esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Popular ANN Architectures Employed for Time Series Forecasting Purposes
### Self-Organizing Maps
#### 3.5.4 SOM Models Combined with Autoregressive Models  
Another possible approach involving the application of SOM models to time series prediction tasks is a direct extension of the procedure described in Section 3.5.2. We can build a hybrid two-stage predictor based on SOM and autoregressive (AR) models. In the first stage, we train an SOM model so as to produce discrete one-step-ahead time series forecasts.This SOM model is then employed to split the available set of training examples into \(N\) clusters, one for each SOM neuron, by simply presenting training example \(\vec{x}\) to trained SOM and assigning a cluster label to \(\vec{x}\) based on the winning neuron. In the second stage, a local linear AR model is fitted to each cluster defined in the first stage. Now, this fully trained hybrid model can be employed to produce a one-step-ahead forecast for any future input \(\vec{x}^{\prime}\): first, we determine to which cluster \(\vec{x}^{\prime}\) belongs, by using our trained first-stage SOM model; then, we use the corresponding AR model to produce the desired forecast. This basic approach is similar in spirit to local function linearization (it seems like a sensible strategy to assume that, locally, a real continuous function can be reasonably approximated by a simpler linear function) and can be extended in many ways; for instance, FFNN (or RBFN, or even RNN) ensembles could replace local AR models in the second stage. Although, this would result in a more complex model, requiring extensive computational resources for its construction. AR parameters can be quickly computed, although statistical training on the modeler's side is required (specifically, the modeler must be familiarized with the Box-Jenkins statistical technique). This SOM-AR approach will work well if each data cluster contains enough consecutive time series observations to adequately train an AR model; otherwise, an ANN alternative for the second stage would be preferable. If all goes well, two-stage SOM-AR models will enable users to make plausible statistical inferences about the relative importance of lagged time series values at a local level. Confidence intervals can additionally be computed for each one-step-ahead forecast produced by the SOM-AR model, giving a statistical quantification of forecast uncertainty. [PERSON] and [PERSON] [65] propose a specific SOM-AR model implementation for predicting electricity demand in Britain and Wales, while [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [66] combine an SOM clustering model with local RBFNs to forecast financial data; [PERSON], [PERSON], and [PERSON] [67] propose an SOM-RNN model to forecast chaotic time series, and [PERSON], [PERSON], [PERSON], and [PERSON] [68] propose a sophisticated SOM-FFNN model to forecast rainfall on multi-step-ahead time scales using precipitation satellite data.
## 3 Results
### General Description of the Studies  
Of the 11 included articles, 9 were prospective studies [8; 17; 36; 37; 38; 39; 40; 41; 42], while 2 were randomized clinical trials [43; 44]. For the placement of MDIs, the majority of the articles used full-thickness flaps (n = 5) [8; 39; 40; 43; 44], followed by flapless technique (n = 4) [17; 36; 41; 42] and minimal thickness flap procedure (n = 2) [37; 38]. In the included studies, the length and the diameter of MDIs used ranged between 8-18 mm and 1.8-3 mm, respectively. Five studies utilized 1-piece implants [17; 36; 39; 40; 44], one study used 2-piece implants [43], while 5 studies did not mention the design of implants used (Table 1) [8; 37; 38; 41; 42].  
In the included studies, 1190 MDIs and 170 SDDIs were placed in 349 patients [198 males + 171 females (range: 8-62 patients)]. The age of participants ranged between 45 and 86 years (66.65 \(\pm\) 6.28 years). A wide variation was noticed regarding the implant follow-up durations, with the lowest being 12 months and the highest being 60 months after implant surgery (Table 2).  
Figure 1: Prisma flow diagram for study methodology.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multicolumn{2}{c}{**Participants (kk)**} & \multirow{2}{*}{**No. of MDIs (s)**} & \multirow{2}{*}{**Follow-Up**} & \multirow{2}{*}{**Pere-Implant**} & \multicolumn{2}{c}{**Peri-Implant**} & \multirow{2}{*}{**Implants Failed**} & \multirow{2}{*}{**S Survival Rate**} & \multirow{2}{*}{**Key Outcome**} \\  & **Age** & & & **Month** & & & **Clinical Parameters** & **Parameters (MB)** & **(s)** & **(s)** & **(s)** \\ \hline \hline Zygyggians & 50 (4 + 26): & 150 (MDIs 100 + & 12 & FP. 0.43 \(\pm\) 0.71 & & & & & & \\ \(\rm{et}\) & (2017) & 67 \(\pm\) 7.7 years & SDOs: 50) & 12 & FP. 1.76 \(\pm\) 0.43 & 0.42 \(\pm\) 0.56 mm & 2 & 98\% & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{[PERSON] et al., (2018)} & 45 (24 + 21): & 177 & 12 & - & 0.50 \(\pm\) 0.75 mm & 5 & 97.2\% & MDIs were observed higher in allually than those with narrow diameters, which may influence impact survival. \\ \hline \hline \multirow{2}{*}{[PERSON] et al., (2019)} & 25 (5 + 15): & &  
The groups of investigated participants varied as per the study type; seven studies were conducted using MDIs only [8; 17; 36; 39; 40; 42; 44]; three studies compared the utilization of MDIs with SDDIs [37; 38; 43]; and one study compared the outcomes of immediate with early loaded MDIs to retain mandibular overdentures [41].
# Smart Healthcare Network Management: A Comprehensive Review
## 2 Literature Review
### Smart Healthcare
Additionally, they introduced a framework designed for various healthcare environments, including hospitals, specializedclinical settings, patient homes, senior citizen residences, gyms, and mobile healthcare services such as ambulances, mobile clinics, and travel health services. [PERSON] et al. [44] conducted a review of research on advancements in IoT-driven healthcare technologies. They analyzed cutting-edge network architectures, platforms, applications, and industry developments within IoT-based healthcare solutions. Furthermore, they proposed an intelligent collaborative security framework aimed at mitigating security threats. Their study also explored how emerging technologies such as big data, ambient intelligence, and wearable devices can enhance healthcare applications. Additionally, they examined global IoT and eHealth policies and regulations, assessing their economic and societal impact on sustainable development. [PERSON] et al. [45] presented an architecture for applying IoT in healthcare and medicine. The architecture is patient-centric, where all constituents, such as the hospital, patients, and services, are seamlessly connected. The architecture consists of three layers, the device, fog computing, and the cloud, to empower handling complex data in terms of variety, speed, and latency. They also addressed the challenges of IoT eHealth, such as data management, scalability, regulations, interoperability, device-network-human interfaces, security, and privacy. Smart healthcare systems may proactively manage the patient's health--prevent disease, detect disease early, and improve healthcare outcomes [46]. [PERSON] et al. [47] developed a platform for monitoring healthcare environments that integrates RFID and WSN technologies, providing the location, status, and tracking of patients and assets.
## 1 Introduction
[15] obtained the distribution of the resulting channel considering only one antenna in the receiver; in this work, we approach a more complex and more comprehensive system model, which includes all the analyses made by the authors, for a special case in which the number of antennas of the user is unitary. This study investigates the fading distribution of a LIS-aided channel with multiple transmitters and users enabling the increase of SINR, allowing the strengthening of a LoS or improving spectrum sharing. It examines the impact of channel parameters (the number of LIS reflectors, users, the Von Mises error concentration parameter, and the number of transmitter antennas) on the bit error probability and the secrecy outage probability. The aim is to assess the impact of LIS design on performance and help design the system based on quality and information security metrics, evaluating performance that can be calculated directly through simple algebraic expressions involving the parameters of the statistical modeling of the system. Recent papers on LISs present various mobile system modeling schemas. [PERSON] et al. [22] focused on joint beamforming design for LIS-aided hybrid satellite-terrestrial relay networks, employing a refracting LIS approach to enhance satellite signals for obstructed users. [PERSON] et al. [23] investigated destructive beamforming by a malicious LIS in IoT networks to minimize received SNR at IoT devices. [PERSON] et al. 's [24] study converted mmWave communications with finite blocklength, optimizing antenna gain for covert transmission against spatially random wardens. [PERSON] et al. [25] analyzed robust beamforming design in a secrecy MISO network with LIS, addressing imperfections in CSI and potential eavesdroppers. These articles considered different beamforming strategies, addressing challenges such as power optimization, security, and robustness to channel variations. In contrast, our paper focuses on the phase error distribution without being restricted to a specific optimization technique and can be applied, regardless of how the LIS is designed, provided that the phase error concentration parameter is known. With this level of abstraction, we analyzed the system performance in terms of physical layer security and bit error probability across various fading scenarios involving multiple users.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 2 Related Work  
### Software Defect Prediction  
Many statistical or machine learning techniques have been used in order to build software defect proneness prediction models, such as logistic regression [6; 13], decision tree [3; 14; 47], artificial neural networks [15; 48], support vector machine [49], naive Bayes [16], Bayesian networks [50; 51], classification based association rule mining [17; 52], deep learning [53; 54], and ensemble learning [18; 55]. Meanwhile, some researchers use the regression modeling techniques to build software defect prediction models for predicting the number of defects in software modules, such as multiple linear regression [56], Zero-Inflated models [57; 58], generalized regression neural networks [59], and regression based ensemble learning [60]. As the independent variables of software defect prediction models, various software metrics have been proposed and used, such as McCabe metrics [9], Halstead metrics [10], CK metrics [11], and process metrics [12].  
However, most software defect datasets are class-imbalanced, which means that the number of defective samples is much more than that of non-defective ones. The class-imbalance problem is a major factor accounting for the poor performance of software defect prediction models [31; 61; 62]. To address class-imbalance problem in software defect proneness prediction, many class-imbalance learning methodshave been used and proposed, such as SMOTE [24; 33; 34; 63; 64; 65], RUS [6; 35; 36; 66], MAHAKIL [44], cost-sensitive learning methods [37; 39], and ensemble learning methods [18; 67].
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 4 Method
### Redefining Noise Nodes
#### 4.2.2 Re-Clustering of Noise Nodes
In the subsequent section, we will provide a detailed implementation of the method for reassigning noise nodes. Proof. : Let \(N\) be the set of all noise nodes. Let \(C_{k}\) denote the cluster \(k\). Define the average structural similarity avg_sim\({}_{u,k}\) of a noise node \(u\) with the nodes in cluster \(k\) as:  
\[\text{avg\_sim}_{u,k}=\frac{1}{|C_{k}|}\sum_{v\in C_{k}}\text{sim}(u,v)\]  
where \(|C_{k}|\) represents the number of nodes in cluster \(k\), and \(\text{sim}(u,v)\) denotes the structural similarity between nodes \(u\) and \(v\). For a noise node \(u\), compute its average structural similarity with each cluster \(C_{k}\) as follows:  
\[\text{avg\_sim}_{u,k}=\frac{1}{|C_{k}|}\sum_{v\in C_{k}}\text{sim}(u,v)\]  
Let avg_sim\({}_{u,k}\) represent the average similarity of node \(u\) with the nodes in cluster \(k\). If:  
\[\text{avg\_sim}_{u,k}\geq\text{avg\_sim}_{u,j}\text{ for all }j\
eq k,\]  
then node \(u\) should be assigned to cluster \(k\). This is because if avg_sim\({}_{u,k}\) is the largest among all clusters, then the overall similarity between node \(u\) and the nodes in cluster \(k\) is the highest. That is, the average similarity of node \(u\) with cluster \(k\) is greater than with any other cluster. By assigning a noise node to the cluster with the maximum structural similarity, we ensure that the node is allocated to the most appropriate cluster. This method enhances the accuracy of clustering results and effectively manages noise nodes. Figure 5: Distance-Based Structural Similarity.
## 2 Materials and Methods
### Selection of Appropriate PEF Pre-Treatment Conditions  
Prior to the application of PEF pre-treatment to OD, a screening of a wide array of treatment conditions was tested, in order to establish the relationship between PEF treatment and quality deterioration. PEF treatments were performed on whole tomatoes submerged in tap water at an electric field strength of 1.8 kV/cm and a number of pulses ranging from 0 (untreated) to 1000, with specific energy inputs up to 12 kJ/kg, respectively. The selection of this treatment condition range was based on previous studies of the cell disintegration index in whole tomatoes [11]. Treatments were performed using the Elcrack-5 kW device (DIL, Quackenbruck, Germany) using a stainless-steel treatment chamber with an electrode gap of 8 cm and a total volume of 300 mL. The pulse width and pulse frequency were fixed at 15 \(\upmu\)s and 20 Hz, respectively. After each treatment, color, firmness and sensory attributes were determined (Section 2.4). Based on the results of this experiment, suitable PEF treatment conditions were chosen such that adequate tissue alteration would occur, contributing to an acceleration in dehydration rates but not leading to unacceptable textural degradation.
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 3 Simulations and Experiments
### Simulation Verification
#### 3.1.1 Presentation of Optimization Results  
Figure 13 presents the Pareto optimal solution and iterative convergence of trajectory point optimization using the improved NSGA-II path optimization algorithm.  
Figure 11: Comparison of initialization with adjacent constraints and random initialization.  
Figure 13a presents the Pareto optimal front. For the objective functions of total distance and total turning angle, each has an optimal solution, but optimizing one may impact the other. Using the improved NSGA-II path optimization method, a set of Pareto optimal solutions were obtained, forming the Pareto front. Figure 13b shows that at 178 iterations, the total distance reached a minimum of 10,594.57; Figure 13c indicates that at 145 iterations, the total turning angle reached a minimum of 3023.74. This study verifies the convergence of the proposed method, and subsequent ablation experiments will further validate the effectiveness of the improvements.
## 4 Applications
### Functional Imaging  
One of the main fields of application of the TG technique is the brain functional imaging. Indeed, the use of the small \(\rho\) approach coupled to the time gating technique allows to increase the mean penetration depth and to realize small probes [100]. This peculiarity opens different research lines from the diagnosis/monitoring of patients' health to the consumer market. From the medical point of view, the possibility of remote monitoring the patient in his daily routine with a small optode (that can be hosted in a cap) will enable the use of personalized telemedicine. For what concerns the consumer market, the use of wearable devices to monitor the performances of the sportsmen requires small probes thus bringing toward the adoption of the small \(\rho\) approach.  
In this view, a first attempt of realization of a time-gating detection system for brain imaging has been carried out using a first prototype system based on a dual-wavelength four-wave mixing laser and two fast-gated modules [115; 116]. In these works, the improvement in detecting a localized perturbation using the time-gating approach was firstly demonstrated on phantoms. To assess this capability, the two figures of merits explained in detail in Ref. [60] were chosen, namely contrast and CNR. As already explained, the former is an indicator of the visibility of the perturbation while the latter is an index of the robustness of the contrast with respect to the measurement noise. For a short \(\rho\) (e.g., 5 mm), the use of time gating is crucial to achieve a sufficient capability (i.e., sufficient contrast and CNR) to detect an inclusion in depth, without being limited by the large amount of scarcely diffused photons. The use of time-gating with a larger \(\rho\) (e.g., 20 mm) is, as expected, less effective and the penetration depth is thus lower (2.6 cm vs. the 3 cm achievable with a small \(\rho\), for an inclusion with \(\Delta\mu_{a}\) = 0.15 cm\({}^{-1}\) with respect to the background [115]). On the other hand, in the same works the usefulness of the timing-gating was demonstrated for brain imaging (brain activation of motor cortex during a finger tapping exercise). Figure 11 reports results of the in-vivo measurements published in Ref. [115]. Using a small \(\rho\), the brain activation was not visible when using a non-gated approach due to the overwhelming effect of the early photons, while it became much clearer when using a TG approach. In agreement with the theory, the improvement of the time-gating was much milder for the large \(\rho\) (30 mm), as it is clearly visible in Figure 11.
# UAV Application for Typhoon Damage Assessment in Construction Sites
## 3 Research Methods and Procedures
### Analytic Hierarchy Process
#### 3.1.1 Defining the Structure Model and Constructing the Pair Comparison Matrix  
Each sub-target established by the disaster chain was decomposed to obtain the complete structure model. The pairwise comparisons of different factors to determine their relative importance formed the judgment matrix, where the comparison result was between index \(i\) and index \(j\). The judgment matrix used the following relationship in Equation (3).  
\[a_{ij}=\frac{1}{a_{ji}} \tag{3}\]  
The weight of the judgment matrix is shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Weights** & **Implication** \\ \hline
1 & The two indicators are of equal importance \\
3 & One is slightly more important than the other \\
5 & One is obviously more important than the other \\
7 & One is mgthily more important than the other \\
9 & One is extremely more important than the other \\
2,4,6,8 & The intermediate value of the above two adjacent \\  & The comparison of factor i to factor j is calculated as \(a_{ij}\) \\ reciprocal & the comparison of factor j to factor i is calculated as \(a_{ji}\) \\  & \(a_{ij}=\frac{1}{a_{ji}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weights of judgment matrix.
# Formation Mechanism and Response Strategies for Urban Waterlogging: A Comprehensive Review
## 3 Analytical Framework
### Experimental Research
#### 3.2.2 Model Applications  
The application of experimental models plays a crucial role in urban flooding research, often used to predict flood dynamics, assess flood risks, and support urban planning.  
[PERSON] et al. [35] conducted experimental validation of two widely used urban flood inundation numerical models, applying rainfall intensity and measuring surface runoff under various rainfall conditions. The experimental results demonstrated the advantages of the dynamic wave model in predicting peak flow and hydrological response. [PERSON] et al. [62] further validated the applicability of the finite volume model under the influence of sewer overflow, confirming the relationship between model sensitivity and overflow ratio, and proving that the finite volume-based model can effectively simulate the local flow interactions between the sewer system and floodplain. [PERSON] et al. [63] explored the sensitivity of 1D and 2D models to input parameters by combining experimental data with variance decomposition methods. The study revealed that upstream inflow and roughness significantly affect water level changes, while downstream water levels are entirely determined by boundary conditions, providing a solid foundation for model parameter calibration.  
In response to complex scenarios such as intricate terrain, multi-branch flow fields, and areas of air concentration, researchers have developed more adaptable models. [PERSON] et al. [64] analyzed the velocity distribution at sewer junctions, assessing typical errors when calculating flow near junctions, and proposed that the local flow structure at junctions significantly affects the accuracy of flow measurements, offering guidance for practical engineering design. [PERSON] et al. [65] investigated the impact of urban heterogeneous porosity conditions on flood model errors, finding that heterogeneous porosity models outperform traditional models in overall predictive accuracy, although further enhancement of local flow characteristics is still needed. [PERSON] et al. [66] validated an air entrainment model based on the volume of fluid method through experimental data, combining it with the k-\(\omega\) turbulence model to simulate air entrainment phenomena within ditches, confirming that this model demonstrates good accuracy in predicting areas of air concentration.
## 4 Algorithms for Core System Functionality
### Order Preemption Algorithm  
As embedded in the G-DSS, order preemption is applicable to orders that have a delayed status, and a portion of their initial quantity may have already been produced in a previous PPP. If \(N_{initial}\) is the initial order quantity of order \(i\) and \(Ni_{remaining}\) is the remaining quantity to be produced, then the following steps are performed:  
1. Create set \(P=\{i:N_{initial}-Ni_{remaining}>0,\forall i\in P\}\)\(\forall i\in P\) do: 1. \(\text{Deem }Ni_{remaining}\) as the quantity of new production order \(i_{remaining}\). 1. \(\text{Set }PPPi_{remaining}=PPPcurrent\) if \(CMm_{i}>0\), else. 2. \(PPPi_{remaining}=PPPcurrent+1\). 2. Assign new production order id for \(i_{remaining}\). 3. \(\text{Set }Ni_{initial}-Ni_{remaining}\) as the quantity of production order \(i\). 4. \(\text{Set }PPPi=PPPinitial\). 5. \(\text{Keep production order id of }i\) for new \(i\). 6. \(\text{Set order status of }i\) as closed.  
Terminate.  
In addition to the above algorithm, the planner can also perform manual preemption by selecting the orders they wish from the \(P\) set and setting the desired \(Ni_{remaining}\) quantities. The scope of preemption is twofold: To alleviate bottlenecks and overloaded work centers by preempting orders for centers with \(CMm_{i}<0\) and compare the PPC-planned production quantities with the quantities actually produced at certain PPPs.