esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example
### Data Splitting  
When working with associated day and time data, as is the case here, the division between the training, validation and test sets must be carried out carefully, in order to ensure that the evaluations obtained correspond to the actual performance that should be expected of the models, since there is an inherent temporal correlation between observations (high similarity between temporally close data). This validation technique is called Holdout.  
In problems of predictive maintenance, in most situations, the best option is to perform a division based on time, that is, choose a point in time, train the model with all records prior to that point, using the later records to validate the model. This methodology also allows to simulate how the model will actually behave in practice.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **machineID** & **Datetime** & **Model** & **Age** & **Failure** \\ \hline
857 & 1 & 2015-04-19 09:00:00 & model3 & 18 & comp2 \\ \hline
858 & 1 & 2015-04-19 12:00:00 & model3 & 18 & comp2 \\ \hline
859 & 1 & 2015-04-19 15:00:00 & model3 & 18 & comp2 \\ \hline
860 & 1 & 2015-04-19 18:00:00 & model3 & 18 & comp2 \\ \hline
861 & 1 & 2015-04-19 21:00:00 & model3 & 18 & comp2 \\ \hline
862 & 1 & 2015-04-20 00:00:00 & model3 & 18 & comp2 \\ \hline
863 & 1 & 2015-04-20 03:00:00 & model3 & 18 & comp2 \\ \hline
864 & 1 & 2015-04-20 06:00:00 & model3 & 18 & comp2 \\ \hline
2297 & 1 & 2015-10-16 09:00:00 & model3 & 18 & comp2 \\ \hline
2298 & 1 & 2015-10-16 12:00:00 & model3 & 18 & comp2 \\ \hline
2299 & 1 & 2015-10-16 15:00:00 & model3 & 18 & comp2 \\ \hline
2300 & 1 & 2015-10-16 18:00:00 & model3 & 18 & comp2 \\ \hline
2301 & 1 & 2015-10-16 21:00:00 & model3 & 18 & comp2 \\ \hline
2302 & 1 & 2015-10-17 00:00:00 & model3 & 18 & comp2 \\ \hline
2303 & 1 & 2015-10-17 03:00:00 & model3 & 18 & comp2 \\ \hline
2304 & 1 & 2015-10-17 06:00:00 & model3 & 18 & comp2 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Example of failure representation in component 2.  
Thus, in the present application, the registrations until 08/31/2015 1:00:00 were assigned to the test set, the registrations between 01/09/2015 1:00:00 and 31/10/2015 1:00:00 to the validation set and registration from 01-11-2015 1:00:00 to the test set. In order to guarantee that the data in different sets do not share time windows, the records at the borders, that is, the records of the 24 h preceding the date of division, have been removed. Thus, Table 12 shows the amount of data that was attributed to each of the sets and the percentage that corresponds to failures.
## 3 Materials and Methods
### Model Classification and Tuning
#### 3.3.3 Random Forest (RF)  
Random Forest is a supervised classifier that can be useful for regression and classification analysis. RF concept is based on constructing a set of decision trees on the training data and giving predictions based on a high accuracy tree and majority vote. RF provides a high classification rate and can handle both outliers and noise; it is also less exposed to overfitting [35]. The RF algorithm hyperparameters are criterion, which is the split quality measurement function; max_depth, which is the maximum depth for the tree; min_samples_leaf, which is the minimum sample number to decide a leaf; min_samples_split, which is the minimum sample number to decide a split; and n_estimators, which is the number of decision trees to be built on the RF.  
After training, many performance metrics were used to evaluate the trained models, and the model with the best performance was chosen based on accuracy, the receiver operating characteristic (ROC), and the confusion matrix, which contains the false positive (FP), false negative (FN), true positive (TB), and true negative (TN). The true positive rate (TPR) and false positive rate (FPR) were based on the following equations:  
\[\text{TPR}=\text{TP}/\text{TP}+\text{FN} \tag{2}\]  
\[\text{FPR}=\text{FP}/\text{FP}+\text{TN} \tag{3}\]  
\[\text{Accuracy}=\text{TP}+\text{TN}/\text{TP}+\text{TN}+\text{FP}+\text{FN} \tag{4}\]
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### Proposed Scheme 1: ACE  
ACE is an adaptive cooperative retransmission routing protocol based on incremental relaying cooperative diversity. Figure 5a shows proposed system model of ACE for single S-D pair. It consists of S, D and two relays R1 and R2. S broadcasts data to D, R1 and R2 in the first phase. Retransmission from R1 and R2 is performed one by one in the second phase if and only if the destination receives erroneous signal at D. The proposed scheme works in time slots. A Time slot is a duration in which all alive nodes transmit their data to the Sink. It is assumed for the proposed protocol that alive nodes always have data to send. BS/Sink keeps the record of all the alive and dead nodes. It is the responsibility of BS to broadcast the Time Division Multiple Access (TDMA) schedule telling the nodes when it can transmit. Nodes send data packet during their allocated transmission time. When all the node finish data transmission, next time slot is initiated by the BS. Each slot consists of three phases given as:  
1. Information exchange phase
2. Path establishment phase
3. Data transmission phase
## 4 Results and Discussion
### Results of Quasi-Static Tests
#### 4.1.1 Uniaxial Tension Tests on Plate-Like Composite Specimens
At the material level, the proper composite tensile behavior of SHCC and textile can be only highlighted with the help of large specimens loaded quasi-statically. This is, however, the matter of a different study by the authors [10]. For a more comprehensive analysis of the textile contribution in the pre-peak and failure localization phases presented above, single-yarn pullout tests were performed, using the same plain matrix and SHCCs, both under quasi-static and impact loading. The results presented in the next section demonstrate the effects of the addition of short polymer fibers on the anchorages of the carbon yarns coated with styrene-butadiene.
# Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection
## 4 Experiments  
### Datasets and Experimental Setup  
To validate the effectiveness of IDF-MOCBLSAE, this section constructs 10 multi-view one-class intrusion detection tasks based on two real-world network traffic datasets (i.e., KDD [33] and UNSW [34]). Table 1 presents the basic information of each task, where the training set consists of normal data, with \(n_{train}\) denoting the number of training samples, and the testing set comprises both normal and intrusion data, with \(n_{test}\) indicating the number of testing samples. For each task, the original feature space is randomly partitioned into two disjoint views and provides the corresponding number of features for each view.  
In the experiments performed, the one-vote veto strategy is used in IDF-MOCBLSAE. All methods are executed 20 times, and their average accuracy and F1-score metrics, as shown in Equations (19) and (20) and Table 2, are reported to comprehensively evaluate the performance.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}, \tag{19}\]  
\[\text{F1-score}=\frac{2 PR}{P+R},\ P=\frac{TP}{TP+FP},\ R=\frac{TP}{TP+FN}. \tag{20}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Intrusion Type** & \(n_{train}\) & \(n_{test}\) **(Normal + Intrusion)** & **Dimensions of Each View** \\ \hline KDD1 & Warezclient & 20,000 & 20,000 + 890 & [20, 21] \\ KDD2 & Portsweep & 20,000 & 20,000 + 2931 & [20, 21] \\ KDD3 & Teardrop & 20,000 & 20,000 + 892 & [20, 21] \\ KDD4 & Nmap & 20,000 & 20,000 + 1493 & [20, 21] \\ KDD5 & Pod & 20,000 & 20,000 + 201 & [20, 21] \\ \hline UNSW1 & Reconnaissance & 18,500 & 18,500 + 3496 & [21, 21] \\ UNSW2 & DoS & 18,500 & 18,500 + 4089 & [21, 21] \\ UNSW3 & Exploits & 18,500 & 18,500 + 11,132 & [21, 21] \\ UNSW4 & Fuzzers & 18,500 & 18,500 + 6062 & [21, 21] \\ UNSW5 & Generic & 18,500 & 18,500 + 18,871 & [21, 21] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-view one-class intrusion detection tasks based on two network traffic datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual Class**} & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{2-3}  & **Intrusion (Positive)** & **Normal (Negative)** \\ \hline Intrusion (Positive) & TP & FN \\ \hline Normal (Negative) & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
Figure 2: The framework of IDF-MOCBLSAE.
# Secure and Dynamic Memory Management Architecture for Virtualization Technologies in IoT Devices
## 4 MIPS Architecture
### Operating Modes:
#### New Instructions:  
Switching the mode of execution between root mode and guest mode is done using two new instructions: HYPCALL and ERET [31]. The HYPCALL instruction moves the control of execution from guest mode to root mode; and the ERET instruction, executed by the hypervisor, moves the control back to guest mode. HYPCALL and ERET instructions are analogous to the execution of SYSCALL and RET instructions in operating systems, to switch between user and kernel modes. These new instructions are part of the virtualization module which are configured through CP0 registers. Other than these instructions, some additional instructions (i.e., _DMFGC0, DMTGC0, MFGC0, and MTGC0_) are also introduced to move the data among guest OS and hypervisor [31].  
When the CPU translates a virtual address, it checks whether the execution is in guest mode or not using the control bits in the _GuestCtl0_ register, which is a part of the CP0 register set. The _Guest_._Cause_ register in the CP0 register set is used for guest interrupts. The guest OS can write to the _Guest_._Cause_ register to trigger interrupts. The external interrupts are rooted to the guest OS by the hypervisor. The _GuestCtl0 PIP_ field in the CP0 registers facilitates handling external interrupts for the guest OS and hypervisor.  
If the CPU is in non-guest mode (root mode), the virtual address is searched for in the root TLB (Translation Lookaside Buffer). If the virtual address is mapped in the root TLB, the physical address is given back to the CPU. If the virtual address is not allocated in the TLB, an exception is raised and handled by the hypervisor. When the CPU is in guest mode, the virtual address is searched in the guest TLB. If the virtual address is found in guest TLB, the corresponding pseudo-physical address is returned, and if not found, an exception is raised. This pseudo-physical address is then converted, in root mode, to a real physical address using the root TLB, in the same pipeline stage [31].  
This two-layer address translation mechanism is a bottleneck for achieving near-native performance in virtual RTOSs. Moreover, pre-assigned memory for virtual machines in an embedded hypervisor would cause memory starvation, since most of the devices are microcontrollers with small memory sizes.  
ASMI is a hardware-assisted memory isolation technology in the literature that can provide memory isolation and dynamic memory allocation, and has a single-level address translation method. These features are essential requirements for security and performance enhancements in an embedded hypervisor. Hence, we propose ASMI on the MIPS embedded platform. The next section explains the proposal in detail.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 3 Related Works
### Problem Formulation
#### 3.1.4 Packet Modification  
The key part of fast network recovery technology is the fast detection of the failure and the subsequent means of its notification to the other routers which were affected by the failure (disrupted routing). In some FRR mechanisms, specific link failure information is distributed by the following techniques:  
* Modifying special bits in the IPv4 header (MRC [11]);
* Encapsulating the packet with another header (Remote LFA [30], Directed LFA [63]);
* Based on the interface through which the packet was received (LFA [46; 64]).  
It should be noted that packet modification causes various compatibility problems as well as problems with exceeding the Maximum Transmission Unit (MTU) on some network links [26].
# Nosemosis Prevention and Control
## 1 Introduction  
_Nosema_ spp. are microsporidia responsible for nosemosis of honey bees (_Apis mellifera_ L. and _Apis cernane_ F.). These pathogens are obligate parasites of the epithelial cells of the ventriculus of adult honey bees [1; 2] and are spread throughout the world [3; 4]. Infection of the bees occurs by the oral ingestion of spore-contaminated honey, pollen or water [5; 6; 7; 8]. Other ways of transmission of the spores may be by trophallaxis [9; 10], foraging activities on the flowers or cleaning tasks in the colonies [11]. The spores are released in large quantities with the faeces of diseased bees and can be infective for more than one year [12]. So far, three _Nosema_ species have been identified to infect honey bees: _N. apis_[13], _N. cernane_[1] and _N. neumanni_[14].  
_N. cernane_ is a microsporidium firstly isolated in the Asian bee Apis cernana, and then found in Apis mellifera [3; 15]. It is currently endemic worldwide [16], as well as the other well-known _Nosema_ species: _N. apis_[13]. However, in some regions, usually with colder climates, _N. apis_ is still prevalent. _N. apis_ has a low prevalence in Southern Europe [16; 17; 18; 19] while _N. cernane_ is widely present in Southern Europe, specifically in areas with a temperate-warm climate [3; 20]. _N. cernane_ infection has important differences with _N. apis_ control due to the microsporidium biology and because _N. cernane_ tends to persist throughout the year [3].  
_N. apis_ is responsible for clinical signs of nosemosis type A. Such signs include a high mortality of adult bees and the presence of fecal spotting at the hive entrance due to digestive disorders in adult bees. _N. cernane_ is responsible for clinical signs of nosemosis type Cthat are represented by evident loss of adult population, decrease in honey production and final collapse [7; 21]. The absence of a clear symptomatology of _N. ceranae_ has complicated its distinction from _N. apis_ in Europe [22].  
_Nosema ceranae_ has been frequently related to colony losses [7; 23; 24], even with the possible contribution of trypanosomatids such as _Crithidia mellifidae_ and _Lotmaria passim_[25; 26; 27; 28]. _N. ceranae_ presence in association with other pathogens such as viruses [29; 30; 31] or varroa [32] and contaminants [33; 34] has been demonstrated to increase honey bee colony losses.  
Recently another _Nosema_ species was identified in Uganda (Africa): _N. neumanni_[35]. The distribution and pathogenicity of this microsporidium is still under investigation.  
This paper aims to give an overview of the main strategies for the prevention and control of nosemosis in order to prevent the previously described damages.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 3 Ground Penetrating Radar
[27] applied GPR for infrastructure assessment and SHM using quantitative attribute analysis. The purpose of the study is to investigate how the physical and mechanical properties of scanned objects are implicated in GPR data. The scans were carried out on Streicker Bridge with a GSSI StructureScan 2.6 GHz antenna to maintain a good balance between high resolution and a reasonable concrete penetration depth. The longitudinal and transverse scans of the bridge deck were collected, where longitudinal transects were collected at 15 cm line spacing with a nominal length of 8 m. The obtained data were processed using a standard set of basic filters focusing on preserving the relative amplitude of the data rather than absolute amplitude as the latter is influenced by the EM properties of the material, surface roughness and other external factors. Findings from the study demonstrated the potential of the attribute analysis method for the characterization of material when combined with other SHM and NDE techniques. One of the advantages of structural health monitoring is the possibility to monitor structures over a long period of time in order to predict potential failures. On the other hand, processing and analysis of such data may present various challenges. [PERSON] et al. [28] proposed a new method to address the challenges of processing long-term monitoring data from the GPR system. The principle of the new signal processing procedure involves individually pre-processing and energy mapping of each input GPR dataset. Then, the GPR signal energy intensity mappings are further registered using Mutual Information (MI) based image registration method. The final stage of the process is to normalize the intensity of the aligned images. In this study, researchers analysed GPR data collected to monitor the accumulative corrosion process over a period of 10 years. The proposed method of GPR signal processing was able to align the monitoring images and normalize their intensities in order to detect reinforcement corrosion in concrete.
## 2 Experimental Setup  
A schematic diagram of the laser system is shown in Figure 1. The all-fiber laser system employed a pulsed laser oscillator, an acoustic-optical modulator (AOM), and three-stage fiber pre-amplifiers, a pre-compressor, and a main amplifier. The laser system was seeded by a SESAM mode-locked oscillator at 1030 nm. The first and second pre-amplifiers consisted of a Yb-doped, 6/125 single mode fiber (SMF) pumped by a pigtailed single-mode laser diode, and an isolator (ISO). The pulses were amplified from 4.5 mW to 230 mW through the first two pre-amplifiers. An AOM was installed to reduce the repetition rate from 25 MHz to 300 kHz. Laser gain for the third pre-amplifier was provided by 0.5 m, Yb-doped double-clad fiber (DCF) with core/cladding diameter of 10/125 \(\upmu\)m. The third pre-amplifier was pumped by two fiber pigtailed 976 nm laser diodes via a \((2+1)\times 1\) fiber combiner. The power at the output of the third pre-amplifier was 105 mW. Since AOM brought extra attenuation for the fundamental pulse, the scheme of three-stage pre-amplification allowed sufficient power gain for the further amplification. As shown in Figure 1, the grating-based compressor contained two adjustable parallel transmission Bragg gratings, 1200 lines/mm, which were set before the main amplification. As for changing the distance between the grating pair, the output pulse duration can be tuned from hundreds of femtoseconds to several picoseconds. In our experiment, the total transmission through the pre-compressor was measured to be 61%, resulting in 64 mW seed power coupled into the main amplifier. The main amplifier was composed of 1-m-long, Yb-doped PCF (DC-200/40-PZ-Yb, NKT, \(\beta_{2}\approx 23\) ps\({}^{2}\)/km, \(\beta_{3}\approx 0.06\) ps\({}^{3}\)/km, \(n_{2}=2.6\times 10^{-20}\) m\({}^{2}\)/W), with a doped core diameter of 40 \(\upmu\)m (NA = 0.03) and a pump cladding of 200 \(\upmu\)m (NA = 0.6). LMA PCF fiber was selected for the main amplifier due to its large mode area, which would guarantee the fundamental and parametric pulses propagating with different modes. This could compensate the walk off between the fundamental and parametric components [10]. To suppress the environmental disturbance and parasitic lasing, both ends of the fiber were sealed and cleaved at 8 degrees. A fiber-coupled multimode laser diode with central wavelength of 976 nm was employed as the pump source. We pumped the Yb-doped PCF from the back with maximum pump power up to 27 W.
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 5 Results and Discussions
### Evaluation of Drone-Based Gas Leak Detection  
In this section, the whole system is evaluated using the collected data from Experiment C Scenario 2, which is explained in Section 4.3. As explained before, the system has two main steps including stabilization and gas leak detection. For this section, PC-RF is employed for the stabilization combined with the proposed gas leak detection to investigate the system performance while deployed on a drone. In Figure 11, the results of the system are presented and they show that the system was able to visualize the gas emanations and determine the area with high gas flow intensity.
## 3 Results  
### Magnetization  
Studies [6] demonstrate that the magnetization method influences both the amplitude and offset of the waveform (Table 1). However, the magnetization method does not alter the overall shape of the waveform. Figure 12 compares the normalized waveforms (reflecting the waveform shape) obtained for different magnetization methods.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & & \multicolumn{3}{c}{**Amplitudea (p.d.u.)**} & & \multicolumn{3}{c}{**Offset (p.d.u.)**} \\  & **SPM** & **OPM** & **NoM** & **SPM** & **OPM** & **NoM** \\ \(B_{\mathrm{x}}\) & 520 & 504 & 251 & 76 & 117 & 108 \\ \(B_{\mathrm{y}}\) & 243 & 137 & 28 & 96 & 245 & 113 \\ \(B_{\mathrm{z}}\) & 403 & 239 & 48 & 94 & 230 & 59 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of amplitude and offset values for different spatial components of \(B\).  
Figure 11: Block scheme of the measuring system.  
Magnetization, regardless of type, enhances the predictability of magnetic measurements, enabling the use of amplitude and offset parameters in analytical and identification processes. Without magnetization, these parameters exhibit random variability (caused by unknown residual magnetization), limiting the identification to waveform shape attributes alone. The considerable diversity can be observed based on Figure 13 (samples P3 and P4 (NoM)) [6].  
Another critical parameter in the identification process is the signal-to-noise ratio (SNR). Figure 13 illustrates a comparative analysis of the SNR (calculated according to (4)) across different magnetization methods.  
Experimental results indicate greater predictability with the Opposite Pole Magnetization (OPM) compared to the Same Pole Magnetization (SPM). Transitioning from the conventional OPM configuration to the SPM significantly enhances the signal amplitude, repeatability, and signal-to-noise ratio. Consequently, the SPM method was selected for subsequent investigations [6].
WBET usually assumes that the change of total water storage (TWSC) is negligible during multi-years, which can be used as an indicator of long-term inter-annual change of LE at basin scale [41]. One the other hand, the TWSC from gravity recovery and Climate Experiment (GRACE) mission combined with water balance equation [42] can be used to calculate monthly LE and evapotranspiration (ET), which also facilitates the evaluation of monthly LE changes at basin scale. Therefore, there is a scope for multi-scale terrestrial LE evaluation by incorporating ground-based measurements and water balance method to improve the LE estimation in Haihe River Basin. In this study, to understand the differences and uncertainties of multiple satellite-based LE products in Haihe River Basin, we evaluated six terrestrial LE products, including five process-based algorithms (PML-V2, BESS, GLEAM, MOD16, and GLASS) and a machine learning method (FLUXCOM). The objectives of this study includes: (1) To validate the accuracies of six LE products using EC flux tower observations; (2) To evaluate the performances of these LE products using water budget balance method at basin scale; (3) To compare the spatial distribution differences among these products by mapping the mean seasonal and annual terrestrial LE from 2013 to 2015 in the Haihe River basin of North China.