esa-sceva/satcom-chunk-collection huggingface dataset:
# Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification
## 1 Introduction
Though NNE exceeds COTE [11] in accuracy under the contribution of huge amount basic classifiers, it does not maximally play the role of ensemble. In addition, approaches like XG-SF and EnRS are imperfect in another aspect that they did not choose the appropriate data perturbation method for time series. Methods such as Bagging will further lose the training instances and Boosting will be complex in fusion phase for multi-class problems, especially facing the datasets with dozens of categories. Based on the above analysis, we propose to employ the simple-structure but excellent FCN and the classical data perturbation manner Random Subspace Method (RSM) [18] to build a deep learning ensemble. RSM, which samples in attributes subspaces, is appropriate for time series with high dimensionality except for a few instances. A classic example of RSM is the Machine Learning algorithm called Random Forest [18]. Other applications such as creating different subsets of the same features [19] or dataset [20] for sentiment classification and image classification support our task as well. However, differently than those discrete attributes or sets, the value-continuous and order-dependent time series cannot be randomly selected. Fortunately, the feature-based TSC solution Shapelet offers an inspiration. Shapelet views a high-dimensional time series as a combination of multiple shape primitives [21] and it learns several most discriminative primitives rather than whole series. Many Shapelet-based studies are proposed and achieve successes in TSC field, such as Shapelet Transformation [22], Logical Shapelet [23], as well as the COTE, XG-SF and EnRS, as mentioned before. Thus, it makes sense to only focus on the discriminative local information of time series. Despite its advantages, the brute force approach of Shapelet has a huge time complexity of O(\(k^{2}m^{4}\)) [21], so we simulated but discarded the Shapelet method. Thus, in our method, the equally-divided time series intervals are regard as primitives. Therefore, in this paper, a lightweight RSE-FCN (Random Subspace Ensembles of FCN) is designed to tackle the challenging TSC problem, where the equally-divided time series intervals are regarded as candidate subsequences, and Top-K ones with significantly discriminative feature are screened out by evaluation model, then with Random Subspace Method deploying on the Top-K and the superior FCN serving as individual classifier, the neural network ensembles RSE-FCN can be constructed. Through processes of screening and random selection, dimension of input can be reduced without losing discriminative features, so that FCN focusing on the key task-related information will be well trained. The basic classifier FCN has superior performance and the ensemble built by RSM will make a further breakthrough. Thus, promising results can be expected. The rest of this paper is organized as follows. Section 2 describes the details of proposed RSE-FCN. Then, experiment results are presented and analyzed in Section 3. Finally, conclusion of this paper is drawn.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Temperature-Based Routing
#### 3.2.5 Comparison and Analysis  
The above routing protocols are focused on solving the node temperature. A simple comparison of the above protocols is made. The comparison of the objectives, characteristics, complexity, delay, and energy efficiency of the protocols is shown in Table 3.  
ER-ATTEMPT [22] and TTRP [23] protocols are built into the static WBAN environment, while MTR [24] considers a dynamic WBAN environment. MTR protocol is more practical to divide network  
Figure 8: TTRP protocol evaluation trustworthiness diagram of relay node R\({}_{\text{j}}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Goal** & **Characteristic** & **Complexity** & **Delay** & **Energy Efficiency** \\ \hline \multirow{3}{*}{TARA (2005)} & \multirow{3}{*}{temperature} & withdraw strategy to avoid high temperature nodes & \multirow{3}{*}{low} & \multirow{3}{*}{high} & \multirow{3}{*}{low} \\  & & & & & \\ \cline{1-1} \cline{5-5}  & & Considering & & & \\ \cline{1-1} \cline{5-5}  & & temperature and hops & & & \\ \hline \multirow{3}{*}{TTRP (2017)} & \multirow{3}{*}{temperature} & Considering & & & \\ \cline{1-1} \cline{5-5}  & & temperature and trust & & & \\ \hline \multirow{3}{*}{MTR (2017)} & \multirow{3}{*}{temperature} & Considering & & & \\ \cline{1-1} \cline{5-5}  & & temperature and trust & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of temperature-based routing protocols.  
nodes into static and dynamic nodes, and use the mobility of dynamic nodes to complete data forwarding, which is innovative and is worth learning.  
When choosing the next hop, ER-ATTEMPT and TTRP have similarities, and both construct cost functions, but TTRP's cost functions are more flexible than using weighting factor to weigh two parameters that can achieve different performance, its method is worth learning.
## 3 Method
### Normalizations  
Using the data acquired from the 3-axial accelerometer, the parameterized data are calculated, and normalization process such as the Min-Max and Z-score normalizations [16]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**Combination**} \\ \hline \multirow{4}{*}{Double parameters} & \(P_{\theta S}\) & \(P_{\theta}+P_{S}\) \\  & \(P_{\theta D}\) & \(P_{\theta}+P_{D}\) \\  & \(P_{\theta G}\) & \(P_{\theta}+P_{G}\) \\  & \(P_{\theta GD}\) & \(P_{\theta}+P_{GD}\) \\ \hline Multiple parameters & \(P_{ALL}\) & \(P_{\theta}+P_{S}+P_{D}+P_{G}+P_{GD}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Combination of single parameters for double and multiple paramters.  
are performed to minimize the parameterized data. In the min-max normalization, the range of the parameterized data is rescaled as the range in [0, 1], which is given by  
\[X_{Min-Max}=\frac{x-x_{min}}{x_{max}-x_{min}}, \tag{7}\]  
where \(x\) is the original parameterized data, \(X_{Min-Max}\) is the Min-Max normalized data, and \(x_{min}\) and \(x_{max}\) are the smallest and largest numbers in each data set, respectively. The \(Z\)-score normalization makes the parameterized data have zero-mean and unit-variance, which is given by  
\[X_{Z-score}=\frac{x-x_{mean}}{x_{std}}, \tag{8}\]  
where \(X_{Z-score}\) is the \(Z\)_-score_ normalized data, and \(x_{mean}\) and \(x_{std}\) are the mean and standard deviation in each data set, respectively. Each normalization is processed before training on the LSTM.
# A Comparative Study of the Pullout Strength of Geostraps and Geogrids in Reinforced Soil
## 3 Results
### Pullout Capacity: Geostrap vs. Geogrid  
The pullout capacity obtained for the M65-grade geostrap was compared with that of a biaxial geogrid reinforcing the same type of subgrade soil (SP) by Ochiai for the same surcharge value of 17 kN m\({}^{-2}\). The comparative plot of the pullout strengths of the geostrap and geogrid is shown in Figure 6.  
Figure 4: Stress contours in the subgrade due to pullout of the geostraps of (**a**) M25 and (**b**) M65 grades (top half removed and rotated for visualisation).  
Figure 5: The plot of pullout force per unit length vs. displacement for different grades of geostraps.  
The geostrap shows high initial pullout resistance (up to 20% more) compared to the geogrid. Before any displacement, the geostrap withstands 18 kNm\({}^{-1}\) of pullout force per unit length compared to 15 kNm\({}^{-1}\) for the geogrid. However, the maximum pullout resistance of the geogrid (53 kNm\({}^{-1}\)) is more than that of the geostrap (49 kNm\({}^{-1}\)). The frictional and interlocking effect of the geostrap dominates at the beginning due to the high contact area. As soon as the displacement started, the effective contact area reduced simultaneously. This constantly reduced the reinforcement effect of the geostrap. Furthermore, this led to the interlocking effect of the geogrid dominating the limited frictional resistance of the geostrap. Therefore, it can be deduced from the findings that geostraps may prove highly effective as reinforcements for low-volume roads and backfill materials. However, geogrids may provide more promising results for poorly graded sand in high-volume roads.
# A Video-Based Fire Detection Using Deep Learning Models
###### Abstract  
Fire is an abnormal event which can cause significant damage to lives and property. In this paper, we propose a deep learning-based fire detection method using a video sequence, which imitates the human fire detection process. The proposed method uses Faster Region-based Convolutional Neural Network (R-CNN) to detect the suspected regions of fire (SRoFs) and of non-fire based on their spatial features. Then, the summarized features within the bounding boxes in successive frames are accumulated by Long Short-Term Memory (LSTM) to classify whether there is a fire or not in a short-term period. The decisions for successive short-term periods are then combined in the majority voting for the final decision in a long-term period. In addition, the areas of both flame and smoke are calculated and their temporal changes are reported to interpret the dynamic fire behavior with the final fire decision. Experiments show that the proposed long-term video-based method can successfully improve the fire detection accuracy compared with the still image-based or short-term video-based method by reducing both the false detections and the misdetections.  
deep learning; fire detection; Faster R-CNN; spatiotemporal feature; LSTM; majority voting; dynamic fire behavior Article
## 3 Results and Discussions  
### Accuracy Comparison among CCSM, HNM and HAMM  
To evaluate the accuracy of the proposed HAM derivation approach, additional laboratory tests are performed on PV-A based on the testbed introduced in Section 2.3, and then CCSM, HNM and HAMM are developed for PV-A operating under a combination of different powers and different supply voltage distortions. Specifically, the typical operating power range of a grid-tied PV system, 10%-100% \(P_{rated}\), is partitioned into 9 power ranges with a fixed power band equaling 10% \(P_{rated}\). Within each power range, 100 tests made up of randomly selected operating powers and supply voltage distortions are applied to PV-A via the testbed, and the recorded voltage and current waveforms are applied for developing HAMM for PV-A. The current spectrum of PV-A operating at \(P_{rated}\) with sinusoidal supply voltage is used as the CCSM for PV-A. To develop HNM for PV-A, the current spectrum for CCSM is used as the harmonic current source of HNM, while the parallel-connected Norton impedance of HNM is determined from the harmonic sensitivity tests in Section 2.3. Unlike HNM and HAMM, CCSM does not consider the power dependency of PV-A's harmonic emission characteristics. The total harmonic current (THC) errors between all the measurements and simulation results are computed using (13) and (14), with the results represented as box plots in Figures 14-16.  
\[THC=\sqrt{\sum_{h=3}^{19}I_{h}^{2}}\;(h=3,\;5,\;\ldots\;,19) \tag{13}\]  
\[THC_{err}=\frac{THC_{sim}-THC_{mea}}{THC_{mea}}\times 100\% \tag{14}\]  
Figure 13: An illustration of HAM self-tuning process.  
It turns out that for CCSM, its THC error gradually increases with the decreasing operating powers of PV-A. As CCSM fully ignores the voltage dependency and power dependency of the harmonic emission characteristics, the application of CCSM can result in a decrease of the power of the harmonic emission.  
Figure 16: The boxplot of THC error by applying HAMM.  
Figure 14: The boxplot of THC error by applying CCSM.  
Figure 15: The boxplot of THC error by applying HNM.  
in unrealistic simulation results. Compared with CCSM, HNM has improved accuracy, as its THC errors generally kept within \(-10\%\) and \(-14\%\) for the whole power range. HAMM achieves the best accuracy among the three harmonic modelling approaches, with the THC errors mainly fluctuating between \(-2\%\) and \(2\%\) for the whole power range.
## 4 Results and Discussion
### Data Communication Reliability along the MDPE Pipe
Even with acoustic data communication at 5 m, these results improve on existing radio-based techniques for reliable wireless underground communication in real-time buried water pipe monitoring (such as in [16; 33]), which are limited to less than 3 m. For example, with conservative sensor deployment at 5 m intervals for an acoustic-based communication system (as opposed to a maximum of 3 m for the radio-based alternative), potential deployment costs for distributed pipeline monitoring can still be reduced by up to 40% (assuming linear sensor deployment and the same cost of the individual wireless sensor nodes) compared to a traditional radio-based alternative. The findings therefore clearly demonstrate the potential of using acoustic data communication along buried water pipes for achieving significantly greater distances than wireless underground communication using radio waves. This can be used for the design of real-time buried water pipe monitoring systems.
## 3 Results
### Verification of PS-InSAR Data Reliability  
The most effective method for verifying the reliability of the final ground deformation data derived from the PS-InSAR technique is to directly compare the results with data from track-mounted monitoring instruments. However, due to difficulties in obtaining such instrumentation data, a direct comparison of settlement measurements could not be performed. Instead, a dedicated steel structure, referred to as a research corner reflector, was installed near the railway track to serve as a reference point for reliability verification.  
In this study, the installed corner reflector was used to derive settlement values through the PS-InSAR analysis. To validate these results, leveling measurements were conducted at the same location, and the two datasets were compared to assess the accuracy of the PS-InSAR-derived deformation values. The reliability confirmed through this comparison  
Figure 7: Comprehensive ground deformation map for the Honam High-Speed Railway (August 2016-September 2018). The white box indicates the Honam High-Speed Railway corridor.  
was then applied as a basis to ensure the overall credibility of the entire PS-InSAR ground deformation dataset.  
The corner reflector was installed at an angle of 100.37\({}^{\circ}\), carefully considering the antenna aperture direction and satellite flight path of the TerraSAR-X and TanDEM-X satellites. The installation site was selected to avoid any obstacles that could interfere with signal transmission.  
The research corner reflector was constructed using a high-reflectivity steel structure to secure a stable point with strong radar backscatter and high coherence. As a result, the corner reflector exhibited a coherence value of 0.91, which is considered very high, exceeding the typical threshold of 0.9 in the PS-InSAR analysis.  
To ensure the reliability of the PS-InSAR data, first-order leveling surveys (with an accuracy of 2.5\(\mathrm{\SIUnitSymbolMicro L}\) mm, where L represents the one-way distance between points) were conducted on the research corner reflector at approximately two-month intervals, taking into account the SAR image acquisition dates. The results of these leveling surveys are presented in Figure 8.
# An Investigation into Healthcare-Data Patterns
## 1 Introduction  
Hospital infrastructures are classified as mission-critical infrastructures [1]. These information infrastructures have become increasingly dependent on information and communication technologies (ICT) to facilitate communication and automate services [2]. Therefore hospitals are predominantly information technology (IT) infrastructures, with an increasing reliance on electronic patient record (EPR) data [3]. Damage to network communications and losing patient data would have a detrimental impact on the healthcare services they provide. However, as with any other network, they are now more vulnerable to cyber-attackers.
## 2 Theoretical Background and Problem Statement
### Input Saturation Nonlinearity  
In applications that require high precision and rapid responses, managing input saturation is a prevalent challenge. The primary role of input saturation nonlinearity in such systems is to regulate the extremities of control inputs, thereby ensuring both system stability and optimal control performance.  
Figure 1: A sketch of a n-degree-of-freedom (n-DOF) robotic arm.  
The saturation nonlinearity can be described as follows [35]:  
\[\tau=D(u)+\Delta\tau \tag{3}\]  
where the term \(\tau\) denotes the approximation error, and the function \(D(u)\) is given \([D_{1}(u_{1}),D_{2}(u_{2}),\ldots,D_{n}(u_{n})]^{T}\).  
\[D_{i}(u_{i})=\frac{2\overline{u}_{i}}{\pi}\text{arctan}(\frac{\pi u_{i}}{2 \overline{u}_{i}}) \tag{4}\]  
where  
\[\begin{cases}\overline{u}_{i}=u_{i\ max}.&u_{i}\geq 0\\ \overline{u}_{i}=u_{i\ min}.&u_{i}\leq 0\end{cases} \tag{5}\]  
By applying the mean value theorem, Equation (4) is formulated as follows:  
\[D_{i}(u_{i})=\frac{\partial D_{i}(\varrho_{i}u_{i})}{\partial u_{i}}u_{i}+D_{i }(0)=\frac{u_{i}}{1+\left(\frac{\pi\varrho_{i}u_{i}}{2\overline{u}_{i}}\right) ^{2}}=u_{i}-\frac{\left(\frac{\pi\varrho_{i}u_{i}}{2\overline{u}_{i}}\right)^ {2}}{1+\left(\frac{\pi\varrho_{i}u_{i}}{2\overline{u}_{i}}\right)^{2}}u_{i} \tag{6}\]  
We initialize \(D_{i}(0)=0\) and assume that \(0<\varrho_{i}<1\) for \(i=1,\ldots,n\). The matrix \(\zeta\) is defined as a diagonal matrix, with diagonal elements given by \(\left(\frac{\pi\varrho_{i}u_{i}}{2\overline{u}_{i}}\right)^{2}/(1+\left(\frac {\pi\varrho_{i}u_{i}}{2\overline{u}_{i}}\right)^{2})\). It is determined that \(\left(\frac{\pi\varrho_{i}u_{i}}{2\overline{u}_{i}}\right)^{2}/(1+\left(\frac{ \pi\varrho_{i}u_{i}}{2\overline{u}_{i}}\right)^{2})\) is less than \(\left(\frac{\pi}{2}\right)^{2}/(1+\left(\frac{\pi}{2}\right)^{2})\) and thus less than \(0.712\). Consequently, it follows that the maximum eigenvalue of the matrix \(\zeta\) does not exceed \(0.712\). Finally, the function \(D(u)\) is reformulated as follows:  
\[D(u)=u-\zeta u \tag{7}\]
## 3 Results and Discussion
### Combustion Analysis
The thermal efficiency, \(\eta_{\text{therm}}\), being a function of, among other parameters, the exhaust temperature shows a trend in which the blends have an advantage over diesel [15]. In particular, at 2 bar of BMEP, the \(\eta_{\text{therm}}\) increase of about 0.6% and 2.1% for DLPG20 and DLPG35, respectively. The increase, as explained before, relates to the blends cooling effect on the in-cylinder charge temperature before the combustion, as illustrated in Figure 7, making the combustion more premixed [16]. The global efficiency (\(\eta_{\text{fuel}}\)) shows approximately the same trend as \(\eta_{\text{therm}}\). The relationships for the calculation of efficiencies are reported in Appendix B. The following analysis discusses the LPG effects on the overall performances in terms of specific emissions at constant engine-out NOx. For an easier comparison among the blends, the results are reported in Figure 8 as radar plots. DLPG20 and DLPG35 are compared with diesel at the same engine calibration and the NOx engine-out emissions. Figure 8 displays the specific emissions for all test points and fuels. The blends show a relevant increase of HC emission, and in particular using DLPG35. At engine load 2 bar, they are about two times higher than diesel, while at the highest load, DLPG20 has values as diesel, and only for DGL35, the values are higher by about 50%. This is attributed to its ignition-related properties, evaporation behavior, and blends composition. The higher CO emissions related to the LPG mass ratio is observed for both tested points because of the longer ignition delay of LPG blends, which causes overmixing due to the evaporation characteristics combined with the relatively high engine swirl number [7]. The smoke emissions reduction is significant compared to diesel and in the range 94-99%. The reduced in-cylinder fuel-rich zones or any exposure to high-temperature combustion products are a precondition for the reduction of soot. Due to the high volatility of LPG, which in the vapor phase reaches high specific volumes, it probably inhibits the formation of fuel-rich areas, soot precursor. Therefore, based on the results obtained, it can be observed that the spray formation using a diesel-LPG mixing is sufficiently inclined to resist the formation of smoke [7]. Indeed, a higher hydrogen/carbon ratio characterizes the tested LPG blends compared to the diesel one (Table 2), with benefits on CO\({}_{2}\) emissions that combined with the higher thermal efficiencies lead to higher efficiencies, proportional to the LPG amount, and up to 10%, for the blends. The EGR rates do not vary regardless of the fuel used for all tested points in the range 35-40%. Figure 7: LPG cooling effect at \(1500\times 5\).
## 11 Conclusions  
We propose an energy-efficient scheme known as the WAS for agricultural application. The system is designed and practically implemented to monitor the climate conditions of crops, including air temperature, air humidity, and soil moisture. System hardware is carefully selected based on energy-efficient criteria to minimize the power consumption of different nodes in the WSN, such as the low-power Zigbee wireless protocol (i.e., XBee S2C), the standalone Atmega 328p microcontroller, and low-power sensors. In addition, two power reduction techniques are proposed to further improve the power consumption of the sensor, router, and main router nodes. These two schemes are the sleep/wake scheme and the _SWORD_ algorithm. The sleep/wake scheme is achieved based on different DCs of the nodes in the WAS (i.e., 0.222% for the sensor node, 1.778% for the router node, and 7.111% for the main router node). The _SWORD_ algorithm combines the sleep/wake scheme and the minimization of redundant data from sensor node packets. The power consumption of the sensor, router, and main router nodes is considerably improved when the two proposed methods are used.  
The power saving and data communication achieved by applying the _SWORD_ algorithm may be increased or decreased depending on the redundant data from soil moisture measurements, which depend on the soil condition (wet or dry). The power savings in the current work are improved by 86.45% and 99.48% relative to the sleep/wake scheme and traditional operation, respectively. In addition, data communication is minimized by 86.45%. The results of current consumption are compared with those in previous studies to validate the performance of the proposed system. The proposed WAS allows data collection for decision support in farming fields and can assist users in automating irrigation systems in agricultural fields in the future. The use of an energy-efficient and advanced WSN technology can achieve highly productive and sustainable precision farming.  
Conceptualization, H.M.J., R.N., and S.K.G.; Methodology, all authors; Software, H.M.J., S.K.G. and A.M.J.; Validation, H.M.J. and R.N.; Formal Analysis, S.K.G., A.M.J., M.I., and M.J.A.-A.; Investigation, H.M.J., A.M.J. and R.N.; Resources, H.M.J., S.K.G., and M.J.A.-A.; Data curation, R.N. and H.M.J.; Writing-Original Draft Preparation, H.M.J., R.N., S.K.G., and A.M.J.; Writing-Review & Editing, R.N., S.K.G., M.L, and M.J.A.-A.; Visualization, A.M.J. and M.J.A.-A.; Supervision, R.N., S.K.G., M.I.  
This research received no external funding.  
The authors would like to thank Al-Rafidain University College for their generous financial assistance.  
The authors declare no conflict of interest.