esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Concept for Fault Detection  
This paper proposes an autonomous WSN solution for predictive maintenance. Three main parts were developed, as shown in Figure 1. The first element expresses the power unit, including an energy harvester based on a vibration converter that can generate a relevant energy level under a weak vibration level from 0.1 g of acceleration. It consists of the electromagnetic converter detailed in Section 3 and is characterized by good robustness and high energy density with a large frequency bandwidth.  
The energy harvester is followed by the passive energy-management circuit described in Section 4, which is advantageous in terms of its charging time and ability to manage the load requirement in terms of energy. The complete power unit enables the continuous powering of the wireless sensor node, which is responsible for the communication of the acceleration measurement; fault detection is conducted by the WSN and is presented in Section 5.  
The developed solution aimed to perform acceleration measurement and detect failures through the presence of high acceleration. Failure is detected based on the measurement of the converter voltage based on the ADC of the used WSN. When the measured acceleration exceeds 0.25 g, the converter voltage increases, and a message will be sent within 1 min to declare that the system has a failure. The treated scenario in terms of the acceleration profile and failure detection is presented in Figure 2.  
Figure 1: Schematic of the proposed concept.
## 7 INCA Algorithm
```
Inputs: features from the PARC model, labels, Output: the selected feature (features_out)
1: features_out = INCA_algorithm (features, labels)
2: begin
3: nca = fscnca (Xtrain, ytrain,'sgd', bestlambda);
4: for i = 1 to N do
5: search the best lambda parameter by using the NCA
6: end for i
7: compute feature weights with the best lambda
8: indeces = weights (indices)
9: for j = 1 to length (features) do
10: if weights (j) >= threshold
11: append j to the new indices list
12: end if
13: end for j
14: features_out = fea[new_indeces]
15: return features_out
16: end
```  
**Algorithm 1** Pseudocode of the PARC model  
The parameter N is the number of optimization iterations and is set to 20 (default value). The threshold value is used to eliminate features including low-weight values. For the binary and multi-class classification, threshold values were selected as 0.5 and 0.2, respectively.
## 2 Measurement System and Data Collection Methodology
### Data Collection Methodology
Table 1 summarizes the performed measurement campaigns for each operator and location in terms of number of measurement campaigns and corresponding number of  
Figure 4: Boxplots of the received power of the detected SSBs for Op1 (multiple SSBs) and Op2 (single SSB) at Location 1. The boxplots named Op1_SSBn (n = \(0,1,2,\ldots,7\)) show the Received Power of the SSBs of the Op1, while Op2_SSB shows the Received Power of transmitted SSB of operator 2. For Op1, SSB 6 is considered the _strongest_ SSB, with the average and median of the Received Power higher than all other SSBs of Op1. In contrast, Op2 operates on a single SSB. collected PDPs for each location. Note that the dataset includes measurements of PCIs/SSBs for which the number of PDPs, collected at a given location during an entire measurement campaign, was more than 50. We set this threshold empirically after observing that a reasonable estimate of the propagation parameters mentioned above can be obtained when the number of PDPs is at least equal to 50. Therefore, PCI/SSB pairs with less than 50 PDPs are not provided in the dataset.
# Volume Contraction in Shallow Sediments: Discrete Element Simulation
## 1 Introduction  
Mineral dissolution and precipitation are concurrent soil processes. The time scale for chemical processes is typically quite long and the \"inert assumption of soils\" applies to many engineering applications. Dissolution and precipitation, however, can also take place in relatively short time scales in advective regimes and when systems are taken far from equilibrium. They occur, for example, in dam foundations, volcanic ash, mine tailings, fly ash, CO\({}_{2}\) injection, infiltration-induced carbonate dissolution, and the dissociation of segregated ice or methane hydrates [1, 2, 3, 4, 5, 6, 7].  
Volume contraction in shallow sediments can take place during mining operations and due to ground loss around underground construction [8, 9, 10, 11]. Similarly, the localized dissolution of carbonates may trigger ground subsidence, cave-ins and regional softening [12, 13]. Ensuing subsidence, collapse and sinkholes are a function of soil properties, volume loss and geometry (size, depth). Displacements induced by underground volume contraction negatively affect the overlying infrastructure and nearby underground structures. As examples, piles experience lateral deflection, bending moments, settlement, and abnormal distribution [14, 15, 16, 17]; lifelines are sheared and disrupted [18, 19, 20, 21]; buildings develop differential settlement and horizontal displacement [22, 23, 24]; and adjacent tunnels may rotate and undergo asymmetric settlement [25, 26].  
Previous studies of mineral dissolution and subsurface volume contraction using the discrete element method have primarily addressed pressure solution [27, 28, 29], randomly distributed soluble grains [30] and volume loss around tunnels [31, 32]. This study takesadvantage of the discrete element method to obtain macroscale and particle-level information following the dissolution of a granular inclusion at shallow depth. Variables include sediment density and granular interlocking, and the inclusion depth.
## 1 Introduction
The extensive experiments on the large-scale NTU RGB+D 60 and NTU RGB+D 120 datasets demonstrate the effectiveness and preponderance of SFCNet, by which human intention can be judged and assisted in remote sensing applications. In general, the main contributions of our work are as follows:  
* We propose an interval-frequency descriptor to characterize the 3D voxels during action execution, which fully preserves the motion details and provides critical clues for key body parts' perception. To the best of our knowledge, our work is the first to handle point cloud sequences in this way. Figure 1: The pipeline of SFCNet. It converts depth frames into a point cloud and applies voxelization operations. A symmetric structure encodes 3D voxels, with crucial parts and global dynamic information processed separately. The attached interval-frequency descriptor initially characterizes the motion information and then is processed by PointNet++[11] for deeper features. Finally, the classifier recognizes 3D actions using the aggregated feature. * We construct a deep learning framework named SFCNet, which first employs a symmetric structure to process point cloud sequences. It encodes the local crucial body parts' dynamics via a fine stream and then supplements these intricate details to the global appearance captured by a coarse stream. The SFCNet can emphasize essential body parts and capture more discriminative motion patterns, addressing the effective action representation problem based on point clouds. * The presented SFCNet has demonstrated its superior accuracy on two publicly available datasets, NTU RGB+D 60 and NTU RGB+D 120, which proves that our method has considerable potential in recognizing various types of actions such as daily actions, medical-related actions, and two-person interaction actions.
## 2 Model Statement
### Wave Surface Dynamics  
A monochromatic unidirectional wave has been considered as to be the input to the implemented numerical model of the OWC system. There exists numerous wave theories in the literature to express the surface dynamics of ocean wave like the Cnoidal wave theory, second- and higher -order Stokes theory and Airy linear theory [26; 27]. In this paper, the Airy wave theory has been adopted because it presents the simplest description and it is the most widely used thanks to it neglecting turbulence, friction losses and other energy losses [27].  
The parameters of a wave are detailed in Figure 1, where \(SWL\) represents the \"Still-Water-Level\" and \(h\), called \"sea depth\", represents the interval from the sea floor to \(SWL\). \(H\) marks the interval from wave trough to wave crest called \"wave height\". \(A\) measures the distance between \(SWL\) and the wave crest known as \"wave amplitude\" and \(\lambda\) representing the interval between successive crests known as \"wavelength\" [27; 28]. Therefore, the surface elevation for a sea wave is given as [29; 30]:  
\[z(x,t)=A\sin\left(\omega t-kx\theta\right)=H/2\sin\left(\omega t-kx\theta\right) \tag{1}\]  
where \(\omega\) is the wave frequency, \(x\) is the wave horizontal coordinate, \(\theta\) marks the the angular opening from the \(x\)-axis to waves' direction and \(k\) represents the wave number linked to \(\omega\) with relation (2) as described in [29]:  
\[k\tanh(kh)=\omega^{2}/g, \tag{2}\]  
where \(g\) represents the acceleration gravity.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 3 System Architecture
### Adaptive Kalman Filtering  
The numerical value of measured noise varies with time due to the influence of the surrounding environment in the practical applications of GPS/INS integrated navigation system. In order to enhance the performance of positioning, it is necessary to adjust the statistical data of the measured variance matrix on line. In addition, under the condition of process noise is known, the Sage filtering method is applied to adjust the measured variance.  
\[R_{m}=(1-d_{m})R_{m-1}+d_{m}\Big{(}e_{m}e_{m}^{T}+H_{m}P_{m}H_{m}^{T}\Big{)} \tag{19}\]  
\[d_{m}=\frac{(1-b)}{(1-b^{m})} \tag{20}\]  
in which \(b\in[0.95,0.99]\) is a forgetting factor, and in this paper it is set to 0.97 according to data analysis. The residual sequence \(e_{k}\) is expressed using the following relationship  
\[e_{m}=z_{m}-H_{m}x_{m} \tag{21}\]  
It should be noted that the abnormal errors of the observations are removed firstly during practical applications.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 5 Experiment and Result Analysis
### Evaluation  
#### 5.3.1 Method Performance  
After determining the key system parameters, we evaluated the performance of the DNCL algorithm. Figure 6 shows the experimental layout. The antenna travels along the guide rail. The distance between the guide rail and the shelf is 2.5 m, and 12 reference tags are evenly arranged on the shelf. The results of the figurative localization of the tags are shown in Figure 11. To show the localization accuracy more intuitively, we marked the actual positions of the reference tags and tags to be measured on the figure. Meanwhile, as shown in Figure 12, the cumulative distribution function (CDF) curves of the proposed algorithm for the localization scenarios in x-axis up, z-axis up, and overall, with median  
Figure 10: The distance from the shelves to reader antenna.  
Figure 9: Number of reference tags selection.  
localization errors of 4.73 cm, 6.76 cm, and 8.47 cm, respectively. In general, based on the localization effect in the figure, our proposed method can distinguish tags on different levels and columns of the shelf without ambiguity.
# Anticipatory Troubleshooting
## 5 Solving the Dilemmas
### Solving the Dilemmas with Heuristic Rules  
The heuristic decision rule described next, which we refer to as \(DR1\), is a solution to the fix-replace dilemma. The input to \(DR1\) is a tuple \(\langle C,t,C_{age},S_{C},S_{C}(\cdot,t)\rangle\) where: (1) \(C\) is a faulty component, (2) \(t\) is the current time, (3) \(C_{age}\) is the age of \(C\), (4) \(S_{C}\) is the survival curve of \(C\), and (5) \(S_{C}(\cdot,t)\) is the survival curve of \(C\) if it is fixed at time \(t\). \(DR1\) outputs a repair action--either _Fix(\(C\))_ or _Replace(\(C\))_.  
**Definition 3** (\(DR1\)): \(DR1\) _returns Replace(\(C\)) if the following inequality holds, and Fix(\(C\)) otherwise._  
\[cost(\mbox{Replace}(C))+(1-S_{C}(T_{limit}-t))\cdot\Big{(}cost \big{(}\mbox{Replace}(C)\big{)}+cost(overhead)\Big{)}\] \[\leq cost(\mbox{Fix}(C))+(1-S_{C}(T_{limit}-t,C_{Age}))\cdot \Big{(}cost\big{(}\mbox{Replace}(C)\big{)}+cost(overhead)\Big{)} \tag{4}\]  
\(DR1\) is optimal if the following assumptions hold:  
1. A component will not fail more than twice in the time interval \([0,T_{limit}]\).
2. A component can be fixed at most once.
3. A replaced component will not be fixed in the future.
4. Components fail independently.
5. We can not replace healthy components.  
Explanation: If components fail independently (Assumption 4), it is sufficient to compare the long-term troubleshooting costs resulting from current and future failures of \(C\), as \(C\) neither affects nor is affected by any other component. The long-term troubleshooting costs consist of the current cost of repairing \(C\) plus the costs of repairing \(C\) should it fail before \(T_{limit}\). The current cost is \(cost(\mbox{Fix}(C))\) or \(cost(\mbox{Replace}(C))\) if \(C\) is fixed or replaced, respectively. Since we assume that a component will not fail more than twice (Assumption 1), computing the expected future troubleshooting costs is reduced to the probability that \(C\) will fail again before \(T_{limit}\) times the cost of replacing \(C\) if it fails (Assumption 2 and 3).  
The probability that \(C\) will fail before \(T_{limit}\) is \(1-S_{C}(T_{limit}-t,C_{qge})\) if we fix \(C\) now, and \(1-S_{C}(T_{limit}-t)\) if we replace it.  
\(DR1\) can be also applied when the assumptions do not hold, but then \(DR1\) may be non-optimal. Still, as we show experimentally in Section 6, using \(DR1\) can be very beneficial.
## 4 Simulation Analysis  
In this section, we evaluate the performance of our R-EERP and S-EERP protocols via simulations. We developed a custom simulator which is written in C# program for this evaluation. We compare our results with the LEACH protocol, since LEACH is the base protocol on which we are building our protocols. Our protocols also proactive routing protocols as LEACH, but different from LEACH, our protocols support reactive applications better than LEACH and with less energy consumption. We would like to see how much we can improve LEACH with our extensions for reactive applications. We are not comparing our protocols with TEEN, since TEEN does not target the scenario that we are  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Network size & 200 m \(\times\) 200 m \\ Node count & 100 \\ Sensing range & 30 m \\ Initial energy of each node & 1.2 J \\ Data packet size & 525 Bytes \\ Broadcast packet size & 25 Bytes \\ E\({}_{\text{elec}}\)(Radio electronics energy) & 50 nJ/bit \\ \(\varepsilon_{\text{fiss}}\) (d \(<\) d\({}_{\text{crossover}}\)) & 10 pJ/bit/m\({}^{2}\) \\ \(\varepsilon_{\text{two-ray}}\) (d \(\geq\) d\({}_{\text{crossover}}\)) & 0.0013 pJ/bit/m\({}^{4}\) \\ Threshold distance (d\({}_{\text{crossover}}\)) & 87.01 m \\ Simulation rounds & 1,000 \\ P (Desired probability of cluster heads) & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 3: First Radio Model.  
targeting: gas detection applications that require very fast responses from time to time and require less energy consumption. Even though TEEN reduces energy consumption by having soft/hard thresholds, it does not have a mechanism and policy for fast response in emergency situations.
## 2 Research Data and Research Methods
### Research Methods
#### 2.3.2 Spatial Auto-Correlation Analysis Based on Moran's I  
Spatial auto-correlation represents the potential interdependence between spatial units and their adjacent units in the form of certain characteristic values, typically measured using Moran's I index [20]. In our spatial auto-correlation analysis, we employed a spatial weighting matrix based on adjacency to capture the spatial relationships between neighboring units. Moran's I is divided into Global Moran's I and Local Moran's I. Global Moran's I indicates whether spatial clustering occurs but does not identify the specific clustering areas. In contrast, Local Moran's I reflects the spatial concentration of regional units and can reveal where spatial clustering is located. Even if the Global Moran's I does not show significant clustering, Local Moran's I can highlight local spatial clustering phenomena. Local indicators of spatial association (LISA) are used to analyze the correlation between adjacent spatial units, measuring the similarity (positive correlation) or difference (negative correlation) between the attribute values of an observation unit and its surrounding units. The calculation formulae are as follows:  
\[Global\,Moran^{{}^{\prime}}sI=\frac{n\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{ n}W_{ij}(x_{i}-\overline{x})\big{(}x_{j}-\overline{x}\big{)}}{\sum\limits_{i=1}^{ n}(x_{i}-\overline{x})\sum\limits_{j=1}^{n}(x_{j}-\overline{x})} \tag{6}\]  
\[Local\,Moran^{{}^{\prime}}sI=\frac{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n} W_{ij}(x_{i}-\overline{x})\big{(}x_{j}-\overline{x}\big{)}}{5^{2}\sum\limits_{i=1}^{ n}\sum\limits_{j=1}^{n}W_{ij}(x_{i}-\overline{x})} \tag{7}\]  
In the formula, \(n\) represents the number of units \(i\) and \(j\); \(W_{ij}\) denotes the spatial weight between \(x_{i}\) and \(x_{j}\); \(x_{i}\) and \(x_{j}\), respectively, represent the EVI (GPP) values of unit \(i\) and \(j\).  
The global Moran's I index ranges from \(-1\) to \(1\). A value greater than \(0\) indicates spatial clustering, with larger values suggesting more pronounced clustering; a value less than \(0\) indicates a dispersed spatial distribution, with smaller values indicating more pronounced dispersion; a value of \(0\) suggests that the observed data are randomly distributed in space [21]. The local Moran's I index results can be categorized into five types: (a) high-high (H-H): spatial units with high EVI (GPP) values are adjacent to other spatial units with high EVI (GPP) values; (b) low-low (L-L): spatial units with low EVI (GPP) values are adjacent to other spatial units with low EVI (GPP) values; (c) low-high (L-H): spatial units with low EVI (GPP) values are adjacent to spatial units with high EVI (GPP) values; (d) high-low (H-L): spatial units with high EVI (GPP) values are adjacent to spatial units with low EVI (GPP) values; (e) non-significant (random distribution): this indicates that the spatial units do not exhibit clear spatial clustering.
# Development of an On-Line Defect Detection System for EDM Process
###### Abstract  
In the electrical discharge machining process, preliminary research has been able to effectively estimate machining accuracy in response to its long machining history and high discharge frequency characteristics. However, when processing abnormalities occur, it is difficult to identify them since the electrical discharge process contains multiple processing parameters, which increases the cost of repair or loss afterwards. Therefore, the question concerning how to monitor the abnormality of the discharge process in real time represents the main purpose of this research. This research develops an EDM process abnormal diagnosis system. First, the data are stored in a circular array to speed up the processing time, and the coefficient of variation feature is added, which has effectively extracted the abnormal characteristics. In terms of diagnostic methods, the composite voting model established by neural networks, random forests, and XGB-RF (extreme gradient boosting applying RF) can provide robust diagnostic results. Finally, through the Node-RED webpage and MQTT agreement, it can provide the ability to monitor machine abnormalities in real time. Through refinement and optimization of the previous research results, this study took the electrical discharge machining diamond grinding wheel as an example, and developed a warning that can be issued within 3 min when abnormalities (abnormal patterns such as polycrystalline diamond high protrusions) occur, with an accuracy of 93% and a false positive rate. The abnormal diagnosis ability is less than 0.2%. Therefore, the online abnormality monitoring system developed by this research institute will be able to provide online abnormality diagnosis for electrical discharge machining.  
electrical discharge machining; anomaly detection; random forest; XGB-RF +
Footnote †: journal: Applied Sciences
# Numerical Fit Modeling for Temperature Mitigation in
## 2 Numerical Fit Model for Arid Regions
### Method Details
The base cooling rate is \(C_{base}\) and is 7.13 K for days 0.5 to 3.5, and the cooling rate is \(C_{rate}\) and is 0.0250 K/K per 20 min. When mitigation occurs, the solar radiation \(I\) is reduced accordingly. These calculations were performed in Excel. Table 1 shows the fit values for each time period. Figure 4: Fit for the cooling rate from day 0.5 to 3.5. The cooling rate has a general trend, but it sometimes oscillates from one hour to the next. A \(p\)-value < 0.001 and a 95% confidence of 3.6 K were found when comparing the numerical model to actual temperatures. A low \(p\)-value indicates that overall, the method predicts the conditions well. However, the confidence in temperature predictions shows that the numerical fit process is less useful for hour-by-hour weather prediction.
## 7 Security Considerations for Proposed Mobility Schemes
### Mutual Authentication of MN and its Future SNs  
When the MN moves within the PAN, it associates with different SNs in the PAN. In order to provide mutual authentication between the MN and its next SN, the current SN informs the next SN, during handoff, about K\({}_{\text{m}}\). The current SN achieves this by encrypting the K\({}_{\text{m}}\) with the pairwise key shared between itself and the next SN. The mutual authentication process using K\({}_{\text{m}}\) is shown in Figure 15.  
Figure 15: Secure Handoff.  
In the hello packets, the next SN includes a random number R1. When MN receives this number, it generates another random number R2 and sends R2 to next SN along with the concatenation of R1 with R2 after encrypting the concatenation with K\({}_{\text{m}}\). Once the next SN verifies that the sender knows the K\({}_{\text{m}}\), it sends an LU to MSP by encrypting with the shared key K\({}_{\text{SP}}\). Also it reverses the order of the concatenation of R1 and R2 and encrypts the concatenation with K\({}_{\text{m}}\)before sending it to MN.  
Note that as the MN moves on then the Mobile Key, i.e., K\({}_{\text{m}}\), would be exposed to many of the SNs within the PAN. Thus it is desirable that the GW revoke the key after some time. In order to do that, the GW generates K\({}_{\text{m}}\) after some amount of time. The time interval at which the key is revoked is implementation specific. The GW then sends this key to the MN by encrypting it with K\({}_{\text{c}}\). At the same time, the GW sends the key to the current SN by encrypting it with the key shared between it and the SN.