esa-sceva/satcom-chunk-collection huggingface dataset:
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 7 Integrating RSA with Large Language Models (LLMs)
### Opportunities for Human-AI Collaboration in RSA with LLMs  
#### 7.2.1 Human Agent Supporting LLM-Based AI  
While LLMs have experienced remarkable advancement in recent years, their accuracy and reliability remain inadequate for effectively aiding PVI in accomplishing diverse complex life tasks. Current AI-powered assistance systems relying on LLMs are primarily confined to tasks like scene description and VQA ([PERSON] et al., 2016). However, for intricate endeavors such as navigating complex environments that necessitate comprehension of 3D space,the current LLM-based assistance systems still struggle. Thus, the involvement of human agents is essential to successfully tackle these multifaceted tasks.  
Even for simple recognition tasks, LLMs are not flawless, let alone handling complex ones. [PERSON] (2016) detailed an encounter with BeMyAI and found some errors and limitations, including occasional misclassifications and inaccuracies in object recognition, such as mistaking wall paneling for cupboards or large windows for doors. In some cases, the app incorrectly identifies or describes objects, like a bear on a shelf that was not there, or misinterprets text, as seen in the misreading of a book title. As shown in Figure 4, the German book title \(HEYMWERK\) was mistakenly interpreted as \(HEIMWERK\). Human agents can readily rectify such recognition errors with ease.
## 3 Materials and Methods
### Type of Multi-Next-Hops Selection
#### 3.2.2 Disjoint Links  
A disjoint path refers to the fact that the link is not shared between the different paths, but may have common nodes. As shown in Figure 4c, the two paths between the source node S and the destination node D (S-N2-N6-D and S-N3-N2-D) are disjoint paths, where node N2 is the common node.
## 2 Simulation Principle and Model Structure
### Model's Material Parameters  
Table 1 lists the various material parameters utilized in the simulation model seen in Figure 1, where the tangential direction of the ZnO piezoelectric film is set to (0, 0, 0) by the rotational coordinate system in COMSOL, and all the studies in this paper are centered around this tangential ZnO piezoelectric film [22]. The model in this study utilizes materials such as Al, ZnO, diamond, and SiO\({}_{2}\). Table 1 presents the primary parameters of these materials, which were obtained from the COMSOL software database [23; 24; 25; 26].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Item** & **Symbol** & **Al** & **ZnO** & **SiO\({}_{2}\)** & **Diamond** \\ \hline  & C\({}_{11}\) & 1.11 & 2.096 & 0.785 & 11.531 \\  & C\({}_{12}\) & 0.59 & 1.205 & 0.161 & 0.864 \\ Elastic constant (10\({}^{11}\) N/m\({}^{2}\)) & C\({}_{13}\) & 0.59 & 1.046 & 0.161 & 0.864 \\  & C\({}_{33}\) & 1.11 & 2.106 & 0.785 & 11.531 \\  & C\({}_{44}\) & 0.26 & 0.423 & 0.311 & 5.333 \\  & T\({}_{C11}\) & \(-\)5.9 & \(-\)1.12 & 2.39 & \(-\)0.14 \\ Temperature coefficients of elastic constants (10\({}^{-4}\)/\({}^{\circ}\)C) & \(T_{C13}\) & \(-\)0.8 & \(-\)1.61 & 5.84 & \(-\)0.57 \\  & T\({}_{C33}\) & \(-\)5.9 & \(-\)1.23 & 2.39 & \(-\)0.14 \\  & \(T_{C44}\) & \(-\)5.2 & \(-\)0.70 & 1.51 & \(-\)0.125 \\  & \(e_{15}\) & - & \(-\)0.48 & - & - \\ Piezoelectric constants (C/m\({}^{2}\)) & \(e_{31}\) & - & \(-\)0.573 & - & - \\  & \(e_{33}\) & - & 1.321 & - & - \\ Relative dielectric constants & \(e_{11}\)/ \(e_{0}\) & 1 & 8.55 & 3.32 & 5.67 \\  & \(e_{33}\)/\(e_{0}\) & 1 & 10.2 & 3.32 & 5.67 \\ Mass density (10\({}^{3}\) kg/m\({}^{3}\)) & \(p\) & 2.7 & 5.665 & 2.2 & 3.512 \\ Temperature coefficients of mass density (10\({}^{-6}\)/\({}^{\circ}\)C) & \(T_{\rm P}\) & \(-\)1.65 & \(-\)10.1 & \(-\)54 & \(-\)3.6 \\ \hline \end{tabular}
\end{table}
Table 1: Material parameters used in calculations.  
Figure 1: Schematic diagram of the construction model: (**a**) IDT/ZnO/SiO\({}_{2}\)/diamond; (**b**) mesh delineation.
# Thermal Exchange and Skid Resistance of Chip Seal with Various Aggregate Types and Morphologies
## 2 Materials and Methods  
### Materials  
Basalt from Hubei province, and two kinds of steel slags, were used to vary the elementary and mineral types of aggregates. Basalt is the preferred natural aggregate in Chinese road construction due to its superior physical performance. Basic oxygen furnace (BOF) slag and ferrochromium (FER) slag were chosen as the industrial by-products supplied from Inner Mongolia and Fujian province, respectively. The basic properties of the aggregates are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample ID** & **BS-1** & **BS-2** & **BOF-1** & **BOF-2** & **FER-1** & **FER-2** & **Method** \\ \hline Specific gravity (g/cm\({}^{3}\)) & 2.65 & 2.68 & 3.15 & 3.21 & 2.98 & 3.02 & ASTM C127 \\ Sand equivalent (\%) & 68 & 66 & 74 & 75 & 79 & 78 & ASTM D419 \\ Water absorption ratio (\%) & 0.42 & 0.49 & 1.35 & 1.69 & 1.07 & 1.14 & ASTM C127 \\ Soundness by Na\({}_{2}\)SO\({}_{4}\) (\%) & 2.96 & 3.02 & 2.34 & 2.25 & 1.86 & 1.97 & ASTM C88 \\ Polished stone value & 48 & 44 & 68 & 62 & 57 & 54 & EN 1097-8 \\ \hline \hline \end{tabular} BS: basalt, BOF: basic oxygen furnace slag, FER: ferrochromium slag.  
\end{table}
Table 1: Basic properties of aggregates.  
The numbers 1 and 2 in the sample ID represent different production batches from the same manufacturer. Therefore, there are in total six kinds of aggregate. Only the aggregates labeled 1 were used for microscopic tests, such as elemental composition, phase analysis and thermal constant, since the chemical compositions of two batches are basically the same. The morphological characteristics, skid resistance and aggregate retention performance were applicable to all aggregates and corresponding seals. All experiments were conducted in quintuplicate and average results were kept. Aggregates with sizes 2.36-4.75 mm were selected in this study according to the literature [15].  
One SBS (styrene butadiene styrene)-modified hot asphalt with penetration of 51, a softening point of 74.8 \({}^{\circ}\)C and a ductility of 35.0 cm, was used as binder in this study. Chip seal specimens were designed and prepared according to the method of [PERSON] [16]. The construction temperatures were 160 \({}^{\circ}\)C and 25 \({}^{\circ}\)C for asphalt and aggregate, respectively. The aggregate application rates of BS, BOF and FER were 11.6 kg/m\({}^{2}\), 13.5 kg/m\({}^{2}\) and 12.7 kg/m\({}^{2}\), respectively, and those of asphalt were 1.3 kg/m\({}^{2}\), 1.5 kg/m\({}^{2}\) and 1.4 kg/m\({}^{2}\).
###### Abstract  
Background: This study aimed to investigate the acute changes in muscle and tendon viscoelastic properties in response to a progressive treadmill VO\({}_{2}\)max test among professional male soccer players. Methods: Bilateral assessments at five sites--the Achilles tendon (AT), biceps femoris, semitentinosus, rectus femoris (RF), and sternocleidomastoid (SCM)--measured tone (oscillation frequency), dynamic stiffness, logarithmic decrement (elasticity), stress relaxation time, and creep. Each site was probed five times and values averaged. Repeated-measures ANOVA (Time \(\times\) Side) with Bonferroni correction tested pre- to post-exercise changes; [PERSON]'s \(r\) examined associations with VO\({}_{2}\)max. Results: Significant Time effects (all \(p<0.05\)) were observed for RF frequency (\(\eta_{p}\)\({}^{2}\) = 0.226), RF creep (\(\eta_{p}\)\({}^{2}\) = 0.144), AT stiffness (\(\eta_{p}\)\({}^{2}\) \(\approx\) 0.035), AT frequency (\(\eta_{p}\)\({}^{2}\) = 0.035), and SCM frequency (\(\eta_{p}\)\({}^{2}\) = 0.037). Post-exercise, right AT stiffness fell by 65 \(\pm\) 14 N/m (\(p\) = 0.015), while left AT stiffness rose by 22 \(\pm\) 9 N/m (\(p\) = 0.015). RF stiffness decreased by 28 \(\pm\) 6 N/m (\(p\) < 0.001) and tone by 1.2 \(\pm\) 0.3 Hz (\(p\) < 0.001), with creep (+0.08 \(\pm\) 0.02; \(p\) < 0.001) and relaxation time (+1.5 \(\pm\) 0.7 ms; \(p\) < 0.001) increasing. SCM tone declined by 0.8 \(\pm\) 0.4 Hz (\(p\) = 0.010). Baseline RF properties--frequency (\(r\) = \(-\)0.597), stiffness (\(r\) = \(-\)0.59), relaxation time (\(r\) = 0.53), and creep (\(r\) = 0.48)--correlated moderately with VO\({}_{2}\)max (all \(p\) < 0.05). Conclusions: These findings suggest that viscoelastic adaptations to exhaustive aerobic exercise are tissue- and side-specific, and that rectus femoris viscoelastic properties may serve as potential indicators of endurance readiness.  
a _Article_
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 3 Foundation  
This section provides a detailed description of the AA-HMM in terms of foundational concepts. It defines and introduces five principle concepts that are necessary to fully understand the underlying approach of the AA-HMM: (1) Pattern Entropy (PE), (2) PE Reduction (PERD), (3) Window Width (WW), (4) Local Optimal Window Width (LOWW), and (5) Dynamic Window (DW). Among these concepts, (1) and (2) are the basis of (3) and (4). In addition, (5) is established based on (3) and (4), which acts as the core feedback mechanism that enables the model to adaptively adjust according to dynamic network patterns.
## 6 Experiment  
### Metric  
To better evaluate the performance, a model is tested by making a set of predictions \(\hat{\textbf{y}}=\{\hat{y}_{1},\hat{y}_{2},\ldots,\hat{y}_{D}\}\) and then comparing it with a set of known actual values \(\textbf{Y}=\{y_{1},y_{2},\ldots,y_{D}\}\), where \(D\) is the size of the test set. Three common metrics are used to compare the overall distance of these two sets, i.e., mean absolute percentage error (_MAPE_), normalized root mean squared error (\(NRMSE\)), and R-squared score (\(R^{2}\) score).  
_MAPE_ As shown in Equation (13), \(MAPE\) is calculated by taking the absolute difference between the predicted and actual values, dividing it by the actual value, and then taking the average of these values over the entire dataset. This calculation results in a single number that represents the average percentage difference between the predicted and actual values. The smaller the \(MAPE\) value, the better the model's performance  
\[MAPE=\frac{100\%}{D}\sum_{i=1}^{D}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{13}\]  
_NRMSE_ Normalized Root Mean Squared Error (\(NRMSE\)) is a metric used to evaluate the accuracy of a prediction model. It measures the normalized average magnitude of the residuals or errors between the predicted values and the actual values, as shown in Equation (14).  
\[NRMSE=\frac{\sqrt{(\frac{1}{D})\sum_{i=1}^{D}(\hat{y}_{i}-y_{i})^{2}}}{y_{max} -y_{min}} \tag{14}\]  
where, \(\hat{y}_{i}\) represents the predicted values, A represents the actual values, and \(sqrt()\) denotes the square root function. The term \((\hat{y}_{i}-y_{i})^{2}\) calculates the squared residuals or errors between the predicted and actual values. The \(y_{max}\) and \(y_{max}\) represent the maximum and minimum values in the actual values, respectively. The smaller the \(NRMSE\) value, the better the model's performance.  
\(R^{2}\) _score_ The \(R^{2}\) _score_, also known as the coefficient of determination, is a statistical measure that indicates the proportion of the variance in the dependent variable that is predictable from the independent variables in a regression model. The \(R^{2}\) _score_ is typically used to evaluate the fitness of a regression model, as formulated in Equation (15).  
\[R^{2}=1-\frac{\sum_{i=1}^{D}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{D}(y_{i}-y^{ *})^{2}} \tag{15}\]  
where \(y^{*}\) the mean of the actual values. In essence, the \(R^{2}\) _score_ is a measure of how well the regression model fits the data and provides an assessment of its predictive performance. A higher \(R^{2}\) _score_ indicates a better fit and stronger explanatory power of the model.
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 4 Simulation Results
### Return Loss  
The following results show the simulated \(\left|S_{11}\right|\) in free-space as a function of the key parameters of the antenna with all other parameters fixed as listed in Table 5.  
Figure 8a shows the simulated \(\left|S_{11}\right|\) as a function of the radiator length \(l_{\mathrm{CD}}\), which was varied from 75 mm to 81 mm (considering \(l_{\mathrm{CE}}\) fixed to 8 mm). The results show that increasing length \(l_{\mathrm{CD}}\) leads to a reduction of the resonant frequency of the antenna. There is a small change in the minimum achievable \(\left|S_{11}\right|\) value, showing that the impedance matching of the antenna is not greatly affected from \(l_{\mathrm{CD}}\) with only a 1 dB variation observed in this case. Figure 8b shows a simulated parametric sweep as the length of the inductive branch \(l_{\mathrm{CE}}\) was varied from 8 mm to 10.8 mm (considering \(l_{\mathrm{CD}}\) fixed to 81 mm). The results show that this parameter \(l_{\mathrm{CE}}\) plays a key role in determining both the resonant frequency and level of impedance matching that can be achieved. The simulations related to the results reported in Figure 8a,b were performed considering the system in free-space, moreover the sweeps are performed considering the parameter values reported in the third column of Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Swept Parameter** & **Starting Value (mm)** & **Final Optimized Value (mm)** \\ \hline \(W_{\mathrm{G}}\) & N & 27.4 & 27.4 \\ \(W_{\mathrm{E}}\) & N & 37 & 37 \\ \(W_{\mathrm{F}}\) & N & 1.6 & 1.6 \\ \(W\) & N & 2 & 2 \\ \(H_{\mathrm{E}}\) & N & 59 & 59 \\ \(H_{\mathrm{R}}\) & N & 19.1 & 19.1 \\ \(H_{\mathrm{G}}\) & N & 19.4 & 19.4 \\ \(H_{\mathrm{T}}\) & N & 6 & 6 \\ \(L_{\mathrm{S}}\) & Y & 6–7.4 & 6 \\ \(H_{\mathrm{S}}\) & Y & 4–5.4 & 4 \\ \(R_{\mathrm{A}}\) & N & 11.9 & 11.9 \\ \(R_{\mathrm{B}}\) & N & 8.5 & 8.5 \\ \(H_{\mathrm{D}}\) & Y & 4–7 & 4 \\ \(l_{\mathrm{CD}}\) & Y & 75–81 & 81 \\ \(l_{\mathrm{CE}}\) & Y & 8–10.8 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tabulated values of the antenna physical parameters (free-space).  
Figure 8: _Cont._The final values of parameters \(l_{\text{CD}}\) and \(l_{\text{CE}}\) were chosen to achieve a free-space resonant response close to 915 MHz with the final parameters shown in the right-most column of Table 5. The simulated response with optimized \(l_{\text{CD}}\) and \(l_{\text{CE}}\) values are shown in Figure 8c. In the free-space case (dashed blue line), it can be seen that the antenna is characterized by a \(-10\) dB bandwidth of approximately 9 MHz which is less than the required value of 26 MHz at 915 MHz [12]. In addition, the on-wrist configuration shows that placing the antenna on the body results in a decrease in resonant frequency and impedance change of the antenna. These issues are addressed in the following section.
## 3 Results and Analysis
### Low Multipath Single Point and Differential Positioning
#### 3.2.2 Epoch-by-Epoch Single Point and Differential Mode  
The GPSMap 66 and P10 measurements were successively processed in epoch-by-epoch single point single frequency code mode and differential code/carrier phase (when available) mode using RTKLib and the resulting coordinates were compared to the test point reference values. The results for GPS are shown in the two plots of Figure 14. In single point mode, the superior helix antenna of the GPSMap 66 showed its advantage with RMS values of 1.51, 1.11 and 2.64 m in the latitude, longitude and height components, compared to the corresponding P10 RMS values of 4.20, 4.69 and 12.9 m. The performance of the GPSMap 66 in differential code/carrier phase mode, with RMS values of 14, 8 and 26 cm in latitude, longitude and height, is impressive. The code/carrier phase RTKLib solution was in float mode 100% of the time. Carrier phase lock was maintained more or less continuously.  
The P10 unit did not output carrier phase measurements nor Galileo data during the test. The single point GPS results are shown in the second plot of Figure 14. No differential processing was pursued with the P10.  
The Galileo coverage was sufficient to obtain solutions with the GPSMap 66 units. The measured C/N\({}_{0}\) values, not shown here, range from 42 to 52 dB-Hz for the R10 and 34 to 46 dB-Hz for the 66 green. The mean difference between the two units is 5 dB-Hz. The Galileo E1 code single point and code/carrier phase position results are shown in Figure 15. The single point RMS values of 5.39, 1.88 and 4.68 m in latitude, longitude and height are predictably higher than the corresponding values reported for GPS, given a PDOP of 4 to 7 for Galileo versus 2.7 for GPS and four to five SVs for Galileo versus 10 to for GPS. The differential code/carrier phase RMS values of 1.42, 0.14 and 0.75 m versus those of GPS are however roughly in the same range, showing a similar level of code and carrier phase performance tracking at the individual signal level within the above error bounds.  
Figure 14: GPSMap 66 and P10 GPS epoch-by-epoch single point and differential code/carrier phase (GPSMap 66 unit only) coordinates variations with respect to reference values ([PERSON], 13 Sep18).  
Figure 15: GPSMap 66 Galileo epoch-by-epoch single point and differential code/carrier phase coordinate variations with respect to reference values ([PERSON], 13 Sep18).
# Probabilistic Resilience Analysis of the Icelandic Power System under Extreme Weather
## 3 Methodology
### Data and Implementation  
The implementation and data requirements of the weather-dependent failure rate models, as described in Section 3.2, are outlined in [20]. The data used to produce the failure rate models are restricted due to them representing sensitive information related to infrastructure of national importance. The look-ahead component survival and failure probabilities required forecast wind speed data. The forecast wind speed data were sourced from the Icelandic Bureau of Meteorology's online repository of forecast weather maps [21]. A tool was created in Python to convert the weather map images from the 10 m elevation HARMONIE model into geospatial wind speed data, at an hourly resolution. This tool was used to generate wind speed time series for a given overhead line, by finding the maximum wind speed measured across all towers at each hour of the storm.  
The weather-dependent failure rate models and wind-speed forecasts were then used to compute look-ahead component survival and failure probabilities at each time step in Python. The resulting probabilities were used to compute contingency probabilities, which represented one of the main inputs to the bi-level attacker-defender model. In addition, system snapshots of the Icelandic power system were provided at 5' intervals in a bus-branch model, which consisted of electrical parameters, component states, and bus injections (load and generation). The bi-level attacker-defender model defined in (5)-(17) was implemented in Julia, and solved while using the CPLEX solver.
# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 2 Related Work
### NeRF-Based SLAM  
With the advent of NeRF, dense visual SLAM has made excellent progress in performing high-fidelity environment reconstruction. IMAP [28] uses implicit neural radiation fields for scene representation, jointly optimizing map and camera pose through the loss between volumetric rendered images and input images. NICE-SLAM [29] allows detailed environment reconstruction on large indoor scenes by introducing a hierarchical scene representation to fuse multi-level local information. ESLAM [30] represents the scene as the multi-scale axis-aligned perpendicular feature planes and shallow decoders that decode interpolated features into TSDF and color values for each point in continuous space. CO-SLAM [31] utilizes the high convergence speed and ability to represent high-frequency local features of a multi-resolution hash-grid to represent the scene. Point-SLAM [32] introduces a neural anchor point representation of the scene that dynamically adjusts the density of points based on the input image information, reducing optimization time and memory usage in fewer detailed regions. Despite these advantages, NeRF-based SLAM tends to lose scene details due to localized over-smoothing, along with the disadvantage of catastrophic forgetting.
## 3 Proposed Solution
### Feature-Selection Algorithm  
Feature selection is performed to obtain the desired number of feature subsets through the algorithm (Algorithm 1). In addition, Algorithm 1 shows the procedure code of binary decoding feature selection. First, the population is initialized, and the fitness of each chromosome is calculated using Formula (1g). A training dataset is used in the calculation and the anomaly-detection training model is built by applying only the feature subset from the current chromosome in the detection method.  
```
featureLength: The number of features in dataset finalFeatureSet: The number of final feature set maxGeneration: Maximum generation popsize: Population Size
```  
Crossover probability = 0.8 Mutation probability = 0.2 p <-initialPopulation(futureLength, popsize) #value of gene = 0 or 1 computeFitness(p) #fitness function calculation-\(>maxF=2\frac{DR(c_{i})}{DR(c_{i})}\) generation = 1 while(maxGeneration >= generation { newPop <- linearRankSelection(p) pbxCrossover(newPop) mutate(newPop) p <- newPop competetFitness(p) generation = generation + 1 ) subFeature <- TopFeatureSet(p) finalFeatureSet <- addFeature(subFeature) returnfinalFeatureSet ```  
**Algorithm 1** Feature Selection Procedure (binary decoding)  
Anomaly detection is similarly performed using only the feature subset from the current chromosome, and then its fitness is calculated from the results. Since the anomaly-detection algorithm used here is available for any method used, it can be applied adaptively to various anomaly-detection domains. When the fitness of all chromosomes in the population has been calculated, optimal solutions are obtained by performing selection and crossover according to the GA process. When convergence is obtained, the stop criteria is met, or a predetermined maximum number of generations is reached; thus, feature selection using the chromosome with the current best fitness is performed.