esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Solid FEA Plugin
### The Class Structure  
In addition to Rhinoceros's native geometry, some extensions are necessary to perform an FEA. Rhinoceros's existing geometry classes, such as _Point3d_ and _Mesh_, are used as attributes also for the finite element classes to keep everything as simple as possible. A simplified illustration of the pertinent classes is given in Figure 4. The Element class of the FEM library stores the equivalent Rhino _Mesh_ instance as an attribute. In addition, some information about the local node's place in the global mesh is necessary; this is the _Connectivity_ field. Its _Nodes_ and _Type_ are also essential information that native Rhinoceros classes cannot provide. The _Node_-class is comparable to a _Point3d_, only with additional information regarding whether or not the nodes should be constrained from displacement during the analysis.  
The _FEMMesh_-class stores all the individual elements and nodes of the structure to analyse. Moreover, it needs information about the object's material. The Material class provides this information. At the moment, only isotropic material is implemented. The _FEMMesh_ also stores the results of the FEA. Stresses and displacements in both nodes and elements are stored as lists.  
Available Element Types  
There are currently four different element types implemented in the solver: First order and second order tetrahedron, Tet4 and Tet10, and first order and second order hexahedron, Hex8 and Hex20. As illustrated in Figure 5, the second order element has an extra node on the midpoint of all element edges in addition to the corner nodes that are shared with the first order element. By adding a middle node on the edges, the shape functions representing the displacement along the element are now nonlinear compared to the linear functions of the first order elements. Second order elements benefit from significantly increasing the accuracy of the analysis while using fewer elements in the mesh. Unfortunately, this comes with the cost of a more computationally expensive analysis, as demonstrated in Section 3.4.
# Power-Efficient Random Access Design for Machine Type Communication
## 3 LTE-nMTC Random Access Design
### Effective Bandwidth and Optimal Resource Configuration
We observe that the confliction between the channel bandwidth and the total number of channels is less severe under a wider bandwidth (\(W=\)1.08 MHz), in which less deficit exists between \(w^{\mathsf{tt}}\) and \(w^{\mathsf{t}}\), thereby, less performance loss is attributable to the bandwidth deficit. Figure 4:0 Relative transmit time in (18) for the access request message with \(W=180\) kHz. Figure 7 is a block diagram illustrating the random access channel configuration for \(W=180\) kHz, where the number of subcarriers is chosen to be \(S=60\) which results in a subcarrier spacing of 3 kHz that matches the optimal bandwidth \(w^{\text{tt}}\) (cf. Table 2) by design. The CP overhead for an OFDM symbol is \(\frac{35\text{ \mu s}}{35\text{ \mu s}+(3\text{ kHz})^{-1}}\approx 9\%\). TTI is the transmission time interval of each random access channel (see Figures 2 and 3), which is related to the link budget of the coverage class, and will be determined in Section 4.
# Vehicle Trajectory Reconstruction Using Lagrange-Interpolation-Based Framework
###### Abstract  
Vehicle trajectory usually suffers from a large number of outliers and observation noises. This paper proposes a novel framework for reconstructing vehicle trajectories. The framework integrates the wavelet transform, Lagrange interpolation and Kalman filtering. The wavelet transform based on waveform decomposition in the time and frequency domain is used to identify the abnormal frequency of a trajectory. Lagrange interpolation is used to estimate the value of data points after outliers are removed. This framework improves computation efficiency in data segmentation. The Kalman filter uses normal and predicted data to obtain reasonable results, and the algorithm makes an optimal estimation that has a better denoising effect. The proposed framework is compared with a baseline framework on the trajectory data in the NGSIM dataset. The experimental results showed that the proposed framework can achieve a 45.76% lower root mean square error, 26.43% higher signal-to-noise ratio and 25.58% higher Pearson correlation coefficient.  
vehicle trajectory reconstruction; outlier detection; Lagrange interpolation; filter denoising; NGSIM
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
## 5 Simulation Results
### MGBD Precoder
The MGBD precoder provides improved throughput performance compared to the GBD precoder at all SNRs and all cases, except for minimum data streams (\(r=3\) or \(r=4\)). In more detail, the MGBD precoder provides about 3 dB (\(r=6\)) gain in comparison with GBD precoder when each of the 3 UE has 3 receive antennas and BS has 9 transmit antennas according to Figure 6. Compared to GBD, the performance gain of the MGBD precoder is about 1 dB (\(r=8\)) to 4 dB (full streams) when each of the 4 UEs has 3 receive antennas and BS has 16 transmit antennas in Figure 7. The improvement for throughput performance can be predicted through Figures 4 and 5. In short, the throughput performance improves when the BER performance improves. Like the BER performance, the throughput performance for proposed scheme is the same as that of the RBD precoder when BS transmits the full data stream and is the same as the GBD precoder when BS transmits the minimum data stream. Because the BER performance is not enough to obtain the reasonable throughput performance, if BS transmits a full stream in Figure 6, this presents poor performance. In other words, although the RBD precoder transmits a signal with maximum data streams, the peak value of the throughput performance for RBD only achieved 33% of the peak value for the MGBD precoder (\(r=6\)) and 60% of the peak value for the MGBD precoder (\(r=3\)) when SNR is less than 30 dB. MGBD and adaptive GBD precoder increases as SNR grows. In other words, the gain of throughput performance for the MGBD precoder increasingly grows as the number of data streams increases compared to GBD. Although the RBD precoder provides the same peak value as MGBD, the RBD precoder does not obtain any throughput performance in low SNR. For these reasons, unlike other schemes, the MGBD precoder maximizes throughput at any SNRs.
## 4 Conclusions  
In this study, the possibility of producing lightweight mortars and concretes with improved thermal performance by introducing R-EPS into the mix as a substitute for aggregate in various proportions and approaches was investigated. After the preparation of the mixtures, analyses were conducted to verify their fluidity, density, thermal, and mechanical characteristics. Based on the objectives of this experimental study, the main conclusions are summarized below:  
* All designed mortars exhibit compressive strength higher than the minimum specified by regulations. Therefore, all designed mortars, even those with no sand content, can be used as masonry mortars.
* All mortars show an improvement in thermal characteristics ranging from 49.28% (M6) for mixtures where there is no sand replacement but only an improvement in its granulometric range, up to an improvement of 89.49% (M4) for mixtures where the replacement is complete across all granulometric ranges.
* All designed concretes have strengths that are appreciable and compatible with the regulations. Therefore, all of them, each in relation to their specific characteristics, can be used as construction concretes, either structural (S0, S6, S7), lightweight structural (S1, S5, S8, S9), or non-structural (S2, S3, S4, S10).
* All concretes show an improvement in thermal characteristics in relation to the quantity of introduced R-EPS.
* Among structural concretes, improvements in terms of conductivity, compared to the reference mix, can be observed up to 27.68% (S8), where 40.2% of sand is replaced with R-EPS.
* Among non-structural lightweight concretes, improvements in terms of conductivity, compared to the reference mix, can be observed up to 74.58% (S4), where 100% of sand is replaced with R-EPS.  
These results have proven to be extremely interesting, as they provide a comprehensive overview of the potential of the concretes both in terms of mechanical strength and thermal insulation. Therefore, based on the obtained performances, it will be possible to choose the product that best suits the required characteristics.
## 4 Performance Metrics for Path Loss Models
### Mean Absolute Error (MAE)  
The mean absolute error (MAE) is a statistical metric used to measure the average absolute difference between the predicted path loss values and the actual or observed path loss values. It is a common performance metric used to assess the accuracy of path loss models in the context of wireless communication systems.  
The mathematical expression for the MAE is thus presented in (24) as follows [52; 55]:  
\[MAE=\frac{1}{N_{test}}\sum_{i=1}^{N_{test}}\Big{|}PL_{i}^{msd}-PL_{i}^{pred} \Big{|}. \tag{24}\]  
Mean Absolute Error (MAE) calculates the average absolute differences between the predicted and observed values for each data point without squaring the differences. This means that all errors, whether they are overestimations or underestimations, are treated equally in the calculation of MAE. A lower MAE indicates a path loss model with better predictive accuracy. A lower MAE suggests that the predicted path loss values are closer to the actual observed values, indicating a better fit of the model to the data [55].  
Mean Error (ME)  
The mean error (ME), alternatively referred to as the mean bias or mean prediction error, is a statistical measurement employed to assess the average variance between predicted path loss values and the actual path loss values within a path loss model. This metric offers valuable insight into the model's systematic or average prediction errors.  
The formula for calculating ME is presented in (25) as follows [54; 55]:  
\[ME=\frac{1}{N_{test}}\sum_{i=1}^{N_{test}}\Big{(}PL_{i}^{pred}-PL_{i}^{msd} \Big{)}. \tag{25}\]  
Mean error computes the mean of disparities between predicted and observed values for individual data points without employing absolute values or squared differences. It offers insights into both the direction and magnitude of the average error. A positive \(ME\) implies a tendency for the model to overestimate path loss values, while a negative \(ME\) signifies a tendency to underestimate path loss values [56].
## 3 Methodology
### Classification Methods  
#### 3.3.1 SVM  
SVM is a widely used learning model, which is applied for classification and regression. The basic idea of SVM is to separate the data by finding a hyperplane that maximizes the margin between the two classes of data. The margin represents the distance between the data points from each class that lies closest to the hyperplane, known as support vectors. SVM uses a kernel function to map non-linearly separable data into a higher dimensional feature space, where it becomes linearly separable. SVM performance can be optimized by tuning the parameters kernel, C, and gamma. The kernel variations used include linear,  
Figure 2: Samples from the face images in the used datasets (top row: Adience dataset [17], bottom row: LFW dataset [36]).  
RBF, and polynomial kernel. The parameter C is used for regularization; if C is set to a large value, a small margin will be used for optimization and vice versa. Gamma is set when a Gaussian RBF kernel is used. Features are fed directly to SVM, but in case of the deep-learned features, they are first flattened from \(7\times 7\times 512\) to a one-dimension vector of size 25,088. In this work, we used SVM with an RBF kernel. The values of the parameters are C = 10 and gamma = 0.001.
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
###### Abstract  
Tea is the second most consumed beverage, and its aroma, determined by volatile compounds (VOCs) present in leaves or developed during the processing stages, has a great influence on the final quality. The goal of this study is to determine the volatilome of different types of tea to provide a competitive tool in terms of time and costs to recognize and enhance the quality of the product in the food chain. Analyzed samples are representative of the three major types of tea: black, green, and white. VOCs were studied in parallel with different technologies and methods: gas chromatography coupled with mass spectrometer and solid phase microextraction (SPME-GC-MS) and a device called small sensor system, (S3). S3 is made up of tailor-made metal oxide gas sensors, whose operating principle is based on the variation of sensor resistance based on volatiloma exposure. The data obtained were processed through multivariate statistics, showing the full file of the pre-established aim. From the results obtained, it is understood how supportive an innovative technology can be, remotely controllable supported by machine learning (IoF), aimed in the future at increasing food safety along the entire production chain, as an early warning system for possible microbiological or chemical contamination.  
volatiloma; tea; MOX sensors; S3; GC-MS +
Footnote †: journal: Sensors  
16 April 2021  
17 June 2021  
1
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 9 Discussion  
Despite its effectiveness, our method faces several challenges. Trust evaluation relies on interaction data, making it difficult to assess newly joined vehicles or those in sparsely populated areas with limited connections. Additionally, in high-density misbehavior node scenarios, collusion attacks may undermine trust assessments, leading to the exclusion of legitimate nodes.  
To address these issues, future research will explore hybrid approaches that combine trust-based evaluation with onboard misbehavior detection. When trust mechanisms become unreliable, vehicles should rely on local sensor data to minimize the impact of malicious nodes. Further work will focus on enhancing these strategies to improve trust assessment and security in dynamic vehicular networks.
## 5 Hierarchical Control Development
### Energy Management System Design
#### 5.2.1 Startup Stage  
In the proposed CDMG there are eight PEC able to supply system loads, source converters. The startup test stage enables all the source PECs at one tenth of rating power in closed loop and in stand-alone operation. The aim of this test is to verify how many and what PECs are able to be connected to the corresponding bus through plug-and-play. Once the PEC is enabled, the test last a period of time enough to let pass the transient and reach steady state, in this condition output voltage measuremet is compared with output reference value in order to verify if measurement value is into \(\pm 10\%\) reference value. If the test is pass by a specific PEC, the corresponding bit is set '1', else is set '0', and data is sent to CC. With the resulting eight bits information, CC takes a decision and set the system initial state of source condition. Let \(SC\) be the source active bit vector defined and ordained as shown in Table 11.  
In a detailed way, \(SGD1\) and \(SGD2\) are bits associated with the DC-AC full bridge converters condition, particularly when perform in rectifier mode as source PEC. If one of them passes the initial test or stays in on mode then \(SGDi=1\), else, \(SGDi=0\) if not passes the initial test or stays in off mode. \(SBT1\) and \(SBT2\) are bits associated with the step-up DC-DC full bridge converters at the ESSs, in batteries discharge mode. If one of them passes the inicial test or stays on mode \(SBTi=1\), else, \(SBTi=0\) if not passes the initial test or stays in off mode. Finally, \(SPV1\), \(SPV2\), \(SPV3\) and \(SPV4\) are bits associated with the boost converters condition, specially when performs in regulation mode. If \(SPVi=1\), PEC is able to operate or stays in on mode, else, \(SPVi=0\) if not able to operate or in off mode.  
With eight PECs in the system, one bit each, there are 256 possible options to initialize the DCMG operation. In the start-up stage, the CC prioritizes the consumption of power collected in PV arrays, then ESS power is harness, finally, power from mains is taken. It means, only 22 combinations are available options to become initial DCMG state, most of the using PV arrays. If there is no PEC able to pass the start-up test, DCMG goes back to system off condition.
## 3 Results
### Exocentric Distance Judgment Accuracy  
The ANOVA result revealed that the effect of the display on the exocentric distance estimation was significant (F [1; 15] = 16.05, \(p\) < 0.01). The overall distance judgment results showed underestimation in the HMDs and overestimation in the SWDs, where the judgment accuracies were 0.94 (SD = 0.06) and 0.85 (SD = 0.06), respectively.  
The result also showed that the main effect of target distances (F [1.2, 17.99] = 7.00, \(p\) = 0.01) and exocentric distance (F [1; 15] = 9.25, \(p\) < 0.01) were significant, whereas the effect of size (F [1; 15] = 0.004, \(p\) = 0.20) was not significant. The post-hoc analysis indicated that the exocentric distance accuracy was significantly higher at 150 than at 65 cm from the participant, where the mean and standard deviation of exocentric distance accuracies at 65, 100, and 150 cm (Figure 3a) were, respectively, 0.86 (SD = 0.12), 0.90 (SD = 0.09), and 0.92 (SD = 0.06). In addition, the result revealed that the distance judgment accuracies improved as exocentric distance increased (Figure 3b).  
The interaction between environment and target distance (F [1.25, 18.79] = 7.97, \(p\) < 0.01) was significant, whereas the interaction results between environment and exocentric distance (F [1; 15] = 1.27, \(p\) = 0.28) and between environment and size (F [1; 15] = 2.68, \(p\) = 0.12) were not significant. The post-hoc analysis showed that the exocentric distance accuracy was significantly improved as target distance increase in the SWDs (Figure 4a), for which the accuracies were 0.76 (SD = 0.19), 0.86 (SD = 0.10), and 0.92 (SD = 0.04) for the target distances of 65, 100, and 150 cm from the participant, respectively. However, no significant effect of target distance on exocentric distance judgment accuracy was observed in the HMDs (Figure 4a). All the three-way interactions and the four-way interactions did not show significant difference in the exocentric distance judgment accuracy.  
In addition, the distance judgment estimations in HMD had shown mixed results as D1, D2, and D5 were overestimated by 0.47, 0.68, and 0.55 cm, respectively, while D3, D4, and D6 were underestimated by 0.5, 0.2, and 1.73 cm, respectively. Similarly, in the SWD conditions D1, D2, D3, and D4 were overestimated by 2.93, 4.7, 2.79, and 3.69 cm, respectively, while D5 and D6 were underestimated by 0.71 and 2.8 cm, respectively.
# Verifying Mutual Exclusion Algorithms with Non-Atomic Registers
## 5 Modeling Examples with Non-Atomic Registers
### Peterson Algorithm for \(N>2\) Processes  
Peterson-N [10] extends the algorithm for \(N=2\) processes using the metaphor of climbing a ladder. The ladder is composed of \(N-1\) steps or levels. By convention, a process at level 0 is one not interested in the critical section. The process that first arrives at the last level \(N-1\) obtains the permission to enter the critical section. Current level \(j\) occupied by process \(i\) is reflected by assigning \(j\) as the value of the output variable: \(flag[i]=j\). In addition, process \(i\) is also assigned the level \(j\) through the non-output variable \(turn[j]=i\). More than one process can occupy simultaneously the level \(j\). When this occurs, one of them moves to the next level.  
The algorithm was modeled in Uppaal and thoroughly investigated in [11]. It emerged the algorithm is fully correct when it executes with atomic registers or MWMR regular registers. In particular, the overtaking factor was found to be: \(ov=\frac{1}{2}N*(N-1)\) when the non-critical section NCS can have an arbitrary duration (also infinite), and \(ov=N-1\) when NCS is made urgent, meaning that a process exiting from the CS is permitted to immediately re-enter for a new competition. In the more general case of MWMR safe registers, that is, when scrambling in the \(turn[.]\) variables and flickering in the \(flag[.]\) variables are allowed (see the Uppaal model in Figure 10 that is based on the _Loop_ flickering technique), the algorithm continues to be free of deadlocks but loses the fundamental mutual exclusion property, that is, multiple processes can enter simultaneously their critical section. The same results were observed when using the _Simple_ and the _Decoration_ technique. These results integrate the results of Peterson-N based on non-atomic memory and flickering reported in [11], where the solution was mainly studied for the assessment of the overtaking factor.  
Figure 10: Uppaal model of Peterson-N algorithm with both scrambling and _Loop_ flickering.
# Complex Human-Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach
## 3 Proposed System
### Creation of Input Data for a Deep Convolutional Neural Network  
#### 3.3.1 Extraction of Skeletal Joint Movement Features  
In order to extract spatio-temporal features, we first detected the moving joints within 15 frames. Next, we calculated the Euclidean distance between two consecutive frames for all joints using Equation (2). Then we found the mean and the standard deviation for joint distance as described in Equations (3) and (4).  
\[Dist_{i,j}=\sqrt{\left(x_{i+1,j}-x_{i,j}\right)^{2}+\left(y_{i+1,j}-y_{i,j} \right)^{2}}\ \text{for}\ i=\ 1,\ 2,\ \ldots,\ T\ \text{and}\ j=\ 1,\ 2,\ \ldots,\ S \tag{2}\]  
\[m_{j}=\frac{1}{T}\sum_{i=1}^{T}Dist_{i,j}\ \ \ \ \ \text{for}\ i=\ 1,\ 2,\ \ldots,\ T\ \text{and}\ j=\ 1,\ 2,\ \ldots,\ S \tag{3}\]  
Figure 3: Illustration of temporal segmentation over the skeletal joint movement data.  
\[sd_{j}=\sqrt{\frac{1}{T}\sum_{i=1}^{T}\left(Dist_{i,j}-m_{j}\right)}\quad\text{ for }i=1,2,\ldots,T\text{ and }j=1,2,\ldots,S \tag{4}\]  
where the value of \(S\) is 15 for total skeletal joints, and the value of \(T\) is 14, which represents \(W-1\) (the frame interval within the temporal sliding window \(W\)). \(Dist_{i,j}\) refers to the Euclidean distance between the locations of joint \(j\) in two consecutive frames \(i\) and \(i+1\). The symbols of \(m_{j}\) and \(sd_{j}\) represent the mean and the standard deviation for joint \(j\). If the value of \(Dist_{i,j}\) is greater than or equal to the value of \((m_{j}+sd_{j}\times 0.5)\), then joint \(j\) at time \(i\) is regarded as a moving joint and its movement frequency (_freq\({}_{j}\)_) increases. RGB value markers are predefined as shown in Table 1, and the values of \(i\), \(j\), and _freq\({}_{j}\)_ for moving joints are used as indices for selecting RGB color values. Sample data for \(Dist_{i,j}\), \(m_{j}\), \(sd_{j}\), and _freq\({}_{j}\)_ for the sliding window for the action \"taking using the right hand\" are described in Table 2. After obtaining the locations of moving joints, we assigned a circular shape marker for the location of moving joint \(j\) in each frame with time \(i\) for creating input images for DCNN. We can see that values for _freq\({}_{j}\)_ for the right shoulder, right elbow, and right hand are higher than those for other joints, indicating the validity of this process of detecting moving joints (highlighted with yellow color).