esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optimization Framework
\begin{array}{c}P^{i}_{x}\\ P^{i}_{y}\\ P^{i}_{z}\end{array} \right],i=1\ldots N\) and locking the coordinates of the RCM, we compute the Euclidean distance between P\({}^{i}\) and RCM using Equation (5) and define the term \(t_{i}=\frac{L}{d_{i}}-1,i=1\ldots N\) that is used to compute the determine the target coordinates of the instrument holder using Equation (21). \[\left\{ \begin{array}{l}X_{i}=a_{01}-t_{i}(P_{x}^{i}-a_{01})\\ Y_{i}=a_{02}-t_{i}(P_{y}^{i}-a_{03})\\ Z_{i}=a_{02}-t_{i}(P_{z}^{i}-a_{03})\end{array} \right.,i=1\ldots N \tag{21}\]  
For each target point determined with Equation (21), the inverse kinematic model is computed. To compute the terms q1_i and q2_i, the terms from Equation (22) are derived using Equation (1). \[\left\{ \begin{array}{l}A_{i}=-X_{i}^{2}+2X_{i}a_{01}-Z_{i}^{2}+2Z_{i}a_{03}- a_{01}^{2}-a_{03}^{2}+a_{1}^{2}-a_{4}^{2}\\ B_{i}=\sqrt{X_{i}^{2}a_{4}^{2}-2X_{i}a_{01}a_{4}^{2}+Z_{i}^{2}a_{4}^{2}-2Z_{i} a_{03}a_{4}^{2}+a_{01}^{2}a_{4}^{2}+a_{03}^{2}a_{4}^{2}}\end{array} \right. \tag{22}\]  
yielding  
\[\left\{ \begin{array}{l}q_{1\_i}=Y_{i}-a_{02}-\sqrt{A_{i}+2B_{i}}\\ q_{2\_i}=Y_{i}-a_{02}+\sqrt{A_{i}+2B_{i}}\end{array} \right.,i=1\ldots N \tag{23}\]  
For computing the value of q3, the \(\lambda\) first needs to be defined using Equation (24):  
\[\lambda_{i}=\arctan\biggl{(}\frac{X_{i}-a_{01}}{Z_{i}-a_{03}}\biggr{)},i=1 \ldots N \tag{24}\]  
and the value of q3 joint is computed using Equation (25):  
\[q_{3\_i}=-a_{2\min}-a_{5}+\frac{1}{2}\sqrt{ \begin{array}{l}-4I_{4}\cos \lambda_{i}C_{i}-8X_{i}a_{4}\cos\lambda_{i}+8a_{01}a_{4}\cos\lambda_{i}-8a_{4} \sin\lambda_{i}(Z_{i}-a_{03})\\ +4X_{i}C_{i}-4a_{01}C_{i}+4(X_{i}^{2}-2X_{i}a_{01})+4(Z_{i}^{2}-2Z_{i}a_{03}+ a_{01}^{2}+a_{03}^{2})+\,i=1\ldots N\\ 4a_{3}^{2}+4a_{4}^{2}-(q_{1i})^{2}+2q_{1i}q_{2i}-(q_{2i})^{2}\end{array} } \tag{25}\]  
where term \(C_{i}\) is computed using Equation (26). \[C_{i}=\sqrt{4I_{3}^{2}-(q_{1\_i})^{2}+2q_{1\_i}q_{2\_i}-(q_{2\_i})^{2}},i=1 \ldots N \tag{26}\]  
For the genetic algorithm, the range of each active joint is given as a constraint q1 \(\in\) [100,400], q2 \(\in\) [100,400], and q3 \(\in\) [100,500]; if a computed value falls outside the constraint interval, the algorithm adds a penalty to the objective function. The multi-objective optimization problem is formulated as follows:  
\[\min f(x)=\left[ \begin{array}{c}f_{1}(x)\\ f_{2}(x)\\ f_{3}(x)\end{array} \right]=\left[ \begin{array}{c}a_{1}+penalty\\ a_{3}+penalty\\ a_{2\min}+penalty\end{array} \right],\forall i=1\ldots N:\left\{ \begin{array}{l} L_{AB\_i}=\parallel P_{i}-RCM\parallel<L\\ q_{1\_i}q_{2\_i}\in[100,400]\\ q_{3\_i}\in[100,500]\\ x\in\left[\begin{array}{c}[30,300]\\ [30,300]\\ [30,150]\end{array}\right]\end{array} \right. \tag{27}\]  
The penalty is a sum of deviation that increases the objective values if the computed q values violate the given bounds. The genetic algorithm used for both optimization problems are based on _gamultiobj_ function and use a population of 100 individuals, with 200 generations and a mutation rate of 1/1000 individuals.
# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 4 Discussion  
### Temporal and Spatial Variations in WUE and Attribution  
Excluding the Tibetan Plateau region, where WUE was rising, the other regions exhibited varying degrees of decline (Figure 2). GPP displayed an ascending trend in the majority of the study area, suggesting that the accumulation of ecosystem organic matter in China's dryland ecosystems was on a rising trend during the study period, and the impact of vegetation restoration was evident. As demonstrated in the superposition analysis (Figure 3), the reduction in WUE across 62.78% of the total area was primarily attributable to an increase in ET, whereas the decrease in WUE in the Tibetan Plateau region was mainly attributed to an increase in GPP. Due to the fluctuating nature of climate change, there is also some uncertainty in the direction of WUE change in different regions of the dry zone over different time periods. However, whatever the changes are, they are the result  
Figure 8: The Hurst index value, reflecting the persistence of the time series (**a**); the trend of the Hurst index results superimposed on the current WUE (**b**). (Notes: The table below shows the future trend classification and the area share.).  
of certain climatic fluctuations and human activities. Table 7 shows the trends of WUE in some of the previous research results.  
Furthermore, as indicated in a study by [PERSON] et al., both WUE and GPP can be utilized to evaluate the process of vegetation restoration [32]. During the early phase of vegetation restoration, biomass accumulation, vegetation cover enhancement, and concurrent increases in the WUE were observed. As vegetation restoration progressed to its later stages, plant stomatal conductance increased, drought-tolerant plant coverage declined, and WUE may have decreased [39; 47]. In the majority of China's arid regions, excluding a significant portion of the Qinghai-Tibet area, the increase in GPP was accompanied by a reduction in WUE during the observation period, indicating that vegetation recovery in these regions was at a more advanced stage. On the other hand, the occurrence of enhanced WUE along with increased GPP in certain areas of the Tibetan Plateau indicated that vegetation recovery in this region may be in its initial stage.
# Anomaly Detection Method for Unknown Protocols in a Power Plant ICS Network with Decision Tree
## 2 Materials and Methods
### Related Work
#### 2.1.4 Anomaly Detection in an Industrial Control System  
Recently, research has been conducted to assess the performance of anomaly detection models using techniques such as preprocessing, filtering, and feature extraction of data, which leverages machine learning and deep learning algorithms [38; 39]. Moreover, methods for detecting anomalies on control systems are being studied to enhance the performance of anomaly detection models for network traffic. However, directly implementing anomaly detection solutions on control systems may not be feasible for systems that prioritize availability.  
For modern industrial control systems, the configuration system changes frequently. Therefore, deep learning neural network techniques are being utilized to detect and monitor rapid asset changes. However, industrial facilities, such as power plants, undergo minimal changes [40]. Generally, autoencoder neural networks individually reconstruct predicted data by learning from normal data. However, methods that simultaneously perform prediction and reconstruction on input data, utilizing a single-mode approach, are also being studied to achieve more effective attack detection [41].  
In the case of industrial control systems connected to the internet, research is also being conducted to enhance the detection rate of abnormal symptoms by identifying data imbalance to address undefined attacks on the network [42].
## 2 Prototyping Software Architecture  
As previously stated, this work was developed within the CHIC project scope. As such, it is important to briefly present it to provide readers with a more thorough context. CHIC aimed to develop, test, and demonstrate a set of innovative processes, products, and services for both the audiovisual and multimedia sectors. Due to the underlying transversality, results are expected to have an impact in other important business sectors within culture's scope (e.g., cultural heritage, exhibiting arts). An extended set of activities was carried out toward three essential domains: (i) open platforms for the management and distribution of digital contents, supported by the cloud; (ii) management of cultural heritage related contents, based in open-systems for preservation and interaction; and (iii) content creation, production and consumption focusing quality of service and experience, resorting to very high-definition immersive environments.  
One of the main tasks established within CHIC's scope was the development of an independent IoT system as-a-service, qualified to enable the swift configuration of scalable mulsemedia setups and their remote management. These mulsemedia setups must support the configuration of groups of wireless mulsemedia equipment--\"things\", such as displays, fans, lights, smell delivery devices, varied types of sensor, etc.--within a given interior space, i.e., an interactive room. To provide meaningful experiences to users and induce engagement, involved device sets--mostly, actuators--are expected to work on-demand, in an orchestrated and time-effective manner, to adequately deliver a specified stimulus. Considering the requirements laid out, Figure 1 presents the proposed architecture for the IoT mulsemedia environments prototyping software. It is composed of a virtual environment of \"things\" and a remote-control panel, which are connected by a publishing/subscriber (P/S) messaging protocol.  
The virtual environment can be regarded as an IoT mulsemedia environment virtual representation, providing both a software and hardware agnosticity layer--device abstraction layer--as well as a virtual representation of each device that is a part of the different mulsemedia experiences. Moreover, each virtual device representation has an profile containing its properties and available functions. As for the IoT server, it enables remote and on-demand control and management capabilities through a context-aware integrated discovery service (setup/device discovery). Communication between both elements is assured by a P/S protocol, specifically the ISO/IEC 20922 standard, also known as message queue telemetry transport (MQTT). The P/S broker is placed in the server side, as usual.  
Figure 1: Architecture overview of the IoT mulsemedia environments prototyping software. It is composed of a control server and, at least, one virtual environment of “things”, managed by a virtual coordinator.
# Surface-Codes-Based Quantum Communication Networks
## 3 Proposed Surface-Codes-Based Quantum Communications Networks  
To enable the next generation of quantum communication networking, we propose to employ the surface codes so that the logical qubits are located at different nodes in the network. The logical qubits are represented by the patches introduced in the previous section. For simplicity, we assume that the surface code is defined on a \(d\times d\) grid. The neighboring nodes are connected by employing \(d\) wavelengths, as illustrated in Figure 4. Some of the optical links could be FSO links.  
By performing the \(Z\otimes Z\) measurement, as described in the previous section, the logical qubits create the Einstein-Podolsky-Rosen (EPR) pair. The results of the measurements have been passed to the SDN controller, which will know the exact EPR pair being created. To create the desired QCN, the corresponding product measurements need to be simultaneously performed. As an illustration, let us consider the ring network composed of four nodes, as shown in Figure 5 (left), with each node being equipped with the surface code patch representing the corresponding logical qubits. In principle, one surface patch can be split between multiple nodes, but, to facilitate explanations, we assume that each node contains a single SC patch. By performing the simultaneous product \(Z\otimes Z\) measurements between logical qubits \(q_{1}\) and \(q_{2}\), \(q_{2}\) and \(q_{3}\), \(q_{3}\) and \(q_{4}\), \(q_{4}\) and \(q_{1}\), we can entangle the nodes 1-4 and thus create the ring QCN. On the other hand, for the four-node mesh network shown in Figure 5 (right), by performing the simultaneous product \(Z\otimes Z\) measurements between logical qubits \(q_{1}\) and \(q_{2}\), \(q_{2}\) and \(q_{3}\), \(q_{3}\) and \(q_{4}\), \(q_{4}\) and \(q_{1}\) as well as the simultaneous \(X\otimes X\) measurements between \(q_{1}\) and \(q_{3}\), \(q_{2}\) and \(q_{4}\), we can entangle the four qubits into the mesh configuration. By providing the results of the measurements to the SDN control plane, the exact maximum entangled state between nodes in the QCN will be known. Clearly, this approach allows us to entangle the logical qubits in an arbitrary network. The trapped ions-based technology represents a perfect candidate for practical implementation of the proposed QCN. By equipping every node in the proposed QCN by multiple qubit patches, in principle, we can simultaneously perform quantum networking and quantum distributed computing. Instead of wavelength-division multiplexing (WDM), the multicore fiber can also be used to connect the logical qubits [16]. The proposed QCN does not require the trusted node assumption, but it is assumed that [PERSON] does not have access to SDN controller.  
Figure 4: Simplified description of connecting two logical qubits from two neighboring nodes by \(d\) wavelengths.
# Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning
## 4 Method
### Action Representation  
The action consists of two parts: \(a=\{a_{phase},a_{duration}\}\), phase information and phase duration.  
Phase Information: the model outputs the Q-values for four predefined phases \([q_{1},q_{2},q_{3},\text{ and }q_{4}]\), and the phase with the highest Q-value is selected as the next phase for the current intersection.  
Phase Duration: the model outputs a continuous value within a predefined range, and this value, when multiplied by the preset maximum duration, gives the corresponding phase duration (\(d_{p}\)).  
\[d_{p}=x\cdot T_{max},x\in\left[\frac{T_{min}}{T_{max}},1\right] \tag{2}\]  
where \(x\) denotes the continuous value of the model output, \(T_{max}\) denotes the preset maximum duration, and \(T_{min}\) denotes the preset minimum duration.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 3 Imaging in BAV
### CT Angiography (CTA)  
CT Angiography (CTA) is another valuable imaging modality, particularly useful for assessing the aorta. CTA provides excellent spatial resolution, enabling detailed visualization of the aortic anatomy, including the presence and extent of aortic aneurysms, coarctation, and other vascular abnormalities. Its multiplanar reconstructions allow precise measurement of aortic diameters and detection of valve calcification, making it highly accurate in evaluating aortic stenosis severity [44; 45]. In cases of severe valve calcification, CTA has been shown to outperform echocardiography in identifying bicuspid morphology, achieving a sensitivity of up to 94% [44].  
CTA also enables the assessment of coronary artery disease and the ascending aorta, both of which are critical for preoperative planning in BAV patients undergoing surgical interventions [46]. Additionally, CTA's ability to detect plaque and assess aortic wall integrity makes it particularly valuable for evaluating aortic stenosis and coexisting vascular abnormalities [45]. However, its use is limited by exposure to ionizing radiation and contrast agents, which may not be suitable for younger or high-risk patient populations [36]. Newer low-dose CT protocols are being developed to mitigate these risks while maintaining diagnostic accuracy.
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
###### Abstract  
The automated optical inspection of a surface mount technology line inspects a printed circuit board for quality assurance, and subsequently classifies the chip assembly defects. However, it is difficult to improve the accuracy of previous defect classification methods using full chip component images with single-stream convolutional neural networks due to interference elements such as silk lines included in a printed circuit board image. This paper proposes a late-merge dual-stream convolutional neural network to increase the classification accuracy. Two solder regions are extracted from a printed circuit board image and are input to a convolutional neural network with a merge stage. A new convolutional neural network structure is then proposed that is able to classify for defects. Since defect features are concentrated in solder regions, the classification accuracy is increased. In addition, the network weight is reduced due to a reduction of the input data. Experimental results for the proposed method show a 5.3% higher performance in F1-score than a single-stream convolutional neural network based on full chip component images.  
automated optical inspection; defect classification; convolutional neural network +
Footnote †: journal: Applied Sciences
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Interference Mitigation in D2D
#### 4.1.3 Spectrum Separation and Mode Selection  
Splitting 3 GPP permits both underlay and overlay D2D operation modes. While spectrum partitioning using TDM can mitigate interference [172], it may lead to underutilization of resources. Hence, hybrid resource partitioning with dynamic MOS based on interference thresholds is also encouraged.
## 4 ML Applications for RIS-Assisted Communications
### Resources for Generating Databases to Train ML on Wireless Communications  
ML algorithms aim to find useful patterns from data to make automatic decisions. Most of the research papers in wireless communication systems feed the ML algorithm employed by performing Monte Carlo realizations of the wireless channel, following the Rayleigh, Rician, and Saleh-Valenzuela models. Since channel realizations are complex-valued and neural networks work only on the real domain, DL-based approaches process the channel values by processing their real and imaginary parts separately or taking their magnitude. Table 1 lists the main datasets for testing ML algorithms applied for the improvement of RIS-aided communications. In [55], a non-publicly available resource that generates data from MIMO mmWave systems is presented. The system includes a ray-tracing simulator and a vehicle traffic simulator in a 5G environment with mobility. DeepMIMO [56] is one of the most popular choices among researchers, since it is an open-source project to generate wireless communication datasets for training and testing ML algorithms. It is based on the ray-tracing simulator Remcom Wireless Insite [57]. [PERSON] et al. [58] gathered physical channel measurements of various geometric antenna arrangements and RIS surfaces to generate a dataset. The data are publicly available on the IEEEDataPort platform [59] and on a GitHub repository [60]. Among the different settings to consider is the RIS-to-antenna distances, the reflection angles, and the positions of the RIS placed in a rotating way. Another publicly available dataset of measurements is presented in [61], where OFDM transceivers are included. The details about the design of the RIS are included.  
Other publicly available datasets exist for ML applications in MIMO wireless systems beyond RISs. The applications comprise automated modulation recognition (AMR) to detect the modulation scheme without needing any prior information using DL methods [62; 63; 64; 65]. For mmWave MIMO applications, the datasets available are used for beam selection as the desired output [66; 67; 68].
# Performance Evaluation of Asphalt and Asphalt Mixtures Modified by Fuel-Resistant Admixture
## 3 Results and Discussion
### Performance of SMA-13 Mixture  
#### 3.2.1 Mass Loss Test  
As shown in Figure 8, after immersing the asphalt mixture specimens in diesel for 1 min, a noticeable change was observed in the diesel inside the container, which transitioned from clear pale yellow to turbid black. This indicates that diesel has a strong eroding effect on asphalt, as it rapidly dissolved the asphalt within the specimens. This is because the main components of diesel are hydrocarbons in the range of C15-C18, which are similar and highly soluble in the saturated fractions of asphalt [27].  
Figure 7: BBR test results: (**a**) S-value; (**b**) m-value.  
The appearance of asphalt mixture specimens after 24 h of oil erosion is illustrated in Figure 9. Following the oil erosion treatment, specimens without added FRM exhibited extensive asphalt stripping, exposed aggregates, and partial aggregate loss on the surface. In contrast, the appearance of the mixture specimens remained relatively intact when FRM was added. Within the range of 1% to 5% FRM, there was no significant difference in the appearance of different FRMAM specimens. However, when the FRM dosage was increased to 7%, localized areas of severe asphalt stripping were observed in the asphalt mixture specimens. This phenomenon may be attributed to an excessively high FRM dosage, resulting in uneven distribution within the asphalt and leading to localized severe oil erosion.  
After the diesel completely evaporated, the mass of each group of specimens was determined. The mass loss of the specimens was calculated using Equation (1), and the test results are shown in Figure 10. The asphalt mixture specimens without FRM exhibited the greatest mass loss after oil erosion, reaching 8.92%. In comparison to the base asphalt mixture specimens, the mass loss of FRMAM specimens was reduced. This indicates that the addition of FRM effectively enhanced the resistance of asphalt mixture to oil erosion.  
When the dosage of FRM reached 1%, the mass loss of FRMAM specimens basically remained stable. It is inferred that the alteration in the rheological performance and oil erosion resistance of FRMA may have reached a certain level with a 1% FRM dosage. However, as the FRM dosage (>1%) is increased, a subsequent decline in the rheological performance of FRMA is observed.  
Figure 8: Marshall specimens after oil immersion treatment: (**a**) immediately after addition of diesel; (**b**) after 1 min of oil immersion.  
Figure 9: Post oil erosion conditions of asphalt mixture with different FRM dosages: (**a**) 0%; (**b**) 1%; (**c**) 3%; (**d**) 5%; (**e**) 7%.
# Machine Learning for Predicting Neurodevelopmental Disorders in Children
## 2 Materials and Methods
### Prediction Model  
The predictive model will use a logistic regression algorithm to forecast the presence of NDs based on the five clusters, C1-C5.  
The logistic regression technique is used to predict binary classifications in healthcare decision making by relying on the given features [18; 47]. It is frequently employed to predict the presence or absence of an ND. This algorithm is known for its computational efficiency and quick training time. The coefficients associated with each feature indicate the direction and strength of the relationship, providing a straightforward interpretation of results. In this technique, the input feature values (e.g., C1-C5) are used to compute a weighted sum based on the acquired coefficients. The outcome is then subjected to the logistic function, which converts the continuous output into a probability value ranging from 0 to 1.  
For example, if the probability is greater than 0.5, the model predicts the presence of NDs (class 1); otherwise, it predicts the absence of NDs (class 0).  
For the external validation of the predictive model, a cohort comprising 184 individuals, approximately 7 years of age, was employed. This subset was strategically chosen to reflect a specific demographic within the target population. In contrast, the training phase utilized an expanded initial dataset of 473 subjects, whose ages ranged from 3 to 52 years. This methodology was meticulously designed to ascertain the model's robustness and its generalizability across a heterogeneous and broader population spectrum.  
Figure 4: Latent factors identified.  
The predictive algorithm was used in age group 3, consisting of 184 participants with an average age of approximately 7 years, whose descriptive statistics are presented in Figure 5.