esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Platform and Sensors Implementation and Evaluation
### Sensors
#### 4.2.2 Inclinometers  
The system has to measure the tilt of the slabs in the installation phase to assist operators and to periodically monitor the slab behavior in the monitoring phase. Three alternatives have been considered to sense the slab tilt. Table 3 shows the technical characteristics of the evaluated inclinometers.  
The first one is using the ADXL345 accelerometer described in Section 4.2.1. In this case, it is necessary to implement some simple transformations to get tilt results from acceleration data provided by the accelerometer. This solution is cheap and implies low energy consumption. However, as established in the system requirements, it is necessary to achieve a precision of 0.1 grades, which is not supported by this sensor. The second option is the use of the SQ-SI-360 DA inclinometer from SignalQuest. This sensor automatically calculates the tilt from multiple acceleration readings carried out transparently with respect to the application.  
As a third option the SCA100T-D2 sensor was also analyzed. This sensor offers practically the same features as the SQ-SI-360 DA sensor, so it not only meets with the project requirements, but it is also cheaper. To be sure that the performance of the SCA100T-D2 is similar to its competitor in terms of project requirements, both of them were tested in our lab in order to see the angle provided by them placing them in different positions and also change from one position to another gradually. The results show that SCA100T-D2 provides the same accuracy measures as the SQ-SI360 DA sensor. Taking into account the tests and the kind of interface they offer, the SCA100T-D2 sensor is the best option.
## 5 Discussion  
### Analysis of Forest Fire Burned Area Detection Errors  
In this study, LightGBM and RF, which are ensemble learning-based machine learning approaches, and U-Net, which is a deep learning-based machine learning approach, were used to detect the burned area caused by forest fires. Sentinel-2A/B satellite images before and after occurring forest fires were used as input data (e.g., SR, NDVI, and NBR). Three Schemes, each of which was composed of different combinations of the aforementioned data, were evaluated. Based on accuracy assessment, the optimal model for detecting burned areas was identified. In both sites, the U-Net showed greater overall accuracy than  
Figure 6: Spatial distribution of burned area detection results in Site B.  
the LightGBM and RF (Figures 5 and 6). Figure 6 also demonstrates this tendency. The diverse patterns of land cover at Site A revealed noise, resulting in misclassification in the burned area [47]. Although the ensemble-based machine learning models generated numerous misclassified pixels in Schemes 1 and 2, the rate of misclassified pixels was reduced in Scheme 3 (Figures 7 and 8). This is because of the incorporation of vegetation indices as input data in Scheme 3. It indicates that use of temporal data (before and after the forest fire) as well as additional input data (vegetation indices) contributed to improve the burned area detection accuracy.  
Figure 7: Visualization of burned area detection errors in Site A.  
The U-Net obtained robust modeling performance in all Schemes. Although misclassified pixels were detected, most of the pixels were identified close to the burned area's boundary. This is because the degree of damage weakens toward the boundary [48]. The ensemble learning-based machine learning models generally exhibits a strong sensitivity to the properties of the training data and weak performance in noise [49]. In contrast, the U-Net model is more resilient to noise and less sensitive to the properties of the training data than the ensemble-based machine learning models [50; 51; 52]. It is therefore appropriate to use the U-Net to identify burned areas from forest fires.  
Figure 8: Visualization of burned area detection errors in Site A.  
In Site B, the ensemble learning-based machine learning models outperformed U-Net in terms of recall value (Table 4). It is because the ensemble-based models over-detected the burned areas. This tendency is clearly shown in Figure 8. The recall value errors are due to the FP pixels, which are presented as gray colors in Figure 8. Detecting more pixels than the actual number of pixels indicates false alarms, and the recall value could be abnormally high [53]. In this case, using the F1-Score and Kappa coefficient can be suitable for evaluating the detection performance rather than the recall error. [54]. The highest F1-Score and Kappa values were consistently achieved by the U-Net for all Schemes in Site B.
## 4 FEM Model  
The effective Young's Modulus was implemented in the FEM software ANSYS. Figure 3 illustrates the applied algorithm of the model and its iterative sequence. It relies on two external inputs: a thermal profile for setting the thermal boundary conditions and the Reimann coefficients derived from uniaxial compression tests for the specific packed bed. This quasi stationary model is able to calculate the acting stresses on the container wall of a packed bed TES for one specific time. For a time-resolved calculation, the model needs to be initialized each time from new.  
At first, the packed bed is confined by geometrical boundary conditions and material properties. The ceramic packed bed (see [12] for material properties) was modeled with a packed bed diameter of 10 m and packed bed height of 40 m, according to [4]. The continuum was subdivided into a grid of \(68\times 12\) elements. This low number of elements was chosen for time-efficient calculations while still obtaining reliable results. Initially, a uniform effective Young's Modulus is assigned for all elements of the grid. In the next step, temperatures corresponding to the temperature profile in Figure 2 are assigned to the elements. The implemented temperature profile is shown in Figure 2. Here, the temperature distribution is considered uniform in radial direction but changes in axial direction over bed height.  
Based on the thermally induced forces, due to thermal expansion, and mechanically induced forces, due to self-weight of the bulk, the axial stresses are calculated for each element. The resulting stresses, together with the previously determined Reimann parameters, serve as input for the calculation of an updated effective Young's Modulus. The updated Young's Moduli are assigned to the elements  
Figure 3: Flow chart showing the implementation of a simplified continuum model using the Finite Element Method.  
of the grid, and in an iterative sequence, the solving process is repeated until the stresses reach an equilibrium state. The assessment of the contact pressures between the packed bed and the confining wall is based on the converged results.
## 6 Analyzing the Graph Database-Based and IFC-Based Construction Management Information Model
### Schedule Network Analysis  
The schedule network of prefabricated buildings' construction process can be automatically obtained after twinning the IFC-based 4D construction management model into the graph database without any manual work. Furthermore, this paper develops a graph-based algorithm to analyze the schedule network. The necessity to analyze the schedule network is reflected in two aspects. Firstly, the analysis of schedule networks can identify the tasks' centrality in the schedule network, which can help managers adjust the resources' allocation. Secondly, the parameters of schedule networks are significant in the Graph Neural Network (GNN), which will benefit further work in machine learning or deep learning [58]. The results of the schedule network analysis will be stored in the Pset_ScheduleNetworkAnalysis. The algorithm calculates three parameters in the schedule network.  
Degree Centrality (DC): The DC is the total number of edges directed to a node and edges directed to others. The degree centrality of the node reflects the centrality of the construction task to other tasks. The greater degree centrality means that the delay of this node is more likely to cause the delay of other tasks in the construction network plan, which means the higher the risk caused by the delay of this task.  
Closeness Centrality (CC): The CC of a node is the average length of the shortest path between the node and all other nodes in the graph, which quantifies how close the node is to others. Closeness centrality reflects the difficulty of intercommunication between nodes [59]. The larger the closeness centrality, the stronger the interaction between the construction tasks in the schedule network.  
Betweenness Centrality (BC): BC is the number of shortest paths that pass through the node and quantifies the number of times the node acts as a bridge along the shortest path between two other nodes. Betweenness centrality reflects the pivotal role of nodes in the network [60]. The larger the betweenness centrality, the greater the construction task's control effect on the schedule network's other tasks.  
The calculation methods of degree centrality \(DC(x)\), closeness centrality \(CC(x)\), and betweenness centrality \(BC(x)\) are represented as follows:  
\[DC(x)=[\text{Count}(\text{E}_{k}\,|\,\text{StartNode}(\text{E}_{k})=\text{x })+\text{Count}(\text{E}_{k}\,|\,\text{EndNode}(\text{E}_{k})=\text{x})]/( \text{N}-1), \tag{1}\]  
\[CC(x)=1/\sum_{y}d(y,x), \tag{2}\]  
\[BC(x)=\sum_{y\
eq x\
eq z}d(y,x), \tag{3}\]  
where \(x\), \(y\), \(z\) are different nodes, \(d(y,x)\) is the distance between nodes \(x\) and \(y\), \(\sigma_{yz}\) is the total number of shortest paths from node \(y\) to node \(z\), \(\sigma_{yz}(x)\) is the number of those paths that pass through node \(x\), and N is the total number of nodes in this network.
## 2 Model and Test Setup
### Experimental Program  
A series of experiments with H-N pencil lead breaks were conducted to examine AE source localization in the 3D model PRF. We applied the H-N pencil lead breaks at three different locations--raft top surface (or RT), raft bottom surface (or RB), and pile middle at a distance of 10 cm from the raft bottom (or PM). Additionally, a total of 15 piles installed on the half surface of the raft was tested, exploiting the symmetry--Zones #1 to #5, #7 to #10, #13 to #15, #19, #20, and #25 (Figure 2c). A total of 45 cases (15 piles at each location) were examined. Moreover, we repeated the test 20 times per case to produce sufficient data and assess accuracy.  
Figure 3 shows the representative AE wave signatures and their frequency spectra, where the artificial AE sources were generated from the three locations in Zone 14, and the AE signals were captured by ACC1. The majority of the AE energy was placed from 5 kHz to 20 kHz, whereas a clear decrease in the amplitudes was confirmed with an increase in the travel distance.  
Figure 3: Acoustic emission (AE) signals acquired at three different locations: (**a**) time-domain signals and (**b**) frequency responses. Note that RT, RB, and PM indicate that a source was generated at the raft top, raft bottom, and pile middle. The AE sources were produced at Zone 14.
## 2 Related Work and Foundational Concepts
### Software and System Development Life Cycles
#### 2.1.1 Waterfall Method  
The Waterfall methodology follows a linear, sequential structure where each phase must be completed before the next begins [9,10]. This rigid approach is effective for projects with stable, clearly defined requirements, such as simple, unclassified systems [11,12]. While its emphasis on detailed documentation can aid in demonstrating compliance with standards like HIPAA or PCI-DSS, its inflexibility makes it poorly suited for dynamic threat environments that require continuous security reassessment and rapid adaptation [4].
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 5 Testbed Deployment and Experimentations
### Duty Cycle  
Figure 10 depicts the average duty cycle (sleep time) per node.  
Figure 8: Average end to end delays per data packet.  
Figure 9: Time of sending and receiving of packets of a sample photo.  
Selecting a random sleep period in the I interval, with the parameters of Table 1 and for \(\alpha=10\) for instance, any node would sleep an average period approximated by random \((50,200\times 10)\simeq 1025\) ms, in MED_N_ADAP. Which leads to a sleep ratio equal to: \(\frac{1025\times 100}{1225}=83\%\). Experiments yield an average value \(\simeq\,70\%\).  
Another observation from Figure 10, is that, as expected, INFR scenario ensures the highest values of the duty cycle. The average duty cycle increases with \(\alpha\) for INFR scenario, while it is almost insensitive to \(\alpha\) for MED_N_ADAP and MED_ADAP. We evaluate the duty cycle of each node in Figure 11 for \(\alpha=10\) and \(\alpha=30\).  
\(\alpha=30\) is globally ensuring higher duty cycle than \(\alpha=10\). However, increasing \(\alpha\) means that less neighbors per node will be awake which increases forwarder search phase and leads to lower duty cycle. This explains the fact that \(\alpha=10\) is more energy effective than \(\alpha=30\) for some nodes in MED_N_ADAP scenario. In contrast, in INFR and MED_ADAP scenarios, the duty cycle increases with \(\alpha\) almost for all nodes. This means that regulating traffic injection (INFR scenario) and tuning the  
Figure 11: Individual duty cycle.  
Figure 10: Average duty cycle per node.  
duty cycle (MED_ADAP scenario) allow the network to take advantage from high sleep durations setting. To conclude, high sleep periods is essential for energy saving, but adapting it to the network state is generally more effective.  
Another observation from these figures is that nodes have heterogeneous duty cycles; nodes close to the source have low duty cycle compared to other nodes as their chances to receive a packet from the source when they turn on their radio are relatively high. Also, from Figure 12, we see that even nodes with high activity reach good duty cycle.
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
## 4 Control Design  
This paper focuses on acquiring the accurate underactuated quadrotor dynamic model using model-free approach. To stabilize the system and reduce the tracking and position errors with minimum computation time and control input energy, a model-free single-dimension fuzzy-based sliding mode control (MFSDF-SMC) is proposed. This entire approach of MFSDF-SMC will ensure the greater accuracy of tracking with unknown and unmodelled dynamic factors.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 5 Algorithmic Design
### Pre-Processing
#### 5.2.1 Data Pre-Processing for Random Forest  
In the pre-processing stage for random forest (RF), statistical features are extracted from the IQ data obtained from the SDR. It includes 64 channels for kurtosis and 64 channels for Fourier coefficients. Information such as the latitude, longitude, and the number of available and utilized satellites, is obtained from the GNSS receiver. Analyzing the feature importance reduced the number of recorded features from 310 down to 36. Each feature is normalized through individual scaling using Equation (7).  
\[\text{Normalization:}\;x^{\prime}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{7}\]
## 2 Principle
### Multispectral Image Reconstruction Method
#### 2.2.1 Traditional Point-by-Point Optimization  
Owing to the smoothness of spectral reflectance for most real-world surfaces, the spectral reflectance can be compressed to several feature vectors using PCA (principal component analysis). [PERSON] et al. (2015) proposed a linear model using the set of orthogonal basis functions, \(b_{k}(\lambda)\). The spectral reflectance can be written as:  
\[S_{\lambda}=\sum_{k=1}^{K}\sigma_{k}b_{k}(\lambda) \tag{2}\]  
where \(\sigma_{k}\) are scalar coefficients, \(k=1\), \(2\),... \(K\) indicates the number of coefficients, and \(b_{k}(\lambda)\) are the orthogonal basis functions. These are eigenvectors derived from the spectral reflectance of Musel color chips. The scalar coefficients can be solved using optimization:  
\[\underset{\sigma}{\text{argmin}}\|\mathbf{F}\mathbf{\sigma}-\mathbf{I}\|_{2} ^{2} \tag{3}\]  
where \(\mathbf{F}\) includes the known quantities \(b_{k}(\lambda)C_{\lambda}P_{\lambda}\), and \(\mathbf{\sigma}\) and \(\mathbf{I}\) are the matrix forms of coefficients and detected values. Imposing the positive constraint and smooth constraint, Equation (3) is changed to:  
\[\underset{\sigma}{\text{argmin}}\Big{[}\|\mathbf{F}\mathbf{\sigma}-\mathbf{I} \|_{2}^{2}+\alpha\|\mathbf{P}\sigma\|^{2}\Big{]},\ \mathbf{A}\sigma>\mathbf{0}, \tag{4}\]  
where \(\mathbf{A}\) is relevant to the positive constraint, \(\mathbf{P}\) is relevant to the second derivative of spectral reflectance, and \(\alpha\) is the regularization parameter. This regularized minimization can be solved using quadratic programming. After obtaining the optimal values of the coefficients, the spectral reflectance can be calculated using Equation (2).  
The above calculation obtains the reflectance of one pixel. This is traditional point-by-point optimization. This optimization of the general quadratic program is NP-hard: it takes more than polynomial time to be solved. If the image has a large number of pixels, it will be very time-consuming to obtain the spectral reflectance. For instance, for 1024 \(\times\) 768 pixels, it takes about 2 h to calculate the spectral reflectance ([PERSON] et al., 2015). Although better computers or parallel computation can reduce the computational time, an improved algorithm with lower computation costs would be better.
# Scaling of Rain Attenuation Models: A Survey
## 3 Existing Scaling Techniques
### Elevation Angle Scaling
Such results are closely related to the value of the effective pathlength. These preliminary results suggest that using a prediction model for low elevation angle application systems such as the LEO satellite should consider the significant impact of rain attenuation, particularly in the heavy rain region.
# Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management
## 3 The Internet of Things and Its Application in Supply Chains
### IoT in Supply Chains: Levers and Application Scenarios
Importantly, cost savings and error reductions are attainable through the use of IoT by automating the warehouse or distribution centre inventory receiving and order dispatch processes, which in turn reduces human intervention (and error) associated with manual storage management. This benefit results from deploying commercial RFID readers and antennas in receiving and shipping docks that send a radio signal to identify RFID tags attached to, or embedded on pallets, totes, or the product cartons leading to a reduction in the time spent in collecting, recording, and retrieving data [54; 55]. Besides, RFID tags help to overcome the (occasional) problematic readability of barcodes in specific industrial environments and result in a higher read accuracy [56]. Production and manufacturing are the second area that can benefit from the implementation of IoT. Industrial machines with embedded sensors can be monitored in real-time and controlled by smart instruments, such as microcomputers, microcontrollers, microprocessors, and intelligent sensors. IoT-based solutions can enhance operational control over the processing capacity, set-up time, and throughput. Therefore, their usage can lead to more efficient machine utilization, reduction of bottlenecks in production and can help in optimizing production planning and scheduling at varying levels within a company. Moreover, [PERSON] and [PERSON] [57] point out that IoT enablement leads to greater agility and proactivity in the company by enabling quick identification of machine errors and enabling predictive maintenance. As a result, this enhanced insight into key manufacturing processes will enable stronger collaboration and value co-creation with suppliers [58] as well as improved machine-to-machine and machine-to-human interaction. Finally, when it comes to transportation activities in supply chains, IoT can also provide potential benefits. For instance, IoT-enabled solutions usher in a well-defined and configured transportation management system (TMS) or \"smart\" TMS [39]. More specifically, a set of IoT devices can help to transform transportation processes and achieve more flexible and efficient operations. For example, a GPS helps to position refrigerated trucks from remote distribution centers and to optimize both routing and delivery time while preserving product quality [59]. In a broader sense, GPS, RFID, and other connected sensors increase the in-transit visibility by measuring conditions such as temperature, humidity, and by precisely localizing vehicles on public roads or at shipping terminals through large scale mapping, traffic data collection and analysis. The data gathered from these IoT devices will help to improve forecasting of delivery times, fleet availability, and routing efficiency [57]. Moreover, these devices can be used for enhancing the sharing of under-utilized resources among vehicles in the parking space or on the road [39].
## 3 Proposed RO-PUF  
Research in finding and improving the quality of RO-PUF has long been carried out. In the conventional RO-PUF, the pulses generated by the ROs of a particular duration are detected by two counters simultaneously. The structure is equipped with a CRP generation process, where demultiplexers and multiplexers are placed before and after ROs, respectively. Therefore, the unequal route distances from ROs to counters caused inaccuracy in counting RO pulses, which were identified as locking phenomena [26; 27] or jitter noise [28]. Meanwhile, this work proposes a modified RO-PUF structure that connects ROs directly to counters (no intermediary circuit between the ROs and the counters), as shown in Figure 1. The circuit of the CRP generation is placed next to the counters. This work has advantages, including:  
* Routing equality--The distance between each RO to the respective counter is equal. Therefore, there will be no locking phenomena or jitter noise.
* One-time run--Each RO will only be run once. This technique would avoid the frequency difference of multiple RO run. In most conventional designs, every RO may be run more than once, leading to a slight difference in frequency. Moreover, multiple RO runs may result in less security due to a side-channel attack [29].
* Flexibility in choosing RO pairs--All CRP generation techniques proposed previously may be applied since the CRP process is independent, as shown in Figure 1.  
Putting a small number of ROs onto FPGA is a must to meet the practical condition of PUF as a hardware-based fingerprint. Therefore, this work realized \(n=30\) ROs to allow more space for the primary circuit. However, the proposed RO-PUF can generate more response bit (\(n!/2(n-2)!\)) compared to the conventional RO-PUF design by [PERSON] et al. of \(n/8\) response bit [20]. Researchers developed a new way of expanding response bit upon the CRP improvement while keeping small area usage. [PERSON] et al. [30] and [PERSON] et al. [31] are among the researchers who proposed expanding response bit techniques  
Figure 1: Propose RO-PUF using direct pulses count.  
where, for \(n\) number of ROs, they can generate as many as \((2^{n}-n-1)\)[30] and \((2^{n}-1)\)[31] response bits. The CRP process is done outside the FPGA chip. Since the CRP generation of this proposed RO-PUF is independent, it may also be realized outside the FPGA chip.