esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 EMD-Resilient Reed-Solomon Codes
### Overall Performance of the Proposed Layer
This indicates the limitation of the proposed layer as it only works flawlessly when the channel controls all symbols of a code word in a same manner, and in case of a partial control, there are always some edge cases that could bypass the inversion layer. Nevertheless, despite the mentioned limitations, the inversion layer still has a great capability to improve the resiliency of RS Codes substantially against single-frequency EMD by eliminating the major CFN and CTP spikes in the graphs. Another limitation of the proposed approach is dependency to a look-up table at the consumer side of the inversion layer. For bigger message lengths, this look-up table is also becoming bigger, which could create a lot of challenges for implementing this technique in embedded systems with small memory. Correspondingly, the obtained results indicate that \(k=1\) provides a significantly higher resiliency as the occurrence probability of CFN and CTP caused by multi-sym-bol-value code words is zero. Moreover, according to the results, using \(k=1\) combined with any symbol sizes (\(m\)) greater than 3 would provide even higher resiliency due to the greater Hamming distance. In the case of \(k=1\), a smaller look-up table becomes generated, which significantly alleviates the mentioned limitation. However, for \(m>3\), the block length also increases, which imposes more overhead. From the safety perspective, the acceptance of this trade-off totally depends on the application.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 3 Methodology
### System Architecture and Dataset Design for IoT-Supported Blockchain Integration
#### 3.1.4 Environmental Monitoring  
During storage and transportation, it is essential to maintain optimal environmental conditions to preserve bean quality. DHT22 temperature [43] and humidity sensors linked to Raspberry Pi 4 IoT gateways [44] with LoRaWAN modules [45] can be used to collect environmental data. Smart contracts can monitor this data stream and automatically downgrade or flag batches that experience harmful conditions such as excessive humidity or temperature swings, thereby protecting the end product quality and consumer trust.
# Age Estimation from fMRI Data Using Recurrent Neural Network
## 4 Experiments  
### Model Parameters  
Hyperparameters of models are optimized via cross-validation as described in Section 4.2. The proposed GRU-based model has three internal layers where each recurrent unit has 300 hidden states, and the width of the final FC layer is 300. The Transformer network consists of three Transformer encoder layers, where each layer consists of two sub-layers. The first is the multi-head self-attention mechanism: we set the number of heads to 2. The second is a position-wise feed-forward layer that sets its network dimension to 8 times the input dimension. We optimize other RNN-based models (vanilla RNN and LSTM) and achieve the same hyperparameters as the GRU-based model. The MLP network consists of three FC layers. Standard dropout probability \(p=0.5\) is used in RNN- and MLP-based models. In the Transformer model, the dropout probability of \(p=0.1\) is used in the positional encoding and the encoder layer.  
Figure 3: Schematic diagram of the model structure. (**a**) The proposed GRU-based model. The GRU module takes the preprocessed data \(X=(x_{1},x_{2},\dots,x_{t})\) sequentially as an input. The output of the last GRU module is connected to an FC layer, which is followed by the BN layer and the ReLU activation. Finally, the last FC layer estimates the age \(y\in\mathbb{R}\). All other RNN-based models have the same structure. (**b**) The Transformer-based model structure.
# Advanced Scheme to Generate MHz, Fully Coherent FEL Pulses at nm Wavelength
## 3 Resonator Considerations
### A Simple Model for the Reflectivity Requirements and Estimated Power Level in the Cavity
This result is independent of the input seed laser power, however, in practice, the energy modulation process depends on both the input seed peak power and the length of the modulator as [47]:  
\[\Delta E=\sqrt{\frac{P_{in}}{P_{o}}}\frac{m_{e}2 KL_{mod}\underline{J}}{\gamma w_{0 }}, \tag{3}\]  
where \(w_{0}\) is the seed waist size, \(K\) is the dimensionless undulator parameter, \(m_{e}\) is the electron mass in \(keV\), \(P_{0}\approx 8.7\,\)GW [47], \(\underline{J}=J_{0}(\xi)-J_{1}(\xi)\), where \(\xi=K^{2}/(4+2K^{2})\) and \(J_{0,1}\) the Bessel function of the zeroth and first order. As the modulator is used both for energy modulation and amplification, both these aspects need to be taken into account for the exact design. Let us consider an example of these analytical estimations by means of a reasonable set of parameters: \(\lambda_{seed}=50\,\)nm, \(K=3.25\), \(w_{0}=286\,\)\(\mu\)m, \(\gamma=2641.9\), \(L_{g}=1.12\) m. In Figure 6, we show the expected energy modulation for a combination of seed laser peak power and modulator length, calculated with Equation (3). In the same figure, we show the reflectivity required as expected by the 1D cold theory and Equation (1) with the dashed black vertical lines, as it is independent of the input seed laser power. It is clear that while the modulator length is fixed and is used to determine the amplification, for a given modulator length, it is still possible to use the seed laser peak power as a knob to adjust the energy modulation. In turn, the energy modulation is related to the energy spread which affects the FEL process in the amplifier, as already discussed. Note that while Equations (1) and (3) are well established approximations valid in the 1D case, diffraction effects should be also taken into account and the exact dependencies may deviate from this result.
## 3 Computational Modeling and Simulation
### Flow Separation Analysis  
The flow separation point (distance in the axial direction) was estimated empirically using Kalt and Badal and Schmucker flow separation criteria, which was about 37 mm after of the throat [18; 19]. To validate the flow separation point, a primary flow simulation of combusted species of RP1 and LOX was carried out without modeling combustion at sea level atmospheric conditions. It is seen from Figure 6 that the flow, after passing through the throat, expanded and detached from the wall of the nozzle, exactly at the location estimated using flow separation criteria.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Units** \\ \hline Nozzle inlet pressure & 2 & bar \\ Nozzle inlet temperature & 1008 & K \\ GOX injection pressure & 2 & bar \\ GOX injection temperature & 300 & K \\ GOX injection velocity & 300 & m/s \\ Nozzle outlet pressure & 0.7 & bar \\ Nozzle outlet temperature & 800 & K \\ \hline \hline \end{tabular}
\end{table}
Table 6: Gas generator design properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Species** & **Mass Fraction** \\ \hline
1 & C (gr) & 0.4768273 \\
2 & C\({}_{2}\)H\({}_{4}\) & 0.000016 \\
3 & C\({}_{2}\)H\({}_{6}\) & 0.0000123 \\
4 & CH\({}_{4}\) & 0.1320777 \\
5 & CO & 0.1579854 \\
6 & CO\({}_{2}\) & 0.0644395 \\
7 & H\({}_{2}\) & 0.0631787 \\
8 & H\({}_{2}\)O & 0.1054770 \\
9 & HCHO & 0.000003 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Species mass fractions.  
The simulation also revealed a complicated shock formation, which was due to the flow adjustment to ambient pressure. The adverse pressure gradient caused the shock wave inside the nozzle, which separated the mainstream flow and boundary layer from the nozzle wall. Due to over-expansion and over-compression, a repeated diamond shock, a triple point, an incident shock, a Mach disk, and a reflected shock were also observed, as shown in Figure 6. These results were validated by [PERSON]'s convergent-divergent rocket nozzle, Munday, and Gvozdeva gas flow in nozzles, as they obtained the same diamond shock pattern formation through CFD. The recirculation region that originated from the flow separation was utilized for a secondary injection of GOX and modeling combustion [20; 21; 22].
# Integration and Field Evaluation of an IoV System for Enhancing Road Safety
## 3 Human-Machine Interface Architecture  
In recent years, new and promising approaches exist to improve the interaction between drivers and vehicles. Among them, new approaches exist in the user interfaces of smart devices. The availability of fast and reliable wireless communication systems (e.g., Bluetooth, 4G and 5G) has enabled the development of driver interaction systems that reside in smart devices in addition to traditional vehicle equipment. Many examples include the use of smartphones as additional screens for driving-related information [30].  
Critical information for road safety is provided traditionally through in-vehicle messages in the form of warnings, which however raise ergonomic issues regarding the driver's workload, distraction [31], understanding or combination of warnings (of varying severity) provided by coexisting applications.
## 2 Materials and Methods
### Dataset
#### 2.2.2 Elevation Data  
This study utilized the PIE-Engine online editing tool to acquire SRTM 250 m resolution DEM data ([[https://engine.piesat.cn/dataset-list](https://engine.piesat.cn/dataset-list)]([https://engine.piesat.cn/dataset-list](https://engine.piesat.cn/dataset-list)), accessed on 3 January 2023). The SRTM (Shuttle Radar Topography Mission) data were from the processed radar images that were measured by NASA and NIMA. It provided elevation data for the Biome-BGC model and the MTCLIM model.
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
###### Abstract  
Contiki's Cooja is a very popular wireless sensor network (WSN) simulator, but it lacks support for modelling sensing coverage, focusing instead on network connectivity and protocol performance. However, in practice, it is the ability of a sensor network to provide a satisfactory level of coverage that defines its ultimate utility for end-users. We introduce WSN-Maintain, a Cooja-based tool for coverage and network lifetime evaluation in an in-building WSN. To extend the network lifetime, but still maintain the required quality of coverage, the tool finds coverage redundant nodes, puts them to sleep and automatically turns them on when active nodes fail and coverage quality decreases. WSN-Maintain together with Cooja allow us to evaluate different approaches to maintain coverage. As use cases to the tool, we implement two redundant node algorithms: greedy-maintain, a centralised algorithm, and local-maintain, a localised algorithm to configure the initial network and to turn on redundant nodes. Using data from five real deployments, we show that our tool with simple redundant node algorithms and reading correlation can improve energy efficiency by putting more nodes to sleep.  
sensor network; simulation tool; coverage maintenance; lifetime evaluation +
Footnote †: _Sens. Actuator Netwm._**2016**, 5, 4; doi:10.3390/jsan5010004
## 1 Introduction  
Currently, research on the magnetic properties of bulk magnetic materials, micro and nanostructures, is an important tool for students in the fields of physics and engineering [1, 2, 3, 4, 5]. The hysteresis curves of a magnetic material can contribute to determining its basic fields of applications. In addition, different magnetic properties, such as remanence magnetization, coercivity, saturation magnetization, hysteresis, Curie temperature, magnetic anisotropy, and susceptibility, can be determined from magnetic hysteresis curves, obtained at room temperature (about 297 K) [3, 6]. Magnetic materials can also be classified according to their magnetic curves, and, in the case of nanoparticles, the average diameter can also be estimated [7]. The use of devices called magnetometers is the most common technique used to obtain hysteresis curves of magnetic materials. It is responsible for detecting the induced magnetic fields of the material in the presence or absence of an applied magnetic field [1, 2, 8, 9]. Several magnetometers have been developed over the years for different applications and different types of reading systems of sample-induced field, such as the vibrating sample (VSM), perhaps the oldest one [2], the system based on the superconducting quantum interference device (SQUID) [10], magneto-optical Kerr effect (MOKE) [11], and the Hall effect [1, 8, 9], with different sensitivities and costs. However, most parts of these devices (magnometers) are high-cost systems, making them unfeasible for low-budget laboratories, academic studies, or teaching laboratories in many universities.  
This study presents a very different approach to measuring magnetic samples using low-cost equipment (magnetometer) developed with items normally accessible to most research and teaching laboratories, such as two magnets (K&J Magnetics, model BX8 CS), a 5-volt battery or a power system directly from mains, one MLX-90215 sensor (Melexis Inc.),one AD-AD22151 sensor (Analog Devices, Inc.), and bench multimeters (HP 34401A or Iminipa ET-2042D). The first version of this device (magnetometer) was developed and published by [PERSON] and his collaborators [12]. This magnetometer has the advantage of being quickly assembled and transported, in addition to the possibility of becoming operational and thus accessible to most research laboratories, being ideal for low-budget basic physics teaching laboratories. The core of this equipment is composed of two magnets, a small circuit board, and an acrylic plate, which serves as a support for the sensors and the sample holder. We use a simple theoretical magnetic dipole model to estimate the magnetic moment. Most commercial magnetometers are not accessible to a teaching laboratory other than the proposed equipment. Thinking about it, we use a simple model to characterize the results obtained in the measurements. The magnetometer was tested using commercially purchased bulk and microparticle samples.
# Sustainable Approach of Generating Water and Energy:
## 3 Mathematical Model
### Storage Tank and Absorption Chillers  
The storage tank is governed by the following energy balance equation. Equation (9) is derived based on the assumption that the water in the tank does not flow through the PVT array and only the brackish water flows through the PVT array to accumulate heat. To keep the tank temperature at the desired setpoint, the concept of the auxiliary heat \(\dot{Q}_{\text{aux}}\) is described in [28].  
\[C_{\text{p}}*V*\rho_{\text{w}}\frac{\text{d}T_{\text{tan}\,k}}{\text{d}t}=\dot {M}_{\text{f}}\,C_{\text{p}}(T_{\text{f}}-T_{\text{tan}\,k})+\dot{Q}_{\text{ aux}}(t)-\ddot{\mathcal{O}}_{\text{losses}}(t) \tag{9}\]  
The multivariable polynomial model (MPR) developed by [PERSON] et al. [31] is used to estimate the performance of the absorption chiller. The developed correlation model accurately calculates the absorption chiller's thermal loads.  
\[\begin{split} Q_{evap}=B_{0}\,+B_{1}\,*Tgen_{in}\,+B_{2}\,*Tac_{ out}\,+B_{3}\,*Teva_{in}\,+B_{4}\,*Tgen_{in}\,*Tac_{out}\,+B_{5}\,*Tgen_{in}\,*Teva_{in}\,+\\ B_{6}\,*Teva_{in}\,*Tac_{out}\,+B_{7}\,*Tgen_{in}^{2}\,+B_{8}\,*Tac_{ out}^{2}\,+B_{9}\,*Teva_{in}^{2}\,\end{split} \tag{10}\]  
In Equation (10), the regenerative temperature is detonated by \(Tgen_{in}\), condenser temperature is \(Tac_{out}\), and evaporative temperature is \(Teva_{in}\). The output of Equation (10) \(Q_{evap}\) is the cooling capacity of the chiller. The coefficients (\(B_{n}\)) considered when calculating Equation (10) can be found in [31].  
Table 1 shows the coefficients (\(B_{n}\)) considered when calculating Equation (10).
## 5 Numerical Simulation and Verification
### Simulation Investigation and Verification
#### 5.3.4 Investigation for Case 3  
Case 3 investigated multiple reflectors in a pipe. The parameters of the steel pipe used in Case 3 are shown in Table 3, and the 3D finite element model constructed and illustrated in Figure 15a is the combination of Model 1 and Model 3, as it contains two types of reflectors: a defect feature (reflector 1) and a geometric feature (reflector 2). The size of the transducer elements is about 60\({}^{\circ}\), and the step is \(\eta\) = 12\({}^{\circ}\). Steps (1) to (3) were performed first. After simulations, 30 sets of data were obtained, then the  
Figure 14: Analysis results based on the proposed method: (**a**) original 2D scanned image (**Up**), enhanced 2D scanned image (**Middle**) and smoothed 2D scanned image (**Down**); (**b**) enhanced 2D scanned binary image; (**c**) distance curves \(\Psi_{1}\) (**Up**) and \(\Psi_{2}\) (**Down**); (**d**) updated judgment results to the enhanced 2D scanned binary image with different colours.  
original scanned image, the enhanced 2D scanned image, smoothed 2D scanned image and enhanced 2D scanned binary image shown in Figure 15b,c were constructed. Compared with Figure 11c or Figure 14a, the reflection waves were strengthened when the size of the transducer elements was increased. To determine the type of the reflectors, steps (4) to (10) were carried out. Through loading the excitation displacement on the excitation nodes, \(S_{r}(n)\) were obtained by the receiving nodes.  
Through steps (6) and (7), \(\Psi_{1}\) and \(\Psi_{2}\) were calculated in MATLAB and shown in Figure 15d. For reflector 1, \(D_{1}=\min\{D(S_{i},S_{ri})\}=33.95\) and \(D_{2}=\min\{D(\overline{\overline{S_{i}}},S_{ri})\}=34.22\), \(D_{1}<D_{2}\), thus the front-edge of the reflector was a step down, indicating the reflector was a defect feature. The \(RlCR\) was equal to 1.31 (\(RlCR>1.05\)), thus the judgement is reliable. For reflector 2: \(D_{1}=36.6\) and \(D_{2}=36.5\), \(D_{1}>D_{2}\), thus the front-edge of the reflector was a step up, indicating the reflector was a geometric feature. The value of the \(RlCR\) was equal to 1.10 (\(RlCR>1.05\)), thus the judgement is reliable. Lastly, the enhanced 2D scanned binary image was updated with the results, as shown in Figure 15e.  
Figure 15: _Cont._
## 3 Proposed Architecture Overview
### Proposed System Architecture: Smart Broker  
In this new blockchain-driven PS architecture, the smart broker has the following new capabilities compared to a traditional PS broker:  
* Calculating the quality of data for the publishers. This is typically performed for individual topics based on various parameters, such as relationship with other topics and timeliness.
* Preparing a list of recommended topics based on the metadata of the topics in case a particular topic is not available.  
The blockchain-driven model consists of several modules communicating with each other as depicted in Figure 2:  
* _Publisher-topic registration_ serves as the first step for any new incoming publisher (IoT device) data within the smart broker. It enables the registration of topics by analyzing the presence of essential tags. Additionally, topics are manually grouped based on their correlation to one another.  
Figure 1: The request–response model consists of a smart broker, publishers, and subscribers.  
2. The _Topic Data Quality Assessment Module_ (_TDQAM_) assesses the _reliability_ of registered publisher IoT data by checking its quality and promptly acting to flag publishers providing poor-quality data. The publisher's data that has gone through reliability checks is granted access to the blockchain-based storage.
3. The blockchain-based _Data Storage Module_ (_DSM_) is designed to store data from all the modules, including data that have passed the assessment checks, accompanied by its corresponding quality score.
4. _Topic Drift Monitoring_ (_TDM_) monitors the publishing intervals of the publishers by detecting changes in the patterns of these intervals over time. It then transmits this information to the blockchain-based _Topic Reputation Scoring_ (_TRS_) model.
5. The TRS continuously monitors the publisher's reputation by using assessments from both TDQAM and TDM, providing reputation scores to each publisher. The _Subscriber Request Assessment Module_ (SRAM) uses these reputation scores to prepare and send a publisher's reputation score when a subscriber requests data from that publisher.
6. The SRAM manages IoT data requests from subscribers. It accepts incoming requests and delivers the requested data, along with its quality score, to the subscriber. It may also recommend substitute topics. _TDQAM_, _TDM_, and _TRS_ operate at regular time gaps of \(\lambda\) and \(w\).
## 3 Sensor Node Position Estimation  
In a WSN, where a mobile sink collects information from sensor nodes, it is common for the sink to be out of range for nodes that have packets to forward. As a result, the forwarding node needs to find a path through other sensor nodes to reach the sink. Location awareness can help the node decide on the best neighbor to forward the packet to. By knowing the positions of its neighbors and the sink, the forwarding node can send the packet to a neighbor that is closer to the sink. However, using a GPS module is not feasible for small, cheap, low-power devices similar to ordinary wireless sensor nodes. To address this, the sink periodically shares beacons with its new position as it moves, allowing sensor nodes to have up-to-date position information for position-based packet routing. Sensor nodes can also use these position packets to estimate their own positions.  
In the proposed position estimation algorithm, a sensor node can estimate its own position using packets from both the sink and neighboring nodes. Once a node estimates its position, it shares it with neighbors to help them estimate more accurate positions. It is important to note that position information from the sink is more reliable because it is based on the GPS position. Therefore, a sensor node will always prefer position information from the sink over information from a neighbor node. Although at least three position data points are needed for more accurate estimation, it is still possible to estimate a node's position using only one or two position data points, as shown in Figure 3.  
The estimation of a node's position can be performed according to the number of position data points (or samples) it receives from position sources:  
* One position sample: The sensor node will assume that its position is the same as the one received in the packet. It is not accurate, but with this information, it is possible to at least determine the region where an event occurred.
* Two position samples: When calculating the intersection between two position packets, we have two possible positions for the node. Since we do not have any other information, the approximate position of the sensor node is the midpoint between these two calculated positions.
* Three position samples: Now a node can estimate a more accurate position and it becomes a _qualified node_. Using the trilateration algorithm, it is possible to estimate the sensor node position by finding the intersection between these three position samples.  
At the start of network operation, the positions of sensor nodes will be inaccurate due to the lack of position packets required for estimating node locations. However, over time, node localization will become increasingly precise as more position packets are disseminated in the sensor field as the sink moves. Subsequently, the network will be able to track events occurring in the area and forward them to the sink.