esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 6 Positioning Experiments
### Robot Localization  
#### 6.1.1 Experimental Setup  
In this experiment, an IMES radio receiver was mounted on a mobile robot as the height of the receiver was set to 400 mm from the floor. The robot moved according to the pre-defined path shown in Figure 8 at a speed of 100 mm/s and made five round trips. The Kalman filtering method requires a relative distance and an orientation angle to update the position estimation, as described in Section 5. This information can be directly obtained from the wheel encoders installed in the robot used in this experiment at the same sampling rate as the IMES (10 Hz).  
The parameters of the EKF were set as follows. For the initial values of the state vector, X, described in Equation (18), (\(-\)500 mm, \(-\)1000 mm and 0\({}^{\circ}\)) were set as the correct initial position/orientation, and (\(-\)1000 mm, 0 mm and 45\({}^{\circ}\)) were set as incorrect values. For the initial values of the error covariance matrix, \(\mathbf{P}\), in both the cases of the correct and incorrect initial values, (250,000, 250,000, 1) were set to the diagonal elements and zero was set to the off-diagonal  
Figure 8: Experimental setup for robot and pedestrian localization.  
elements; this means that the uncertainty of the estimated state is \(x\) = 500 mm, \(y\) = 500 mm, and theta = 1 radian as their standard deviation. The system noise covariance matrix, \(\mathbf{\Omega}\), shown in Equation (18), was set as \(\mathbf{\Omega}[0][0]=0.4\), \(\mathbf{\Omega}[1][1]=0.4\), \(\mathbf{\Omega}[2][2]=0.000001\), and all off-diagonal elements were set to zero. These values were determined empirically based on several dead reckoning results, so that a good EKF estimation result could be obtained. The observation noise covariance matrix, \(\mathbf{\Psi}\), shown in Equation (20), was set as \(\mathbf{\Psi}[i][j]=386,001\) (\(i=j\)) and \(\mathbf{\Psi}[i][j]=0\) (\(i\
eq j\)). Note that the square root of 386,001 is about 621 (the unit is mm); this value is calculated from the data shown in Figure 1 as the weighted average (according to the number of plots) of the standard deviation of the distance for each integer value of the CNR.
## 2 Related Work
### Optimal Location Query  
Optimal location query [1] seeks to identify the optimal location for a new facility among candidate sites, considering a set of existing facilities and a group of customers. However, an optimal location needs to be determined mathematically from a set of given points, which requires the introduction of the concept of reverse nearest neighbor (RNN) [16; 17; 18]. For example, for a data point \(p\), its nearest neighbor (NN) [8] distance can be concretized as the distance from \(p\) to its nearest point in the dataset \(D\). After that, it is easy to check whether \(p\) is an RNN that queries \(q\) by checking whether \(q\) falls in a circle centered at \(p\) and with a radius equal to \(p\)'s NN distance. The RNN queries exist as one of the following two main variants: the monochromatic version and the bichromatic version. In the monochromatic version, all points belong to a single category. In the bichromatic version, the points are divided into two distinct categories. Previous studies on the facility location problem generally assume that the customer's location is known. However, in the given scenario of Figure 1, the business does not know the location of the customer, and the business needs to submit a query request to use location data stored by cloud service providers for data analysis. In other words, assuming that every customer prefers the nearest business, the bichromatic RNN query (RNNQ) is transformed into a business with an optimal location that can attract more potential customers. In addition to RNNQ, there are two other types of optimal location queries: the average distance query (AVGQ) [19],which minimizes the average distance between a customer and the nearest facility, and the maximum distance query (MAXQ) [20], which aims to maximize the maximum distance to the nearest facility for a customer.
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 3 Results and Discussion
### Adsorption Isotherms  
The [PERSON] and [PERSON] isothermal adsorption models were utilized to fit the defluorination process, and the related equations are presented in Equations (3) and (4):  
\[Q_{\rm e}=\frac{Q_{\rm m}K_{\rm L}C_{\rm e}}{1+K_{\rm L}C_{\rm e}} \tag{3}\]  
\[Q_{\rm e}=K_{\rm F}C_{\rm e}^{\frac{1}{2}} \tag{4}\]  
where \(Q_{\rm m}\) (mg\(\cdot\)g\({}^{-1}\)) denotes the maximum adsorption capacity, \(C_{\rm e}\) (mg\(\cdot\)L\({}^{-1}\)) represents the equilibrium fluoride concentration, and \(n\) is the Freundlich linear index. \(K_{\rm L}\) (L\(\cdot\)mg\({}^{-1}\)) and \(K_{\rm F}\) (L\(\cdot\)mg\({}^{-1}\)) represent the equilibrium constants for the Langmuir and Freundlich model, respectively.  
Figure 6 shows the adsorption isotherms, and the fitting parameters are summarized in Table S3. As the initial fluoride concentration increases, the adsorption capacity of Al-S957 also rises. At low fluoride concentrations, abundant adsorption sites on the adsorbent surface lead to a rapid increase in adsorption capacity. However, as the fluoride concentration continues to increase, adsorption sites gradually become saturated, causing the isotherm to level off.  
Figure 5: Adsorption kinetics of fluoride onto Al-S957.  
Furthermore, the adsorption capacity increases with temperature, indicating that the defluorination process is endothermic. Among the two models, the Langmuir model fits the adsorption data better, suggesting a monolayer adsorption on a homogeneous surface [38]. The fitted \(K_{\rm L}\) values consistently fall between 0 and 1, confirming that the adsorption is favorable [39]. The maximum adsorption capacity of Al-9957 at 298 K reaches 15.49 mg/g, surpassing many metal-modified adsorbents reported in the literature (Table 2).
# Modulated Noncollinear Optical Parametric Amplifier Output Induced by Stimulated Raman Scattering
## 4 Results and Discussion
### Stimulated Raman Processes in Optical Parametric Amplification
#### 4.1.1 Characteristic Features of Amplified Signal Spectra  
Two spectral features of the spectra in Figures 2 and 3 are of special interest. First, at certain pump-seed delay times, the amplified signal spectra are not smooth with a single peak. Instead, they have modulated structure with several peaks as shown in Figure 3b. Second, at specific signal wavelengths, the intensity is modulated in the pump-probe delay time as shown in Figure 2b-e and in Figure 3 more clearly.
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 5 Experiment and Evaluation  
In this section, we compare the performance of the proposed Ad-BBR algorithm with traditional congestion control algorithms (BBRv1, BBRv3, BBQ, Cubic, and Reno) in terms of throughput, RTT fairness, and transmission stability.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Classification** & **Age** & \multicolumn{3}{c}{**Sex**} & \multicolumn{3}{c}{**Both**} \\ \hline
**Feature Selection Algorithm** & **IGR** & **SGD** & **IGR** & **LASSO** & **Relief** & **SGD** & **SGD** & **FG frequency (N)** \\
**Classification Model** & **NB** & **GB, NB** & **NB, KNN** & **NB, KNN** & **KNN** & **KNN** & **NB** & **RF** & \\ \hline First-order features & & & & & & & & & \\ Mean intensity & & & & & & & & & \\ Intensity skewness & & & & & & & & & \\ Intensity kurtosis & & & & & & & & & \\ 75 th intensity percentile & & & & & & & & & \\ Standard deviation & & & & & & & & & \\ Maximum intensity & & & & & & & & & \\ Discretized intensity skewness & & & & & & & & & \\ Discretized intensity kurtosis & & & & & & & & & \\ Discretized intensity entropy (based log10) & & & & & & & & & \\ Discretized intensity entropy (based log2) & & & & & & & & & \\ Discretized intensity uniformity & & & & & & & & & \\ High-order features & & & & & & & & & \\ GLCM Joint variance & & & & & & & & & \\ GLCM Joint entropy (based log2) & & & & & & & & & \\ GLCM Joint entropy (based log10) & & & & & & & & & \\ GLCM Angular second moment & & & & & & & & & \\ GLLM Long runs emphasis & & & & & & & & & \\ GLRM Low gray level run emphasis & & & & & & & & & \\ GLRM Short run high gray level emphasis & & & & & & & & & \\ \hline \hline \end{tabular} AUC, area under the receiver operating characteristic curve; IGR, information gain ratio; LASSO, least absolute shrinkage and selection operator; SGD, stochastic gradient descent; NB, naive Bayes; KNN, k-nearest neighbors; GLCM, gray-level co-occurrence matrix; GLRLM, gray-level run length matrix.  
\end{table} 
Table 3: Selected radiomic features contributing to perfect classification performance (AUC = 1.000). Classification models using the same feature selection algorithm utilized the same features.
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 2 LPWAN and LoRa
### LoRa PHY
The CR is related to Forward Error Correction (FEC), which allows for the recovery of information bits corrupted by interference. Higher CR values allow for lower sensitivity because the number of recoverable bits increases, but the overhead used for encoding also increases, implying longer Time on Air (ToA). Since the actual performance of FEC can be evaluated in the event of burst errors due to interference, the use of a high CR may result in longer ToA without the benefit of sensitivity if such interference does not exist. Therefore, an appropriate trade-off between receiver sensitivity and ToA should be made when designing the LoRa radio link.
## 3 Results and Discussion
### Diffusivity
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{3}{*}{**Temperature**} & \multirow{3}{*}{**Corrosion Step, \(i\)**} & **Diffusivity Using Sherwood** & **Diffusivity Using Sherwood** & **Error (\%),** \\  & & **Modified Tachibana and Fukui.** & **et al. [62] and [PERSON] [63], \(D_{i,G}\)** & **(\(\times\)10\({}^{-11}\) m\({}^{2}\)/s)** & \(\frac{(D_{i,T}-D_{\rm{ev}})}{D_{i,\rm{ev}}}\times\)100 \\ \hline \multirow{8}{*}{**1450 \({}^{\circ}\)C**} & 1 & 5.45072 & 6.24493 & \(-\)12.72 \\ \cline{2-5}  & 2 & 9.04414 & 10.08591 & \(-\)10.33 \\ \cline{1-1} \cline{2-5}  & 3 & 13.33486 & 14.24404 & \(-\)6.38 \\ \cline{1-1} \cline{2-5}  & 4 & 9.84786 & 10.02625 & \(-\)1.78 \\ \cline{1-1} \cline{2-5}  & 5 & 6.83412 & 6.65800 & 2.65 \\ \cline{1-1} \cline{2-5}  & 6 & 13.21858 & 12.28140 & 7.63 \\ \cline{1-1} \cline{2-5}  & 7 & 12.22045 & 10.80049 & 13.15 \\ \cline{1-1} \cline{2-5}  & 8 & 11.43392 & 9.64851 & 18.50 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of the diffusivities of magnesia using different Sherwood relations. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Mass Loss (g)**} & **Bulk Concentration** & **Corresponding** & **Activation** \\  & & **(wt\%/100)** & **Corrosion Steps \(i_{T}\)** & **Energy (kJ/mol)** \\ \hline m1 & 4.8 \(\pm\) 0.71 & 0.074 \(\pm\) 0.0011 & 2\({}_{450}\)\({}^{\circ}\)C; 1\({}_{1500}\)\({}^{\circ}\)C; 1\({}_{1550}\)\({}^{\circ}\)C & 432 \\ \hline m2 & 11.7 \(\pm\) 1.31 & 0.088 \(\pm\) 0.0019 & 5\({}_{1450}\)\({}^{\circ}\)C; 3\({}_{1550}\)\({}^{\circ}\)C; 2\({}_{1550}\)\({}^{\circ}\)C & 409 \\ \hline m3 & 15.8 \(\pm\) 0.51 & 0.096 \(\pm\) 0.0007 & 7\({}_{1450}\)\({}^{\circ}\)C; 4\({}_{1500}\)\({}^{\circ}\)C; 3\({}_{1550}\)\({}^{\circ}\)C & 398 \\ \hline m4 & 18.9 \(\pm\) 1.45 & 0.102 \(\pm\) 0.0027 & 8\({}_{1450}\)\({}^{\circ}\)C; 5\({}_{1550}\)\({}^{\circ}\)C; 4\({}_{1550}\)\({}^{\circ}\)C & 396 \\ \hline \end{tabular}
\end{table} 
Table 3: Test intervals applied for the Arrhenius plot with the associated mass loss, bulk concentrations of magnesia, and the corresponding activation energies for magnesia.
###### Abstract  
In this paper, a novel, robust fin controller based on the backstepping control strategy and sliding mode control is proposed to handle the problem of ship roll stabilization. First, the mathematical model of the fin control system is established, including the modeling errors and the external disturbances generated by sea waves. In order to address the side effects caused by differential expansion, a command-filter is implemented within the backstepping controller design. By introducing a new performance function and a corresponding error transformation, the compensated tracking error can be bounded to achieve the desired prescribed dynamic and steady-state responses. The sliding mode disturbance rejection control with prescribed performance is realized by combining the disturbance observer. Simulations are presented to demonstrate the effectiveness of the proposed control scheme.  
Keywords:fin stabilizer; command-filtered backstepping; sliding mode control; prescribed performance; disturbance observer +
Footnote †: journal: Applied
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
## 2 Preliminaries
### Bilinear Pairing  
For three cyclic groups \(G_{1},G_{2},G_{T}\) of a prime order \(q\), a map \(e:G_{1}\times G_{2}\to G_{T}\) is a bilinear pairing if and only if three properties hold:  
* [noitemsep]
* Computable: given any \(g\in G_{1},h\in G_{2}\), calculating \(e(g,h)\in G_{T}\) is efficient;
* Bilinear: for \(x,y\in Z_{q}\), the equation \(e(g^{x},h^{y})=e(g,h)^{xy}\) always holds;
* Nondegenerate: if \(g\) is a generator of \(G_{1}\) and \(h\) is a generator of \(G_{2}\), \(e(g,h)\) will also be a generator of \(G_{T}\).
## 2 Background and Related Work
### Related Work
For instance, systems that utilize InfluxDB (2.7.6 version) and those integrated with Amazon Web Services (AWS) cloud illustrate the efficacy of cloud-based solutions for extensive data management [16; 22]. Furthermore, the calibration of low-cost sensors within IoT networks has been addressed, contributing to enhanced data interpretation and system accuracy [25]. Practical applications and case studies are often incorporated to highlight the tangible impact of these systems in real-world scenarios. For instance, the successful deployment of the Smart-Air system at Hanyang University [13] and the operational efficacy of modular systems at Qatar University [14] serve to illustrate practical implementations. Additional studies have emphasized the application of these systems within industrial contexts [23] and the development of mobile applications aimed at pollution management [26]. This synthesis encapsulates the diverse methodologies and innovations in IAQ monitoring, thereby underscoring the pivotal role of IoT technology in enhancing real-time data collection, analysis, and user engagement across a multitude of indoor environments. Unlike other works that emphasize air quality monitoring and alerting systems, our approach focuses on the monitoring of ozone with an automated management of UV-C lamps to increase safety and efficiency. Moreover, with respect to these related works, the proposed system focuses on sanitization rather than air quality or comfort by exclusively monitoring the ozone level and, for maintenance reasons of UV-C lamps, the environmental conditions (i.e, humidity and temperature). As for many other work, IoT technologies are widely used but, as an innovative element, EI techniques are exploited to achieve a higher level of operational efficiency, automation, and safety, as discussed in the following sections.
## 3 Methodology
### Image Segmentation  
Generally, object-based image classification is based on image segmentation, which is a procedure of dividing an image into separated homogenous non-overlapping regions based on the pixel gray values, texture, or other auxiliary data [23]. One of the most popular image segmentation methods is Multi-resolution image segmentation, which is used in this study, for initial segmentation adopting eCognition software. For multi-resolution image segmentation in eCognition, there are three parameters to be specified: scale, shape, and compactness. Generally, the eCognition default values for shape and compactness are used for initial segmentation, which are 0.1 and 0.5 respectively. Scale is also specified so that the resulting segments are smaller than real objects. Considering the spatial resolutions of the used data, which are around 0.5 m, a scale equal to 10 is fine. Here, the initial segmentation results are enough to be used to define shadows, vegetation, and roads, but for building detection due to the existing complexity a second level segmentation is required, which is described in Section 3.2.4.
## 4 Model Training and Experimental Results
### Experiment Based on Publicly Available Dataset  
The NinaProDB1 dataset encompasses 52 hand gestures, classified into three categories for training: (A) 12 finger gestures; (B) 8 gestures of uniform opening and closure with equal length, along with 9 wrist gestures; (C) 23 basic grasp gestures. A total of 10 healthy volunteers (7 males and 3 females, aged 22 to 30 years old, without any medical history and of similar physique) participated in the collection of sEMG signals from forearm muscles using an ELONXI electromyography system equipped with 18 dry electrodes, including 2 for grounding. The sampling frequency was set to 100 Hz. Each participant performed the 52 hand gestures 10 times, with each gesture lasting 10 s. A 3-s rest was allowed between gestures, and subjects could rest for 10 min between different sessions.  
The NinaProDB5 datasets encompass 52 hand gestures, classified into three categories for training: (A) finger gestures; (B) gestures characterized by uniform opening and closure of equal length, along with wrist gestures; (C) basic grasp gestures. Ten healthy volunteers participated in the collection of sEMG signals, utilizing a 16-channel sampling system. The sampling frequency was set at 200 Hz. Each participant was required to perform the 52 hand gestures in the experiment, repeating each gesture 6 times, with each repetition lasting 5 s. Participants were allowed a 3-s rest period between different hand gestures.  
In the experiment, the sEMG data from the second and sixth repetitions of each gesture constituted the test sets, while the remaining data formed the training and validation sets. The distribution among the training, validation, and test sets was approximately in a 6:2:2 ratio. We employed data augmentation techniques, such as random and center cropping, to enhance the diversity of the training data. Each recognition model was trained individually, and the corresponding trained model was then utilized for each individual. The batch size for training was set at 64, with the Adam optimizer (adaptive moment estimation method) selected. The learning rate was established at 0.001, and a dropout rate of 0.4 was applied to improve the model's generalization capability.
###### Abstract  
To address the contradiction between ride comfort and handling stability, a new vibration-absorbing wheel structure with time-delay feedback control was proposed by applying a time-delay feedback-controlled dynamic vibration absorber to the wheel structure. Using H\({}_{2}\) optimization and the particle swarm optimization algorithm (PSO), the design parameters of the time-delay vibration-absorbing wheel structure were obtained with sprung mass acceleration (SMA), suspension deflection (SD) and dynamic tire deflection (DTD) as the optimization objective functions. The stability interval of the system was analyzed using the Routh-Hurwitz criterion and the Sturm criterion. The vehicle vibration reduction performance of the new vibration-absorbing wheel structure was simulated and verified in the time domain under harmonic excitation and random excitation. The simulation results show this new structure can effectively solve the contradiction between the ride comfort and handling stability. It provides a new technical idea for reducing vehicle vibration.  
time-delay feedback control; wheel structure; vehicle vibration reduction; time-delay stability; particle swarm optimization algorithm Article  
A New Vibration-Absorbing Wheel Structure with Time-Delay Feedback Control for Reducing Vehicle Vibration
## 3 Results and Discussion
### Comparison with Other Works  
In this section, the accuracy of the presented model in this paper will be compared to the previous works published in the literature. As can be seen with Table 9, the RMSE of the proposed model in this paper is the lowest in comparison with other published papers. Also, in terms of R-squared, the proposed model in this paper has an R-squared of almost 0.999, which is higher than other published papers. Moreover, in this paper, relative error never exceeded 5.4% (worst case), while for other published papers, it was reported between 3% to 190%.
###### Abstract  
Pavement marking retroreflectivity, a critical factor for safe driving, depends on the characteristics of both the paint and the embedded glass beads. However, traditional methods for predicting pavement marking service life often overlook these materials properties. This study investigates the influence of paint and glass bead characteristics on pavement marking retroreflectivity performance and addresses the characterization of glass bead size distribution by the coefficient of uniformity and curvature. Three field test sites on a Brazilian highway with various paint and glass bead combinations were evaluated. A statistical model, GAMLSS (Generalized Additive Model for Location, Scale, and Shape), was adjusted to evaluate the performance of the markings' retroreflectivity as a function of paint and glass bead characteristics. The model revealed that well-graded glass beads increased retroreflectivity by around 10%, while paints with a higher volume of solids improved service life around 65%. Therefore, the results show that acrylic water-based paints with higher volumes of solids and well-graded glass beads with better shape characteristics should be preferred to improve pavement markings' retroreflectivity and service life. The statistical model identified the key characteristics with the greatest impact on pavement marking retroreflectivity, offering valuable insights for real-world applications, which will assist pavement marking practitioners and road authorities in selecting appropriate materials to achieve enhanced durability.  
road markings; test sites; GAMLSS; particle shapes; gradation; volume solids Article