esa-sceva/satcom-chunk-collection huggingface dataset:
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 5 OpenFOAM FVTM Comparison
### Sensitivity Analysis on Spacer Contact Interfaces  
The conductive interfaces are the ones that have the most impact on the temperature of the components [7; 43; 44], indeed the sensitivity analysis is performed on the contact interfaces of the internal components to push their temperature closer to the experimental one. All the boards in the stack are in contact through a set of spacers, thus the thermal contact coefficient between the spacers and the boards is modified in order to reach a better correlation. Such value is changed for all the spacers at once, indeed all the spacers have the same contact area with the boards and are mounted in a stack configuration (they are tightened axially), thus the pressure at those interfaces is equal. Since the spacers are made of the same material, they have the same surface roughness values. On the board side, there is an exposed conductive area around each mounting hole to guarantee the electrical connection with all the ground layers throughout all the electronics (Figure 6), so also in this case there is the same surface roughness value. In conclusion, the spacers' contact interfaces have the same pressure and same surface roughness, being these are the factors which influence the thermal contact coefficient, it is assumed that all those interfaces have the same thermal contact coefficient. The value has been changed from 10,000 W m\({}^{-2}\) K\({}^{-1}\) to 100 W m\({}^{-2}\) K\({}^{-1}\) with the latter resulting as the most accurate. The starting value of the contact coefficient is taken from a CubeSat stack dedicated test campaign [44], the last one is the reference conservative value for a low pressure contact between two aluminum interfaces [45]. The results are presented in Figure 23 and discussed in the next section.  
It must be noted that the most responsive component is the middle board, i.e., the PSU, while the TCs on the upper part are quite insensitive to such changes. With high probability the thermal dynamics of the upper part (i.e., tc6 and tc1) is dominated by the links inside the detector. Unfortunately, there are no TCs inside the detector thus without data in that region it is not possible to investigate possible correlations.
## 1 Introduction
Unfortunately, the collection of remotely-sensed data is fixed by a given satellite's orbit and return interval, and thus observations are not always captured at ideal times (e.g., green-up or harvest). Optical data availability is also problematic in areas with frequent cloud cover, such as humid tropical and sub-tropical environments. Having stated this, it is important to point out the technological advances made in remote sensing. For example, satellite constellations such as Rapideye with 5 meter spatial resolution and providing data in five spectral bands has already covered nearly 95% of USA geographic area in less than one year after launch. Further, multi-sensor data fusion (e.g., IRS, Rapideye, Landsat) are becoming increasingly important and feasible. Finally, looking at the limitations of conventional datasets such as subjectivity in data collection and varying statistical design in different studies, limitations from remote sensing by itself are less certain. The final limitation considered here comes from the fact that identification of agricultural fields using remote sensing is difficult because irrigated landscapes are a subclass of croplands that themselves have traditionally been difficult to map [22-24]. Agricultural fields (and especially irrigated fields) are highly dynamic because each field may be at a different stage of development, and thus subject to being confused with natural land cover classes. Accuracy of land-cover maps is often inversely related to their categorical detail. Since agriculture is already inherently difficult to identify and map, the task of identifying irrigated areas as a subclass of cultivation becomes even more difficult. Perhaps this is where temporal data profiles will be invaluable to separate irrigation from rainfed agriculture as successfully demonstrated by [21,61]. Moreover, ancillary datasets on precipitation and evapotranspiration will come in handy when interpreting these temporal profiles [20].
# Monitoring of Time and Cost Variances of Schedule Using Simple Earned Value Method Indicators
## 1 Introduction  
The growing number and growing importance of unique, often complex construction projects, results in unflagging interest in project management. Despite the undisputed, more significant than ever, development of effective methods of planning, coordinating, and controlling, the increase in the complexity of operating conditions of the enterprise on the market means that success in project management is not easier. The selection of production control methods depends on many factors-primarily the degree of production repeatability, as well as the degree of details of available data, the details of the records kept, the use of planning documentation, the degree of use of computer techniques and the organizational culture prevailing in the company. Construction production is characterized with significant seasonal fluctuations. They constitute a cycle of repetitive changes, in more or less equal periods of time, with similar intensity. Their identification and inclusion considerably increases the precision of predictions [1; 2; 3].  
Earned Value Method (EVM) is recognized as an advanced method of controlling production ventures, which provides working results in the form of quantitative and qualitative indicators.  
The purpose of developing the EVM method was to link the material progress of works and the costs incurred-against the background of planned values. This tool, \"introduced\" to construction sites, has been effectively implemented and disseminated. Managers managing complex construction contracts use key words to assess the progress of their schedules, fully describing planned and actual turnover, budget, and timeliness of implementation [4; 5]. These include simple control indicators of the earned value method, used in the strategic assessment of the condition of the project-when monitoring the progress of works using the indicator method [6; 7; 8; 9]. The EVM method is one of performance-based project management methods and is an effective tool for controlling projects in terms of costs, time, and scope of works done [10]. It consists of controlling the implementationof the project by comparing the scope of works executed and the costs actually incurred against the project schedule and budget adopted in the base plan.
# Long-Distance Measurements Using a Chromatic Confocal Sensor
## 3 Simulator Software
### Ray-Surface Intersection  
The surfaces of the aspheric lenses are defined by the following conic equation:  
\[z(y)=\frac{y^{2}}{R\bigg{(}1+\sqrt{1-(1+k)\big{(}\frac{y}{R}\big{)}^{2}} \bigg{)}}+z_{0} \tag{5}\]  
where \(R\) is the radius of curvature, \(k\) is the conic constant, and \(z_{0}\) is the offset of the surface. To find the intersection of a ray with this surface, a merit function was constructed to describe the geometry of the ray relative to the surface. The system of equations is solved numerically using the _fsolve_ from a Python scientific package called scipy [25], which is based on the Powell's minimization routine [26]. The solver computes the ray's coordinates (\(y\), \(z\)) at the point of intersection with the lens, ensuring the geometric consistency between the ray's path and the surface.
## 3 Results and Discussion
### Case Study 2  
In this study, the simultaneous minimization of dual machinability indices with three different combinations using evolutionary algorithms is considered. The Pareto front analyses of all the algorithms with respect to CF vs. SR, CT vs. CF, and CT vs. SR are given in Figure 5a-c, respectively. Further, the TOPSIS method is used to convert the dual machinability indices into a single objective. Hence, the global minimum value of the machinability indices is obtained using TOPSIS results (Figure 5d-f) for all the evolutionary algorithms. In addition to that, the performance of evolutionary algorithms is validated using the hypervolume indicator. From these Figure 5, it is inferred that the MFO algorithm outperformed others in all three cases. The results are presented in Table 5.  
The general observations are when CF increases, and SR worsens after machining; When CF increases, TE increases significantly and affects the tool life; and when TE increases, then SR decreases. Further, the LN\({}_{2}\) environment minimizes the CF, CT, and SR due to a fine droplet of LN\({}_{2}\) acting as a film barrier in the tool and workpiece interface, thus reducing chatter vibration and built-up edge formation. This is used to improve the tool life too.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & & \\ \hline GHO & & 128.00 & 0.050 & 3 & 127.75 N & 2.26 μm & 0.301 \\ GA & CF & 126.00 & 0.062 & 3 & 127.12 N & 2.27 μm & 0.302 \\ PSO & \& & 128.00 & 0.065 & 3 & 141.07 N & 2.17 μm & 0.319 \\ MFO & SR & 124.00 & 0.060 & 3 & 136.57 N & 2.20 μm & 0.324 \\ GWO & & & 129.00 & 0.060 & 3 & 136.57 N & 2.20 μm & 0.321 \\ \hline GHO & & 53.87 & 0.050 & 3 & 206.31 N & 42.89 \({}^{\circ}\)C & 0.652 \\ GA & CF & 34.06 & 0.060 & 3 & 218.52 N & 32.79 \({}^{\circ}\)C & 0.633 \\ PSO & \& & 35.06 & 0.062 & 3 & 218.52 N & 32.79 \({}^{\circ}\)C & 0.624 \\ MFO & TE & 35.11 & 0.050 & 3 & 217.98 N & 31.26 \({}^{\circ}\)C & 0.697 \\ GWO & & & 46.90 & 0.050 & 3 & 211.12 N & 39.03 \({}^{\circ}\)C & 0.657 \\ \hline GHO & & 34.32 & 0.052 & 3 & 2.60 μm & 36.16 \({}^{\circ}\)C & 0.391 \\ GA & SR & 34.32 & 0.062 & 3 & 2.60 μm & 36.16 \({}^{\circ}\)C & 0.415 \\ PSO & \& & 49.38 & 0.058 & 3 & 2.52 μm & 43.72 \({}^{\circ}\)C & 0.416 \\ MFO & TE & 50.00 & 0.053 & 3 & 2.52 μm & 34.06 \({}^{\circ}\)C & 0.443 \\ GWO & & & 52.00 & 0.056 & 3 & 2.52 μm & 44.06 \({}^{\circ}\)C & 0.441 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Minimization of machinability indices based on pareto front analysis and TOPSIS method for Case study 2.
# PEMFC RUL Prediction for Non-Stationary Time Series Based on Crossformer Model
###### Abstract
[7] utilized an attention-based sequence-to-sequence model (ATS25) and proposed joint optimization with reconstruction loss to endow the model with higher predictive capabilities. [PERSON] et al. [8] in 2021 applied LSTnet (long- and short-term time series network) to the domain of multidimensional time series forecasting. [PERSON] et al. [9] proposed the Time Convolutional Network (TCN) for rapidly processing long time series and extracting comprehensive information. [PERSON] et al. [10] in 2022 introduced a time series forecasting model based on Long Short-Term Memory (LSTM) networks and the multi-head attention mechanism (LSTM-ATTN). The Transformer [11] neural network was initially designed to tackle Natural Language Processing (NLP) tasks. However, over time, Transformers have demonstrated outstanding performance in fields such as Computer Vision (CV), time series, and others. The Crosformer model proposed by [PERSON] et al. [12] effectively extracts cross-temporal dependencies and cross-dimensional dependencies through two-stage attention mechanisms. In time series prediction, we generally consider stationary time series, but for PEMFC data, there exists strong non-stationarity. [PERSON] et al. [13] proposed DAIN, which adaptively stabilizes time series using nonlinear neural networks. [PERSON] et al. [14] employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to handle non-stationary time series with seasonality and trends. [PERSON] et al. [15] utilized two-stage normalization to mitigate non-stationarity in the original data and enhance the predictability of the model. [PERSON] et al. [16] introduced a non-stationarity attention mechanism to capture non-stationarity in data. The main contributions of this paper lie in the following aspects:  
1. This paper proposes a remaining useful life (RUL) prediction model for Proton-Exchange Membrane Fuel Cells (PEMFCs) based on Crosformer. This model leverages a two-stage attention mechanism, deep adaptive normalization, and a non stationary attention mechanism to address the limitations of traditional Transformers in capturing dependencies between different features, the information loss caused by conventional normalization methods, and the traditional attention mechanism's neglect of non-stationarity information across different dimensions. 2. Existing time series prediction models commonly use the mean squared error (MSE) as the loss function, which primarily focuses on the discrepancy between predicted and actual values at each time step, neglecting the overall trend and shape of the sequence. To address this limitation, this paper introduces TILDE-Q as the loss function, which accounts for both the gap in and shape of the predicted values, enabling the model to better capture the overall trend and structure of the sequence during training. 3. Real datasets from logistics vehicles equipped with PEMFCs are utilized to compare the proposed model with five mainstream time series forecasting models.
# Suitability of Low-Cost Sensors for Submicron Aerosol Particle Measurement
## 3 Results and Discussion
### Influence of Aerosol Humidity on Measurement Capability and Accuracy of the Low-Cost Sensors  
This section describes the influence of humidity on the measurements of the low-cost sensors. The indoor relative humidity is usually between 40 and 60% [57]. Initially, the test aerosol must be adequately conditioned, and for this purpose particle-free air is led through one or two columns (series) and then mixed with the dry NaCl aerosol (cf. Section 2.3). The mixing ratio of these two flows (C:D) determines the resulting humidity. Figure 6 shows the possible relative humidity for both configurations at different flow rates.  
An increase in the saturated air-flow rate leads to an increase in the relative humidity of the total air flow, while the air-flow rate of the test aerosol is constant at 4 L-min\({}^{-1}\). Using one column, a maximum humidity of approx. 60% can be achieved; with two columns, a maximum of approx. 80% is possible. The divergence of the results is caused by the uneven mass transfer in a random packing. The study of the influence of humidity on the measuring capability of PM is carried out with the low-cost sensor SDS011. Figure 7 shows the resulting PM concentrations of the SDS011 sensor for the particle-free air flow and for the NaCl aerosol, depending on the relative humidity.  
The results for the particle-free air clearly show that the humidity has no impact on the measurement of PM values. At a constant volume flow with varying humidity, no particles are detected. Consequently, condensation on the detector or in the measuring volume can be excluded as well. Under identical conditions, the results with the conditioned NaCl aerosol show a correlation between the two parameters, and the PM values increase with increasing humidity (cf. Figure 7). Up to 60% humidity, the correlation is approximately linear, while for higher humidity levels the detected PM rises significantly. Values at 70% are about four times higher than the values at 40%. The reason for this increase in the PM values is the hygroscopic growth of the particles in humid air [58; 59]. Initially, the adsorption of water-vapor molecules takes place until the maximum capacity (max. adsorption capacity of the salt) is reached. Then, water-vapor condensation and particle dissolution occur (solubility point), which leads to a growth process, and the solid particle  
Figure 6: Relative humidity depending on the flow rate of the humid air (D), at a constant NaCl (C) aerosol flow of 4 L-min\({}^{-1}\), using a single column or two in series.  
becomes a droplet [60]. With the change in size, the optical properties of the particles also change--for example, their interaction with light [61].
## 4 Discussion  
We detected the area damaged by windthrow and landslides after the typhoons hit with PlanetScope data. The posttyphoon images were collected 21 days after the typhoons hit, and no cloud-free images were available during these 21 days. The high temporal resolution of the PlanetScope data increased the chances of obtaining cloud-free images. Although these data have a relatively low spectral resolution of only four bands, the results showed that they were suitable for detecting windthrow and landslides using the chosen methodologies.
# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 5 Experiments
### Case Study  
Table 6 shows the mentions and their real entities, as well as the results predicted by the model. Examples of incorrect model predictions are shown in red, e.g., \"Scotland\" is predicted to be \"Scotland_national_cricket_team\". This shows that in some cases, only semantic information cannot complete the link to the entity. We note that a document contains a knowledge graph structure. As shown in Figure 6, there is a certain connection between the entities \"Scotland\" and \"England\". When calculating the global score, the score between \"Scotland\" and \"England\" will be higher than the scores between other entities, indicating that mentions \"English\" and \"Scotland\" are more likely to refer to entities \"England\" and \"Scotland\", respectively. Therefore, we can guide the prediction of mention \"Scotland\" based on this connection. Similarly, we can use the knowledge graph structure between \"Edgbaston\" and \"Birmingham\" to guide the prediction of \"Edgbaston\". In summary, the introduction of the knowledge graph structure solves the problem of incorrect prediction of some mentions.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Mention** & **Pred** & **Gold** \\ \hline...Arrive in **London** May 14... & London & London \\...matches against **English** county sides... & England & England \\...Counties and **Scotland** Tour itinerary... & Scotland\_national\_cricket\_team & Scotland \\...match (at **Edgbaston**,Birmingham)... & Edgbaston\_Cricket\_Ground & Edgbaston \\...Edgbaston**,Birmingham**) June... & Birmingham & Birmingham \\...international (at **The Oval**, London)... & The\_Oval & The\_Oval \\...Sussex or **Surrey** (three days)... & Surrey\_County\_Cricket\_Club & Surrey\_County\_Cricket\_Club \\ \hline \hline \end{tabular}
\end{table}
Table 6: The examples predicted by the baseline model. The bold font in the first column denotes the mention, the second column is the entity predicted by the model, and the last column is the real entity corresponding to the mention.  
Figure 5: F1 scores of different baselines on out-domain datasets.
## 3 Failure Mode Analysis
### Horizontal Failure 1 Set  
When any one of the horizontal sensors X1, X2, Y1, or Y2 fails, it means the measurement signal d\({}_{x1}\) is invalid. Assuming that eddy current sensor Z1 fails, Equation (10) is rewritten as:  
\[\left\{\begin{array}{c}x-y\theta_{z}+z\theta_{y}+\theta_{z}R_{x2}=d_{x2}\\ -y-x\theta_{z}+z\theta_{x}+\theta_{z}R_{y1}=d_{y1}\\ y+x\theta_{z}-z\theta_{x}+\theta_{z}R_{y2}=d_{y2}\\ z-x\theta_{y}+y\theta_{x}+R_{z1x}\theta_{y}+R_{z1y}\theta_{x}=d_{z1}\\ z-x\theta_{y}+y\theta_{x}-R_{z2x}\theta_{y}-R_{z2y}\theta_{x}=d_{z2}\\ z-x\theta_{y}+y\theta_{x}+R_{z3x}\theta_{y}-R_{z3y}\theta_{x}=d_{z3}\\ z-x\theta_{y}+y\theta_{x}-R_{z4x}\theta_{y}+R_{z4y}\theta_{x}=d_{z4}\end{array}\right. \tag{22}\]  
The relative position between the base and the floating platform can be expressed as:  
\[\begin{bmatrix}x\\ y\\ z\end{bmatrix}=\begin{bmatrix}1&-\theta_{z}&\theta_{y}\\ \theta_{z}&1&-\theta_{x}\\ -\theta_{y}&\theta_{x}&1\end{bmatrix}^{-1}\begin{bmatrix}m_{1}\\ m_{2}\\ m_{3}\end{bmatrix} \tag{23}\]  
At present, well-known brands of eddy current displacement sensors include KAMAN from the United States, Keyence from Japan, and ZSY from the United Kingdom. Compared to others, KAMAN from the United States has the best overall performance and range coverage.  
At present, the measurement requirement for displacement sensors is a range of \(\pm\)20 mm, with an accuracy better than 5 \(\upmu\)m. Referring to the selection of KAMAN eddy current sensors, the KAMAN 12 CU probe (paired with the KD-2306 adapter) can be selected, with a measurement range of 50.8 mm and an accuracy of 5 \(\upmu\)m, meeting the measurement accuracy requirements. Considering the relatively good thermal control environment on the satellite, and considering the use of reliable sensor products, such as KAMAN sensors, the operating temperature can reach \(-\)55 \({}^{\circ}\)C to \(+\)105 \({}^{\circ}\)C.
## 5 Result and Discussion
### Protocol Comparison: Delay Metric  
Delay in a network refers to the amount of time it takes for data packets to travel from the source node to the destination node. High delay in a network can lead to poor performance. The entire network is influenced by each node's estimation of the average latency for its neighbor. For 70 nodes, the delay for conventional, proposed optimized OLSR and AODV (Ad-hoc On-Demand Distance Vector) technique is evaluated. While enhanced OLSR experiences less delay than standard OLSR [16] and AODV [26], flooding causes the delay to rise. The clustering in enhanced optimized OLSR selects a cluster head for each round, lessening the time spent in transit from source to destination. In each round, the cluster head is selected with probability p, which is the same for all nodes, indicating that every node has an equal chance of becoming a forwarder node. The strategy used greatly reduces the delay. The value of delay obtained is highlighted in Table 2 and the delay comparison graph of all three techniques considered is shown in Figure 5.  
Per round, the cluster head is determined with probability p, which is identical for every node, implying that each node has an equal chance of being nominated as the forwarder node. The strengthening approach reduces the delay in the proposed OLSR.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Simulator & NS-3 \\ \hline Routing Protocol & Existing OLSR, Proposed OLSR \\ \hline Simulation Area & 1000 m \(\times\) 1000 m \\ \hline No. Of Nodes & 70 \\ \hline Node Speed & 5 m/s \\ \hline No. of Sources & All nodes \\ \hline Simulation Repetition’s & 3 \\ \hline Topology Control & 1 s \\ \hline Methodology & Routing with Clustering \\ \hline MAC Layer & IEEE 802.11 \\ \hline No. of packet send & 5 packets/s \\ \hline Packet length & 512 bytes \\ \hline Simulation Time & 30, 80, 120, 180 \\ \hline Node Speed & 1, 2, 3, 4, 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration Setup.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Delay** \\ \hline Standard OLSR & Proposed OLSR & AODV \\ \hline
37.5 & 17.0 & 38.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: OLSR vs. Proposed OLSR vs. AODV—Average End to End delay.
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
###### Abstract  
Medium Access Control (MAC) protocol is one of the key network protocols that ensure Wireless Sensor Networks (WSNs) maintain high performance during communication. MAC protocol design plays an important role in improving the performances of the whole network. First, Wireless Passive Sensor Networks (WPSNs) and MAC protocols are introduced in this paper. Second, some existing MAC protocols are introduced. Sensor MAC (S-MAC) protocol is analyzed and existing improved backoff algorithms are introduced. A new MAC protocol called Improved Sensor MAC (IS-MAC) is then proposed to solve the problem that the contention window (CW) during carrier sense is fixed in S-MAC protocol. IS-MAC protocol is able to adjust CW in terms of network load, so energy consumption can be decreased. Finally, according to the simulation results on NS2, the proposed protocol has better performance in terms of throughput and energy consumption.  
Wireless Passive Sensor Networks (WPSNs); Medium Access Control (MAC) protocol; backoff algorithm; contention window (CW); energy efficiency +
Footnote †: journal: Future Internet