esa-sceva/satcom-chunk-collection huggingface dataset:
# Implicit A-Stable Peer Triplets for ODE Constrained Optimal Control Problems
## 2 Materials and Methods
### Existence of Boundary Methods Imposes Restrictions on the Standard Method
#### 2.3.2 Combined Conditions for the Starting Method  
The starting method has to satisfy only two conditions  
\[\begin{split} A_{0}V_{s}=& ae_{1}^{\mathsf{T}}+be_{2}^{ \mathsf{T}}+K_{0}V_{s}\bar{E}_{s},\\ A_{0}^{\mathsf{T}}V_{q}=& B^{\mathsf{T}}V_{q} \mathcal{P}_{q}-K_{0}^{\mathsf{T}}V_{q}\bar{E}_{q}.\end{split} \tag{68}\]The first two columns of the first equation may be solved for \(a=A_{0}V_{s}e_{1}\) and \(b=(A_{0}V_{s}-K_{0}V_{s}\bar{E}_{s})e_{2}\) leading to the reduced conditions  
\[(A_{0}V_{s}-K_{0}V_{s}\bar{E}_{s})Q_{3}=0,\quad Q_{3}:=I_{s}-e_{1}e_{1}^{\sf T}-e _{2}e_{2}^{\sf T}.\]  
The presence of the projection \(Q_{3}\) leads to changes in the condition compared to the end method.  
**Lemma 6**: _A necessary condition for the starting method \((A_{0},K_{0})\) to satisfy (68) is_  
\[\Big{(}\mathcal{L}_{q,\beta}\big{(}\mathcal{P}_{q}^{-T}V_{q}^{\sf T}K_{0}V_{s} \big{)}-V_{q}^{\sf T}BV_{s}\Big{)}Q_{3}=0. \tag{69}\]  
Transposing the second condition from (68) and multiplying with \(V_{s}Q_{3}\) gives  
\[(V_{q}^{\sf T}A_{0}V_{s}+\bar{E}_{q}^{\sf T}V_{q}^{\sf T}K_{0}V_{s}-\mathcal{ P}_{q}^{\sf T}V_{q}^{\sf T}BV_{s})Q_{3}=0,\]  
and \(V_{q}^{\sf T}A_{0}V_{s}Q_{3}\) may now be eliminated from both equations, yielding  
\[(\bar{E}_{q}^{\sf T}V_{q}^{\sf T}K_{0}V_{s}+V_{q}^{\sf T}K_{0}V_{s}\bar{E}_{s} )Q_{3}=\mathcal{P}_{q}^{\sf T}V_{q}^{\sf T}BV_{s}Q_{3}.\]  
Again, \(P_{q}^{\sf T}\) may be moved to the left side and leads to (69) since it commutes with \(\bar{E}_{q}^{\sf T}\).  
The situation is now similar to the one for the end method (also concerning a diagonal form of \(K_{0}\)) and we consider again the Fredholm condition. The matrix belonging to the matrix product \(\mathcal{L}_{q,s}(\cdot)\cdot Q_{3}\) is \(Q_{3}\otimes\bar{E}_{q}^{\sf T}+(\bar{E}_{s}Q_{3})^{\sf T}\otimes I_{q}\) and it has the transpose \(Q_{3}\otimes\bar{E}_{q}+(\bar{E}_{s}Q_{3})\otimes I_{q}\). This matrix belongs to the map  
\[X\mapsto\bar{E}_{q}(XQ_{3})+(XQ_{3})\bar{E}_{s}^{\sf T}=\mathcal{L}_{q,s}^{ \sf T}(XQ_{3}). \tag{70}\]  
For \(q=3,s=4\), images of this map are given by  
\[\mathcal{L}_{3,A}^{\sf T}(XQ_{3})=\begin{pmatrix}0&2x_{13}&x_{23}+3x_{14}&x_ {24}\\ 0&2x_{23}&2x_{33}+3x_{24}&2x_{34}\\ 0&2x_{33}&3x_{34}&0\end{pmatrix}.\]  
Here, the map \(\mathcal{L}_{3,A}^{\sf T}\) alone introduces no new kernel elements, the kernel of (70) coincides with that of the map \(X\mapsto XQ_{3}\) given by matrices of the form \(X=\bar{X}(I-Q_{3})\), \(\bar{X}\in\mathbb{R}^{q\times s}\). Since the right-hand side of (69) is \(V_{q}^{\sf T}BV_{s}Q_{3}\), the condition for solvability  
\[tr\big{(}X^{\sf T}V_{q}^{\sf T}BV_{s}Q_{3}\big{)}=tr\big{(}\bar{X}^{\sf T}V_{q }^{\sf T}BV_{s}Q_{3}(I-Q_{3})\big{)}=0\]  
is always satisfied since \(Q_{3}(I-Q_{3})=0\). Hence, no additional restrictions on the standard method are introduced by requiring the existence of starting methods.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 5 Experimental Results
Therefore, buffer occupancy continuously fluctuates between slow and urgent state. Therefore, \(\beta\) should be carefully chosen. From Figure 9a, for small \(\beta\), we can observe that buffer occupancy comes to the normal state and then goes back to the urgent state. Whereas in Figure 9b, for large \(\beta\), buffer occupancy does not go to the normal state at all and continuously fluctuates between urgent and normal state. Figure 8: Buffer occupancy with NoIVSP for different data rates: (**a**) 0.1 Mbps; (**b**) 0.5 Mbps; (**c**) 1 Mbps; and (**d**) 2 Mbps. Figure 10 shows the packet delivery ratio of I frame packets with respect to time. The \(x\)-axis shows the time and \(y\)-axis represents packet delivery ratio. The objective of our protocol is to protect the packets with high priority; therefore, IVSP has more mechanisms to protect I Frame packets. If I frame packet is lost in transmission, all P frame packets after it cannot be decoded correctly and will be useless. Since there is no retransmission mechanism for lost packets in ECODA and NoIVSP, the packet delivery ratio of I frame packets in NoIVSP and ECODA is much lower than IVSP. ECODA protocol drops some packets with high dynamic priority in severe congestion, whereas IVSP saves high priority packets in congestion situation and retransmits those high priority packets when free channel is available. Figure 10 shows that I frame packets have 98.7%, 93.5% and 69.2% packet delivery ratio for IVSP, ECODA and NoIVSP, respectively, when time approaches 2 min. Less I-frame packet loss in IVSP means that the video will not be interrupted and video quality in IVSP will be much superior to NoIVSP and ECODA. Figure 10: Packet Delivery ratio of I Frame packets. Figure 9: (**a**) Buffer occupancy with IVSP when \(\beta=0.25\); (**b**) buffer occupancy with IVSP when \(\beta=0.5\). Figure 11 represents the packet delivery ratio of P frame packets with respect to time. Similar to I frame packets, lost P-frame packet retransmission in IVSP should be arranged when the link is not busy to avoid congestion. ECODA drops P frame packets in congestion situations to save I frame packets. Therefore, P frame packet loss in ECODA and NoCC is much higher than P frame packet loss in IVSP. Figure 11 shows that P frame packets has 95.8%, 87.5%, and 65.3% packet delivery ratio for IVSP, ECODA, and NoIVSP, respectively, when time approaches to 2 min. Again, NoIVSP has the highest number of lost P-frames. IVSP shows lower number of lost P-frames in comparison with NoIVSP and ECODA.
# Emerging Applications of Machine Learning in 3D Printing
## 4 Discussion
### Limitations of ML Use in 3D Printing  
The use of ML in 3D printing faces limitations related to data requirements, computational costs, and interpretability. ML models require huge amounts of high-quality data (complete, reliable, unique, class-balanced) to work well, but such data are often scarce, inconsistent, or expensive to obtain in the context of 3D printing. The computational requirements for training and implementing advanced ML models can be prohibitive, especially for smaller organizations with limited resources. The black box of many machine learning algorithms makes it difficult to interpret and trust decisions, especially in critical manufacturing applications [64]. Another challenge is generalization, as ML models trained on specific printers or materials may not work effectively in different configurations. Real-time ML applications in 3D printing require robust hardware and software integration, which can be complex and error-prone. The rapid development of new materials and processes in 3D printing often outpaces the ability of ML models to adapt and remain relevant. Over-adaptation to specific data sets or scenarios can lead to suboptimal performance in real-world conditions. Ethical and safety risks, such as misuse of generative designs or gaps in automated decision-making systems [65]. The regulatory framework for AI-based manufacturing is still evolving, potentially delaying the widespread adoption of machine learning. From an implementation point of view, the high upfront investment costs of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Strengths** & **Weaknesses** & **Efficiency** \\ \hline \multirow{3}{*}{Logistic regression} & Simple & Limited & \\  & Interpretable & to linear problems & High \\  & Efficient & & \\ \hline \multirow{3}{*}{ANNs} & \multirow{3}{*}{Handles complex data (images, text, speech)} & Requires large datasets & \\  & & Computationally & Low \\  & & expensive & \\ \hline \multirow{3}{*}{K-Nearest Networks} & Simple & \multirow{3}{*}{Slow for large datasets} & \multirow{3}{*}{Low} \\  & Effective & & \\  & on small datasets & & \\ \hline \multirow{3}{*}{Gradient boosting} & High accuracy & Requires careful & \\  & Overfitting & & \\  & & expensive & \\ \hline \multirow{3}{*}{Naive Bayes} & Fast & Assumes & \\  & Works well & feature independence & High \\  & with small datasets & & \\ \hline \multirow{3}{*}{Reinforcement learning} & Learns from interactions & High computational cost & \\  & Adapts over time & Requires well-defined rewards & Low \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._ML technologies may discourage companies from realizing their full potential in the 3D printing industry.
## 8 Future Directions and Challenges  
We intend to offer safe routing solutions that are energy-efficient in the future for various environments. The routing solutions will include a protection mechanism to fend off various security intrusions. To ensure that the suggested solution is still appropriate for the UWSNs environment, the computation cost must be taken into account while creating a security mechanism. When designing a security solution for the UWSNs environment, energy efficiency and security should be balanced and should be treated as equally important.  
Further exploration in this research area can be proposals for a key distribution scheme having support for different mobility models in the UWSNs environment, proposing more energy-efficient secure solutions for UWSNs. The following are the key future research issues:  
* Secure and energy efficient solutions: to design secure UWSNs with intelligent sensor nodes and self-localization for combating DOS attacks in mobile UWSNs [35; 38].
* Trust: to establish the trust when the nodes are moving in the underwater environment, establishment of trust when the sensor nodes are sparsely deployed and they are far away from each other, considering block cipher algorithms such as ARIR and SEED for UWSNs environment, using the technology for underwater security with other network systems such as IEEE 802.15.3 (UWB), IEEE 802.11 (WLAN), IEEE 802.15.4 (ZigBee) [39; 40; 41; 42; 43].
* Intelligent sensor environments: to use AI models for reducing intelligent attacks in the network leading to robust systems. The transfer rate of packets in UWSNs environment can be reduced by utilizing intelligent sensor nodes that are self-localized; to address the DoS problem in UWSNs environment, secure UWSNs having intelligent sensor nodes and self-localization should be designed [35].  
Conceptualization, K.S. and W.K.; methodology, K.S. and I.A.; software, K.S.; validation, A.S.A.-S. and A.G.; formal analysis, S.Z.A. and A.A.; investigation, I.A. and A.G.; resources, A.A. and A.S.A.-S.; data curation, S.Z.A., A.A. and A.S.A.-S.; writing--original draft preparation, K.S. and W.K.; writing--review and editing, W.K., I.A. and S.Z.A.; visualization, S.Z.A. and K.S.; supervision, W.K. All authors have read and agreed to the published version of the manuscript.  
This research has not received any external funds.  
Not applicable.  
We would like to extend our appreciation to Al-Ahliyya Amman University and Umm AL-Qura University, Al-lith, Saudi Arabia, for providing all necessary support to conduct this research work.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
# Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
## 3 Results and Discussion
### Evaluation of In Vivo Hepatoprotective Activity of GPPs
#### 3.5.3 Effects of GPPs on Oxidative Stress in Liver  
The levels of SOD, CAT, GSH and MDA were determined with an ELISA kit to evaluate the oxidative stress state of CCl\({}_{4}\)-induced acute liver injury in mice [39]. As shown in Figure 4E-H, the injection of CCl\({}_{4}\) could obviously reduce SOD, CAT and GSH activities (\(p<0.05\)) and elevate MDA contents (\(p<0.05\)) when compared with the normal control. Mice pretreated with GPPs showed significant improvement in concentration dependence. The high-dose (200 mg/kg body weight) GPP pretreatment increased the levels of SOD, CAT, MDA and GSH in liver by 31.24%, 41.73% and 57.18%, respectively, and decreased the MDA level by 1.6 times compared with the model group. These results demonstrate that GPP pretreatment was able to mitigate lipid peroxidation and possessed protective action on the oxidative damage of liver tissue caused by CCl\({}_{4}\).
# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
## 2 Framework  
The framework of this study is shown in Figure 1, which includes four parts: Training data, supervised ML solution, semi-supervised ML solution, and case study.  
Training data: This part includes five attributes of a building, i.e., structural type, construction year, story number, story height, and story area. The building data of two cities in China was used for the training in this study. One city is Tangshan, which has 230,683 buildings; the other city is Taiyuan, whose downtown has 31,154 buildings. These data were provided by the department of urban planning in the local government.  
Supervised ML solution: The ML models and implementation platforms suitable for the prediction of structural types are determined by comparing prediction accuracies. In addition, the scale sensitivity and regional applicability of the designed solution are discussed.  
Semi-supervised ML solution: First, the sampling fraction of the building investigation is determined based on the supervised ML solution above. Subsequently, the semi-supervised self-training procedure is designed based on the sample data. Finally, the prediction performance of the semi-supervised ML solution is assessed.  
Case study: The downtowns of Daxing and Tongzhou, the districts of Beijing, were selected as a case study, which has 69,180 and 34,763 buildings, respectively. The structural types of buildings in the downtowns of Daxing and Tongzhou were predicted using the designed semi-supervised ML solution, and the prediction performances were assessed based on the sample data. Furthermore, the seismic damage of Daxing and Tongzhou downtowns were simulated using the predicted structural types.  
Figure 1: The framework of this study.
## 1 Introduction
In this way, they come with disadvantages like being time-consuming processes to carry out intricate chemical separation procedures and involving costly isotope testing, which is challenging to incorporate into regular monitoring evaluations [29]. On the other hand, fiber optic sensors have shown satisfactory results in detecting the adulteration of glucose in honey as reported in Reference [30] and both fructose and glucose as detailed in References [31; 32]. Additionally, fiber optic sensors have been used to measure honey adulterated with distilled water through reflection techniques, as is noted in References [33; 34]. This paper presents a novel study that lies in the development and application of an SMS-configured fiber optic sensor specifically designed to detect low levels of adulteration in honey, particularly through the addition of glucose and moisture, with sensitivity and stability compared to conventional methods. The sensor has a simple structure that consists of a non-core multimode fiber (NC-MMF) segment spliced between two standard single-mode fibers (SMFs). In this scheme, the medium surrounding the NC-MMF plays the role of its coating, which makes the multimode interference taking place in the NC-MMF sensitive to variations in the external refractive index (RI). This effect allows for the detection and quantification of different concentrations of honey adulterated with water or glucose standard optical equipment. An advantage with respect to the published sensors is that their manufacturing does not require functionalization procedures, or the deposition of materials, and does not require special care in handling. The proposed sensor was tested on commercial honey brands, showing a linear response with respect to the level of adulteration, thus demonstrating its potential for low-cost, real-time detection suitable for online analysis in the food industry.
# A Review of Unmanned Aerial Vehicle Based Antenna and Propagation Measurements
## 2 UAV-Based Measurements
### UAV-Based Far-Field Measurements
#### 2.1.2 Aerial Measurement of Broadcasting Antennas
In the following sections, we discuss UAV-based antenna measurements in near-field regions. This technique enables the assessment of antennas that are significantly large. Additionally, conducting measurements in the near-field offers the advantage of reducing the flight times required for UAV operations. In Table 1, we present an overview of the reflector and broadcasting antennas measured in far-field regions using UAVs. Figure 9: SixArms airborne measurements for broadcasting systems.
# An Inspection of IFC Models from Practice
## 2 Industry Foundation Classes
### IFC Modelling  
Parametric modelling is usually employed in BIM and IFC, which makes it possible to encode many kinds of geometries. The IFC geometry is mostly defined or derived from a different standard, i.e., ISO [32], which also specifies the STEP Physical File (SPF).  
Interoperability is affected by the kind of modelling and geometry used, since software can implement only subsets of the allowed ones, to adopt different conventions for their description and coding (e.g., extrusion directions, Boolean operations, etc.), or even to choose to support additional kinds of geometries with respect to the group identified in standard prescriptions [23].  
Furthermore, the way designers model the BIM can have consequences for the interoperability with other formats: especially, conversion procedures should define the allowed objects and agree on other modelling specifications. Inaccuracies, such as intersections or gaps between geometries, can also produce issues with respect to automatic processing.  
Two constructs (and related IFC classes) are particularly important: _IfcOpenings_ and _IfcSpaces_.  
Subtraction relationships are part of the IFC model, representing openings by means of the voiding mechanism: _IfcOpening_ defines the objects used to be subtracted from another geometry (e.g., an _IfcWall_ or an _IfcSlab_) in order to generate an opening in a consistent way, according to the data model. The _IfcOpening_ can be in turn filled by an element, like an _IfcWindow_ or _IfcDoor_.  
A second important element is the explicit modelling of _IfcSpaces_, which are filling the empty spaces (for example, rooms) and defining them. It is possible to associate attributes to them, and their geometry is generally very relevant to conversion procedures.
# Torque Allocation of Hybrid Electric Trucks for Drivability and Transient Emissions Reduction
## 2 Hybrid Vehicle Powertrain Layout
### Dynamic Equations
#### 2.2.4 NOx Modelling  
As proposed in [14], the NOx emissions were modelled considering the transient dynamics of the ICE. It has been experimentally proven that, in case of sudden acceleration by the driver, there is a spike in NOx emissions--which is expected to be 80% higher than the steady state value. Thus, in the Simulink model, a transfer function of the second order was adopted with a variable gain with respect to the engine torque \(T_{ICE}\). The NOx emissions were estimated using the transfer function reported in Equation (14):  
\[NO_{X}(s)=G(T_{ICE}(s))\ H(s)T_{e}(s) \tag{14}\]  
where \(G(T_{ICE}(s))\) is a variable gain between the steady state NOx and the engine torque, while the second order transfer function \(H(s)\) is defined as:  
\[H(s)=\frac{\omega_{n}^{2}}{s^{2}+2\zeta\omega_{n}+\omega_{n}^{2}} \tag{15}\]  
where \(\omega_{n}\) and \(\zeta\) have been properly tuned to reproduce the transient dynamics relationship between the NOx emissions and the engine torque proposed by [14]. The NOx model here adopted takes into account only the engine-out emissions, without considering an after-treatment system device.
## 3 Proposed Model
### Recurrent Variational State-Space Model for Liquid Rocket Engines  
We introduce a deep state-space model with two primary features: (1) using a recurrent neural network (RNN) to capture long-term dependencies in sensor data and (2) employing neural networks to nonlinearize the state-space transition and emission matrices.  
Figure 1: Schematic diagram of recurrent variational state-space model.  
The initial state-space model (SSM) is based on linear Gaussian models and hidden Markov models (HMM), which include linear transition and emission matrices [14]. However, liquid rocket engine sensor data exhibits complex nonlinear relationships and long-term dependencies. Traditional frameworks are insufficient for modeling these complexities. The interpretable recursive variational state-space model (IRVSSM) employs a recurrent neural network (RNN) to capture long-term dependencies in sensor data, relaxing the Markov assumption [20]. The RNN then extracts deterministic sensor states. The generative model for the state-space is illustrated in Figure 2a and can be expressed mathematically as follows:  
\[h_{t}\sim\delta(f_{\theta_{k}}(h_{t-1},s_{t-1})) \tag{3}\]  
\[z_{t}\sim N(u_{\theta_{2}}(z_{t-1},h_{t}),\mathbf{\Sigma}_{\theta_{2}}(z_{t-1},h_{ t})) \tag{4}\]  
\[x_{t}\sim\mathcal{AR}(f_{\theta_{x}}(z_{t})) \tag{5}\]  
where \(f_{\theta_{x}}\) is the RNN function, and \(\delta(\cdot)\) is a delta distribution. For stochastic latent states, \(u_{\theta_{2}}\) mean and \(\mathbf{\Sigma}_{\theta_{2}}\) covariance functions for Gaussian distribution of state transitions, which are also parameterized by neural networks. \(AR\) is an arbitrary probability distribution, and its parameter \(f_{\theta_{x}}\) is also parameterized by neural networks.  
The deep state-space model represented in Equations (3)-(5) is parameterized as \(\theta=\{\theta_{h},\theta_{z},\theta_{x}\}\). The parameterization is implemented as follows: a long short-term memory (LSTM) network is employed to capture the temporal dependencies in the data, and the hidden state transition matrix \(\theta_{x}\) is parameterized using the following method:  
\[\begin{array}{l}u_{\theta_{2}}(t)=\text{NN}_{1}(z_{t-1},h_{t},s_{t})\\ \sigma_{\theta_{2}}(t)=\text{SoftPlus}[\text{NN}_{2}(z_{t-1},h_{t},s_{t})]\end{array} \tag{6}\]  
where \(\text{NN}_{1}\) and \(\text{NN}_{2}\) denote two neural networks parameterized by \(\theta_{z}\), and \(\text{SoftPlus}[x]=\text{log}(1+\text{exp}(x))\).  
The parameterization of the emission matrix \(\theta_{x}\) also uses neural networks \(\text{NN}_{3}\) to approximate arbitrary distributions [21].  
\[f_{\theta_{x}}(t)=\text{NN}_{3}(z_{t}) \tag{7}\]