, which is a variation of the concentric type, is commonly used. The cross-sectional shape of the grooves refers to their shape in the direction of depth, which can be rectangular, arch, or wedge-shaped. Because arch and wedge shapes can change the width of the groove as the platen wears down and thereby change the lapping characteristics, a rectangular shape with a consistent profile in the direction of depth is generally used. The parameters of the spiral groove with a rectangular shape can be classified into groove width (w) and pitch (p), as shown in Figure 1. _Land_ is the area of direct contact with the wafer and is determined by the groove width and pitch. In this study, the groove density parameter, \(d_{g}\), was introduced to understand the effect of these groove parameters on material removal, which can be expressed as shown in Equation (1). The groove density parameter is defined as the ratio of the groove width to pitch and represents the ratio of the groove area to the total area of the platen.  
\[d_{g}=\frac{groove\ width}{pitch}=\frac{w}{p}. \tag{1}\]
# Evaluation of Green and Grey Flood Mitigation Measures in Rural Watersheds
## 2 Materials and Methods
### Study Area and Flood Mitigation Options
#### 2.1.2 Flood Mitigation in the Tamnava Watershed
It should be noted that the distinction between green and grey measures is not a clear, straightforward one. Concerning the building material, some elements of green infrastructure (e.g., the dams of the detention basins) may be made of (reinforced) concrete or steel, while the levees (grey measures) are mainly made of local earthen material. In this paper, the distinction between the two groups of measures is not made upon the building material, but rather according to the ecosystem services that the measures can provide. This categorization is in line with the definition provided by European Commission, stating that \"green infrastructure provides great benefits for both citizens and biodiversity\" [23]. Detention basins can provide habitat for wildlife and keep a fish stock, and their application is deemed to \"contribute to meeting the objectives of the 2020 Biodiversity strategy\" [23]. In addition, detention basins can offer some recreational opportunities. For the above reasons, the detention basins are considered a green flood mitigation measures in this study, as opposed to the conventional levees and a bypass canal.