esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of _Jatropha curcas_ and Pistachio Shell Particles as Modifier for Asphalt Binder
## 3 Results and Discussion
### Fluorescence Microscopy  
Figure 1 shows micrographs of modified asphalt blends prepared with _latropha curcas_ seed shell. The 2 and 4 wt.% micrographs, observed in Figure 1a,b, exhibit less fluorescent effect, which is a direct consequence of the homogeneous dispersion of the modifier into the asphalt. This result confirms what was previously discussed in the phase separation test, where compatibility between the asphalt and the _latropha_ seed shells was observed at the same concentrations. Nevertheless, as the proportion of _latropha_ increases in the tested samples, the modifier particles tend to coalesce and form aggregates that are not well dispersed in the asphaltic matrix, as observed in Figure 1c,d; this fact is also responsible for the greater phase separation of the samples containing 6 and 8 wt.% of _latropha_, compared with the results of the 2% and 4% samples. The elevated lignin content of _latropha_ seeds [30], which is compatible with the polar compounds of the asphalt, may explain the good dispersion of the modifier particles. The compatibility between asphalt and modifier allow the integral transport of malting and highly polar fractions from the asphaltene-rich phase, causing a phase inversion thatcan be spotted in the fluorescent background of the micrographs [31].  
Figure 2 shows the fluorescence micrographs of the asphalt blends modified by pistachio shells. The sample containing 2 wt.% pistachio shells is depicted in Figure 2a, which is the most homogeneous sample. The 4 wt.% sample is also homogeneous, as shown in Figure 2b. The effect discussed in Figure 1 for _latropha_-modified asphalt is also valid in Figure 2 for pistachio shell as a modifier; the more concentrated the modifier in the asphaltic preparation, the more agglomerated the particles become; however, the aggregation of pistachio shell particles is less pronounced than that of _latropha_ seed shells, as noticed by comparing Figure 1c,d with Figure 2c,d. These results are also consistent with the previous observation that the phase separation is more evident in the asphalt samples modified by _latropha_ than those modified by pistachio shells. Given that the lignin content is proportionally lower in pistachio shells than in _latropha_ seed shells, an excess of lignin in the modified asphalt could be related to greater phase separation.
# 6G Visible Providing Advanced Weather Services for Autonomous Driving
## 2 Evaluation of Autonomous Vehicle LiDAR and Camera Sensor Sensitivity to Harsh Weather  
To estimate the autonomous vehicle sensors' sensitivity to harsh weather, we composed a fixed measurement system for our winter testing track. The Sod5G test track is in Sodankyla, Northern Finland, and is used as a testing and development environment for intelligent traffic, autonomous vehicles, and road weather services [9]. The test track is supplemented with a multitude of communication systems, e.g., vehicle-to-vehicle (V2V) radios for ITS-G5 and C-V2X, a WiFi hotspot, and 5G test networks. All of FMI's special instrumentation and knowledge related to intelligent traffic road weather services are employed as part of the 6G Visible development architecture.  
In this work, autonomous vehicles' sensor systems are being evaluated for their sensitivity to harsh weather conditions. For this purpose, FMI employs a self-built, small, autonomous robotic platform to study the instrumentation used for autonomous vehicles. However, to evaluate the weather dependency, we are interested in having measurements in a fixed environment, with all environmental parameters known. Special focus will be placed on LiDAR and camera sensor instruments, and vehicular radar systems are part of the future work. We are studying LiDAR's vulnerability to weather in the tasks of (1) road sign recognition, (2) identifying snow cover, and (3) snowbank height monitoring. LiDAR and a camera are installed into the wall of the hub (Figure 1) and monitor the same location on the track, where two traffic signs, the snow cover, and the snowbank height are all visible. Parallel images from LiDAR and the camera are shown in Figure 2 in their test locations. The LiDAR instrument used is the mid-range Ouster OS1 lidar sensor features a 90 m range on a dark 10% target, a 42.4\({}^{\circ}\) vertical field of view, and high reliability for the most rugged conditions. It is designed for all-weather environments and use in industrial automation, autonomous vehicles, mapping, smart infrastructure, and robotics [10]. The camera instrument used is a standard security monitoring camera device. The LiDAR is also used in our miniature autonomous vehicle platform to perform on-board measurements and 3D mapping of the test track, but the weather vulnerability evaluation is based on fixed installation only.  
Figure 1: Vehicle LiDAR and camera in parallel installation.  
The measurement system collects parallel measurements from LiDAR and the camera in a one minute-interval, which then go into temporary storage. Whenever the weather conditions become challenging for some imaging system, the images of that case will be saved for permanent storage. In addition, the road weather information collected from test track road weather stations and local road weather forecast information is stored as well [9]. As soon as we have comprehensive material from different harsh conditions, we can conduct an estimation of the vulnerability of our sensors.
## 2 Integrated Navigation System of SINS/DVL/MCP/PS
### Error Propagation Models of MCP and PS  
MCP and PS are important aided navigation systems. To shorten the length of this paper, the characteristics of the MCP and PS measurements are not given in detail but can be found in [5; 6; 12].  
Similarly to DVL, white Gaussian noise is used to describe the measuring noise in MCP and PS as follows:  
\[w_{\text{MCP}}\sim\left(0,R_{\text{MCP}}\right) \tag{10}\]  
\[\delta\dot{h}_{\text{PS}}=0,\ w_{\text{PS}}\sim\left(0,R_{\text{PS}}\right), \tag{11}\]  
where \(w_{\text{MCP}}\) is the random errors in MPC, and \(R_{\text{MCP}}\) is the variance of the white noise corresponding to \(w_{\text{MCP}}\). \(w_{\text{PS}}\) is the random noise in PS, and \(R_{\text{PS}}\) is the variance of the white noise corresponding to \(w_{\text{PS}}\).  
In AUV, SINS, DVL, MPC, and PS are discrete systems and operate independently; thus, the measurement noise of each is assumed to be non-related.
## 4 Proposed Method
### Path Generation Module
#### 4.4.1 Pseudo-Label Generation Decoder  
The decoder computes an attention (sub)layer atop the encoder, limiting message exchanges solely with the context node for efficiency reasons [33]. The final probabilities are computed using a uni-directional attention technique. Consult Figure 3 to obtain a graphical depiction of the procedure for decoding.  
By utilizing attention instead of recursion, the consistency of node input sequences is increased, thereby enhancing learning efficiency. This approach also allows for parallelization, leading to enhanced computational efficiency. The multihead attention mechanism can be conceptualized as a communication system where nodes transmit significant information through distinct pathways. As a result, the embedded node in the encoder can acquire substantial contextual information about nodes in the graph. The graph embedding \(h_{g}^{l}\) is derived from the embeddings \(h^{l}\) of all nodes.  
\[h_{g}^{l}=GraphEmbedding(h^{l}) \tag{26}\]  
The input \(h_{y}^{l}\) to the label generation decoder is a concatenation of the graph embedding \(h_{g}^{l}\) and the context vector \(h_{c}^{l}\):  
\[h_{y}^{l}=Concat(h_{g}^{l},h_{c}^{l}). \tag{27}\]  
The operator \"Concat\" denotes the horizontal concatenation, resulting in a (3 dh)-dimensional vector denoted as \(h_{y}^{l}\). This indicates that it is being interpreted as the incorporation of the unique context node \(h_{c}^{l}\). The superscript \(l\) ensures that the alignment with the node embeddings is maintained. Then, we compute a fresh context node embedding \(h_{c}^{(l+1)}\) employing the (8-head) attention mechanism outlined. The keys and values are sourced from the embedded node \(h_{i}^{l}\), while only a singular query \(q_{c}\) (per head) is computed from the context node:  
\[q_{c}=W^{q}h_{c},k_{i}=W^{K}h_{i},v_{i}=W^{n}h_{i} \tag{28}\]  
For each node, we derive the probabilities concerning which nodes to access by computing the interaction between \(q_{c}\) and \(k_{i}\), concurrently applying a mask to previously visited nodes:  
\[u_{i}=\begin{cases}\frac{q_{c}^{T}k_{i}}{\sqrt{d_{k}}}&\text{if }i\
eq\pi_{t^{ \prime}}\\ -\infty&\text{otherwise.}\end{cases}\quad\forall t^{\prime}<t \tag{29}\]The calculation of \(u_{i}\) in both the multihead attention and single-head attention mechanisms is determined by Equation (29). For unmasked nodes, the calculation proceeds normally. However, if a node has been masked with \(pi_{\text{e}^{\prime}}\), which Indicates a prior visitation--its value is set to negative infinity to avoid selecting this node.  
Then, we evaluate these interrelations as unnormalized log-probabilities and subsequently compute the ultimate output probability vector \(p_{i}\) with a SoftMax function:  
\[p_{i}=p_{\theta}(\pi_{t}=i|s,\pi_{1,t-1})=softmax(u_{i}) \tag{30}\]  
Ultimately, the next vector \(\widetilde{h}_{i}\) is computed by \(p_{i}\) and \(v_{i}\):  
\[\tilde{h}_{i}=\Sigma_{j}p_{i}v_{i} \tag{31}\]
## 4 Eliminating the Low Weight Non-Structured Codewords
### Improved Distance Spectrum  
To delete the non-structured codewords of weight 24 in the (21,3,5) code, we generate a new polynomial syndrome former matrix  
\[\mathbf{H}_{New}^{T}(D)=\begin{bmatrix}1&1&D^{18}\\ D^{13}&D^{11}&D^{12}\\ D^{3}&D^{18}&1\\ D^{3}&D^{4}&D^{16}\\ D^{21}&D^{13}&D^{21}\end{bmatrix}_{5\times 3} \tag{12}\]  
where the syndrome former memory \(m_{s}\) is still 21, and both the codes defined in Equations (8) and (12) have girth of 8.  
In the case of Equation (12), the super code formed by the second and the third columns contains 10 structured codewords of weight 12. Compared to the super code \(\mathbf{H}_{b}^{T}(D)\) in Equation (11), the two non-structured codewords \(\mathbf{v}_{b}^{11}(D)\) and \(\mathbf{v}_{b}^{12}(D)\) in Table 3 are eliminated by respectively substituting \(D^{11}\), \(D^{18}\), and \(D^{16}\) for the (2,2)-th, (3,2)-th, and (4,3)-th elements in Equation (11). In addition, similar to the code in Equation (8), arbitrary two columns of Equation (12) representing a super code does not contain any non-structured codewords of weight smaller than 6. As a result, the number of the codewords of weight \(24\) for the code Equation (12) is reduced to five, and these five codewords are exactly the same as those obtained from Lemma 1.  
Applying the distance estimation algorithm in [17] to Equation (12), the number of the codewords of weights 24, 26, 28, 30, and 32 have been reduced from 6, 5, 8, 34, and 53 for the code Equation (8) to 5,0, 0, 0, and 0, respectively. No codewords of weights 26, 28, 30, and 32 are found. The number of low weight codewords has been significantly reduced by carefully choosing the monomials in the polynomial syndrome former matrix. It shows that eliminating the low weight non-structured codewords in the super code improves the distance spectrum of the mother codes.  
As shown in Figure 1, the decoding performance of the designed code in Equation (12) is compared to that of the (21, 3, 5) Tanner LDPC-CC in Equation (8). The simulation was carried out assuming binary phase-shift keyed (BPSK) modulation on an additive white Gaussian noise (AWGN) channel with \(50\) iterations of the on-demand variable node activation [20] sum-product pipeline [4] decoding algorithm for LDPC-CCs. As a benefit of the improved distance spectrum, the bit error ratio (BER) curve of the designed code is consistent lower than that of the Tanner code.
# A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language
## 3 Semantic Encoding Module  
### Semantic Encoding Elements  
The key to the NL2 Cypher transformation model is to accurately extract semantic information from natural language temporal questioning and deeply understand the intent of the questioning so that accurate Cypher query statements can be automatically generated.  
A study by [PERSON] et al. [26] provides an in-depth analysis of patterns in location-based questions through large-scale question/answer datasets and generalizes five core semantic categories related to location: location, location type, activity, spatiotemporal relationship, and quality. In order to further improve the comprehension of natural language spatiotemporal questions, this paper builds on the research of [PERSON] et al. and the two datasets GeoQuestions and GeoQuery mentioned in Section 4.1, and the semantic coding schema of locations has been extended by increasing the number of categories from five to eight and introduces a more fine-grained coding mechanism to realize effective extraction of semantic information, which is designed to cover the complex semantics of natural language questions more comprehensively and thus represent the information in the spatiotemporal questions more accurately and in spatiotemporal interrogative sentences.  
According to Table 2, the basic semantic encoding elements for Chinese spatiotemporal queries include eight categories:  
(1) Interrogative words: locative interrogatives (where, which place, etc.), specific interrogatives (what, etc.), selective interrogatives (which one, which ones, etc.), quantitative interrogatives (how many, how much, etc.), measurement interrogatives (how long, how far, etc.), and judgment interrogatives (is it, whether, etc.):  
(2) Place names: Names of geographic entities, such as Shanghai, Beijing's Forbidden City, etc.;  
(3) Location types: Classifications of geographic entities, such as hospitals, parks, etc.;  
(4) Attributes: Properties of locations, such as population, area, time, etc.;  
(5) Quality: Quality of locations and their attributes and location types, such as nearest, highest, etc.;  
(6) Activities: Indicating actions, such as crossing, flowing through, building, etc.;  
(7) Spatiotemporal relationships: Describing spatial and temporal relationships, including direction, topology, and temporal relationships, such as northeast, nearby, surrounding, before, after, etc.;(8) Numbers and units: Including time, distance, quantity, etc., such as the year 2021, five kilometers, and three units.  
Based on the above description, the query \"Which cities are within 600 km around Beijing?\" is encoded as \"prd3t\".
# An Intelligent Approach for Contact Pressure Optimization of PEM Fuel Cell Gas Diffusion Layers
## 4 Results and Discussion  
### FE Model Validation  
Pressure-sensitive films (200 \(\upmu\)m, Fuji LLW) were used to detect the contact pressure between BPP and the GDL after a single fuel cell is packed with bolts. The effective range of the sensitive film is 0.5-2.5 MPa. After 5-min of standing still, the sensitive film was taken out, showing a contour of contact pressure on the GDL.  
Static analysis of the FE model under exactly the same clamping force was carried out to calculate the contact pressure on the GDL of the same fuel cell, and the result is given in Figure 7. From the measurement, it was found the pressure distributes quite uniformly on the GDL surface; solid contacts can be found at the four corners and along the width edges. The contact pressure of the central area along the longitudinal direction was very low. The contact pressure inside the black dashed line was even lower than 0.6 MPa--a very poor contact between the BPP ribs and the GDL, which may result in high contact resistance. The longitudinal edges also showed a relatively low level of contact pressure, which is unexpected since these two edges are close to the clamping bolts. There may be two reasons: (1) The tightening sequence of bolts is the four corners first and then the middle ones, so the capsules at the corners broke first and led to a deep color at the contour, but the contact in the middle area was not as good as the corners despite that the same clamping forces are given; (2) the machining tolerance of EP, BPP, and the gasket may also have prevented the GDL from contacting the ribs of BPP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Material** & **Young’s Modulus (GPa)** & **Poisson’s Ratio** \\ \hline EP [31] & Steel & 200 & 0.3 \\ Insulation plate [32] & Epoxylite & 1 & 0.38 \\ Collector [31] & Copper & 110 & 0.34 \\ Gasket [32] & PTFE & 0.28 & 0.4 \\ BPP [31] & Graphite & 10 & 0.25 \\ GDL & - & 0.01 & 0.25 \\ PEM & - & 0.249 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties used in the FE model.  
Figure 6: Boundary and loading conditions of the finite element (FE) model.  
Overall speaking, the FE simulation shows the same distribution pattern of the contact pressure on the GDL surface. The pressure gradually decreases from outside to the center, and the very central area shows a pressure lower than 0.6 MPa. From the comparison, very similar distributions can be seen between the experimental and calculated contact pressures in both magnitude and trend. That is to say, the FE model developed is able to reflect the mechanical performance of the GDL after packing.
# Performance Improvement of an NMP Mini Segway Using Sample and Hold Inputs
## 5 Experimental Hardware
### Sensors  
In order to balance the MS system about its upright equilibrium point, the measurement signals of the cart body pitch angle, pitch angular velocity, individual wheel displacement, and velocity are required.  
**Cart body pitch angle and velocity**: An MPU-6050 sensor from IvenSense Inc. is used for reading cart body angular information. It contains a MEMS (microelectromechanical system) gyroscope and a MEMS accelerometer in a single chip. The gyroscope is a sensor that measures the angular velocity in roll, pitch, and yaw directions, and the accelerometer measures gravitational and motion accelerations in X, Y, and Z directions. After disabling the sleep mode of the chip in Simulink, the gyroscope and accelerometer readings are sent to the microcontroller via the Arduino I\({}^{2}\)C bus. The cart body pitch angular velocity is obtained directly from the gyroscope raw data. Theoretically, integrating the angular velocity yields the pitch angle, but the sensor noise and bias cause the integrated angle signal drift. Note that the pitch angle can also be obtained from the arc-tangent value of the X and Z direction gravitational accelerations from the accelerometer signals; however, the sensor noise and external forces could also affect its accuracy easily. Hence, a complementary filter, developed in [29], is used to have an accurate pitch angle signal; see the complementary filter in Figure 5. To compensate the gyroscope drift with the accelerator reading, it applies a low-pass filter and a high-pass filter to accelerometer and gyroscope signals, respectively. For the MS used in this paper, \(\beta=0.98\) was used for the filter.  
**Wheel displacement and velocity**: The MS displacement is measured with two hall-effect rotary encoders mounted on the two motors. The motor encoders output digital signals of both motor shaft positions with a resolution of 1320 samples per revolution. The  
Figure 5: The complementary filter.  
encoder readings can be converted into cart displacement and velocity based on gear ratio and wheel radius.
# Thermal Relaxation Spectra for Evaluating Luminescence Quantum Efficiency of CASN:Eu2+
## 2 Materials and Methods
### Materials  
Eu\({}^{2+}\)-doped CaAlSiN\({}_{3}\) (CASN:Eu\({}^{2+}\)) is a red phosphor with high quantum efficiency used for a white-light emitting diode (LED). We used CASN:Eu\({}^{2+}\) powder with 1 mol\(\%\) Eu and particle size of 17 \(\upmu\)m. The PL/PLES spectrum and the quantum efficiency were measured using an absolute quantum efficiency measurement system (QE2000, Otsuka Electronics, Osaka, Japan). Internal quantum efficiency was measured to be 90%. The PTES was measured by the method 2 on the conditions described above as follows. CASN:Eu\({}^{2+}\) powder was mounted and glued on a glass slide as a sample. The PTES spectrum was measured in the air. The modulation frequency of the pump was 20 Hz, and maximum excitation light intensity was 5 mW/ cm\({}^{2}\) @ 470 nm (210 \(\upmu\)mol m\({}^{-2}\).s \({}^{-1}\)). For normalization of the signal spectrum, activated carbon was put on the same slide and measured absolutely in the same manner. Then the raw photothermal spectrum of CASN:Eu\({}^{2+}\) powder was divided by that of activated carbon to obtain the PTES of CASN:Eu\({}^{2+}\).
## 2 Methodology
### Simulation of Sensor Viewing Extent  
In order to simulate the viewing extent of the single sensor, we use the Trade-space Analysis Tool for Constellations (TAT-C) simulator to explore the ground track of the sensor orbit under different orbital configurations. The module for simulating the sensor orbits in TAT-C has been employed to investigate the nadir position track of a variety of different satellite sensors [44]. The second step in simulating the viewing extent of a single sensor is to adjust the sensor swath width to enhance the hypothetical sensor coverage for this study. Specifically, the satellite viewing extent is generated by extending the ground track in the cross-track direction to a given swath width of interest. The viewing extent simulation is ultimately expressed as a binary map marking the global surface as viewed (or not) in the absence of clouds. The viewing extent simulation is conducted at a 0.01\({}^{\circ}\) spatial resolution in this study, and subsequently aggregated in space to match relatively coarse-scale geophysical retrievals in the following analysis. Finally, a realistic cloud mask is convolved with the viewing extent to explore the effects of cloud attenuation on terrestrial snow observability.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 4 Algorithm Description
### Routing
#### 4.2.2 Determination of the Best Next Jump  
Nodes in the forwarding area reply to the current relay node with its remaining energy and distance from the sink. The relay node is calculated in accordance with the weight formula. The best node is chosen as the next hop relay.  
\[W_{k}^{f}=a_{4}\frac{E_{k}^{o}-E_{k}^{r}}{E_{k}^{o}}+b_{4}\frac{D_{k0}}{D_{j0}}, \tag{16}\]  
where \(W_{k}^{f}\) represents the weight of the relay node in the forwarding region; \(D_{j0}\) stands for the distance between the current relay node and the sink; \(D_{k0}\) denotes the distance between the candidate relay node and sink in the forwarding region; \(a_{4}\) and \(b_{4}\) are adjustment factors, and \(a_{4}+\ b_{4}=1\). It can be seen that the more remaining energy there is, the more likely the node closer to the sink will be the next hop node.