esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 RSS Difference-Aware Sparse Graph-Based Semi-Supervised Learning Method and Experimental Results
### Experimental Results  
Since the labels of all the vertices in the graph are necessary for sparse reconstruction of the graph weight matrix, CS method can only be used in the offline phase. The weighted matrices calculated by the _heat-kernel_ function and CS method are shown in Figure 14a,b, respectively. Each pixel in the figure represents the weight value \(w_{ij}\) between two vertices and \(0\leq w_{ij}\leq 1\). A larger value of \(w_{ij}\) between vertex \(S_{i}\) and \(S_{j}\) means a stronger correlation between them. If the vertices around the vertex \(S_{i}\) have strong correlations with the vertex \(S_{i}\), we can get a more accurate RSS estimates for the vertex \(S_{i}\). As we can see from Figure 14a, since the measurements are noisy, the weight matrix contains some errors. In the weight matrix, the weight values are very small between different vertices, which means the relationship between different vertices is very weak. Therefore, the information transferred between different vertices is inaccurate and the estimated RSS values using this weight matrix are not accurate enough. As a result, the localization accuracy is reduced by the inaccurate relationship between different locations.  
Due to the sparsity of the graph and robustness to noise, the weight matrix is recovered more precisely than the traditional _heat-kernel_ function. The relationship between different vertices in Figure 14b is much clearer than Figure 14a. Comparing Figure 14b with Figure 14a, the graph weight values calculated by the CS method are much larger than those obtained using the _heat-kernel_. As a result, it is possible to get more useful information between different vertices using the matrix in Figure 14b. Therefore, the estimated RSS values are more accurate than those calculated by the _heat-kernel_ as shown in Figure 15a,b. Based on the matrix calculated by the CS method, the localization results are more accurate in Figure 15b. From Figure 16 we can learn that the cumulative probability is 71.4% when the location error is 2 m and the localization accuracy is increased by 7.9% relative to RG-SSL, 11.5% relative to G-SSL and SPORT, 17.1% compared to SCTW algorithm. Thanks to the more accurate radio map, the maximum localization error has been further reduced to 3.5 m. Meanwhile, the average localization error has been reduced from 2.07 m to 1.98 m. In summary, the RSG-SSL algorithm is more robust to noise and has achieved a better performance than RG-SSL algorithm and much better than other algorithms. By using the RSG-SSL method, the localization accuracy is improved significantly in crowdsourcing WLAN indoor localization system. As a result, the localization system could provide us with much better service.  
Figure 14: Comparison of Weighted graph. (**a**) Weighted graph calculated by heat kernel method; (**b**) Weighted graph calculated by CS method.
# Risk Assessment of Soil Contamination with Heavy Metals from Municipal Sewage Sludge
## 2 Materials and Methods
### Contamination Assessment Methodology
#### 2.3.1 Risk Assessment Code (RAC)  
The RAC was also used to assess the environmental risks posed by heavy metals. The RAC was used to assess soil contamination with heavy metals from sewage sludge and sewage sludge ashes [42]. The RAC takes into account the percentage of heavy metals present in the mobile fraction (F1). The risk level can be classified into 5 categories: <1--no risk; 1-10--low risk; 11-30--medium risk; 30-50--high risk; >50--very high risk [36]. It is defined by the formula [42; 43]:  
\[Rac=\frac{F1}{HM}\cdot 100\% \tag{5}\]  
where:  
\(F1\)--concentration of heavy metal in acid-soluble/free fraction, mg\(\cdot\)kg\({}^{-1}\); _HM_--total heavy metal concentration, mg\(\cdot\)kg\({}^{-1}\).
## 2 Methodology
### Structure Learning Based on Variable Chromosome Genetic Algorithm  
In this study, VCGA is used to search the neural network architecture. Figure 1 provides an overview of methodology used in this paper in the structure learning process. In Phase 1, the chromosome set is generated first. These chromosomes represent neurons and connections. An individual has multiple chromosomes and it composes an ANN structure. In Phase 2, each generated ANN structures are simulated. Simulation results are calculated by fitness function and ANN structures are sorted by result score. In Phase 3, genetic operations are implemented to make offspring: Selection, cross over, mutation, and attachment. These new generated chromosome set make new individuals and an ANN structure on Phase 1. Equation (1) shows a fitness function.  
\[\textit{Fitness value}=\ \frac{1}{2}\textit{score}-\frac{1}{2}D\times N[2 \times(\textit{score}-0.5)] \tag{1}\]  
Figure 1: Overall methodology.  
_Score_ means the accuracy of predicted output value of the ANN structure and it has a real value between 0.0 and 1.0. \(D\) means a dependency on the number of chromosomes. The larger \(D\) is, the more important the minimum structure of the ANN is. And the smaller \(D\) is, the more important the accuracy of the predicted output value is. \(N\) means relative number of chromosomes; (_number of own chromosomes_)/(_number of average chromosomes_). In this case study, the constant \(D\) was set to 0.2.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 7 Potential Real-World Applications
### Internet of Things (IoT)  
In IoT networks, the proposed model can aid in efficiently classifying modulation types used by various devices, improving resource allocation and energy efficiency. As IoT devices increasingly rely on modern communication methods, such as multiple input-multiple output (MIMO) systems, accurately identifying the modulation types of received signals becomes vital for optimizing the performance. In [69], an efficient convolutional neural network for automatic modulation classification (AMC) is proposed, demonstrating that the authors' model significantly reduces the number of parameters while maintaining the performance, making it suitable for low-power IoT devices. Additionally, [PERSON] et al., in [70], introduce an innovative approach using transformer networks to enhance AMR for IoT applications. Their work emphasizes the importance of real-time processing for edge devices, which is crucial for IoT ecosystems. The proposed methods show promising results in terms of the accuracy and efficiency, addressing the unique challenges faced in the IoT context. Moreover, [PERSON] et al., in [71], highlight the necessity of AMC in spectral monitoring, especially given the challenges posed by the proliferation of IoT devices that may misuse spectral resources. Their research emphasizes that effective modulation classification can aid in spectral regulation and ensure efficient the utilization of electromagnetic space, even under non-cooperative communication conditions.
## 4 Performance Evaluations
### Experimental Environments
#### 4.1.4 Experimental Setup and Evaluation Scenarios  
The performance evaluation was conducted using a pre-extracted test dataset consisting of 6186 samples. Each original sample in this dataset represents a sequence of images extracted from videos at 30 frames per second (fps), capturing the keypoints of sign language utterances by the signers. These keypoint images serve as the input for our sign language recognition model.  
To assess the effectiveness of our dynamic sampling adjustment method, we used a modular arithmetic method to simulate lower frame rates from the original 30 fps video sequences. Specifically, we adjusted the frame rates to 5, 10, 15, 20, and 25 fps by selectively choosing frames from the original data. For example, to simulate a 15 fps mobile computing  
Figure 3: Illustration of the keypoints information used by the MediaPipe models. (**Left**) The Pose model handles keypoints for full-body tracking. (**Right**) The Hands model focuses on hand keypoints detection.  
environment, we retained frames that satisfy the condition \(i\mod 2=0\) (i.e., keep frames 0, 2, 4, 6,...from the original frames). This approach allowed us to evaluate how effectively the dynamic sampling rate adjustment method adapts to different frame rates and maintains recognition accuracy.  
The experiments were conducted using three different methods: (1) the original dataset without any modifications, (2) the proposed dynamic sampling rate adjustment method, and (3) the fixed sampling rate method proposed in previous research. We evaluated the performance of each method on the test datasets at 5, 10, 15, 20, and 25 fps. This approach allowed us to compare the recognition accuracy of the original, unmodified sequences against the proposed method and the previous fixed sampling rate method across different frame rate scenarios.  
Hereafter, the methods for processing the input data for the sign language recognition model will be referred to as follows: the original dataset without any modifications will be called the Original Method, the proposed dynamic sampling rate adjustment method will be called the Dynamic Method, and, finally, the fixed sampling rate method proposed in previous research will be called the Fixed Method.
## 3 Results and Discussion
### Raman Spectra of Reference Biological Macromolecules
#### 3.3.3 Rat Collagen Type I  
Fibrillar collagens are connective tissue proteins with a periodic sequence of amino acids and a specific triple helical conformation [35]. They are structurally different from common globular proteins such as HSA and, thus, have distinct Raman spectra with high contributions from the secondary amino acids, Pro and Hyp, and the tertiary amide bonds they form [36; 37; 38].  
The Raman spectra of collagen type I from rat tail showed a broad band of amide I vibrations with a maximum at 1672-1676 cm\({}^{-1}\) (Figure 4c, Table S1). The spectrum obtained with Ahura FirstDefender has this band more broadened than those obtained by the other devices. For all instruments, the amide III region has two bands at 1239-1246 and -1268-1270 cm\({}^{-1}\)[36; 37; 38]. In the DXR SmartRaman spectrum, five bands of C-C stretching and Pro/Hyp ring vibrations observed at 867, 883, 920, 939, and 976 cm\({}^{-1}\) are well-resolved, but only two or three of them are distinguishable in the spectra recorded with the portable devices. The first two bands are expected to be particularly useful in detecting collagens in Raman spectra of colon tissues [37; 38].
## 4 Results
### Simulation Results
#### 4.4.3 Model Precision Results  
The precision for each model should follow the trends to find the accuracy values over similar trends. RF and GBT outperform other models with 60 to 70 percent attack densities over GBT. The proposed MLPC performs better in training the LR models. The comparison is performed over the number of vehicles within the network which accounts for over 40% of the scores. Based on the regression values, the SVM does not perform well compared to other models for the final and effective confirmation of the values from these models. Figure 15 shows the precision values over all attack densities for the final and effective model comparisons. Table 5 shows the precision values from GBT, LR, MLPC, RF, and SVM models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time Percentage under Attack**} & \multicolumn{5}{c}{**ML Algorithms**} \\ \cline{2-6}  & **GBT** & **LR** & **MLPC** & **RF** & **SVM** \\ \hline
10\% & 0.92 & 0.89 & 0.87 & 0.94 & 0.84 \\ \hline
20\% & 0.94 & 0.83 & 0.85 & 0.95 & 0.79 \\ \hline
30\% & 0.95 & 0.85 & 0.86 & 0.98 & 0.82 \\ \hline
40\% & 0.94 & 0.92 & 0.78 & 0.97 & 0.88 \\ \hline
50\% & 0.95 & 0.94 & 0.87 & 0.96 & 0.89 \\ \hline
60\% & 0.95 & 0.92 & 0.91 & 0.97 & 0.92 \\ \hline
70\% & 0.94 & 0.93 & 0.97 & 0.97 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model Accuracy result of the networks comparison.  
Figure 14: Models’ accuracy over multiple nodes.  
Figure 15: The precision of the models for the proposed system.
# Wearable Haptic Device for Stiffness Rendering of Virtual Objects in Augmented Reality
## 3 Experimental Study: Stiffness Rendering of Virtual Objects
### Procedures  
#### 3.2.1 Stiffness Referencing Phase  
Over the experiment, the goal of the experiment is \"how similar a feedback condition can deliver an illusion of stiffness with real objects in AR\". We think these would be more applicable results for general finger-based haptic manipulation, for instance, what type of feedback should be delivered for rendering the stiffness of a real object with specific material (e.g., steel, wood, rubber, sponge). For this, we want to directly compare the stiffness of virtual objects rendered by feedback, with the stiffness of real objects. Thereby, we design and perform a \"stiffness referencing\" of real objects phase before the main experiment.  
For referencing the stiffness of real objects, we prepare a sensing box, which includes three sphere-shaped real objects \(o_{r}\) (rigid), \(o_{e}\) (elastic), and \(o_{s}\) (soft) with different stiffness; a plastic ball, a rubber ball, and a sponge ball with the same shape as shown in Figure 5A. The stiffness of the objects is measured by \(o_{s}\simeq 200\) N/m, \(o_{e}\simeq 2000\) N/m, and \(o_{r}>>o_{e}\) for the rigid object, which is selected with clearly the distinguished values to cover a wide  
Figure 4: (**A**) A snapshot of the experimental environment; (**B**) four types of feedback conditions for stiffness rendering; rigid kinesthetic haptic feedback (\(KH_{r}\)), elastic kinesthetic haptic feedback (\(KH_{e}\)), vibrotactile haptic feedback (\(VH\)), and pseudo-haptic feedback (\(PH\)).  
range of stiffness of daily objects. The diameter of the objects is equally 4 cm, and the subjects are instructed to perform the two-finger grasping task for the objects. To block the visual feedback of the objects (e.g., color, texture, shape deformation) and enforce the subjects to sense the stiffness only through haptic feedback, the objects are occluded with a box during the task.  
We instruct the subjects to grasp each object in turn as the object can be deformed enough. Since the users are asked to answer multiple-choice questions to represent their perceived stiffness and realness in the next section, we implement a GUI panel in the AR environment as shown in Figure 5B (left). Then, we mark the stiffness level of the grasping objects (i.e., levels 2, 4, and 6), and the subjects are given enough time to touch the real objects until the subjects can memorize the sense of the stiffness firmly. Moreover, we also give all types of experimental feedback conditions to the subjects ahead, to avoid the effect of learning or adaptation.
# Research on the Tie Cable Replacement Method of Half-through Tied-Arch Bridge
## 4 Results
### Results of the Impact of Each Replacement Scheme on the Superstructure
#### 4.2.1 Axial Force of Arch Rib  
The influence of each tie cable replacement scheme on the axial force of the arch rib is shown in Figure 7.  
It can be seen from Figure 7 that the axial force of the side arch rib sections changes greatly, and the section of the side arch crown S6 changes the most. It can also be seen that  
Figure 7: Change in the arch rib axial force.  
1. The change in the axial force of the arch rib caused by scheme 1 and scheme 3, which use the non-substitution method, is similar, and both are greater than that of the temporary substitution method schemes.
2. Comparing scheme 5 and scheme 7, it is found that their axial force changes are similar in each section. The change in axial force caused by scheme 6 and scheme 8 in each section is similar, and their value is about twice that of scheme 5 and scheme 7.
3. In each scheme, the axial force of the side arch crown S6 section is the most sensitive, and the axial force of the S3 section at the arch foot of the main arch is the least sensitive to the replacement of tie cables.
## 5 Two-Stage Iterative Algorithm Design  
\(\mathcal{P}1\) is a complex MINLP problem for the following reasons: (1) the variables in \(\eta\) are discrete, while the variables in \(\beta\) are continuous; and (2) the objective function and constraints \(\text{C}4\)-\(\text{C}5\) are nonlinear. Additionally, the variables in \(\text{P}1\) are high-dimensional and strongly coupled, posing challenges for traditional solution methods. Specifically, the communication resources allocated by the RSU to vehicles depend on different task offloading decisions. Moreover, due to the impact of resource allocation, the effectiveness of the offloading decision cannot be evaluated until the resource allocation is determined. Therefore, we design a two-stage iterative optimization algorithm to solve \(\mathcal{P}1\) efficiently.  
In the first stage, we solve the task offloading decision (TOD) problem to determine the offloading decision for each vehicle. Since the generation of offloading decisions is a typical NP-hard optimization problem, finding the optimal decision is challenging. Deep Reinforcement Learning (DRL) combines deep learning with reinforcement learning, using neural networks to optimize task decision-making in complex environments. Therefore, we propose a DQN-based task offloading algorithm that optimizes offloading decisions for each vehicle to minimize the total task processing delay.  
In the second stage, communication resources are allocated based on task offloading decisions to further reduce the overall task processing delay. Specifically, we prove that the communication resource allocation (CRA) subproblem is a convex problem and obtain its optimal closed-form solution through the Lagrange multiplier method.  
Finally, the TOD and CRA subproblems are solved iteratively until the objective function value converges or the maximum number of iterations is reached, ultimately obtaining the suboptimal solution for \(\mathcal{P}1\).
# Approaches to Parameter Estimation from Model Neurons and Biological Neurons
## 2 Model Neurons: Lessons Learnt from Well-Posed Models
### Observability  
Observability is a measure of how well the state and parameters of a conductance model can be inferred from measurements of the membrane voltage and other experimental observations. [PERSON] theorem prescribes that all information needed to constrain the model is contained in an observation window of finite duration [52]. [PERSON] [53] refined this argument by predicting that the number of observations be no less than \(2L+1\), where \(L\) is the number of state variables in the system.  
Electrophysiology and calcium imaging only allow a few state variables to be observed, namely the membrane voltage and calcium concentration. Gate variables \(m_{\mathrm{i}}\) and \(h_{\mathrm{i}}\) need to be inferred indirectly from time delayed observations of the membrane voltage. [PERSON] et al. [54; 55; 56; 57; 58] and [PERSON] et al. [59; 60; 61] have studied the properties of the Jacobian matrix, embedding the state vector at time \(t\) in the time delayed measurements of the membrane voltage. They show that, when the embedding space is small, this matrix is singular in localized regions of dynamic maps. In those regions, the data do not contain enough information to uniquely constrain the neuron state, and hence, the system is not observable. Increasing the size of the embedding vector to match the number of state variables plus parameters was found to eliminate domains where the Jacobian is singular. The length of the assimilation window used to construct neuron models typically has 10,000-100,000 data points for models with less than 100 state variables and parameters [7; 8; 62; 63; 64]. This suggests that the volume of data is sufficient to fulfill the observability criterion. This has been empirically verified by reliably retrieving the parameters of the original model in twin experiments, where the assimilated data were generated by the same model, configured with known parameters. Twin experiments are the ultimate validation tool of well-posed inference problems where the model generating the data is fully known.  
Naturally, increasing the number of state variables that can be matched to observation increases observability across the entire phase space [54; 55], and also stabilizes convergence towards the global minimum of the objective functions, by reducing the occurrence of positive conditional Lyapunov exponents [65].
# A Molecularly Imprinted Polymer for Selective Extraction of Phenolic Acids from Human Urine
## 3 Results and Discussion
### Polymer Characterizations  
Polymer structures (MIP and NIP) were studied by BET and SEM. In the BET procedure, nitrogen adsorption isotherm determination is used to obtain information about the total pore volume and the surface area [26]. BjH desorption pore volumes obtained were 4.80 cm\({}^{3}\)/g for MIP and 0.75 cm\({}^{3}\)/g for NIP. The specific surface determined by BET relates to the reactive area (total surface area) as all porous structures adsorb the small gas molecules. BET surface areas were found to be 274.8 and 230.7 m\({}^{2}\)/g for MIP and NIP, respectively. MIP and NIP showed a similar physisorption isotherms and similar surface areas, which could indicate that MIP had a similar structure to NIP. However, when considering the pore volume, the MIP exhibited 6 time more pore volume than the corresponding NIP. Figure 2 shows BET data of surface areas and the linear plot obtained for MIP and NIP for N2 at 77 K.  
Figure 1: Chemical structures of the template (4-HPA) and its analytes analogues (3-HPA, PCA, 4-HBA, SyA, and HA).  
Figure 2: BET data of surface areas and linear plot obtained for molecularly imprinted polymer (MIP) and nonimprinted polymers (NIP) for N\({}_{2}\) at 77 K (Q is quantity adsorbed in cm\({}^{3}\)/g).  
MIP and NIP particles morphology were explored by SEM. SEM is especially useful for convenient inspections of grain structures. Figure 3 presents the SEM microscopic images of the MIP particles by crushed polymer monolith obtained by bulk polymerization, displaying a non-spherical shape (Figure 3A). The textural surface of polymers revealed a typical pattern on the imprinted (Figure 3B) and non-imprinted surfaces (Figure 3C). A different roughness and smoother surface of the NIP was observed [16; 28].  
Figure 4 presents ATR-FTIR spectra of the MIP, NIP, and the template (4-HPA). ATR-FTIR analysis provides information about the state of the polymerization [15; 16]. The non-existence of bands in the region 1680-1640 cm\({}^{-1}\) indicates the absence of vinyl group in the polymers, and confirms their total polymerization. 4-HPA presents characteristic bands at 3200 and 1690 cm\({}^{-1}\) associated with the O-H and C=O stretching of carboxylic acid; 1400 cm\({}^{-1}\) is assigned to vibrations of the benzene ring, and 1270 cm\({}^{-1}\) corresponds to O-H stretching of phenols. The absence of characteristic bands for template also indicates a successful extraction process [15; 16]. Some authors are associated other characteristic MIP and NIP bands to crosslinker or the existence of poly(EGDMA) [14; 22]. MIP and NIP present similar spectra, except for the intensity of the C=O stretching vibration band (1724 cm\({}^{-1}\)). In NIP, this band is lower than that of MIP [14; 22].  
Figure 3: SEM microscopic images of crushed polymers produced by bulk polymerization method. (**A**) and (**B**) show MIP particles, magnification \(\times\)150 and \(\times\)3200, respectively, while (**C**) shows NIP particles, magnification \(\times\)3200.