esa-sceva/satcom-chunk-collection huggingface dataset:
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 3 SOC Estimation Based on the HIF Algorithm
### SOC Estimation Accuracy Verification
#### 3.3.2 Uncertainty of SOC Initial Value  
In actual use, it is difficult to obtain an accurate initial SOC value. Therefore, the algorithm should have the ability to track the system state when the initial SOC value is inaccurate, and correct the initial SOC value. To evaluate the convergence ability of the HIF under uncertain initial values, the initial value of the SOC is set to 0.7 (the exact initial value of the SOC is 1), and the results of its convergence curve and error are shown in Figure 10.  
Figure 10 shows that in the case of the inaccurate initial SOC value, the SOC values estimated by the two algorithms can quickly converge to the accurate value after algorithm calculation. However, for the convergence rate, HIF needs approximately 1.5 s to make the SOC converge to the initial value, which is 50% higher than the 3 s of the EKF algorithm. Moreover, the estimated error of HIF fluctuates within \(\pm\)0.02 after convergence, which proves that the algorithm is not sensitive to the setting of the initial value of SOC, can calculate the true value recursively, and has relatively better robustness.  
Figure 9: Comparison of SOC estimation results and errors based on DST conditions (colored noise added to current and voltage).
## 3 Proposed Methodology
### Proposed Flow Table Architecture

Table 2: Summary of notations used in the proposed routing framework. in Figure 11, where we have shown the architecture and a sample entry that is compatible with the existing OpenFlow protocol flow table. As evident from Figure 11, the key component that we propose to include as a statistic in the flow table is the route valid timestamp. The route valid timestamp for the path from source node \(j\) to destination node \(i\) is computed by the controller by using the \(V_{i}[j]\) returned from the routing algorithm and adding it to the present timestamp. However, at the switch, for a given matched packet whose source and destination addresses are the same, it means that packet has reached the destination, so that packet will be logged, as shown in Figure 11, irrespective of the route valid timestamp. Otherwise, for a matched packet, if the drop action has been explicitly defined by the controller, then the packet will be dropped. Such a drop action will be defined by the controller when there are no existing routes from a given source to a given destination (e.g., if the destination is a node that is not known by the controller). For a matched packet with a present route timestamp less than route valid time whose destination and source addresses are not the same and action is not explicitly defined as drop, then the first packet's source fields will be modified with the next hop addresses, and then the packet will be forwarded to the next hop using the given communication channel (by forwarding through the appropriate source port), as shown in Figure 11. Otherwise, for a matched packet, the packet will not be forwarded, and the switch will send a packet-in message to the controller, requesting to send a valid route from the given source to destination as the current path in the flow table is no longer valid. The current path is no longer valid when the present time exceeds the route's valid timestamp in the flow table because one or more links' lifetimes in the present path have expired due to the mobility of the nodes. Thus, by including the field route valid timestamp as a statistic in the flow table, we successfully prevent packet forwarding on expired paths. Furthermore, a packet_in message will be sent to the controller if a flow table match is not found, as shown in Figure 11. Note that this approach reduces communication overhead, as the controller is consulted only when a given path expires or when a flow-table mismatch occurs (when the switch is unaware of actions to undertake for the packet). Therefore, the key novel aspect in the architecture of the proposed flow table is that, due to the inclusion of the \"route valid timestamp\" field in the flow table, the controller is contacted to update the switch with the latest flow rules when the existing path in the switch for a matched packet has expired. Figure 11: Proposed flow table architecture with a sample flow table entry.
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 5 Proposed Coexistence Solutions for Uncoordinated LTE-U and Wi-Fi Networks
### Q-Learning Based Coexistence Scheme
Equation (2) shows that the maximum reward is obtained when LTE gets the highest possible throughput range (state \(L_{10}\)) and WiFi is unsaturated (\(W_{\text{U}}\)). Similarly, the highest reward can be achieved if both technologies are spectrum hungry while both technologies are given equal spectrum share, i.e., if LTE is not at state \(L_{10}\) and WiFi is at state \(W_{\text{S}}\) while \(N_{\text{b}}\) = 5. Otherwise, the reward function depends on the values of \(\beta\), \(\rho\), and \(N_{\text{b}}\). As \(N_{\text{b}}\) increases, LTE gets a lower spectrum share and hence the value of \(\beta\) decreases. On the other hand, as \(N_{\text{b}}\) increases, there is a higher probability that the WiFi gets sufficient spectrum to attain unsaturated state leading to \(\rho=1\). Generally, the values of \(\beta\) and \(\rho\) are selected such that an action with a higher aggregated throughput and fairness leads to a higher reward value.
## 2 Materials and Methods
### Response Measurement  
In the current work, titanium grade 9 was machined using the micro-milling process. The process outcome was assessed by measuring the surface roughness, burr size, and tool flank wear using an Olympus digital microscope (DSX1000; Olympus Corporation, Tokyo, Japan) with a repeatability of 1 \(\upmu\)m. Slots were machined, as shown in Figure 1. The surface roughness was measured by taking multiple readings in the machined slot, as indicated by the yellow square. The black outgrowth visible on both sides of the machined slot, known as burr, is an unwanted and undesirable phenomenon. Burr is a sharp edge or ridge of material that forms along the edge of the workpiece. In the current research, the burr height and burr width were determined using microscopy images. Since the burr length could not be measured accurately owing to the curve of the burr, it was calculated using the Pythagorean theorem, incorporating the burr height and width, as illustrated in Figure 2. Although some previous researchers have used chip thickness and/or chip area calculations using imaging software for burr area measurements, the current study focused on directly measuring the burr height and burr width using an Olympus 3D digital microscope, which was then used to determine the novel burr length.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No. of Exp.**} & **Feed Below Edge Radius** & **Cutting Speed** & **Depth of Cut** \\  & **(f\({}_{\mathrm{z}}\) (\(\upmu\)toth\({}^{-1}\)))** & **V\({}_{\mathrm{C}}\) (m min\({}^{-1}\))** & **a\({}_{\mathrm{p}}\) (\(\upmu\)m)** \\ \hline
1 & 0.25 & 25 & 30 \\
2 & 0.25 & 50 & 60 \\
3 & 0.25 & 75 & 90 \\
4 & 0.45 & 50 & 90 \\
5 & 0.45 & 75 & 30 \\
6 & 0.45 & 25 & 60 \\
7 & 0.65 & 75 & 60 \\
8 & 0.65 & 25 & 90 \\
9 & 0.65 & 50 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Levels of parameters used in the experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Condition**} & **Specification** \\ \hline Workpiece & Ti-3 Al-2.5V \\ Cutting fluid & Dry cutting \\ Milling type & Full immersion \\ Cutting tool & Micro end mill \\ Number of flutes & 2 \\ Tool diameter & 500 \(\upmu\)m \\ Cutting edge radius & 4 \(\upmu\)m (measured) \\ Tool material & Tungsten carbide \\ Length of cut & 12 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental conditions.  
With regards to tool wear in micro-milling, ISO 8688 standards were followed, which encompass different forms of tool wear, including flank wear, chipping, face wear, outside edge wear, and catastrophic failure [39]. Flank wear is particularly significant, as it directly impacts product quality by increasing surface roughness. Consequently, this study concentrated on the analysis of flank wear, as depicted in Figure 3.  
Figure 1: Burr during up milling and down milling.  
Figure 2: Burr measurement (not to scale).
# Reflection Characteristics Measurements of Indoor Wireless Link in D-Band
## 2 Principle of Reflection Measurements and Theoretical Model
### Minimum Mean Square Error Criterion  
Mean square error (MSE) represents the matching degree between the predicted values and the real values, and it usually acts as the loss function of regression problems, as shown in (8),  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i}-\overset{\wedge}{Y_{i}}\right)^{2}, \tag{8}\]  
where \(\overset{\wedge}{Y_{i}}\) is the predicted values, \(Y_{i}\) is the real values, and n is the number of samples. When the model has been established, whereas some parameters  
The proposed method is introduced in detail as follows:  
1. The reflection coefficients, expressed as \(|\Gamma|_{M}\), are measured based on Section 2.1;
2. The ranges of two parameters, \(\varepsilon_{r}\) and \(h_{rms}\), are set to calculate the theoretical reflection coefficients \(|\Gamma|_{T}\);
3. The dataset of theoretical \(|\Gamma|_{T}\) is compared with that of measured \(|\Gamma|_{M}\) to calculate the MSE;.
4. With MMSE criterion, the set of theoretical \(|\Gamma|_{T}\) closest to the measured \(|\Gamma|_{M}\) is selected, and therefore, the optimal relative permittivity \(\varepsilon_{r}\) and optimal surface roughness \(h_{rms}\) are obtained in two ranges;
5. These two optimal estimated parameters are compared with the data in previous research to prove the rationality and robustness of our method and experiment;
6. Based on the steps above, the reflection characteristics can be analyzed. This process is depicted in Figure 2.
## 4 Discussion: Mechanistic Studies of the CL Detection System
### Verifying Free Radicals by ESR Spectra  
The presence of free radicals in Ni(IV) complex-Luminol-SAL molecule CL system was further verified by electron spin resonance technique. In the above CL system, there may be free radicals including OH\({}_{\bullet}\), O\({}^{2-}\)\({}_{\bullet}\) and free radicals of luminol and salbutamol molecules as reaction reductants. The free radical life of OH\({}_{\bullet}\), O\({}^{2-}\)\({}_{\bullet}\), luminol and target molecules can be as short as 10\({}^{-15}\) s, therefore, free radical trapping agent (5,5-dimethyl-1-pyrroline N-oxide, DMPO) was added to the reaction solution, and the ESR spectra of the reaction system was determined under nitrogen protection. The results showed that resonance spectra types of the free radicals in these systems were R\({}_{\bullet}\) or RO\({}_{\bullet}\) type, which verified the existence of free radicals of luminol and target drug molecules. Figure 6a is the ESR spectrum of luminol radical in the Ni (IV)-Luminol system. Figure 6b is the ESR spectrum of Ni (IV)-Luminol-Salbutamol system. It can be seen from the diagram that the free radicals in the two systems are R\({}_{\bullet}\) or RO\({}_{\bullet}\) type, and after adding salbutamol to Ni (IV)-Luminol system, the peak area of ESR spectrum of the system is enhanced, but the radical type remains unchanged.
# Fungal Pigments and Their Roles Associated with Human Health
## 3 Biosynthesis of Fungal Pigments
### Nitrogen-Containing Metabolite Pathways  
Chalciporone (Figure 9) is a type of 2H-azepine alkaloid pigment produced by the mushroom _Chalciporus piperatus_ (Basidiomycetes). This pigment is believed to act as a deterrent to insects and other predators and thereby can potentially protect the mushroom. The biosynthesis of chalicporone was illustrated in 2001 [110]. In their experimentations, the radioactive labelled sodium [U-\({}^{13}\)C\({}_{2}\)] acetate was  
Figure 8: Simplified scheme of the biosynthetic pathways for \(\beta\)-carotene (black arrows) and the astaxanthin and neurosporaxanthin (blue arrows) from GPP.  
not incorporated into chalicporone by _C. piperatus_. In contrast, the supplementation of a mixture of [U-\({}^{13}\)C]-labelled fats led to chalicporone, in which seven intact acetate units were incorporated (_ca._ 10 atom % enrichment) between C\({}_{3}\) and C\({}_{16}\). However, the enrichment of the C\({}_{1}\) methyl group and the adjacent carbon (C\({}_{2}\)) was not observed during this experiment, suggesting that the CH\({}_{3}\)-CH-N moiety was derived from an \(\alpha\)-amino acid. This was confirmed by the observation that administration of a mixture of [U-\({}^{13}\)C]-labelled amino acids to _C. piperatus_ gave rise to the enrichment of both C\({}_{1}\) and C\({}_{2}\) and of all the other carbon signals in the spectrum of chalicporone [107]. The work clearly demonstrated that the carbon backbone of chalicporone is generated from an amino acid plus seven acetate (=malonate) units.  
The wood-rotting fungus _Pycnoporus cinnabarinus_ (Basidiomycetes) can produce a red pigment, cinnabarinic acid (Figure 10), via oxidative dimerization of the precursor 3-hydroxyanthranilic acid in sporocarps as well as in culture broth [111]. This reaction is catalyzed by laccase and necessary for the production of antibacterial compounds by the fungus. Cinnabarinic acid shows inhibitory effects towards several Gram-positive bacteria of the _Streptococcus_ genus. It is known that cinnabarinic acid shares structural homology with the antibiotic group of actinomycins (e.g., actinomycin D) produced by _Streptomyces_ spp., having two cyclic pentapeptides linked to the phenoxazinone chromophore.  
Two indole pigments were isolated as free radical scavengers from the fruiting bodies of the mushroom _Agrocybe cylindracea_[112]. Based on spectroscopic data, they were identified as 6-hydroxy-\(1H\)-indole-3-carboxaldehyde and 6-hydroxy-\(1H\)-indole-3-acetamide (Figure 11). 6-hydroxy-\(1H\)-indole-3-acetamide is an amide derivative of 6-hydroxyindole-3-acetic acid. A previous study identified that 6-hydroxyindole-3-acetic acid could be produced through the transformation of indole-3-acetic acid by _Aspergillus niger_[113]. These two indole compounds were found to possess potent inhibitory activity on lipid peroxidation in rat liver microsomes, with IC\({}_{50}\) values of 4.1 and 3.9 \(\upmu\)g per ml, respectively [109].  
Figure 10: Cinnabarinic acid.  
Figure 9: Chalicporone.
## 2 Mathematical Preliminaries
\[r(t_{0})=r_{0}\quad,\quad\theta(t_{0})=0\quad,\quad u(t_{0})=0\quad,\quad v(t_ {0})=\sqrt{\frac{\mu}{r_{0}}} \tag{3}\]  
If the final value of the spacecraft polar angle \(\theta(t_{f})\) is unconstrained, the conditions for a circle-to-circle orbit transfer give the following 3 scalar constraints at the final time instant  
\[r(t_{f})=r_{f}\quad,\quad u(t_{f})=0\quad,\quad v(t_{f})=\sqrt{\frac{\mu}{r_{ f}}} \tag{4}\]  
The spacecraft equations of motion (2) and the boundary constraints given by Equations (3) and (4) can be rewritten more conveniently in a dimensionless form by introducing the (dimensionless) variables  
\[\dot{r}\triangleq\frac{r}{r_{0}}\quad,\quad\dot{u}\triangleq\frac{u}{\sqrt{ \mu/r_{0}}}\quad,\quad\dot{v}\triangleq\frac{v}{\sqrt{\mu/r_{0}}}\quad,\quad \ddot{t}\triangleq\frac{t}{\sqrt{r_{0}^{3}/\mu}}\quad,\quad\ddot{a}_{p} \triangleq\frac{a_{p}}{\mu/r_{0}^{2}} \tag{5}\]  
so that Equation (2) becomes  
\[\dot{r}^{\prime}=\ddot{u}\quad,\quad\theta^{\prime}=\frac{\partial}{\dot{r}} \quad,\quad u^{\prime}=-\frac{1}{\dot{r}^{2}}+\frac{\dot{v}^{2}}{\dot{r}}+ \ddot{a}_{p}\ \cos\alpha\quad,\quad\dot{v}^{\prime}=-\frac{\ddot{u}\,\partial}{\dot{r}}+ \ddot{a}_{p}\ \sin\alpha \tag{6}\]  
where the prime symbol indicates the derivative with respect to the dimensionless time \(\ddot{t}\). Accordingly, the boundary conditions written in Equations (3) and (4) become  
\[\ddot{r}(\tilde{t}_{0})=1\quad,\quad\theta(\tilde{t}_{0})=0\quad,\quad\dot{u} (\tilde{t}_{0})=0\quad,\quad\dot{v}(\tilde{t}_{0})=1 \tag{7}\]  
and  
\[\ddot{r}(\tilde{t}_{f})=\tilde{r}_{f}\quad,\quad\dot{u}(\tilde{t}_{f})=0\quad, \quad\dot{v}(\tilde{t}_{f})=\frac{1}{\sqrt{\tilde{r}_{f}}} \tag{8}\]  
in which \(\tilde{r}_{f}\) is the dimensionless target radius, and \(\tilde{t}_{f}\) is the dimensionless flight time to be minimized through a suitable selection of the spacecraft controls \(\{\dot{a}_{p},\ \alpha\}\). Note that Equations (6)-(8) are independent of both the actual value of the primary body gravitational parameter and the radius of the circular parking orbit. The optimization of the spacecraft transfer trajectory, i.e., the calculation of the time variation in both the thrust angle \(\alpha\) and the propulsive acceleration (dimensionless) magnitude \(\dot{a}_{p}\) that minimizes the flight time, is described in the next section.
# Valkyrie--Design and Development of Gaits for Quadruped Robot Using Particle Swarm Optimization
## 2 Kinematic Modeling of the Quadruped Robot
### Inverse Kinematics  
In Inverse Kinematics, the joint angle is determined given the end-effector position and link lengths. Inverse kinematics may not always have a unique solution. Figure 1c represents the kinematic model for one leg of the quadruped robot where \(x_{2}\) and \(z_{2}\) represent the end-effector position. \(a_{1}\) and \(a_{2}\) are the length of link 1 and link 2, respectively. \(\theta_{1}\) and \(\theta_{2}\) represent the joint angles of link 1 and link 2, respectively. The joint angles \(\theta_{1}\) and \(\theta_{2}\) are determined using the equations.  
\[\theta_{1}=tan^{-1}\bigg{(}\frac{z_{2}}{x_{2}}\bigg{)}+tan^{-1}\bigg{(}\frac{ a_{2}sin\theta_{2}}{a_{1}+a_{2}cos\theta_{2}}\bigg{)} \tag{4}\]  
\[\theta_{2}=cos^{-1}\bigg{(}\frac{{x_{2}}^{2}+{z_{2}}^{2}-a_{1}^{2}-a_{2}^{2}}{ 2a_{1}a_{2}}\bigg{)} \tag{5}\]
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis  
The methodological foundation of multimodal analysis in education is anchored in the integration of diverse data streams, the application of advanced analytical techniques, and the systematic transformation of raw signals into meaningful educational indicators. This section categorises and describes the principal methods and tools employed in MMLA, structured across data collection modalities, feature extraction and preprocessing, modelling techniques and tools and frameworks.  
To bridge the theoretical foundations established in Section 2 with the methodological approaches detailed in this section, it is important to outline the process by which the four key components of MMLA were identified. The 177 studies selected for this review underwent a structured qualitative analysis to capture the diversity of methodologies employed in multimodal research. This process included multiple stages: initial coding, thematic clustering, and iterative refinement. Initially, each study was coded based on its primary methodological focus, including data collection strategies, feature extraction methods, modelling approaches, and the computational frameworks used to process multimodal data. During this phase, studies were grouped according to the types of data they utilised (e.g., visual, auditory, physiological), the preprocessing techniques applied, the modelling methods employed, and the specific tools or platforms used.  
Subsequently, an iterative thematic analysis was conducted to identify common patterns and refine these initial groupings. This involved several rounds of manual review to ensure consistency and comprehensiveness, allowing for the identification of emerging trends and methodological gaps. The final structure, comprising four core components, emerged as a natural grouping of the most frequently cited methods and tools: (1) data collection modalities, (2) feature extraction and preprocessing, (3) modelling techniques, and (4) tools and frameworks. This hierarchical organisation captures the critical steps required to collect, process, and interpret multimodal data in educational contexts, reflecting both the technical and conceptual frameworks that guide the interpretation of multimodal signals. Figure 1 provides a visual summary of this structure, illustrating the relationships between the various components.
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 3 Results and Analysis
### USD Detection Performance in Presence of Thermal Noise  
Unlike MED, where the impact of noise on MED only increases its average error probability and reduces its channel capacity, noise challenges USD's advantage of having zero errors, so it is necessary to study how much thermal noise affects USD's errors.  
Figure 6 plots the inter-user detection error probability for the average photon number of the thermal noise power \(N\) from 0.01 to 0.5, according to the calculation results of Equation (28). The red line in the figure indicates the noise-free case, where the error probability is 0, i.e., the effect of error-free discrimination of USD is achieved when the noise is absent.  
As can be seen from Figure 6, when the noise average photon number increases, the inter-user detection error probability of USD also increases. A clear peak is formed between 1 and 1.5 of the signal average photon number. There are two reasons for this. On the one hand, with the increase in the signal power, the probability of detection errors between users tends to decrease; on the other hand, the USD measurement degenerated into a general quantum measurement due to the noise that cannot be ignored in relation to the signal. At this time, although there is a small detection error probability between users, the detection error probability of the BPSK signal owned by each user increases, but the overall detection performance decreases.  
Depending on the calculation result of Equation (30), Figure 7 depicts the USD's channel capacity curve with the noise power varying uniformly from 0.1 to 0.5. The red  
Figure 6: Detection error probability between two users of USD in the background of thermal noise.  
line here indicates the channel capacity for the noiseless case, which is in fact consistent with the USD bound in Figure 5.  
When the average photon number increases further beyond five, the degrading effect of noise on the USD measurements will gradually decrease, and USD will still obtain a low probability of detection error and a high channel capacity, as can be seen in Figures 6 and 7. This means that after a certain value of signal power, e.g., an average photon number above five, increasing the SNR allows error-free detection to continue to be effective.