esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
## 4 RUL Prediction Experiment
### Experimental Setup  
All experiments were performed on a laptop computer equipped with AMD Ryzen 7 5800H CPU (AMD, Sunnyvale, CA, USA), 16-GB RAM, and GeForce RTX 3070 Laptop GPU (Nvidia, Santa Clara, CA, USA). A software environment with Keras 2.6.0 based on Tensorflow 2.6.0 and Python 3.9.0 was used.  
Figure 6: Diagram of the sliding time window operation.  
Figure 7: Diagram showing the generation of RUL labels using the piecewise function.  
The model parameters of MSA-FCN are summarized in Table 3. These parameters were tuned by grid search. The training epoch was set to 180, and the batch size was set to 512. The initial RUL value was set to 125. The window lengths of the sliding time window operation were set to 20 and 18 on FD002 and FD004, respectively. The Adam algorithm was used as the optimizer to minimize the loss function, and the learning rate was set to 0.0005. \(\delta\) is an adjustable hyperparameter in the loss function, and \(\delta\) was set to 1 for Huber loss. \(\sigma\) for L2 regularization was set to 0.001.
## 2 System Model  
We consider the uplink of a two-cluster C-RAN system illustrated in Figure 1. The system consists of two nearby clusters, where each cluster has \(K\) single-antenna users, \(M\) single-antenna RUs, and a single BBU. There are no overlapped users, RUs, and BBUs between the two clusters. We refer to the \(k\)th user, the \(r\)th RU, and the BBU in cluster \(i\) as user \((i,k)\), RU \((i,r)\), and BBU \(i\), respectively. The users \((i,k)\), \(k\in\mathcal{K}\triangleq\{1,2,\ldots,K\}\), in cluster \(i\) transmit digital messages to their serving BBU \(i\) through the RUs \((i,r)\), \(r\in\mathcal{M}\triangleq\{1,2,\ldots,M\}\). Each RU \((i,r)\) is connected to BBU \(i\) through a _fronthaul link_ of capacity \(C_{F}\) bit/symbol. To efficiently manage inter-cluster interference signals, each BBU \(i\) can send some information to BBU \(\tilde{l}\triangleq 3-i\) through a _backhaul link_ of finite capacity \(C_{B}\) bit/symbol. We assume that the association between users and clusters is given a priori, and the design of association is left as a future work.
# Thrust Augmentation of Micro-Resistojets by Steady Micro-Jet Blowing into Planar Micro-Nozzle
## 3 Numerical Setup and Methodology  
### Numerical Approach and Setup  
The numerical simulations of the gas flow through the micro-nozzle were performed by using the open source CFD toolbox OpenFOAM(r) Version 3.0.1, based on a Finite Volume formulation. The density-based solver _rhoCentralFoam_[34] was used for computations. In particular, the compressible Navier-Stokes (NS) equations were solved in combination with the laminar flow approximation in reference to the turbulence modeling. This choice resulted from computations that revealed the Reynolds number of the steam flow along the mid axis of the nozzle ranging from about 3150 at the throat section up to about 600 at the exit section. Furthermore, the gas rarefaction effects have been considered by setting a partial slip boundary condition at walls with tangential momentum accommodation coefficient \(\sigma_{TMAC}\) equal to 0.80. The last settings result from previous CFD computations [4], which have demonstrated that the maximum Knudsen number into the divergent is strictly below 0.1.  
Concerning numerical schemes, the central upwind scheme of [PERSON] and [PERSON] [35] was used for the flux terms and the Total Variation Diminishing (TVD) van Leer limiter [36] for interpolation. Moreover, the Gauss linear scheme was used for the divergence, the gradient, and the Laplacian operators. Time derivatives were computed with the first order, bounded and implicit Euler scheme. The time step was determined based on a maximum Courant number \(Co_{max}\) of 0.2. In particular, the pre-conditioned conjugate gradient/diagonal incomplete Cholesky scheme with a residual tolerance of \(1\times 10^{-8}\) was used to solve the viscous governing equations, while the inviscid equations of momentum and energy were explicitly solved by means of a Gauss-Siedel Smooth solver with a residual tolerance of \(1\times 10^{-10}\). The numerical stability during transients was ensured by splitting each simulated temporal window into three temporal intervals, i.e., [0-0.5 \(\times 10^{-4}\)] s, [0.5 \(\times 10^{-4}\)-1 \(\times 10^{-4}\)] s, and [1 \(\times\) 10\({}^{-4}\)-3 \(\times\) 10\({}^{-4}\)] s, and increasing the mass flow rate at the beginning of each step. The establishment of the steady state regime was ensured by monitoring the Mach number at mid-point of the nozzle exit. The Peng Robinson model [37] was used as equation of state for both water vapor and gas nitrogen, which allowed for better reproduction of the compressibility effects.
# Estimation of Structural Deformed Configuration for Bridges Using Multi-Response Measurement Data
## 2 Estimation Algorithm
### Step 3: Deformed Shape Estimation  
The weight factor required to estimate SDS can be calculated using \(MD\) and \(\widetilde{ADS}\). Owing to the different MD matrix sizes of \(MD\) in Equation (13) and the \(\widetilde{ADS}\) in Equation(9), it is necessary to adjust the matrix size of the \(\widetilde{ADS}\) to construct an error function. To adjust the matrix size, \(\widetilde{ADS}\) is converted to \(ADS\) by removing the row, excluding the data measurement point for each response. The least-squares method is adopted to calculate the weight factor that can minimize the error between \(ADS\) and \(MD\), and the calculation process is shown in Equations (14)-(16). The error function \(E\) is constructed as the sum of the square errors between \(ADS\) and \(MD\) in Equation (14). To minimize \(E\), the partial differential equation for the weight factor should be zero, as shown in Equation (15). Then, Equation (16) can be derived by substituting Equation (8) into Equation (15) and transformed into a matrix form as shown in Equation (17).  
\[E\ =\ \sum_{j=1}^{N_{md}}(MD_{j}\ -\ ADS_{j})^{2} \tag{14}\]  
\[\frac{\partial E}{\partial x_{k}}\ =\ 2\sum_{j=1}^{N_{md}}\biggl{[}\left(MD_{j}\ -\ ADS_{j}\right)\left(\frac{\partial MD_{j}}{ \partial x_{k}}\ -\ \frac{\partial ADS_{j}}{\partial x_{k}}\right)\biggr{]}\ =\ 0\quad\mbox{ where }k\ =\ 1,\ 2,\ \cdots,\ N_{sf} \tag{15}\]  
\[\frac{\partial E}{\partial x_{k}}\ =\ \sum_{j=1}^{N_{md}}\left[\left(\sum_{i=1}^{N_{sf}} \alpha_{i}\[PERSON]_{ij}\right)\left(\[PERSON]_{kj}\right)\right]\ =\ \sum_{j=1}^{N_{md}}\Bigl{[}\left(MD_{j}\right)\left(\[PERSON]_{kj} \right)\Bigr{]}\ \mbox{ where }k\ =\ 1,\ 2,\ \cdots,\ N_{sf} \tag{16}\]  
\[[\Phi]_{N_{md}\times N_{sf}}^{T}[\Phi]_{N_{md}\times N_{sf}}^{N}\{\alpha\}_{ N_{sf}\times 1}\ =\ [\Phi]_{N_{md}\times N_{sf}}^{T}\{MD\}_{N_{mf}\times 1}. \tag{17}\]  
The weight factor that minimizes the square error between \(ADS\) and \(MD\) can be calculated from Equation (17). [PERSON] and [PERSON] used MSF and proposed the calculation of the weight factor using the inverse matrix of the \([\Phi]^{T}[\Phi]\) term [12]. However, if the number of shape functions is larger than the number of measured data, then the inverse matrix cannot be derived because \([\Phi]^{T}[\Phi]\) becomes a rank-deficient matrix. In general, the amount of data that can be measured on a structure is limited owing to the cost. In the case of using MSF, the inverse matrix can be adopted because a small number of shape functions are used.  
When using the SSF derived by applying a unit load to each node of the FEM model, a different method is required to calculate the weight factor. To solve this problem, in this case, the singular value decomposition (SVD) method [22] was adopted to calculate the weight factor. The rank-deficient linear algebra problem can be solved reliably by using the SVD method. Finally, the estimated deformed shape (EDS) can be obtained by substituting the calculated weight factor into the displacement part of Equation (9).
# A Simple Model to Predict Machined Depth and Surface Profile for Picosecond Laser Surface Texturing
## 2 Modeling of LST and Experimental Procedures  
### Modeling of Laser Ablation in a Single Location  
The energy distribution of a Gaussian beam can be approximately expressed as [30]:  
\[I(x,y)=I_{\text{max}}e^{-(\frac{x^{2}+y^{2}}{\sigma^{2}})} \tag{1}\]  
where \(I\) is the laser fluence at point \((x,y)\), \(I_{\text{max}}\) is the maximum fluence and \(\sigma\) is the beam spreading parameter (proportional to the beam diameter). The laser ablation occurs once the local fluence exceeds the threshold value of the material removal. Hence, the depth at any point of the laser irradiated zone for a single pulse can be estimated using the Gaussian function.  
\[D(x,y)=D_{\text{max}}e^{-k(\frac{(x-x_{0})^{2}+(y-y_{0})^{2}}{\sigma_{0}^{2}})} \tag{2}\]  
where \(D\) is the machined depth at any point, \(D_{\text{max}}\) is the maximum depth of a single pulse, \((x_{0},y_{0})\) represents the laser pulse center, \(w_{0}\) is the beam diameter, and \(k\) is a scaling factor for adjusting the practical profile of laser irradiated zone to the Gaussian shape.  
The total ablation depth in a single location after multi-pulses irradiation can be considered as the accumulation of multiple independent pulses. Equation (2) is rewritten as:\[D(x,y)=D_{\text{max}}(N)e^{-k(\frac{(x-x_{0})^{2}+(y-y_{0})^{2}}{w_{0}^{2}})} \tag{3}\]  
where \(D_{\text{max}}(N)\) is the accumulated depth for \(N\) pulses. For given laser process parameters, the \(D_{\text{max}}(N)\) and \(k\) are both estimated simulation parameters, which are determined according to experimental data.
# Formal Safety Assessment and Improvement of DDS Protocol for Industrial Data Distribution Service
## 4 DDS Protocol Modeling
### DDS Protocol Message Flow Model  
In DDS security, the identity authentication process is split into two parts: the local participant authentication process and the mutual authentication and key negotiation  
Figure 3: DDS standard architecture.  
process between the local participant and the remote participant, this paper only studies the latter. There is no client side or server side in the mutual authentication and key negotiation process between the local participant and the remote participant because it is a Peer to Peer (P2P) protocol, and the status of both sides in the authentication process is equal [13]. The DDS security specification stipulates that before authenticating the identity of the participant, the participant must first be configured with a certificate and private key through a shared CA, and the CA must be safe and reliable. The descriptions of the required symbols are shown in Table 2.  
Figure 4 shows the transmission process of its information:  
Step 1: Participant [PERSON] first sends its own certificate CertM and a random number Nm to participant [PERSON], and [PERSON] verifies the validity of the certificate after receiving the message from [PERSON];  
Step 2: After verifying that the certificate information is legal, [PERSON] sends its own random number Ns and its own certificate CertS to [PERSON], and signs the random number Ns with the private key. [PERSON] verifies the validity of the certificate and signature after receiving the message from [PERSON];  
Step 3: After verifying the validity of the message, [PERSON] sends information to [PERSON], uses the public key to encrypt the shared key K and the message signature, uses the hash function to calculate the random number Ns and encrypted information, and then uses the private key to sign. The role of the shared key K is to determine whether the identity of the other party is legal by judging whether the other party has the same key. After receiving the information from [PERSON], [PERSON] verifies the validity of the message, thereby completing the identity authentication.
## 2 Theoretical Behavior of Distributed versus Concentrated Volt/var Local Control Strategy
### Distributed versus Concentrated Var-Sinks
#### 2.5.1 Reactive Power Exchange  
Figure 6 shows \(Q^{ex}\) as a function of the bus number, \(n\), for the distributed and concentrated var-sink cases. \(Q^{ex}\) for the distributed var-sinks case is calculated as in:  
\[Q^{ex}_{d}(n)=n\cdot Q, \tag{17}\]  
while for the concentrated var-sink case:  
\[Q^{ex}_{c}(n)=Q_{c}(n). \tag{18}\]  
The results of the calculations show that the reactive power needed, i.e., \(Q_{ex}\), to achieve the same total voltage drop is smaller in the case of a concentrated var-sink than in the case of the distributed var-sinks. \(Q_{ex}\) is the same for both strategies only when the feeder has one bus, \(n=1\). With increasing \(n\), the difference between \(Q^{ex}_{d}\) and \(Q^{ex}_{c}\) increases monotonically.
###### Abstract  
Weed management is becoming increasingly important for sustainable crop production. Weeds cause an average yield loss of 11.5% billion in Pakistan, which is more than PKR 65 billion per year. A real-time laser weeding robot can increase the crop's yield by efficiently removing weeds. Therefore, it helps decrease the environmental risks associated with traditional weed management approaches. However, to work efficiently and accurately, the weeding robot must have a robust weed detection mechanism to avoid physical damage to the targeted crops. This work focuses on developing a lightweight weed detection mechanism to assist laser weeding robots. The weed images were collected from six different agriculture farms in Pakistan. The dataset consisted of 9000 images of three crops: okra, bitter gourd, sponge gourd, and four weed species (horseweed, herb paris, grasses, and small weeds). We chose a single-shot object detection model, YOLO5. The selected model achieved a mAP of 0.886 IOU 0.5, indicating that the model predicted a large number of true positive (TP) with much less prediction of false positive (FP) and false negative (FN). While SSD-ResNet50 achieved a mAP of 0.53@IOU 0.5, the model predicted fewer TP with significant outcomes as FP or FN. The superior performance of the YOLOv5 model made it suitable for detecting and classifying weeds and crops within fields. Furthermore, the model was ported to an Nvidia Xavier AGX standalone device to make it a high-performance and low-power computation detection system. The model achieved an FPS rate of 27. Therefore, it is highly compatible with the laser weeding robot, which takes approximately 22.04 h at a velocity of 0.25 feet per second to remove weeds from a one-acre plot.  
Keywords:real-time detection; deep-learning; single-shot detector (SSD) model; light-weight; YOLO; weed dataset; standalone device +
Footnote †: journal: _Sciences_
## 4 Experimental Evaluations and Results
### Explanations Using Existing XAI Methods  
By implementing existing methods, we obtain the results shown in Figure 4. The importance of every input feature generated from DeepLiftSHAP [4] is shown in Figure 4a without showing its contribution to different components. We can see that the RSRP feature contributes significantly to the prediction of the agent, but there is no information on how it is distributed to the reward components. Similarly, Figure 4b shows the explanation of how each component constructs the total reward, as in [8] without showing the contribution of every input feature. For example, when the RSRP reward component (\(R_{RSRP}\)) contributes the most to the final reward calculation, the contributions of the input features are not shown.
# Engineering Vehicle Detection Based on Improved YOLOv6
## 4 Experiments
### Experimental Results
#### 4.4.2 Contrasting Various Backbone Architectures  
To assess the performance of the Swin Transformer, we replaced multiple feature extraction networks of YOLOv6 and compared the corresponding experimental results [40; 41].  
According to the results presented in Table 4, YOLOv6 utilizing the Swin Transformer base as its backbone outperforms other backbone options. In comparison to the default EfficientRep and PVTv2-B5, our proposed method exhibited improvements in \(mAP_{50:95}\) performance by 5.7% and 0.7%, respectively, affirming the effectiveness of the Swin Transformer.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**IoU Based on Prediction Results**} & \multicolumn{4}{c}{**IoU Based on Ground Truth**} \\ \cline{2-9}  & **Number** & **Mean** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \(p\)**-Value** & **Number** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \(p\)**-Value** \\ \hline YOLOv5-L [18] & 679 & 0.88 & 0.19 & 0.102 & 822 & 0.74 & 0.36 & 0.000 \\ YOLOv6-L [5] & 793 & 0.87 & 0.24 & 0.005 & 822 & 0.84 & 0.28 & 0.005 \\ YOLOv7-L [19] & 665 & 0.85 & 0.22 & 0.000 & 822 & 0.70 & 0.37 & 0.000 \\ YOLOv8-L [20] & 764 & 0.89 & 0.22 & 0.222 & 822 & 0.83 & 0.30 & 0.002 \\ YOLOv-L [21] & 775 & 0.82 & 0.24 & 0.000 & 822 & 0.78 & 0.29 & 0.000 \\ Faster R-CNN R50 [14] & 868 & 0.78 & 0.27 & 0.000 & 822 & 0.80 & 0.25 & 0.000 \\ Faster R-CNN R101 [14] & 879 & 0.77 & 0.28 & 0.000 & 822 & 0.79 & 0.27 & 0.000 \\ Casade R-CNN R50 [15] & 792 & 0.81 & 0.24 & 0.000 & 822 & 0.78 & 0.28 & 0.000 \\ Casade R-CNN R101 [15] & 788 & 0.82 & 0.24 & 0.000 & 822 & 0.78 & 0.28 & 0.000 \\ YOLOv6-L Swin-B (Ours) & 795 & 0.90 & 0.22 & 0.500 & 822 & 0.87 & 0.25 & 0.500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mean, standard deviation and significance level of sample IoU based on prediction results, and the mean, standard deviation and significance level of sample IoU based on ground truth. \(p\)-value represents the significance level. The significance level indicates that the mean IoU of our proposed method is significantly greater than that of the corresponding methods.
# Quantification and Analysis of Carrier-to-Interference Ratio in High-Throughput Satellite Systems
## 3 Quantification Method of CIR  
The system CIR refers to the power ratio of the carrier signal to the sum of interference signals. As for the calculation of the satellite system CIR, the CIRs of the forward uplink (i.e., feeder uplink), forward downlink (i.e., user downlink), return uplink (i.e., user uplink), and return downlink (i.e., feeder downlink) should be comprehensively considered. The details are as follows:  
\[\left(C/I\right)_{f}=10\lg\left(\frac{1}{1/10^{\left(C/I\right)_{fu}/10}+1/10 ^{\left(C/I\right)_{d}/10}}\right), \tag{11}\]  
\[\left(C/I\right)_{b}=10\lg\left(\frac{1}{1/10^{\left(C/I\right)_{hu}/10}+1/10 ^{\left([PERSON]\right)_{du}/10}}\right), \tag{12}\]  
where \(C\) is the carrier power and \(I\) is the interference signal power, \(\left([PERSON]\right)_{fu^{\prime}}\left([PERSON]\right)_{fu^{\prime}}\left([PERSON]\right)_{ bu^{\prime}}\), \(\left([PERSON]\right)_{bi}\), \(\left([PERSON]\right)_{f}\), and \(\left([PERSON]\right)_{b}\) refer to the CIR of the forward uplink, forward downlink, return uplink, return downlink, the entire forward link, and return link, respectively.  
In this paper, the uplink interference and downlink interference are discussed. The quantification method of user's uplink interference and downlink interference (corresponding to return uplink and forward downlink) is expounded. Due to the same principle, feeder uplink interference and feeder downlink interference (corresponding to the forward uplink and return downlink) can be quantified in a similar way. Moreover, the number and location of the ground gateways are deterministic, making it easier to handle the feeder link. Based on the above results, the system CIR can be obtained according to Equations (11) and (12).  
Next, we will discuss the quantification method of the CIR for user uplink and downlink.
## Materials and Methods
### Test Procedure  
For the study in this paper, two POM gears were fitted with an ISM and run alternately on the test bench. In the following, these are called gear 1 and gear 2. The BBSs were used to compare the in situ data with the conventional method of recording vibration data. The sampling rate of the piezo sensors was 10 kHz. The drive ran at a constant 1000 rpm, which is a shaft frequency of 16.7 Hz, and the load was a constant 10 Nm. An overview of the main frequencies is given in Table 5. Both the ISM and the BBSs collected data synchronously for 1 min every 20 min. The test bench was stopped after running for two hours to measure the actual wear and recharge the batteries. These two hours are referred to as two-hour sequences in the following. They consist of 1.2 \(\times\) 10\({}^{5}\) cycles of the polymer gear. After recharging, the gears were remounted and the next two-hour sequence began. Figure 3 displays the flow of the test procedure. Additionally, a 1 min acceleration sample was taken at the beginning of the tests to provide a comparison with the acceleration signal of a completely healthy gear.  
Polymer gears have a risk of melting at elevated temperatures. The melting would lead to a very fast and abrupt failure. To ensure progressive wear, the temperature had to be below the melting temperature. To confirm that this temperature was not reached in the study, the surface temperature of the loaded flanks of gear 1 was measured. The measurement was also performed to validate the measurements of the integrated temperature sensors on the ISM. The temperature was measured for the second two-hour sequence beginning from 2 h running time. During this period, the temperature was measured every 5 min until a steady-state tooth surface temperature was reached at 3:40 h. The temperature comparison measurements were made using a handheld infrared camera (FLIR E6, FLIR Systems Inc., Wilsonville, OR, USA).  
Because of the complex relationship between the vibration signal and wear, it was also necessary to measure the actual wear. For this study, the normed wear was defined as the difference between the measured value and the initial value. To quantify it, the mass loss and the loss of Lateral Surface Area (LSA) was measured. Therefore, the weight of the POM gears was measured using a scale with a precision of \(\pm\)0.0004 g (PCE-ABI 220, PCE Deutschland GmbH, Meschede, Germany). The ISM and battery holder were not removed as their weight remained the same. Weighting is a common method for measuring wear [12; 28]. The LSA was measured using a digital microscope (VHX2000, Keyence, Osaka, Japan). A similar method was used by [PERSON] et al. who measured the tooth width [29]. The LSA was measured for two different teeth on each POM gear.
## 5 Results and Discussion
### Parameter Experiment  
In this research, further investigation was conducted to explore the impact of different relation filtering thresholds on model performance and determine the optimal model parameters. The results of experiments performed on the CMeIE dataset by adjusting relation filtering thresholds are presented in Figure 3, where'relation_threshold' represents the threshold for determining relation filtering and'relation_adding' represents the threshold for adding after relation filtering to the original model.  
Figure 2(a) shows that when'relation_adding' remains fixed, the'relation_threshold' fluctuates up and down, reaching a peak at 0.9 before decreasing. Figure 2(b) shows that when'relation_threshold' is fixed,'relation_adding' reaches a peak at 0.3 before stabilizing around 51.7%. It is evident that these two thresholds do not exhibit a consistent or sensitive pattern with the overall model. Therefore, after conducting threshold parameter comparison experiments, this work selected the optimal parameter values of'relation_threshold' as 0.9 and'relation_adding' as 0.3 to achieve the best performance for the model.