esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The shear strength (\(Ss\)) problem related to the structural engineering field has been investigated using the feasibility of AI models that have demonstrated positive progress [8; 21; 22]. Several versions of AI models have been developed for beam \(Ss\) prediction, such as artificial neural network (ANN) [15; 23; 24; 25], support vector machine (SVM) [26; 27; 28; 29], evolutionary computing models (ECM) [30; 31; 32; 33], and adaptive neuro-fuzzy inference system (ANFIS) [34; 35; 36; 37; 38]. Among all the aforementioned AI models, ANFIS confirmed its potential in modeling beam \(Ss\) mechanisms over the other models. The ANFIS model is characterized by the capability to mimic and capture the associated non-linearity and stochasticity of data time series [39]. However, the ANFIS model is associatedwith a major drawback, which is the membership function tuning parameters. Thus, combining the optimization algorithms, which are inspired by the behavior of animals and plants in nature, with a standalone ANFIS model appears as a new alternative model for improving its performances in solving difficult problems [40; 41]. The hybrid ANFIS model exhibited a noticeable implementation for diverse civil engineering applications [42; 43; 44]. In the current research, some parameters of the optimization algorithms (e.g., mutation probability) were assigned based on the reported literature review studies, while the appropriate values of those parameters can be obtained using the Taguchi approach [45]. This study assumes that the prediction modeling is associated with only input variables and model structures uncertainties, while the other uncertainty sources such as measurement errors, data handling, and inadequate sampling were ignored. The modeled dataset was hypothesized to be associated with redundant observations, and thus the dataset was constructed based on two scenarios of non-processed and pre-processed. The main motivation of this study is to investigate the feasibility of the novel hybrid ANFIS models for modeling high-strength concrete beam _Ss_. The modeling procedure is involved in several experiments of HSC slender beams. Being that deep beams behave differently compared to the slender beams (owing to size effect), only slender beams were used in this research. The data analysis focused on ascertaining the model validity and establishing its limitations. Before the prediction process, several input combinations were constructed using the related physical properties including the effective depth of beam (_d_), shear span (_a_), maximum size of aggregate (_a_g), compressive strength of concrete (_f_c), and percentage of tension reinforcement (_\(\rho\)_). The _Ss_ of the HSC slender beams is predicted using two different modeling scenarios based on (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP) to investigate the impact of the non-homogeneity of the dataset on prediction result accuracy.
# Design of MIMO Antenna with Double L-Shaped Structure for 5G NR
## 3 Results and Discussion
### Radiation Patterns and Antenna Peak Gain  
The computed gain of single antenna element is larger than 1.9 dBi in Figure 10, whereas the observed gain is greater than 1.5 dBi over the whole frequency range. In addition, the simulated and measured radiated efficiency of antennas 1 and 2 in the XOZ and YOZ planes are shown in Figure 11. It can be seen that for the XOZ radiation plane, the higher radiation gain directions of Ant1 and Ant2 are close to 90\({}^{\circ}\) and 270\({}^{\circ}\), respectively. On the other hand, Ant1 and Ant2 can both efficiently generate an omnidirectional radiation pattern in the YOZ plane. However, due to the influence of the test environment, there are inevitably some errors between the simulation and actual measurement of the two-dimensional radiation pattern of the antenna.  
Figure 10: Simulated (Sim) and Measured (Mea) peak gain values of Ant1 and Ant2.  
Figure 9: The total efficiency of Ant1 and Ant2.
## 2 Channel Measurement and Simulation
### Communication Scenario  
Figure 1 depicts a representative communication model between a high-speed rail base station (BS) and a mobile station (MS) within the train carriage. In this model, the projection of the base station onto the xoy plane is set as the coordinate origin. The vertical distance from the base station to the railway track is defined as the station-track distance \(D\), \(d\) represents the Euclidean distance between the transmitting and receiving antennas, \(h_{T}\) is the height of the transmit antenna, and \(h_{R}\) is the height of the receive antenna. The communication link between the high-speed rail base station and the train terminal involves multiple paths. These include the direct line-of-sight path from the base station to the train terminal, the reflection path inside the carriage, and the ground reflection path. All signal paths traverse the high-speed rail carriage windows.  
Figure 1: The communication scenario between the base station and the terminal in HSR.
## 2 Short-Term Traffic Flow Prediction Model Construction
### Construction of SVR Short-Time Traffic Flow Prediction Model Based on Associated Roads
#### 2.2.2 SVR Principle
 \tag{8}\]  
This can be obtained by substituting (3) and then making the partial derivatives of \(w\), \(b\), and \(0\):  
\[w={\sum_{i=1}^{m}}\left({{\alpha_{i}}^{*}-\alpha_{i}}\right)x_{i} \tag{9}\]  
\[0={\sum_{i=1}^{m}}\left({{\alpha_{i}}^{*}-\alpha_{i}}\right) \tag{10}\]  
\[C={\alpha_{i}}+{\mu_{i}} \tag{11}\]  
\[C={\alpha_{i}}^{*}+{\mu_{i}}^{*} \tag{12}\]  
Substituting (9)-(12) into Equation (8) yields the dyadic problem of SVR:  
\[ \begin{array}{l}\max\sum_{i=1}^{m}y_{i}({\alpha_{i}}^{*}-\alpha_{i})- \varepsilon({\alpha_{i}}^{*}+\alpha_{i})-\frac{1}{2}{\sum_{i=1}^{m}}\sum_{j=1 }^{m}({\alpha_{i}}^{*}-\alpha)\big{(}{\alpha_{j}}^{*}-\alpha_{j}\big{)}{x_{i}} ^{T}x_{j},\\ s.t.{\sum_{i=1}^{m}}({\alpha_{i}}^{*}-\alpha_{i})=0,\\ 0\leq{\alpha_{i}}_{i}{\alpha_{i}}^{*}\leq C\end{array}  \tag{13}\]  
Figure 3: The schematic of SVR with the addition of relaxation variables. The above process is subject to the KKT condition, i.e., requirement:  
\[ \begin{array}{l}\alpha_{i}(f(x_{i})-y_{i}-\varepsilon-\zeta_{i})=0,\\ \alpha_{i}{}^{*}(y_{i}-f(x_{i})-\varepsilon-\zeta_{i}{}^{*})=0,\\ \alpha_{i}\alpha_{i}{}^{*}=0,\zeta_{i}\zeta_{i}{}^{*}=0,\\ (C-\alpha_{i}{}^{*})\zeta_{i}=0,(C-\alpha_{i}{}^{*})\zeta_{i}{}^{*}=0\end{array}  \tag{14}\]  
Finally, the solution of SVR is shaped as \(f(x)=\sum_{i=1}^{m}(\alpha_{i}{}^{*}-\alpha_{i})x_{i}{}^{T}x+b\). The samples where \((\alpha_{i}{}^{*}-\alpha_{i})\
eq 0\) are the support vectors of SVR. From the KKT condition (14), it follows that, for each sample \((x_{i},y_{i})\), \((C-\alpha_{i})\zeta_{i}=0\) and \(\alpha_{i}(f(x_{i})-y_{i}-\varepsilon-\zeta_{i})=0\). Thus, after obtaining \(\alpha_{i}\), if \(0<\alpha_{i}<C\), then \(\zeta_{i}=0\), and hence,  
\[b=y_{i}+\varepsilon-\sum_{j=1}^{m}(\alpha_{j}{}^{*}-\alpha_{j})x_{j}{}^{T}x_{i} \tag{15}\]  
If the feature mapping form is considered, Equation (9) will be shaped like the following:  
\[w=\sum_{i=1}^{m}(\alpha_{i}{}^{*}-\alpha_{i})\phi(x_{i}) \tag{16}\]  
Then, the SVR can be expressed as \(f(x)=\sum_{i=1}^{m}(\alpha_{i}{}^{*}-\alpha_{i})K(X,X^{\prime})+b\), where \(K(X,X^{\prime})=\phi(x_{i}){}^{T}\phi(x_{i})\) is the kernel function.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 5 Proposed Method for Privacy Preservation in Healthcare Data
### Experimental Results on the COVID-19 Home Nursing Dataset
#### 5.5.1 Varying Privacy Budget Using the COVID-19 Home Nursing Dataset  
In order to examine how noise affects the same query, we ran an experiment where we varied the value of epsilon (0.8, 0.6, 0.4, 0.2, 0.01, and 0.02; Figure 23). In the given results, we can notice that with a decrease in the epsilon value, the amount of added noise also increases. Therefore, the smaller the value of epsilon, the greater the privacy required and the more noise is added. A compromise between privacy and utility exists. Adding more noise increases privacy but also reduces data utility. In differential privacy, the epsilon parameter (\(\epsilon\)) is used to control this trade-off between privacy and accuracy.  
For further demonstration, a graph was plotted for different values of epsilon (\(\epsilon\)), as shown in Figure 24. The amount of noise introduced to the query results grows significantly as the \(\epsilon\) value decreases toward zero. This enhances privacy but may result in less accurate results. Increasing the epsilon value results in less noise added to the output of query results, which typically provides higher accuracy and utility, as data remain closer to their true values. This helps data analysts to make informed decision to maintain the privacy level while also considering the usability and reliability of the data.  
Figure 23: Varying epsilon values in the Nursing Home COVID-19 Dataset.  
Figure 22: Comparison of results using the COVID-19 Home Nursing Dataset.
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 4 Propagation of Uncertainties
The scaling of the variance is a function of the proportion of cloudy pixels within a grid-cell; thus the sampling uncertainty reduces to zero as the cloudy proportion reduces to zero. The total uncertainty values in Figure 6 show larger uncertainties: (i) in the tropical regions where water vapour errors are likely to be higher; (ii) in the bare soil regions, such as the Sahara, where emissivity errors are likely to be higher; and (iii) at the edges of cloud-cleared cells where missed cloud detections of cloud edges are more likely. In the propagation the random components, such as the radiometric noise component and the sub-pixel emissivity component, are reduced by \(1/\sqrt{N}\). Over much of the land surface, taking the example in Figure 6, this reduction in random components is offset by the additional sampling term. For other regions, such as the Sahara, which is principally cloud-free; this additional term is also near zero for most grid cells. On propagation of the LST uncertainties to Level-3 differences between day and night become apparent. This is because the final term in Equation (18) introduces divergence. There are two relevant factors: (i) LST heterogeneity is generally higher during the day within each corresponding grid-cell; and (ii) there is on average a difference between the proportion of pixels masked as cloudy between day and night since the cloud masking algorithms utilise different channel combinations for day and night.
# Edge and Cloud Computing in Smart Cities
## 1 Introduction
* Systematically assesses the impact of edge-cloud computing in smart transportation, healthcare, industrial automation, smart cities, energy management, AR/VR, disaster response, and cybersecurity, demonstrating its transformative potential. * Highlights critical challenges outlining future research directions to address existing limitations. Figure 1 presents a comprehensive overview of edge and cloud computing in smart cities, categorizing architectural models, enabling technologies, application domains, and emerging challenges. It depicts four key architectural frameworks: multi-tier hierarchical, fully distributed, clustered edge-cloud architecture with FL, and hybrid digital twin-enabled models, highlighting their strengths and trade-offs in latency, scalability, fault tolerance, and energy efficiency. The figure also maps enabling technologies such as 5G/6G networking, AI-driven resource allocation, blockchain security, and edge virtualization, illustrating their role in enhancing performance, security, and decentralized intelligence. Furthermore, it outlines critical application domains, including smart healthcare, transportation, industrial automation, energy management, AR/VR, and cybersecurity, demonstrating the transformative potential of edge-cloud synergy. Lastly, future research directions such as AI-driven autonomy, federated intelligence, quantum computing, and sustainable edge-cloud ecosystems are highlighted, providing a roadmap for next-generation smart-city infrastructures.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 2 Hierarchical Structure of Cyber-Physical Systems
### Application Layer  
The application layer represents the computing system with which intelligent tasks are conducted by software in the cyber-world. The applications manage the physical systems [52], predict the state of the physical systems in the next time step [53; 54], and provide intelligent functions to CPS users, where various CPS applications are executed based on sensor measurements from physical systems. From the results of the application execution, a computing system determines the system dynamics (1) in the next time step.  
Due to the power limitation, the physical system depends on the computing system for the execution of intelligent functions requiring complex computations. In most CPSs, the application layer is supposed to have enough computing power and no electrical power constraints. In addition, due to real-time interactions in network layer, the power-limited physical systems can operate more intelligently than conventional embedded systems.  
In intelligent transport systems, the RSUs and other traffic control equipment on the ground negotiate among autonomous vehicles, control the traffic lights at intersections, and distribute road traffic in congestion situations [55]. Furthermore, simultaneous localization and mapping (SLAM) [56], which positions a UAV and configures a map at the same time, and path planning, which determines the motion of vehicles in real time, are the most representative applications of a vehicular CPS. In a smart factory, digital twin technology [53; 54], which realizes sophisticated and comprehensive factories in the cyber-world by using big data, is implemented at the application layer, where it is possible to expect the throughput of each production line per unit of time and to improve production lines continuously. Likewise, in a smart grid, a supervision system predicts power consumption for entire regions in real time based on sensor measurements from massive numbers of smart watt-hour meters in the physical system layer and from previous time-series data related to power consumption. Therefore, from power demand predictions, it is possible to feed back the control of electricity generation at the power plants [34].
# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 4 Experiments
### Main Results
#### 4.3.3 Cross-Domain Word Segmentation Results  
To comprehensively illustrate the performance of cross-domain data, we also evaluate our UnifiedCuttmodel (trained on BEST2010 dataset) on another three datasets for Thai: (i) ORCHID [32] is a Thai part-of-speech (POS) tagged corpus. It contains 23,125 sentences with about 2 M annotated words. (ii) Thai National Historical Corpus (TNHC) consists of human-annotated Thai classical literature documents; it contains 20,791 samples, with about 599 K words, and 2.14 M characters, and (iii) the Wisesight-1000 corpus [5] contains 1000 social media text samples with around 22 K words and 75 K characters.  
Table 5 shows the results of different models on cross-domain datasets. Our models outperform the state-of-the-art system on the Orchid and the Wisesight-1000 datasets. We noticed that PyThaiNLP achieved the highest word-level F1 value on the Orchid and TNHC datasets, while other neural network baselines performed poorly. Compared to neural network baselines, UnifiedCut obtains significant improvements on the Orchid dataset and achieves a comparable result to the PyThaiNLP model on the TNHC dataset. The experiment's result shows that UnifiedCut has better generalization performance and domain adaptability than other neural baselines.  
It is worth noting that all neural network models failed to perform well on the TNHC dataset. The main reason may be that TNHC is significantly different from BEST-2010 in writing styles and vocabulary. The literature in TNHC is typically written with archaic words, and the poems in the document need additional linguistic structures that are quite different from the training samples.
## 2 Case Report  
A 22-year-old female patient presented to the College of Dentistry clinics at Imam Abdulrahman Bin Faisal University with complaining of \"a gummy smile\" (Figure 1). Upon initial examination the patient did not show any gingival tissues at rest, however on full smile, 4 mm of gingival band was shown below the lower border of the maxillary lip. The medical history of the patient did not show any relevant medical issues, while dental history revealed that patient had completed orthodontic teeth alignment with clear aligner therapy 6 months earlier.  
Furthermore, there were no significant findings on the extra-oral examination and patient was normally asymmetrical. The facial three thirds were analyzed and found to be normal. A lateral cephalometric analysis confirmed absence of vertical maxillary excess and dentoalveolar extrusion. The maxillary lip length was 19 mm, the incisal display at rest was 4 mm, and the lip mobility was also assessed and found to be 6 mm. The clinical crown lengths of central incisors, lateral incisors, and canines teeth were 8 mm, 7 mm, and 8 mm, respectively. The gingival phenotype was assessed using the probe-transparency test4 and was determined to be thick. A CBCT scan (CS9300; Carestream Health Inc., Kodak, Rochester, NY, USA) was acquired to analyze the level of the alveolar bone crest in relation to the cementoearned junction. The radiographic parameter was set as 90 kVp/4 mA/s, with voxel size 200 \(\upmu\)m, according to manufacturer's exposure settings with a field of view 10 \(\times\) 5 cm (diameter \(\times\) height). Based on the sagittal cross section of the maxillary anterior sextant, the distance between the bone crest and the respective cementoearned junction was at a maximum of 1.5 mm (Figure 2). The CBCT scan also showed that the patient had a thick buccal cortex. Due to the short clinical crowns presentation, the squarish appearance of the teeth, and the aforementioned clinical findings, the patient was diagnosed with excessive gingival display as a result of altered passive eruption APE type 1 with a low bone crest. Treatment options were discussed with the patient and esthetic crown lengthening with bone resection was proposed.  
Figure 1: Preoperative smile view showing excessive gingival display.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 4 Principle of NOMA System
### Resource Allocation
The user over the second strongest channel decodes its signal by removing the best user's interference and shares it with the others. The last user over the worst channel will have zero interference from other users after applying (n\(-\)1) SIC and decodes its signal. Assuming that \(n\) users are assigned for each sub-channel, the signal sent by the BS to each sub-channel can be expressed as:  
\[x_{v}=\sum_{i=1}^{n}\sqrt{P_{i,v}}s_{i} \tag{1}\]  
where \(s_{i}\) is the modulated symbol of the \(i\)th user. According to the principle of NOMA, multiple users can be superposed on the same sub-channel where each user not only receives its own signal but also receives possible interference signals from other users on the same channel. The received signal at the \(j\)th user over \(v\)th sub-channel can be expressed as:  
Figure 5: Downlink sub-channel system model of NOMA. \[y_{j,v}=x_{v}h_{j,v}+w_{j,v} \tag{2}\] \[=(\sum_{l=1}^{n}\sqrt{P_{l,v}}s_{i})h_{j,v}+w_{j,v}\] \[=\sqrt{P_{l,v}}s_{i}h_{j,v}+\sum_{i=1,\
eq j}^{n}\sqrt{P_{l,v}}s_{ i}h_{j,v}+w_{j,v}\]  
where \(h_{j,v}=g_{j,v}PL^{-1}(d)\) is the coefficient of sub-channel \(v\) from BS to the \(j\)th user, \(g_{j,v}\) is the Rayleigh fading channel gain and \(PL^{-1}(d)\) is the path loss function between the \(BS\) and the \(j\)th user at distance \(d\). Further, \(w_{j,v}\) is the additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma_{v}^{2}\) where \(\sigma_{v}^{2}=E[|w_{j,v}|^{2}]\). Then the signal to interference plus noise ratio (SINR) (without SIC) received by the \(j\)th user can be expressed as [51]:  
\[SINR_{j,v}=\frac{P_{j,v}|h_{j,v}|^{2}}{\sigma_{v}^{2}+\sum_{i=1,\
eq j}^{n}P_{ i,v}|h_{j,v}|^{2}} \tag{3}\]  
The normalized signal to interference plus noise ratio can be expressed as [51]:  
\[SINR_{j,v}=\frac{P_{j,v}H_{j,v}}{1+\sum_{i=1,\
eq j}^{n}P_{i,v}H_{j,v}} \tag{4}\]  
where \(H_{j,v}=\frac{|h_{j,v}|^{2}}{\sigma^{2}}\) represents the channel response normalized by noise of the \(j\)th user on sub-channel \(v\). Recalling the principle of SIC approach and the optimal decoding order, we assume that the channel conditions of \(n\) users superposition on sub-channel \(v\) are sorted by the channel response normalized by noise (\(CRNN\)) as:  
Figure 4:0  
As previously mention, in NOMA protocol, more power is allocated to user with poor channel conditions. As a result, the power allocated to each user on sub-channel \(v\) can be expressed as:  
Figure 4:1  
After removing the interference from the users having poorer channel condition, the received signal to interference plus noise ratio (with SIC) of user \(j\)th can be expressed as:  
Figure 4:2  
Recalling the [PERSON]'s capacity expression, the data rate of the \(j\)th user on sub-channel \(v\) is given by:  
Figure 4:3  
The sum rate of the sub-channel \(v\) is given by:  
Figure 4:4  
Therefore, the overall sum rate of system can be given by:  
Figure 4:5