esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### Data Source
#### 2.1.2 Ground Motion  
Two different sets of data have been used in this study. Set-1 included responses of ground motions from the PEER NGA-West2 [36; 37] database. This database has 21,539 records of shallow crustal earthquakes in active tectonic regions. Due to the possibility of anomalies from older data collection systems, only records from the past 30 years were selected. Moreover, records with peak ground acceleration (PGA) less than 1% of the acceleration of gravity (\(g\)) may not produce sufficient excitation useful for this study.  
Figure 1: Proposed framework using acceleration data from instrumented structures to assess their damage conditions following an earthquake event.  
Therefore, only records with PGA of more than 1%g were considered. Lastly, to avoid homogeneity in the response, not more than 20 records from a single event were selected. A total of 1710 records matched these criteria. Figure 2 shows the magnitude and hypocenter distance ranges of these selected records. The magnitude of these records varied from 3 to 8 with the majority (565) of records having a magnitude between 5 and 6 and about 200 records coming from events with magnitudes of 7-8. On the other hand, 28% of these records (480) had a hypocenter distance less than 25 km and 15% of the records corresponded to a hypocenter distance greater than 100 km. This shows that the records covered near-field and far-field events with variability in magnitude.  
Set-2 comprised responses to site-specific ground motions used in [38]. These ground motions were selected by matching the uniform hazard spectrum and associated causal events for a site in Oakland, California. It consisted of 120 ground motions representing three hazard levels, namely 2%, 10%, and 50% probabilities of exceedance in 50 years. Figure 3 shows that these records were mostly higher magnitude events (M > 6) and 98% of the records had hypocenter distance less than 50 km. With such a set, it was expected that the majority of these records would push the structure into the nonlinear range of the response to evaluate the ability of the proposed features for damage assessment.
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
###### Abstract  
Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these models employ batch normalisation (BN) for centring and scaling the input for each neuron. This study includes a novel examination of a competitive approach for layer input normalisation in the scope of fault diagnosis. Network deconvolution (ND) is a technique that further decorrelates the layer inputs reducing redundancy among the learned features. Both normalisation techniques are implemented on three common 1D-CNN-based fault diagnosis models. The models with ND mostly outperform the baseline models with BN in three experiments concerning fault datasets from two different rotor systems. Furthermore, the models with ND significantly outperform the baseline models with BN in the common CWRU bearing fault tests with load domain shifts, if the data from drive-end and fan-end sensors are employed. The results show that whitened features can improve the performance of CNN-based fault diagnosis models.  
CNN architecture; normalization techniques; intelligent fault diagnosis; vibration
# Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework
## 1 Introduction
### State of The Art
This emulation is based on augmenting packet in traditional Mininet instances with wavelength/channel information, which is used in modified instances of Open vSwitch. In fact, the entire Mininet-Optical emulation relies on a software structure that invokes the Open vSwitch instances as Python processes. Consequently, this approach diverges from realistic deployment/production scenarios in which optical systems are managed/controlled by its own light-weight software agent in dedicated physical or virtual resources.
# Integrated Design Methodology of Automated Guided Vehicles Based on Swarm Robotics
## 4 Case-Study: Application to the Design of AGVs
### Phase 2: ROS Implementation  
To verify the development performed in the previous phase, the developed models of the AGV system need to be implemented in the ROS environment. For this, the designer creates a workspace composed of a set of packages. To take into account the behavior of the swarm, the individual AGVs are associated with nodes in the ROS system, and a communication subject is created for subscribing and posting information exchanged between the individual AGVs. The ROS packages are implemented based on the different SysML diagrams developed in the first design phase. Indeed, the BDD block definition diagram and the IBD internal block diagrams are used to create the URDF file and the WORLD file in ROS. The URDF file describes the general structure of the AGV system and the WORLD file describes the working environment (factory). Then the state machine diagrams and the activity diagrams are used to develop the control algorithms of the AGV system in ROS. Figure 15 shows the ROS integration methodology.  
One of the strong points of ROS is the reuse of codes developed by others that can be found and shared by ROS users (on GitHub site for example). Users can find versions written in Python and C++ which differ in their restrictions, performance, and the lighten code. AGV developers can download them and make them usable by compiling them (using cakthi build processes), and new versions of these codes will be available as other ROS-based packages. Based on the SysML descriptions made in the first design step, AGV developers can integrate the most suitable codes that respect the AGV requirements, the functional architecture, and its physical architecture. The software architecture will therefore be more easily integrated based on the existing ROS packages. The developer's task after that is to adapt the code and integrate it with the remaining ROS elements. In this study, we choose to adapt the \"agvs\" packages developed by the company Robotnik Automation [47] to our case study to validate our methodology. We followed a bottom-up approach guided by a SysML information model to select a baseline solution of an AGV in order to create a new AGV design that met the system requirements. This saved a lot of time in the AGV design process. In the next sections, we will detail how to implement one single AGV system in the ROS platform. Some remarks will therefore be given on the simulation of the swarm of AGVs.
## 2 Problem Formulation
### Dynamic Searchable Encryption  
A dynamic searchable encryption scheme typically consists of the \(Setup\), \(Update\), and \(Search\) algorithms.  
The role of the \(Setup\) algorithm is to generate keys and construct the encrypted database. Specifically, it takes the security parameter \(\lambda\) and the database \(\mathbf{DB}\) as inputs, and outputs the data owner's key \(sk\), state \(\sigma\), and an encrypted database \(\mathbf{EDB}\).  
The \(Update\) algorithm is jointly executed by the data owner and the server, and its purpose is to insert or delete data from the database. Specifically, the data owner provides the key, state, keyword-document pairs, and update type as inputs, while the server provides the encrypted database \(\mathbf{EDB}\) as input. This algorithm will return an updated state \(\sigma^{\prime}\) and an updated encrypted database \(\mathbf{EDB}^{\prime}\) to the data owner and the server, respectively.  
The \(Search\) algorithm is executed collaboratively by the data user and the server, aiming to search for files related to keyword \(w\). Specifically, the data user provides the key  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Descriptions** \\ \hline \(x\overset{\$}{\rightarrow}X\) & \(x\) is uniformly and randomly sampled from the finite set \(X\). \\ \(\lambda\) & The security parameter. \\ \(poly(\lambda)\) & The polynomial function. \\ \(negl(\lambda)\) & The negligible function. \\ \(\mathbf{DB}\) & The files database. \\ \(\mathcal{W}\) & The set of all keywords that appear in \(\mathbf{DB}\). \\ \(w\in\mathcal{W}\) & The keyword in \(\mathcal{W}\). \\ \(\mathbf{DB}(w)\) & The set of identifiers for all files that contain the keyword \(w\). \\ \(|\mathcal{W}|\) & The number of distinct keywords. \\ \(bs\) & The file identifier \\ \(st_{c}\) & The state of keyword. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common Notations.  
Figure 1: Overview of system model.  
\(sk\) and the keyword \(w\) as input, while the server provides the encrypted database **EDB** as input. The algorithm returns a collection of files \(\textbf{DB}(w)\) related to \(w\) to the data user.  
The above content follows the general definition of searchable encryption as outlined in reference [34; 35; 36]. Additionally, our scheme includes the _Verify_ algorithm, which will be elaborated in detail later in this paper.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 2 Materials and Methods  
### Study Area  
The Yellow River Basin (YRB) is located in northern China (Figure 1), between \(95^{\circ}53^{\prime}-119^{\circ}05^{\prime}\) E and \(32^{\circ}10^{\prime}-41^{\circ}50^{\prime}\) N, and is the second largest river in China. It originates in the Bayan Kara mountains of the Qinghai Tibet Plateau and flows through nine provinces and regions including Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong, with a total drainage area of \(79.5\times 10^{4}\) km\({}^{2}\)[31]. YRB stretchesacross four geomorphic units from west to east: Qinghai Tibet Plateau, Inner Mongolia Plateau, Loess Plateau, and Huang Huai Hai Plain. YRB belongs to continental climate, and the climate of different regions in the basin is significantly different. Farmland, grassland, and woodland are the main land-use types in YRB [32].
## 3 Proposed Work
### Link Communication
#### 3.4.1 Direct Communication  
Zone 1 is equipped with normal nodes, since they have low energy and allocated close to BS. These nodes sense environment, then send collected data to BS in direct mode.  
In the first round, clusters and cluster heads are formed using normal LEACH algorithm, where cluster head is elected using Equation (17) as in a SEP algorithm for normal nodes. After transmitting data, each sensor node in the network consumed a certain amount of energy, which is different for each node. The power consumption depends on the distance between the sending and receiving nodes, represented as \(d\). Therefore, in the next round, cluster head is selected using the modified equation in Equation (24).  
The cluster head sends its cluster head announcement information to member nodes in the corresponding cluster, once it is elected for the current round. Depending on this received message signal strength, other remaining non-cluster head sensor nodes decide whether to join this cluster head or not. Further, to avoid the data collision in the network, cluster head broadcasts and allocates TDMA schedules in the member nodes to transmit data in different timeslots. This process continues until the end of all rounds and until all the nodes in the network are exhausted and lost its energy. This time period is called Set-Up Stage.
# An IoT-Based Glucose Monitoring Algorithm to Prevent Diabetes Complications
## 3 System Architecture
\(Y_{t}=\) series real value in period  
Figure 3: Elements of the server. Figure 2.0 Sensing stage. \(p\) = number of periods to be prognosticated in the future  
Tests were performed in a private and controlled environment. Several trials were carried out to validate the correct sending of the interpretations. The algorithm takes readings every thirty minutes, this periodicity was consulted with a specialist. It requires three historical readings (\(n\) = 3). In a first round, the first average is calculated using these values. Then, the same period, n, is used to calculate the second average of the first round. We propose a third period for p (this period can be adapted according to the patient's profile) to produce a prognosis. The prognosis obtained is used by the system to determine whether a critical condition exists or not. It is important to clarify that the caregiver must provide the time considered for each meal and it should be considered for the system. The visualization of the information is based on a web page. To develop it, tools such as Adobe Dreamweaver, JavaScript, and Jquery libraries were used. Ajax methods play an important role in maintaining asynchronous communication between the browser and the server, adding flexibility to the page. To validate the values obtained from the system, we characterized our sensing stage through a comparison with a traditional glucometer. A variation of less than a digit was achieved (one mg/dL). To ensure end to end functionality, we divided the tests into three stages as with the architecture. In the first stage, we confirmed the sensor and embedded device assigned to the patient (identifier) and the value obtained at the output. The second stage refers to the verification of the network configuration, the transmission between the embedded device and the router, and how the reading within messages are forwarded. In the third stage, we verified correct data reception and profile authentication in the servers. Afterwards, the information is processed by the prognosis algorithm. Furthermore, the appraisals are displayed on the web page for the defined period and the assigned profile. We also checked notifications when they existed. The web page shows a message when no data is received or if there is a communication problem. Thus, we assure the delivery of the proper reading.
## 4 Discussions and Comparative Analysis  
Many MPPT techniques can be found in the literature. This research conducted a comprehensive evaluation of the literature on MPPT approaches for both partial shade (non-uniform) and uniform irradiance. These techniques are divided into the following three categories: online, offline, and intelligent MPPT techniques. All the MPPT methods aim to optimize the output power of a PV system in an unrelated way. The following  
Figure 16: Flow chart of Java-based MPPT.  
aspects are explored in this section: the GMPP tracking capability, convergence speed, complexity, and environmental change sensitivity.
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 3 Dataset Description  
The dataset utilized in this study is an extension of the synthetic magnetometer-based data framework originally developed in our previous work [3]. The dataset was generated using a digital twin approach employing finite element method (FEM) simulations to replicate the magnetic signatures of underwater objects under controlled environmental conditions, thereby overcoming the challenges associated with the scarcity and high cost of empirical data collection in underwater UXO detection.
## 4 Simulation Results and Discussion
### Path Planning in Static Scenarios
Figure 7: Comparison of path length for five algorithms in six different test environments. Another important evaluation parameter is search time. Table 2 gives the mean value of search time for the four algorithms mentioned above in six different test environments described in Table 1. All simulations were performed 20 times. As can be seen in Table 2, the APF-IRRT* algorithm could find the optimal solution faster than other algorithms. Again, taking the environment M5 as an example, it gives a difference of 1.14 s between the APF-IRRT* and IRRT* algorithms. According to the search time of the algorithm, the search efficiency of APF-IRRT* algorithm increases by about 35%, 30.5%, and 24% more than that of IRRT*, A*-RRT*, and P-RRT* algorithm, respectively. The simulation results indicate that the APF-IRRT* algorithm, by introducing the virtual force of APF into the expansion stage of the search tree, increases the guidance for the algorithm, greatly improves the search efficiency, and further shortens the path length of the algorithm. To verify the effectiveness of the APF-IRRT* algorithm in more complex environments, we construct a grid map based on a real environment of our laboratory, as shown in Figure 8. We can see from Figure 8 that the proposed algorithm can find a best path which has the shortest collision-free path length from the start point to the target point. It also indicates that the proposed
## 4 Methodology
### Time Series Generation
#### 4.3.2 Decoder  
The input of the decoder is the current trajectory C, which contains spatial information, state information, period information, and travel time information for the current trajectory. In order to facilitate the extraction of information by the decoder, we use a linear layer to embed the current trajectory C, mapping it to a higher-dimensional hidden space. The specific operation is as follows:  
\[C^{h}=Linear(C) \tag{22}\]  
\(C^{h}\) is the output of the linear layer.  
After embedding the trajectory, the decoder uses a double-layer self-attention mechanism to capture the spatiotemporal relationships within and between the trajectories. In the first layer of the self-attention module and the residual connection and layer normalization, the processing of the spatial trajectories is consistent with the self-attention mechanism in the encoder.  
However, in the second layer of the multi-head self-attention module, a cross-attention mechanism is used to allow the final output of the encoder to be different based on the different inputs of the decoder. In this layer, the final output of the encoder is mapped to the hidden spaces K and V, and the output of the decoder's previous layer is mapped to the hidden space Q. Then, these are used to perform the multi-head self-attention operation on the mapped Q, K, and V.  
Subsequently, to address training problems and enable faster convergence, a residual connection and layer normalization are used to process the output of the cross-attention mechanism.  
At the same time, the encoder also uses a multi-layer structure, with the output of the previous decoder layer as the input of the next decoder layer, until the output of the last layer.  
Finally, in order to capture the spatiotemporal relationships between the trajectories, a long short-term memory (LSTM) network is used to process the output of the decoder. Before this, in order to better capture the temporal relationships between the trajectories, the decoder's output is merged with the original spatial trajectory, i.e., they are connected together. The final output of the LSTM is the desired time series, denoted as T in (23), where \(O^{d}ec\) represents the output of the decoder and C represents the embedded spatial trajectory obtained from (22). The model uses the mean squared error (MSE) as the loss function.  
\[T=LSTM(Concat(O^{dec},C)) \tag{23}\]  
After generating the time series, the reconstructed spatiotemporal trajectory is obtained by simply concatenating it with the non-embedded spatial trajectory bit by bit.
## 6 Image Compression
### The Implementation on a Distributed System
At the \(k\)-th step, if areas \(A_{(i-1)2^{k-1}+1,j}\), \(A_{(i-1)2^{k-1}+2,j}\), \(\cdots\)\(A_{i2^{k-1},j}\), with \(i\) odd, were merged for \(w_{1}\leq j\leq w_{2}\), the processor storing area \(A_{(i-1)2^{k-1}+1,w_{1}}\) will broadcast to the processors storing the areas \(A_{i2^{k-1}+1,j}\), \(A_{i2^{k-1}+2,j}\), \(\cdots\)\(A_{(i+1)2^{k-1},j}\) to merge with the above areas for \(w_{1}\leq j\leq w_{2}\), if they are monochromatic with the same color. The broadcasting will involve processors up to level \(2h-2k+1\). The same is done horizontally, that is, if \(A_{i,(j-1)2^{k-1}+1}\), \(A_{i,(j-1)2^{k-1}+2}\), \(\cdots\)\(A_{i,j2^{k-1}}\), with \(j\) odd, were merged for \(\ell_{1}\leq i\leq\ell_{2}\), the processor storing area \(A_{\ell_{1},(j-1)2^{k-1}+1}\) will broadcast to the processors storing the areas \(A_{i,j2^{k-1}+1}\), \(A_{i,j2^{k-1}+2}\), \(\cdots\)\(A_{i,(j+1)2^{k-1}}\) to merge with the above areas for  
Figure 6: Storing the image into the leaves of the tree. \(\ell_{1}\leq i\leq\ell_{2}\), if they are monochromatic with the same color. The broadcasting will involve processors up to level \(2h-2k\). After O(\(\log M\)) steps, the procedure is completed. If the waste factor is less than 2, as conjectured in [14], we can make a second assumption that each pixel is covered by a constant small number of monochromatic matches. It follows from this second assumption that the information about the monochromatic matches is distributed among the processors at the same level in a way very close to uniform. Then, it follows from the first assumption that the amount of information each processor of the tree must broadcast is constant. Therefore, each step takes O(\(\alpha\)) time and the image parsing phase is realized with O(\(\alpha\log M\)) time and O(\(n/\alpha\)) processors. The sequence of pointers is trivially produced by the processors which are leaves of the tree. For the monochromatic rectangles, the pointer is written in the leaf storing the area at the upper left corner. Differently from the shared memory machine decoder, the order of the pointers is the one of the leaves. As previously mentioned, some areas could be entirely covered by a monochromatic match and the subsequence of pointers corresponding to a given area is ended with 1111 followed by the index of the leaf storing the next area to decode. We define a variable for each leaf. This variable is set to 1 if the leaf stores at least a pointer, 0 otherwise. Then, the index of the next area to decode is computed for each leaf by parallel suffix computation. Moreover, with the possibility of a parallel output the sequence can be put together by parallel prefix. This is, obviously, realized in O(\(\alpha\)) time with O(\(n/\alpha\)) processors. The interested reader can see how the decoding phase is implemented with the same complexity in [8].
## 3 System Performance Analysis
### System Throughput  
In Figures 1 and 2, the individual system model has two transmission blocks. Each odd or even transmission block is separated into two time slots. The achievable system throughput in an odd or even transmission block is the sum of the minimal device throughput at the relay and device in the same transmission block. Therefore, the system throughput is expressed as follows:  
\[TP^{(\theta)}=\sum\limits_{n=1}^{N}{\left(1-\text{max}\Bigg{\{}OP_{\mathcal{R}-x _{n}}^{\left(\tau_{1}^{(\theta)}\right)},OP_{n-x_{n}}^{\left(\tau_{2}^{(\theta)} \right)}\Bigg{\}}\right)R_{n}^{*}} \tag{40}\]