esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Model for GNSS/INS/LEO/5G Integrated Navigation
### Simulation Method of Each Signal Source
#### 2.3.3 5G Signal Simulation  
The simulation of 5G ranging signal in this paper, obtained from Formulas (26) and (27), is obtained by adding simulation signal error based on theoretical distance value and range rate between the base station and receiver, namely:  
\[\hat{\rho}_{5G}=\rho_{5G}+\delta\rho_{c-5G}+\delta\hat{\rho}_{cd-5G}\times\Delta t \tag{26}\]  
\[\hat{\hat{\rho}}_{5G}=\dot{\rho}_{5G}+\delta\hat{\rho}_{cd-5G}+\delta\hat{\rho} _{cd-5G} \tag{27}\]  
where \(\hat{\rho}_{5G}\) and \(\hat{\hat{\rho}}_{5G}\) are the pseudo-range measurement and pseudo-range rate measurement of the 5G signal, respectively. \(\rho_{5G}\) and \(\hat{\rho}_{5G}\) are the theoretical distance and the distance rate. \(\delta\rho_{c-5G}\) is the clock bias of the receiver. \(\hat{\vartheta}_{cd-5G}\) is the clock drift deviation. \(\dot{\rho}_{cd-5G}\) is the distance rate tracking error. \(\Delta t\) is a 5G positioning time interval.  
Table 5 shows the parameters of the 5G-ranging models used in this paper. In order to simulate the coverage of 5G signal on the motion track, in this paper, according to the coordinates of the center point of the motion track, taking the base station coverage radius r as the axis, four base stations are evenly distributed in the same height plane with an azimuth interval of 90\({}^{\circ}\).The coordinates of each base station are calculated by the above distribution method and 5G model parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Accelerometer bias(m/s\({}^{2}\))—\(b_{a}\) & \([0.0088,\ -0.0127,\ 0.0078]\) \\ gyroscope bias (deg/h)—\(b_{g}\) & \([10,\ 10,\ 10]\) \\ Accelerometer scale factor and cross-coupling error— \(M_{a}\) & \(\left[\begin{array}{ccc}500&-300&200\\ -150&-600&250\\ -250&100&450\\ \end{array}\right]\times 10^{-6}\) \\ Gyroscope scale factor and cross-coupling error— \(M_{g}\) & \(\left[\begin{array}{ccc}400&-300&250\\ 0&-300&-150\\ 0&0&-350\\ \end{array}\right]\times 10^{-6}\) \\ Gyroscope gravity acceleration correlation bias & \(\left[\begin{array}{ccc}0.9&-1.1&-0.6\\ -0.5&1.9&-1.6\\ 0.3&1.1&-1.3\\ \end{array}\right]\) \\ Accelerometer random noise PSD root(m\(\cdot\)s\({}^{-1.5}\))\(w_{a}\) & \(\begin{array}{ccc}9.80665\times 10^{-4}\\ 2.9089\times 10^{-6}\end{array}\) \\ Gyro random noise PSD root(rad\(\cdot\)s\({}^{-0.5}\))—\(w_{g}\) & \(\begin{array}{ccc}2.9089\times 10^{-6}\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: IMU model parameters.
## 2 Materials and Methods
### Slam  
SLAM [14], originally developed by [PERSON] and [PERSON], based on earlier work by [PERSON], [PERSON], and [PERSON] [15; 16] and the first paper to declare the complete process of SLAM, is the method by which an agent maps an environment whilst understanding its position within the newly mapped environment. The importance of this is obvious within the context of a robotic colony mapping an environment. This methodology is so robust and ubiquitous in modern robotics that it is a natural backbone for the robotic agents used within this thesis. The basic idea behind SLAM is broken down into these simplified steps:  
* Gather sensor data from the agent;
* Collect sensor data into a map of the environment;
* Track agent movement at time T via the use of onboard odometry;
* Detect features/landmarks within the environment;
* Track features/landmarks at time T;
* Move agent;
* Update predicted position based on odometry;
* Update position based on features/landmarks accounting for uncertainty in both odometry and sensor readings.  
This paper considers multiagent SLAM. A good review can be seen in [17]. A more recent overview of the state of the art still separates the problem into single and multiagent SLAM. The evaluation is of particular interest, with the difference between traditional landmark-based SLAM, the graph method, and the Monte Carlo.  
There are examples of multiagent SLAM [4; 18; 19] that look toward solving the multiagent problem. Although these examples are proven to work, the identified gap in this paper is in decentralised communication between agents; furthermore, multiagent SLAM remains to be generalised between heterogeneous agents. With multiagent SLAM, the pose of the agent, the feature detection, and the use of odometry are rigid. This should not present an issue; however, with the voxel grid approach proposed in this paper, certain advantages can be conferred. Firstly, the choice of feature detection will be taken out of the equation and moved to the specific agents. In this paper, the use of SURF (Speeded Up Robust Features) and frame-to-frame alignment will be explored; however, this is not core to the philosophy of the voxel grid approach--more pertinent is that the environment is aligned and there is a closed loop described in 3D space.  
While abstracting the feature detection, changes will need to be recorded in the environment over time. This is key to agents' collaboration in dynamic environments. It should be stated that it is not that this is impossible in multiagent SLAM, more that it is seldom considered.
## Extraction methods using organic solvents
### New trends in extraction methods without using organic solvents  
The establishment of new analytical methods which improve quality and sensitivity in the determination of environmental pollutants, in liquid and solid samples, is one of the main lines of research in environmental chemistry. These new methodologies must be able to achieve the low concentrations which have been established by the new environmental directives for priority pollutants. Moreover, they must be fast enough and respectful to the environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Matrix** & **Extraction technique** & **Characteristics** & **Recoveries (\%)** & **LOD (\%-g\({}^{\text{-4}}\))** & **Instrumental Analysis** & **Ref.** \\ \hline Soil & Soxhlet & Acetone, _n_-hexane & - & - & GC-MS (SIM) & [46] \\ Soil & Soxhlet & Methanol & 83–97 & - & LC-UV Vis & [64] \\ Soil & MAE & Methanol & 53–92 & 0.03–0.08 & LC-AV and \\ Soil & MAE & Methanol & & 0.02–0.55 & LC-APCI- \\ Soil & MAE & Acetone, _n_-hexane & 32–78 & 0.010–0.025 & GC-MS & [61] \\  & Ultrasound agitation & & 81–99 & 0.005–0.276 & GC-FID & [65] \\ Soil & MAE-HS-SPME & PA, H\({}_{2}\)O & 86–98 & 0.0001–0.002 & GC-ECD & [71] \\ Soil & MAE-SPE & C\({}_{18}\), acetic anhydride, & 94–97 & 0.01–0.2 & GC-ECD & [72] \\ Sludge, & & triethylamine & & & \\   sediments & MAE & Methanol, acetone & 78–106 & 0.0001–0.0003 & GC-MS/MS & [68] \\ Wood, leather, & & C-18, acetic anhydride, & & & \\ textiles & MAE-SPE & triethylamine & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Methods for the determination of phenolic compounds in solid samples using organic solvents.  
Among these new methods, extraction and preconcentration of organic compounds, such as phenolic compounds, using aqueous solutions of surfactants are a real alternative to conventional methods. The use of surfactants solutions offer several advantages over organic solvent like extractant, such as reduction of solvent amount usage, low cost, easy handling and non-toxic procedures.  
It is well known that surfactants, or surface active agents, have the capability to solubilise different kind of solutes as they are constituted by amphiphilic molecules. Surfactant molecules can associate in aqueous solutions to form molecular aggregates called micelles. The minimum concentration of surfactant concentration required for this phenomenon to occur is called the critical micellar concentration (CMC) [73].  
Among the different types of surfactants, the non-ionic ones have been widely used to extract organic substances from diverse types of samples [74-80] which demonstrates their high potential as extractants. Moreover, they are compatible with aqueous-organic mobile phase in chromatographic analysis, which facilitates these applications.
## 5 Experiment Results
### Experiment Results  
#### 5.3.1 Comparison Experiment  
The experiment results are displayed in Table 1. The results show that the F1 score is significantly higher when the FEG-AE method is used on actual production datasets. The cause is the proposed model's ability to identify error sequences with lower false negatives and higher precision. In other AE-based methods, the lower score is mainly caused by overfitting. The experiment also shows that the proposed method has an overall slightly lower recall rate compared to the other methods. The dynamic threshold method can greatly improve the precision, but in some extreme cases, it will produce an extremely high threshold in a few abnormal areas, on which FEG-AE could produce more FNs.
# Recognition of Stress Activation by Unobtrusive Multi Sensing Setup
## 6 Results
### SFFS Analysis  
Results of the feature selection process are summarized in Table 2 where features are ranked according to the SFFS score and selection frequency is also reported. In particular, the median RR was selected in all runs, and is constantly the most relevant single feature. This suggests that it carries a significant piece of information, irrespective of how the data are split between training and test set. The number of GSR peaks obtains the second score in the process, being selected in almost 99% of the runs.  
After the first two features, we observed a marked drop in the score. Indeed, LF/HF (the feature with the third best score, i.e., \(>\)9200) was selected in 34% of the runs only. Similar results were observed for the RR interquartile range and NN. Finally, all other features have scores that are very close to the maximum (\(>\)10,000) to end up with the last one (the maximum RR) never being selected by the process. Table 3 shows values for the two most relevant features for all subjects of SRAD used in this work. Analogous data are reported for MCA in the Table 4.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Features** & **Score** & **SF (\%)** \\ \hline Median RR & 1000 & 100 \\ Number of GSR peaks & 2146 & 98.7 \\ LF/HF & 9220 & 34.2 \\ RR Interquartile range & 9571 & 30.9 \\ NN & 9842 & 26.2 \\ SDNN & 10,958 & 11.8 \\ LF & 11,501 & 7.0 \\ Minimum RR & 11,700 & 4.0 \\ Maximum GSR peak amplitude & 11,850 & 1.7 \\ HF & 11,974 & 0.5 \\ Median GSR peak amplitude & 11,991 & 0.1 \\ Maximum RR & 12,000 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SFFS score and selection frequency (SF) for each feature.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**Median RR (bpm)**} & \multicolumn{2}{c}{**Number of GSR Peaks**} \\ \hline
**ID** & **Rest** & **Driving** & **Rest** & **Driving** \\ \hline
04 & 13.5 (3) & 24.5 (3) & 8 (6) & 22.5 (5) \\
05 & 15.5 (3) & 23 (4.5) & 0 (0) & 21 (8.5) \\
07 & 14 (2) & 20 (2) & 10.5 (2) & 25 (5) \\
08 & 12 (2) & 20.5 (2)  
In Figure 3 we plot the accuracy of LDA classifier obtained on the SRAD dataset with different numbers of features according to SFFS ranking. A similar plot is also given for MCA as test set. For SRAD the accuracy amounts at about 93% for a single feature rising to about 98% with two features with no further significant changes using the remaining features. For the MCA data we observe a rapid increase to about 90% with two features, fluctuations being present when using more than six features.
###### Abstract  
With the increase in rooftop photovoltaic (PV) systems at the residential level, customers owning such renewable resources can act as a source of generation for other consumers in the same network. Peer-to-peer (P2P) energy trading refers to a local trading platform where the residential customers having excess PV power (prosumers) can interact with their neighbors without PV resources (customers) to improve the social welfare of society. However, the performance of a P2P market depends on the power system network constraints and trading strategy adopted for local energy trading. In this paper, we compare different trading strategies, i.e., the rule-based zero intelligent (ZI) strategy and the preference-based game theory (GT) approaches, for a constrained P2P platform. Quadratic trading loss and impedance-based network utilization fee models are suggested to define the network constraints for the P2P system. Additionally, a reluctance-based prosumer-sensitive model is developed to adjust the trading behavior of the participants under the heavy distribution losses/network fee. The presented results show that the suggested trading strategies enhanced the average welfare of the participants by approximately 17%. On average, the customers saved about $33.77 monthly, whereas the average monthly earnings of the prosumers were around $28.3. The ZI strategy enhanced the average monetary advantages of all the market participants by an average of 7% for a system having small distribution losses and a network fee as compared to the GT approach. Contrarily, for a system having high losses/a utilization fee, the GT approach improved the average welfare of the prosumers by around 75% compared to the ZI strategy. However, both trading strategies yielded competitive results compared to the traditional market under the standard values of network coefficients.  
g ame theory; network constraints; P2P trading; photovoltaic solar energy; prosumer sensitivity; trading strategies +
Footnote †: journal:  
_Applied_  
_sciences_
## 4 Computational Simulations
### Study the Mesh Size  
In order to establish the degree of mesh refinement required to obtain reliable results, several preliminary calculations using a microcapsule with a 2 mm diameter and shell thickness is 0.1 mm as an average size for the four values of the core/shell thickness ratio were studied. Three meshes with different discretization were modelled as shown in Figure 6. The coarse mesh has 5103 elements for the matrix and 116 for the microcapsule with 2 elements through its thickness. The medium mesh has 16,078 elements for the matrix and 357 for the microcapsule with 3 elements through its thickness. The fine mesh has 31,741 elements for the matrix and 476 for the microcapsule with 4 elements through its thickness. Figure 7 shows the damage dissipation energy (fracture energy) for each mesh discretization. The medium mesh and the fine mesh density are very close to each other. Therefore, the medium mesh discretization was employed for simulations and to present the results to compromise between accuracy and computational effort.  
Figure 6: Three meshes with different discretization: (**a**) Coarse mesh (5219 elements); (**b**) Medium mesh (16,435 elements); (**c**) Fine mesh (32,217 elements).  
Figure 7: Damage dissipation energy from different mesh discretization.
# Leveraging User Comments for Recommendation in E-Commerce
## 5 Experimental Results
### Experiment 7--Impact of Parameter Settings  
The energy threshold \(\theta\) determines the degree of reduction on the dimensionality of the vectors produced by PCA. Table 10 compares the performance of four different values of \(\theta\) for the Amazon Digital Music dataset. The columns \"URdim\" and \"IRdim\" indicate the reduced dimensionality of user vectors and item vectors, respectively, after PCA. Note that the original vectors have 100 dimensions. When \(\theta=0.7\), the reduced user vectors have nine dimensions, while the reduced item vectors have 28 dimensions. As expected, a larger \(\theta\) results in a higher dimensionality of the reduced vectors. Note that CFUCC with \(\theta=0.7\) performs the best. However, the performance is pretty stable with values near 0.7.  
Next, we show the impact of different values of \(\rho\) on the recommended results obtained by CFUCC. Note that a different \(\rho\) value can lead to a different number of clusters produced. A higher \(\rho\) will produce more clusters. Table 11 compares the performance of different values of \(\rho\) for the Amazon Digital Music dataset. In the left part of the table, the \(\rho\) for clustering user vectors is kept at 0.6, while the \(\rho\) value varies for clustering item vectors. In the right part of the table, the \(\rho\) for the clustering item vectors is kept at 0.7, while the \(\rho\) value varies for clustering user vectors. As can be seen, the value of \(\rho\) does affect the performance of CFUCC, but not significantly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **URdim** & **IRdim** & **MAE** & **RMSE** \\ \hline \(\theta=0.6\) & 6 & 18 & 0.716 & 1.012 \\ \(\theta=0.7\) & 9 & 28 & **0.709** & **0.996** \\ \(\theta=0.8\) & 18 & 43 & 0.712 & 0.998 \\ \(\theta=0.9\) & 37 & 62 & 0.730 & 1.216 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparisons among different values of \(\theta\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MAE** & **RMSE** & **Time(s)** \\ \hline Method 1 & **0.709** & **0.996** & **51.9** \\ Method 2 & 1.180 & 1.410 & 3248.0 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparisons between using and without using user comments.
## 2 Coal Samples and Experimental Procedure
### Experimental Procedure  
Mine water was collected from underground coal reservoirs, and coal samples were immersed in the mine water in batches. During the immersion experiment, a preliminary study revealed that after approximately 5 days of immersion, the coal samples reached a state of near saturation. To investigate the effects of extended immersion durations, the specified time intervals were 0 days, 30 days, 60 days, and 120 days. Rapid fracture mechanics testing was subsequently conducted on the coal samples after immersion, utilizing an INSTRON testing machine at Sichuan University, as illustrated in Figure 3. The displacement loading mode was employed, with different loading rates set at 0.0002 mm/s, 0.0005 mm/s, 0.002 mm/s, and 0.005 mm/s.  
The three-point bending fracture mechanics experiment for coal is capable of testing the fracture toughness of coal. In accordance with the standards recommended by the International Society for Rock Mechanics (ISRMs) [33], the calculation of fracture toughness is performed using Formulas (1)-(3).  
\[K_{IC}=\gamma^{\prime}\frac{P_{\max}\sqrt{\pi a}}{2 RB} \tag{1}\]  
\[Y^{\prime}=-1.297+9.516(L/2R)-[0.47+16.457(L/2R)]\beta+[1.071+34.401(L/2R)] \beta^{2} \tag{2}\]  
\[\beta=a/R \tag{3}\]  
In the formula, \(K_{IC}\) represents the fracture toughness (MPa\(\cdot\cdot\)mm\({}^{1/2}\)); \(P_{max}\) denotes the peak load (N); \(Y^{\prime}\) is the dimensionless stress intensity factor; \(a\) indicates the length of the  
Figure 2: Schematic diagram and photograph of coal sample types (dashed lines indicate bedding planes).  
manually cut groove (mm); \(B\) refers to the thickness of the SCB sample (mm); \(R\) represents the radius of the SCB specimen (mm); and \(L\) specifies the distance (mm) between the lower two support ends of the specimen during loading.
# Applications of the Open-Source Hardware Arduino Platform in the Mining Industry: A Review
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Purpose**} & \multicolumn{2}{c}{**Field Monitoring Systems**} & \multicolumn{2}{c}{**Wearable Systems**} & \multirow{2}{*}{**Autonomous Mine Systems**} & \multirow{2}{*}{**Sum (Percentage)**} \\ \hline Atmospheric monitoring & 18 (26.9\%) & 10 (14.9\%) & 6 (9.0\%) & 34 (50.7\%) \\ \hline Slope monitoring & 4 (6.0\%) & 0 (0.0\%) & 0 (0.0\%) & 4 (6.0\%) \\ \hline Vibration monitoring & 1 (1.5\%) & 0 (0.0\%) & 0 (0.0\%) & 1 (1.5\%) \\ \hline Borehole monitoring & 1 (1.5\%) & 0 (0.0\%) & 0 (0.0\%) & 1 (1.5\%) \\ \hline Roof monitoring & 1 (1.5\%) & 0 (0.0\%) & 0 (0.0\%) & 1 (1.5\%) \\ \hline Helmet wearing detection & 0 (0.0\%) & 2 (3.0\%) & 0 (0.0\%) & 2 (3.0\%) \\ \hline Collision/event detection & 0 (0.0\%) & 3 (4.5\%) & 0 (0.0\%) & 3 (4.5\%) \\ \hline Heart rate (pulse) monitoring & 0 (0.0\%) & 6 (9.0\%) & 0 (0.0\%) & 6 (9.0\%) \\ \hline Navigation/position monitoring & 0 (0.0\%) & 2 (3.0\%) & 1 (1.5\%) & 3 (4.5\%) \\ \hline Machine/robot control & 0 (0.0\%) & 0 (0.0\%) & 7 (10.4\%) & 7 (10.4\%) \\ \hline UAV-based monitoring & 0 (0.0\%) & 0 (0.0\%) & 3 (4.5\%) & 3 (4.5\%) \\ \hline Efficiency improvement of mineral recovery & 0 (0.0\%) & 0 (0.0\%) & 2 (3.0\%) & 2 (3.0\%) \\ \hline
**Sum** & 25 (37.3\%) & 23 (34.3\%) & 19 (28.4\%) & 67 (100.0\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Reviewed literature classified by application purpose. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Arduino Type**} & \multicolumn{2}{c}{**Field Monitoring Systems**} & \multicolumn{2}{c}{**Wearable Systems**} & \multirow{2}{*}{**Autonomous Mine Systems**} & \multirow{2}{*}{**Sum (Percentage)**} \\ \hline UNO & 14 (28.6\%) & 4 (8.2\%) & 4 (8.2\%) & 22 (44.9\%) \\ \hline Mega & 8 (16.3\%) & 2 (4.1\%) & 5 (10.2\%) & 15 (30.6\%) \\ \hline Nano & 2 (4.1\%) & 5 (10.2\%) & 0 (0.0\%) & 7 (14.3\%) \\ \hline Due & 1 (2.0\%) & 0 (0.0\%) & 0 (0.0\%) & 1 (2.0\%) \\ \hline Leonardo & 0 (0.0\%) & 0 (0.0\%) & 1 (2.0\%) & 1 (2.0\%) \\ \hline Etc. & 1 (2.0\%) & 0 (0.0\%) & 2 (4.1\%) & 3 (6.1\%) \\ \hline
**Sum** & 26 (53.1\%) & 11 (22.4\%) & 12 (24.5\%) & 49 (100.0\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Reviewed literature classified by Arduino type.
## 5 Experiments and Results
### Comparison of Security and Practicality  
In the previous hardware-enhanced architectures, security and practicality are two most important metrics to evaluate the superiority of protection techniques. The extended SPU was implemented without any modification on corresponding compiler and processor, and thus it is easily transplanted to other embedded processors with different ISAs.  
Moreover, the fully hardware-implemented SPU has a high processing performance in monitoring program execution and calculating the LHash digest; therefore, it can keep a low performance overhead to keep up with the CPU's execution pipeline. Therefore, we evaluated the SPU practicality with ISA extension, compiler modification, and performance overhead. The SPU security capability was evaluated based on the protection abilities of attack detection and fault self-repairing. We divided the system security capability into the following three levels.  
* Level-I: Only monitor instruction tampering behaviors by a coarse-grained partition.
* Level-II: Only monitor instruction tampering behaviors by a fine-grained partition.
* Level-III: Both monitor instruction tampering behaviors and instruction fault recovery.  
The different hardware security mechanisms reported in the literature in terms of security capability and comprehensive practicality are compared in Table 5. Our integrated SPU achieves both instruction tampering detection and instruction fault self-repairing at a low performance overhead of (1.92%), which is a lower value compared with the state-of-the-art security techniques while providing a comprehensive protection feature. In addition, its fine-grained BB integrity monitoring is not necessary to extend ISAs and modify compilers; these enable the SPU to be transplanted into other ISA hardware platforms.  
The fine-grained BB integrity checking can provide a sensitive violation detection for instruction tampering behaviors, and it does not incur significant performance overhead or speed degradation in each BB integrity verification. Therefore, we evaluate the security capability of SPU into the level-III, and it has a better practicality.