esa-sceva/satcom-chunk-collection huggingface dataset:
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 3 Materials and Methods
### Hyperparameter Tuning  
To maximize the performance of the proposed PELM ensemble model, hyper parameter tuning was performed using a grid search strategy. The goal was to identify the optimal combination of training parameters that would enhance convergence speed, reduce overfitting, and improve generalization on unseen data. The following parameters were tuned:  
* Learning Rate: Tested within the range of 0.0001 to 0.01, with 0.001 yielding the most stable and accurate results when using the Adam optimizer.
* Batch Size: Values of 16, 32, and 64 were evaluated. A batch size of 32 offered the best trade-off between training stability and computational efficiency.
* Dropout Rate: Dropout values between 0.2 and 0.5 were explored in the dense layers of the classification head. The final architecture included dropout rates of 0.4 and 0.3 in successive layers, effectively reducing overfitting.
* Optimization: The Adam optimizer was used and momentum parameters were kept constant as (\(\beta 1=0.9\), \(\beta 2==0.999\)).
* Number of Dense Units: Various configurations of dense layer sizes were tested after feature concatenation. The best performance was achieved using three fully connected layers with gradually decreasing units (e.g., 1024, 512, 128).
* Activation Functions: ReLU activation was used for intermediate layers, while a sigmoid activation function was applied to the final output layer for binary classification.  
This structured tuning process played a critical role in improving classification performance, helping the model achieve robust accuracy, high F1-score, and reliable generalization across diverse validation and test samples.
## 2 System and Channel Model
### Generalized Pointing Errors Model
Indeed, when the boresight displacement at the \(n\)-th hop \(s_{n}=\sqrt{\mu_{x,n}^{2}+\mu_{y,n}^{2}}\) is equal to zero, i.e., \(\mu_{x,n}=\mu_{y,n}=0\), and \(\sigma_{x,n}=\sigma_{y,n}\) then \(g_{n}=1\). In other words, when \(g_{n}=1\), we have only ZB pointing errors and more specifically the Beckmann model is reduced to a Rayleigh distribution with \(s_{n}=0\), and thus, expression (12) is simplified into [24], Equation (10) while also (14) reduces to [24], Equation (11).
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 2 Error Control Scheme (ECS)
### Hybrid Automatic Repeat Request (HARQ)  
As we discussed in Section 2.1, ARQ requires re-transmission for reliability, and re-transmission is frequent during poor channel conditions (which is costly) and seldom happens during good channel condition. Therefore, the reliability of communication link can be achieved using ARQ. However, the energy consumption is high due to frequent re-transmission during poor channel condition. On the other side, as we discussed in Section 2.2, FEC eliminates re-transmission, but it introduces redundancy bits for transmission. These redundant bits are unwanted costs during good channel conditions. The HARQ [39] scheme is developed by manipulating the advantages of both ARQ and FEC. In HARQ, the receiver tries to correct the detected errors first using FEC and asks for re-transmission only if error correction is not possible using FEC.
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 3 Air Resistance Approximation and Motion Primitive Generation
### Motion Primitive Generation
#### 3.2.1 Dynamic Modeling  
We plan the motion of a quadcopter based on the jerk of each axis, as specified in reference [30]. By ignoring input constraints, the axes can be decoupled, and motion trajectories can be generated for each axis separately. This section will describe how to calculate thrust and body velocity inputs from this trajectory. The following chapters will recombine the decoupled axes to consider the feasibility of the trajectory.  
Calculate the input thrust \(f_{t}\) by applying the Euclidean norm to (3), with the jerk denoted as \(\mathbf{j}=\hat{\mathbf{a}}_{t}\):  
\[f_{t}=\|\mathbf{a}_{t}-\mathbf{g}\| \tag{18}\]  
Take the first derivative of (3) and (18) to obtain:  
\[\mathbf{j}=\mathbf{R}\mathbf{\omega}\times\mathbf{e}_{3}f_{t}+\mathbf{R}\mathbf{e}_{3}\hat{f}_ {t} \tag{19}\]  
\[\hat{f}_{t}=\mathbf{e}_{3}^{T}\mathbf{R}^{-1}\mathbf{j} \tag{20}\]  
Figure 3: Linearization approximation of air resistance for quadcopter drones.  
After substituting and evaluating the product \(\mathbf{\omega}\times\mathbf{e}_{3}\), according to reference [6], the jerk \(\mathbf{j}\) and thrust \(f_{t}\) fixed the two components of body velocity:  
\[\begin{bmatrix}\omega_{2}\\ -\omega_{1}\\ 0\end{bmatrix}=\frac{1}{f_{t}}\begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&0\end{bmatrix}\mathbf{R}^{-1}\mathbf{j} \tag{21}\]  
As \(\omega_{3}\) does not affect linear motion, in order to simplify the writing expression, it will be considered as \(\omega_{3}=0\) in the rest of this article.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 3 Principles of Tolerance-Aided Interference Degradation Algorithm
### Channel Model  
OWC systems usually use LDs, LEDs, and infrared (IR) diodes as light sources for signal transmission. In addition to the peak transmitted power constraint, it is important to recognize that these light sources inherently exhibit low-pass characteristics. Typically, the behavior of these light sources can be effectively approximated through the utilization of first-order low-pass filter models, with the parameters often estimated using pilot signals within OFDM systems, as elucidated in [18].  
In this paper, we emphasize that channel estimation lies outside the scope of our research focus. We make the fundamental assumption that the channel has been accurately estimated, and for the purpose of compensating channel losses on each subcarrier, a single-tap equalizer is deployed. In the context of OFDM systems, the mitigation of intersymbol interference necessitates the inclusion of a cyclic prefix (CP), a standard component that can be meticulously designed based on prevailing channel conditions, as elaborated in [17; 19; 20]. For the purposes of this article, our analysis is centered on the system following the extraction of the cyclic prefix at the receiver stage. To streamline both the calculations and the ensuing analysis, we simplify our modeling by assuming an ideal channel with unity loss.
## 3 Results and Discussions
### Radial Hydraulic Force on the Separate Pump  
In this section, the characteristics of radial hydraulic force on the pump section operated independently are studied in this part. The pump delivers water in a certain way, and the flow is controlled by the pump structure and operation condition. The water flows in the pump without many impacts, not like in the turbine. Figure 12 presents pressure distributions on the radial mid-planes of the pump at three different rotation speed values (0.8\(n_{\mathrm{T}}\), \(n_{\mathrm{T}}\) and 1.2\(n_{\mathrm{T}}\)). The water pressure gradually increases along the flow channel of pump. The pressure experiences a uniform variation in the pump, and is different from the abrupt change in the transition zone between turbine suction and turbine impeller. In the conditions of 0.8\(n_{\mathrm{T}}\), flow separation structure is existed in the blade passage, which generates force excitation on the blade. Meanwhile, there is obvious pressure difference between blade pressure surface and blade suction surface in the 1.2\(n_{\mathrm{T}}\) condition; this phenomenon causes the larger force magnitude, as compared with 0.8\(n_{\mathrm{T}}\) and \(n_{\mathrm{T}}\).  
Figure 13 presents the initial direction and magnitude of radial hydraulic force on the pump section under three operation conditions. The value of the force on the normal impeller rotation is obviously smallest. There is little difference between the conditions of 0.8\(n_{\mathrm{T}}\) and 1.2\(n_{\mathrm{T}}\) for the force on the impeller. The direction of the force on the impeller is in the second quadrant, while that on the volute is in the fourth quadrant. Figure 14 shows the force variations in one full cycle of the impeller rotation. Radial force characteristics of five  
Figure 11: Frequency domain results of radial hydraulic forces. (**a**) Turbine suction. (**b**) Turbine impeller.  
The time-domain analysis of the radial hydraulic force on the pump section in one full working cycle was conducted as shown in Figure 15. The obvious periodicity of force fluctuation can be observed, and the periods of fluctuations on the impeller and volute are close, but the fluctuation extents are different. The \(K\) amplitude on the impeller is 0.046, while that on the volute is approximately 0.09. Figure 16 displays the frequency domain results of radial hydraulic forces on the pump. There are obvious fluctuation amplitudes on the frequencies of \(5f_{R}\), \(10f_{R}\) and \(15f_{R}\). The frequency of \(5f_{R}\) has the bigger amplitude of the fluctuating force, which is affected by the impeller structure.
# A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization
## 3 The Proposed Framework
### Experience Reuse and Parallel Computing
```
0: Current problem's features \(\mathbf{f}_{new}\), last problem's features and nearly optimal PS \(\mathbf{f}_{last}\), \(p\), external archive \(Archive\)
0: Initial population of the new problem \(p^{*}\)
1:\(A=UpdateArchive(\mathbf{f}_{last},\ p,\ Archive)\)
2:forall\((\mathbf{f}_{i},\ p_{i})\in A\)do
3:\(s_{i}=DTW(\mathbf{f}_{i},\ \mathbf{f}_{new})\)
4:\(iteration_{i}+=1\)
5:endfor
6:\(index=0\)
7: Select the minimal \(s_{i}\), \(mindex=i\)
8:if\(s_{mindex}\leq MinimumSimilarity\)then
9:\(p^{*}=RandomRemoveNIndividuals(p_{mindex})\)
10:\(p^{*}=RandomAddNIndividuals(p_{mindex})\)
11:\(trial_{mindex}+=1\)
12:else
13:\(p^{*}=p_{mindex}\)
14:endif
15:return\(p^{*}\)
```  
**Algorithm 2** Experience Reuse Method Based on Problem Similarity  
To further enhance the computational speed of dynamic algorithms, the algorithms in the framework could utilize Apache Spark for parallel computation when performing population evaluation. Due to the presence of resilient distributed datasets (RDDs) and the fact that Spark is very Java-programming-friendly, it is relatively easy to implement parallel computation on a Spark cluster. As shown in Figure 2, a population of _List_ type can be transformed into an RDD by the _parallelize_ method in \(JavaSparkContext\). Then, the data are loaded into memory by transformations in RDD operations (e.g., \(map\)), and then the _take_ operator is called to execute the computation and re-persist the data to the external memory. The _collect_ operator is not used here because it is prone to memory overflow, and its operation mechanism conflicts with the distributed concept of Spark. Readers can refer to the official documentation for more details and principles about the operations of RDDs.
# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
###### Abstract  
An outside mutual correction (OMC) algorithm for natural scene text detection using multibox and semantic segmentation was developed. In the OMC algorithm, semantic segmentation and multibox were processed in parallel, and the text detection results were mutually corrected. The mutual correction process was divided into two steps: (1) The semantic segmentation results were employed in the bounding box enhancement module (BEM) to correct the multibox results. (2) The semantic bounding box module (SBM) was used to optimize the adhesion text boundary of the semantic segmentation results. Non-maximum suppression (NMS) was adopted to merge the SBM and BEM results. Our algorithm was evaluated on the ICDAR2013 and SVT datasets. The experimental results show that the developed algorithm had a maximum increase of 13.62% in the F-measure score and the highest F-measure score was 81.38%.  
scene text detection; multibox detector; semantic segmentation Article
# Load Models Representative of Brazilian Actual Traffic in Girder-Type Short-Span Highway Bridges
## 1 Introduction
Load values have been increased over time as an attempt to update the code to account for heavy vehicles weights, although without any calibration process; therefore, loads from the Brazilian design code may not properly reproduce the extreme effects of the real traffic. There is a need to verify the adequacy of the code model and to develop more realistic design criteria. Research focusing load models for highway bridge design is under development in Brazil [24; 25; 26; 27]. The current fatigue live load model from the Brazilian code NBR 7188, which consists of the TB-450 model (see Figure 1) affected by a reduced factor, was evaluated to verify the unlimited fatigue life criterion of typical structures as box girders or multiple girders [28]. As a result, a new fatigue live load model was proposed. Live load models for ultimate limit states in Brazilian highway bridges are being developed by the authors to reproduce extreme values of bridge effects due to actual traffic [6] (including dynamic effects) with approximately the same reliability index [17; 29] among typical structural systems [13]. Two-girders reinforced concrete (RC) bridges, with span lengths up to 40 m, were considered. The adopted methodology, involving the developed traffic database, assessment of static and dynamic effects due to the real traffic, their histograms, and extrapolations, are described in Section 2. Figure 1: Current Brazilian load model: plan view and cross section. Comparisons were provisionally made [30; 31] between the values of the effects produced by the NBR 7188 load model and the characteristic values of the effects obtained by the simulations of actual traffic. Results showed that the Brazilian code load models could not adequately reproduce the real traffic of heavy vehicles and may, in many cases (particularly the negative moments at the cantilevers), be non-conservative. A similar situation also occurred in other countries [6; 20; 21; 22]. Present work offers two main contributions to the research line. First, the grid models that represent the decks have undergone certain adjustments in order to simulate the static and dynamic behavior of the bridges in terms of displacements and natural frequencies. The new grid models were calibrated to shell element models of the bridges. Finally, two load models that are representative of Brazilian real traffic on small span bridges are proposed and comparisons between their results and those of the NBR 7188 current model are presented.
# Electolytically Ionized Abrasive-Free CMP (EAF-CMP) for Copper
## 1 Introduction
According to [PERSON] et al. [30], dicarboxylic acids, such as oxalic acid and malonic acid, can be used for abrasive-free CMP solutions under acidic conditions. [PERSON] et al. [31] studied the tribological characteristics of an abrasive-free solution for Cu. The use of an abrasive-free solution can help reduce dishing, erosion, and oxide loss during Cu CMP [32; 33]. The advantages of the abrasive-free solution are as follows [34]:  
1. Effortless maintenance of particle dispersion stability;
2. Continuous mixing in a slurry tank and filtration are not required;
3. Maintenance of the flow velocity of the slurry to prevent particle agglomeration is not necessary. The abrasive-free CMP solution has a high concentration of toxic chemicals to compensate for the low MRR. Recently, a method using electrochemical ionization of slurry was introduced to improve the MRR in CMP. [PERSON] et al. [35] proposed a Cu CMP method using an electrolytically ionized slurry to reduce slurry consumption. The slurry was ionized by attaching electrodes to the existing slurry nozzle and applying a voltage using a power supply (Figure 1). The electrolytically ionized slurry contains a high amount of -OH radicals, which activate the chemical reaction between the copper and the slurry. However, applying a voltage while the particles are dispersed within the slurry may cause particle agglomeration. In this study, we investigated the use of electrolytically ionized abrasive-free CMP (EAF-CMP) to enhance the MRR of Cu. The abrasive-free CMP and the proposed electrolytically ionized abrasive-free CMP were compared in terms of electrochemical analysis and MRR. The effects of the oxidizer (H\({}_{2}\)O\({}_{2}\)), complexing agent (citric acid), and corrosion inhibitor (BTA) concentrations in the electrolytically ionized CMP solution were examined.
## 6 Future Research Directions  
Along with achievable rate concerns, there are a number of unresolved communication and networking research questions that must be addressed in order to advance the implementation of the metaverse.  
Figure 8: The results of throughput versus SNRs with different IRS reflecting elements and analytical cases, including BPSK, QPSK, and the 16 QAM modulation schemes of the proposed wireless system in the metaverse.  
Figure 7: The results of SE versus user numbers with different IRSs reflecting elements and the BPSK modulation scheme of the proposed wireless system in the metaverse [27].  
* Green communication networking: For constant sensing, data transfer, and real-time communication in the metaverse, significant energy resources are needed. In order to achieve green networking, it is necessary to develop energy-efficient [28] communication protocols and use energy harvesting techniques to ensure network functioning. These topics should be researched in future works.
* Enhanced ultra-reliability and low latency system: To support VR/AR devices, metaverse services demand excellent data dependability and transmission stability in expanding high-bandwidth situations. In order to increase the transmission dependability of 6G wireless networks, IRS elements and artificial metasurfaces that may change the reflector array of incoming electromagnetic waves by autonomously manipulating the phase shift are being investigated as viable solutions. Furthermore, extremely low latency is required for critical real-time metaverse applications in order to ensure user satisfaction and avoid vertigo from the delay. To design a metaverse-enabled system, improving the latency in 6G communication is a challenging task that can be explored in future research.
* Management of heterogeneous networks: Diverse communities are brought together in the metaverse. An important area that needs to be researched is how to manage various resources and services while integrating numerous heterogeneous networks.
* Combining multiple systems: The network must incorporate several sensing, communication, and computation techniques in order to meet the stringent criteria for 6G, including Tera-Hertz (THz) communication, edge AI, energy transfer and harvesting, and communications with huge IRS elements.
* Standard specifications: Standardized network access, networking, routing, and congestion management protocols are necessary for the communication and network technologies underpinning the metaverse. While this needs a great deal of rigorous follow-up study efforts because of the heterogeneity of the unified metaverse network, these standards must be tailored to each subsystem in the metaverse.
* User experience enhancement: In a metaverse scenario, user experience is given ultimate preference to make the virtual environment real. Different wireless communication perspectives can be utilized to test the performance of metaverse environments. Parameters can be considered as channel impairment effects, channel paths, modulation techniques, data rates, and so on.
## 4 Discussion
Of these, five patents can be highlighted for the combination of elements that characterize their operation. The patent 3.1 (WO 2010/042826 A1), which, as mentioned, through the electrodes and the system detects anomalies for diagnosis, can also be used for the treatment of patients with pacemakers since it allows the use of potential to correct rhythm disorders. Patent 3.9 (US/8688189), due to its patch modality, makes it more comfortable for the patient, which makes it possible to detect abnormal rhythm patterns, either due to disease or medication. It also allows for the monitoring of the patient's response to treatments and evaluating their effectiveness. Patent 3.11 (US/9161705) focuses mainly on detecting regions of cardiac ischemia; it is programmed to identify specific patterns of the ST segment and the T wave, comparing them with normal values, which are characteristic areas where cardiac muscle tissue damage can be seen. Finally, patent 3.27 (WO 2020/086112), where the main feature is the real-time analysis since it not only focuses on ECG measurements but also saves the information to be able to make reconstructions of those signals after having been registered. Unlike other equipment that only identifies by wave, in this case, it completely evaluates the complete signal complex, which makes it possible to obtain a more accurate position of the lesion caused by CVD. These patents raise what could be a breakthrough as part of preventive medicine. Being able to analyze, not only through the interrogation and clinical history of patients but also having first-hand physiological data from the patient, can lead to a reduction in CVD cases with irreversible results. As was established from the beginning, CVD is a public health problem with a growing number of cases each year and with a high mortality rate, which means it favors treatment in the earlier stages of CVD that is present in individuals.
###### Abstract  
Motion planning by considering it as an optimal problem is an effective and widely applicable method. Its comprehensive performance greatly depends on the vehicle dynamics model, which is highly coupled and nonlinear, especially under the dynamical scenarios and causes much more consumption of computation resources for the numerical optimization. To increase the real time performance of the motion planner designed by nonlinear model predictive control (NMPC), a unified and simplified vehicle dynamics model (SDM) is presented to make a balance between the accuracy and complexity for dynamical driving scenarios. Based on the statistical analysis results of naturalistic driving conditions, a unified nonlinear vehicle dynamics model is set up, which considers the tyre cornering characteristic and is also applicable to conditions with large turning angle. After the validation of this coupled dynamics model (CDM) by comparisons with other widely used models under a variety of conditions, the coupling effect is analyzed according to the transfer functions, which are obtained by linearizing CDM at equilibrium points. Furthermore, SDM is derived by ignoring the weak part of the coupling effect. The accuracy of SDM is validated by several comparative studies with other models and it is further applied to design a motion planner by NMPC to validate its contribution on the performance improvement under dynamical driving conditions.  
automatic driving; motion planning; vehicle dynamics; model predictive control Article