esa-sceva/satcom-chunk-collection huggingface dataset:
# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
## 3 The Proposed Approach
### Motion Prior from IMU and Wheel Encoder
The relationship between the velocity vector \(v_{\rm k}^{\rm W}\) in the coordinate system \(\{W\}\) and the velocity vector \(\mathbf{v}_{\rm k}^{\rm B}\) in the coordinate system \(\{B\}\) is:  
\[\mathbf{v}_{\rm k}^{\rm W}=\mathbf{R}_{\rm k}^{\rm WB}\,\mathbf{v}_{\rm k}^{\rm B}. \tag{7}\]  
Using \(\mathbf{p}_{\rm k-1}^{\rm WB}\), \(\mathbf{R}_{\rm k}^{\rm WB}\), and \(\mathbf{v}_{\rm k}^{\rm W}\), the translational component \(\mathbf{p}_{\rm k}^{\rm WB}\) can be computed as:  
\[\mathbf{p}_{\rm k}^{\rm WB} =\mathbf{p}_{\rm k-1}^{\rm WB}+\mathbf{v}_{\rm k}^{\rm W}\,\left(t_{\rm k}- t_{\rm k-1}\right) \tag{8}\] \[=\mathbf{p}_{\rm k-1}^{\rm WB}+\mathbf{R}_{\rm k}^{\rm WB}\,\left[\triangle s \quad 0\quad 0\right]^{\rm T}\,.\]  
Since both the IMU and the wheel encoder output data at a frequency of 100 Hz, we use two separate buffers to cache these two kinds of data. These two sources of information are then fused by Equations (6) and (8). This fusion process only involves a few operators, and usually takes less than 1 millisecond. Therefore, the fusion output, which we term as the motion prior data, is also generated at 100 Hz.
## 3 Design of Different OFDM Based Systems  
OFDM systems use three different types of design option (algorithm) for signal transmission (Figure 2). The first algorithm is based on the rate-adaptive (RA) design, which tends to maximise the instantaneous data rate with respect to the BER ad Tx power limitations. When the Tx power is kept at a fixed value in order to ensure equal BER (i.e., equal QoS), the transmitter must adjust the OFDM modulation scheme (m-PSK/m-QAM) according to the conditions in the wireless channel. A lower transmission rate realized through the selection of the lower constellation order (m) of the OFDM modulation will be used in the case of poor channel quality and vice versa. Different real OFDM systems transmit with fixed Tx power, since the concept which exploits adaptive modulation selection is simpler in terms of the practical implementation of circuit design [22]. Implementations of such OFDM systems include WLAN, WiMAX, etc. However, in RA systems, information about the quality of the wireless channel, commonly detected at the receiver end, must be returned through a reverse channel to the transmitter which will adjust the appropriate modulation.  
The second OFDM design algorithm is based on margin-adaptive (MA) system design, which minimises the Tx power based on the BER and data rate limitations (Figure 2). To ensure the same QoS (i.e., the same BER) while keeping the unchanged transmission rate (constellation order m must be kept unchanged), the Tx power should be changed based on the conditions in the wireless channel: a higher Tx power where the quality of the wireless channel is poor, and vice versa [22].  
The third algorithm takes into account the recently adopted power and bit loading design option, which functions as a combined MA and RA approach (Figure 2). This approach tends to minimise the Tx power and maximise the data rate with respect to the instantaneous BER constraints in the wireless channel [22]. The incentive to employ, in conjunction, margin and rate optimisation can be found in the newest wireless systems that operate in diverse conditions and requirements. More precisely, minimisation of Tx power is important when operating in shared interference-prone spectrum environments or near frequency-adjacent users. Additionally, if sufficient OFDM guard bands exist to differentiate the users, maximisation of throughput can be performed for the purpose of better utilisation of the wireless channel.  
Besides OFDM-based transmission where users are only scheduled on the time domain scale, in the OFDM access (OFDMA) systems the users are scheduled on a frequency and time domain scale. Hence, OFDMA systems are based on OFDM and have a broad practical implementation, which is defined in IEEE 802.16 (WiMAX), IEEE 802.11 ax (WLAN), IEEE 802.20 (mobile broadband wireless access), Log Term Evolution/Advanced (LTE/LTE-A) and 5G systems [6; 42; 43]. OFDMA is also a possible access method for the Wireless Regional Area Networks (WRAN) defined in the IEEE 802.22 standard.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 2 Materials and Methods
### The Proposed Segmentation Method
#### 2.2.1 Training Set Cropping Rule  
As the resolution of remote sensing images is too large to be directly input into the neural network for training, the original remote sensing images need to be cut into the required size for training by cropping. The segmentation scheme in this paper crops the training samples into \(256\times 256\) and \(512\times 512\) sizes respectively. In order to verify the robustness of the network structure, this paper uses a controlled ratio of positive and negative samples for cropping.  
Figure 3: The architecture of the Swin Transformer.
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 4 Performance Analysis
### Validation Experiment  
The performance of the \(SFBT\) is given in this section. First, the diagnosis accuracy for faulty cluster-header detection utilizing \(STUKF\) and \(MB\) only is validated through experiments. Then, we compare the \(SFBT\) with the existing DFD [30], Jiang [28], and DSFD [38] in terms of detection accuracy (\(DA\)), false positive rate (\(FPR\)), and false alarm rate (\(FAR\)), respectively.  
The resistor of a meter easily suffers from the jump type fault. Suppose the resistor gets faulty at \(k=1000\). Let \(\theta^{0}=10\)\(\Omega\), \(\sigma_{\theta^{0}}^{2}=1000\), \(N_{1}=20\), \(\beta_{\theta}=4000\), \(\epsilon=0.1\), and \(T_{2}=50\).  
Figures 1 and 2 show that the fault is detected at \(k=1030\) and it is isolated at \(k=1250\) with the fault amplitude equal to \(4.8\)\(k\Omega\). That implies the detection accuracy is about \(1-\frac{4.8}{4000}=99.88\%\).  
The capacitor is subject to the attenuation type fault, which is a case of the incipient fault. Now, we show the proposed strategy can detect the attenuation type fault. Let \(\theta^{0}=1000\)\(pF\), \(\sigma_{\theta^{0}}^{2}=0.1\), \(N_{1}=40\), \(\beta_{\theta}=500,000\). Suppose the degradation of the capacitor begins with \(\theta(k+1)=\frac{\theta(k)-1}{2}\) at \(k=600\).  
Figures 3 and 4 show that, the attenuation type fault has been detected at about \(step=1110\) with the magnitude about \(235\)\(pF\). That implies the detection accuracy is \(1-\frac{235}{1000}=97.65\%\).  
Figure 1: Real value vs. estimated value of Strong Tracking Unscented Kalman Filter (\(STUKF\)) for jump type fault.  
Figure 2: \(STUKF\) and Modified Bayes’ classification algorithm (\(MB\))-based detection for jump type fault.  
In Figure 5, it is clear that the detection accuracy of each strategy drops with the increase of the fault probability. However, the \(SFBT\) achieves higher detection accuracy than the others.  
Although the false alarm rate and the false positive rate increase with the fault probability (see Figures 6 and 7), the \(SFBT\) still outperforms baseline approaches.  
Figure 4: \(STUKF\) and \(MB\)-based detection for attenuation type fault.  
Figure 5: Detection accuracy comparison.  
Figure 3: Real value vs. estimated value of \(STUKF\) for attenuation type fault.  
Figure 6: False alarm rate comparison.  
To sum up, the \(SFBT\) performs better than DFD [41], Jiang [29], and DSFD [30] in terms of detection accuracy, false positive rate, and false alarm rate, respectively. The reason for that is the \(SFBT\) employs fault-free cluster-heads, which are located by the strong tracking UKF and the modified Bayes' classification algorithm, to detect the fault states of cluster-members through the modified three sigma test.
## 6 Discussion  
### Overview  
This study's regional focus on Turkiye, segmented into seven distinct areas, provided a unique opportunity to explore GPP trends in varied ecological and climatic contexts. The use of six different GPP datasets--that could not be validated due to the limited presence of eddy covariance systems--aligns with the existing literature, showing an increasing trend in GPP across many global regions [2; 3]. Our methodology, which incorporated multiple GPP estimates and trend analysis methods, lends confidence to the overall finding that GPP increased at the study locations but also leads to important nuances when interpreting results; we will describe these nuances in more detail below.  
The datasets utilized, derived from various remote sensing methodologies, underscore the potential and challenges of using satellite data for GPP estimation. The inclusion of different calculation methods, namely Light Use Efficiency models (e.g., MODIS, MuSyQ, TL-LUE), machine learning (FLUXCOM), GPP proxies (GOSIF), and biophysical models  
Figure 9: Trends in GPP extracted from the last mode of an Empirical Mode Decomposition (EMD) on GPP estimates from different remote sensing data products from different regions of Turkiye, (**a**) Antalya, (**b**) Erzincan, (**c**) Izmir, (**d**) Kirklareli, (**e**) Konya, (**f**) Samsun and (**g**) Sanliurfa.  
(PMLV2) reinforces our results; for example, there are numerous outliers in the MODIS, GOSIF, and TL-LUE GPP datasets in the Konya region, whereas in the Sanliurfa region, similar findings are observed in the MODIS, PMLV2, GOSIF, and TL-LUE GPP datasets. Moreover, ITA analyses have demonstrated that there is a pronounced increasing trend in TL-LUE across all regions at high GPP values, meaning that large carbon uptake events are becoming larger. Significant increases at high values of the PMLV2 dataset were observed in the regions of Antalya, Erzincan, Izmir, and Konya. At the same time, the TL-LUE data have exhibited a declining trend in recent years across all areas, GPP values from the GOSIF dataset have shown a slight decreasing trend in recent years across all regions, except for Erzincan, and PMLV2 data have maintained a continuous increasing trend, which demonstrates the importance of using multiple datasets for a conservative interpretation of GPP trends.  
Although most trend analysis methods agree on the presence of an increasing trend across most regions, they diverge in certain details. The MMK method follows a classical ranking-based approach, allowing for the interpretation of the presence of a trend based on a certain Z value, but lacks the graphical extrapolation capabilities of ITA and EMD. ITA provides detailed information about the trend status at low, medium, and high values, whereas EMD is more successful in explaining how the trend changes over time. The multi-method analysis ensures that the results are less likely to be an artifact of the particular method used [98].
# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 3 Proposed Strategy
### Timeslots as Time Base
Thus, after the UAV collects the sensory data from SN2 and shuts it down, the UAV flies to SN3, which is distant from its current position. Consequently, it needs more than two TSs to displace to its new target, so SN3 must be configured to be active only on TS6. This procedure is repeated between SN3 and SN4, and the UAV finds SN4 transmitting on TS8. Changes in the topology and SNs redistribution often occur. The impact caused by SN addition, removal, or moving directly affects (3), as they change the total UAV flight distance and its flight time. The insertion of SN in new points will often be necessary, due to the decision from the agronomists responsible to detect blind spots in the analysis of the crop, or even due to issues of territory expansion. Furthermore, the removal of sensors for maintenance purposes or even the exchange of sensors may also be considered. Therefore, for such situations, we treat it as a new scenario. Figures 7 and 8 show a simple scenario created by the UAV Simulator with 10 SNs, in an area of 500 \(\times\) 500 m, and with SNs communication range set to 50 m. In Figure 7, we have the total flight path  
Figure 5: Shared Timeslots scenario. Figure 6: SN activation timeslot scheduled according to the UAV flight. representation taken by the UAV during the SNs reconfiguration phase. As shown in Figure 8, after all SNs were reconfigured, each SN active time is scheduled according to the UAV flight path.
## 3 Results and Discussion
### Catalytic Conversion of Methanol  
The results of methanol conversion over the tested clays and selectivity to products are gathered in Figure 13. Ethylene, propylene, dimethyl ether (DME), formaldehyde and CO\({}_{2}\) are the reaction products detected. Eventually, C1 and C2 clays were found to be the more promising clays for production of alkenes. However, the maximum selectivities to ethylene observed over C1 and C2 clays were 79.2% and 68.9% respectively, with conversion of 6.6% and 2.1%. It is well known that selectivity of clay to alkene formation is related to the number of strong acidic centers [65]. In this sense, the higher selectivity of C1 and C2 clays for production of ethylene can be attributed to their acidic nature (see Figure 7a). Additionally, the narrow pore dimensions (frequent hydraulic radius at ca. 2.0 nm) of these clays may impose a limitation to the chain growth of the produced ethylene, where propylene is the largest hydrocarbon detected but with a low selectivity percentage [66]. According to the above reason, it is acceptable that basic clays (viz., C4 and C5) exhibited low methanol conversion.  
On the other hand, activation of Al-Hasa clays with HCl acid enhanced their conversion of methanol and selectivity to ethylene and reduced the CO\({}_{2}\) production (Figure 14 and Figure S8), especially for C2 clay (ethylene selectivity was 78.9% with conversion of 39.1%). The maximized methanol conversion and ethylene selectivity of C2 clay can be referred to as the existence of high amounts of montmorillonite in this clay, where [PERSON] and [PERSON] found that treatment of montmorillonite with hydrochloric acid enhanced the conversion of methanol to olefin-rich hydrocarbons [67].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Kinetic Model** & **Constants** & **C1** & **C2** & **C3** & **C4** & **C5** \\ \hline Pseudo-first order & \(\mathrm{q_{e}}\) (mg/g) & 53.36 & 21.62 & 35.50 & 26.27 & 3.56 \\  & \(\mathrm{k_{1}}\) (min\({}^{-1}\)) & 0.0124 & 0.0094 & 0.0061 & 0.0063 & 0.0094 \\  & \(\mathrm{r^{2}}\) & 0.789 & 0.901 & 0.979 & 0.976 & 0.348 \\ Pseudo-second order & \(\mathrm{q_{e}}\) (mg/g) & 53.76 & 40.32 & 50.0 & 36.90 & 32.47 \\  & \(\mathrm{k_{2}}\) (g/mg min) & 0.00034 & 0.00094 & 0.00027 & 0.00039 & 0.0028 \\  & \(\mathrm{r^{2}}\) & 0.993 & 0.999 & 0.990 & 0.990 & 0.996 \\ Intraparticle & \(\mathrm{k_{i}}\) (mg/g min\({}^{0.5}\)) & 1.68 & 1.02 & 1.57 & 1.16 & 0.57 \\ diffusion & \(\mathrm{C(mg/g)}\) & 13.15 & 17.58 & 9.67 & 7.59 & 20.98 \\  & \(\mathrm{r^{2}}\) & 0.881 & 0.768 & 0.941 & 0.933 & 0.316 \\ \hline \hline \end{tabular}
\end{table}
Table 5: List of estimated parameters from [PERSON] pseudo-first-order, pseudo-second-order and intraparticle diffusion kinetic models; r\({}^{2}\) is the regression coefficient.  
Figure 12: Statistical fitting of data from adsorption of lead onto different clays using different kinetic models: (**a**) pseudo first order, (**b**) pseudo second order, (**c**) intraparticle diffusion.
# A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement
## 3 Range Extension of the Indoor Standard Device for Large-Scale Length Measurement  
In our research, corner reflectors were employed to extend the measuring range of the indoor standard device for large-scale length measurement. A comparative analysis of the data obtained from the laser interferometer upon the range extension demonstrated the significance of extending the range of an indoor standard device for large-scale length measurement, whose primary reference instrument is represented by the laser interferometer due to its exceptional linear measurement accuracy. Beyond a major lab-based standard device for length measurement, the laser interferometer is typically used for the field test of such high-accuracy surveying systems as machine tools. The Renishaw XL-80 laser interferometer is capable of measuring linear distances of up to 80 m, and by leveraging its strengths, we managed to double the measuring distance within a range of 52 m by equipping it with special corner reflectors.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 2 Overview of Smart Grid
### Smart Grid Infrastructure  
Both international and national organizations have developed roadmaps, defined standards and definitions on what makes a power grid a Smart Grid [9,11,12,13,14,15,16]. There is no single definition of what a Smart Grid is, however common in the definitions is the emphasis on communication for measurements, monitoring, management, and control. Communication plays an essential role in providing reliable, efficient and secure  
Figure 1: The structure of the paper.  
power generation, transmission, and distribution. The communication systems provide information exchange between the distributed sensing equipment, monitoring systems, and data management systems. These solutions require fast communications as the generation, delivery, and consumption all happen at the same time. With the introduction of distributed energy resources and energy storage systems, the importance of fast and reliable communication increases. The expectations from end-users also change, with real-time information on electricity prices, customers feeding the grid with electricity, and electric vehicles acting as batteries in the grid. A key goal for SGs are reduced cost and environmental impact, and maximizing reliability, resilience, and stability [9]. The smart meter is a key component of the SG infrastructure, and part of the Advanced Metering Infrastructure (AMI). AMI is responsible for enabling a reliable and secure high speed two way communication between smart meters at the end-user, and data control centers at the utilities companies for monitoring and control [11; 17; 18]. The full benefit of the SG infrastructure is achieved when smart meters, sensors, and measuring devices located throughout the power grid communicate in order to ensure stability, detect, predict, and prevent faults, forecast load changes and facilitate demand response [19]. Table 1 shows the main differences between a traditional grid and a SG.
## 4 Results
### Real-Time Testing of IDS on Smart Thermostat
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ML Model** & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**FNR**} & \multirow{2}{*}{**FPR**} & \multirow{2}{*}{**Computation Time**} & \multirow{2}{*}{**Program**} \\ \multicolumn{1}{c}{**(No. of Estimators)**} & & & & & & & \(\mu\)s (Average) & \multicolumn{1}{c}{**Storage**} \\ \hline
**XGBoost (Estimator = 50, Depth = 8)** & **97.59\%** & **0.97** & **0.97** & **0.97** & **2.76\%** & **2.24\%** & **1502.3** & **77.0\%** \\ \hline XGBoost (Estimator = 100, Depth = 8) & 97.66\% & 0.97 & 0.97 & 0.97 & 2.76\% & 2.12\% & 3515.9 & 84.01\% \\ \hline XGBoost (Estimator = 150, Depth = 8) & 97.50\% & 0.97 & 0.97 & 0.97 & 3.26\% & 2.12\% & \
\begin{tabular}{c} Memory Out \\ of Bound \\ \end{tabular} & – \\ \hline XGBoost (Estimator = 200, Depth = 8) & 97.58\% & 0.97 & 0.97 & 0.97 & 3.01\% & 2.12\% &
\begin{tabular}{c} Memory Out \\ of Bound \\ \end{tabular} & – \\ \hline RF (Estimator = 50, Depth = 8) & 95.17\% & 0.93 & 0.96 & 0.94 & 1.19\% & 2.48\% & 439 & 69.53\% \\ \hline RF (Estimator = 100, Depth = 8) & 95.17\% & 0.93 & 0.96 & 0.94 & 1.00\% & 6.61\% & 1357.6 & 73.46\% \\ \hline RF (Estimator = 150, Depth = 8) & 95.25\% & 0.93 & 0.96 & 0.95 & 1.00\% & 6.50\% & 2331.4 & 77.29\% \\ \hline RF (Estimator = 200, Depth = 8) & 95.33\% & 0.93 & 0.96 & 0.95 & 1.00\% & 6.38\% & 3314.4 & 80.96\% \\ \hline DT (Depth = 5) & 90.59\% & 0.88 & 0.90 & 0.89 & 8.54\% & 9.81\% & 3.7 & 66.54\% \\ \hline DT (Depth = 10) & 94.61\% & 0.93 & 0.94 & 0.94 & 4.77\% & 5.67\% & 6.2 & 66.61\% \\ \hline DT (Depth = 15) & 96.54\% & 0.96 & 0.96 & 0.96 & 5.77\% & 2.36\% & 7.3 & 66.71\% \\ \hline DT (Depth = 20) & 96.38\% & 0.96 & 0.95 & 0.96 & 6.03\% & 2.48\% & 8.0 & 66.78\% \\ \hline ANN (Layer 1 =32, Layer 2 = 16) & 92.76\% & 0.85 & 0.93 & 0.89 & 6.28\% & 7.68\% & 117.5 & 78.02\% \\ \hline ANN (Layer 1 = 64, Layer 2 = 32) & 91.23\% & 0.80 & 0.97 & 0.87 & 3.01\% & 11.46\% & 286.6 & 76.11\% \\ \hline ANN (Layer 1 = 64, Layer 2 = 32, Layer 3 = 16) & 92.92\% & 0.84 & 0.95 & 0.89 & 4.77\% & 8.15\% & 313.9 & 75.92\% \\ \hline ANN (Layer 1 = 96, Layer 2 = 64, Layer 3 = 32) & 93.41\% & 0.85 & 0.96 & 0.90 & 3.26\% & 8.15\% & 2498.1 & 75.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: IDS implementation on smart thermostat using IDSH dataset.
# Risk Assessment of Compound Dynamic Disaster Based on AHP-EWM
## 3 Establishment of Evaluation Model
### Determination of Weight
#### 3.1.1 Determination of Subjective Weight  
The AHP, which was founded by [PERSON], is a method of combining qualitative analysis and quantitative analysis to analyze decision objects under the influence of multiple factors. The AHP is used to determine the main control factors of compound dynamic disaster and divide the hierarchical structure [48; 49], and then the judgment matrix is constructed based on the subjective weighting of experts on the analysis of mine geology and mining data, and the statistical analysis of on-site dynamic events. The specific process is shown in Figure 1. The AHP has been successfully applied to maintain policy selection [50; 51; 52; 53]. The analytic hierarchy process is also used for risk assessment.  
The \"satyy1-9 scale\" is used to express the importance of factors at all levels. According to the relative importance, the judgment matrix of mutual comparison can be obtained when \(C=(C_{ij})_{n\times n}\) where \(C_{ij}\) is the scale of the \(i\)th index and the \(j\)th index, \(i=1\), \(2\), \(3\),..., \(n\), \(j=1\), \(2\), \(3\),..., \(n\). Then, normalize each column element in the judgment matrix \(C\), see formula (1) for details. After normalization, the judgment matrix is added by rows, and the eigenvector can be obtained by summing and multiplying each row of matrix by \(1\)/n. Furthermore, the eigenvector \(W_{1}=(w_{1},w_{2},\ldots,w_{n})\) is finally obtained using Formulas (2) and (3), The maximum characteristic root \(\lambda_{max}\) of matrix \(C\) is obtained with the calculation using Formula (4).  
\[O_{i}=\frac{c_{ij}}{\sum_{i=1}^{n}o_{ij}} \tag{1}\]  
\[w_{0i}=\sum_{j=1}^{n}c_{ij} \tag{2}\]  
\[w_{i}=\frac{w_{0i}}{n} \tag{3}\]  
\[\lambda_{max}=\sum_{i=1}^{n}\frac{(Bw_{i})}{nw_{i}} \tag{4}\]  
In order to ensure the rationality of the weight distribution obtained by using the AHP method, avoid the situations that do not conform to the actual requirements and are self-contradictory. The importance of each element is checked for coordination. Therefore, the consistency index of the judgment matrix is calculated with Formulas (5) and (6). If the value of the result is larger, the consistency is worse.  
\[CR=\frac{CI}{RI} \tag{5}\]  
\[CI=\frac{\lambda_{max}}{n-1} \tag{6}\]  
Figure 1: Flow chart of AHP.  
In the formula, \(CI\) represents the consistency inspection index; \(n\) represents the matrix order established; \(CR\) stands for consistency ratio; \(RI\) stands for mean randomness index. The specific criterion is that when \(CR\) < 0.1, the consistency test is passed, and the subjective weight set \(Z\) = {\(z_{1}\), \(z_{2}\),..., \(z_{m}\)} is obtained. If the consistency conditions are not met and the representative matrix is wrong, the judgment matrix shall be modified until the consistency test is met. The \(RI\) data is closely related to the order of the matrix established, and the specific values of the average random consistency index are shown in Table 1.