esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
## 5 Simulation Results and Discussion  
### Dataset  
The proposed method in this paper primarily learns color differences in images to discern faulty lines, with the intention of being unaffected by line parameters or line length. Consequently, two distinct 10 kV distribution network models with small current grounding were constructed using PSCAD/EMTDC. These models are depicted in Figure 7, while their line parameters are detailed in Tables 2 and 3 respectively. Each feeder's head in both distribution network models was equipped with a PMU device to collect zero-sequence current data. The transformers had a capacity of 50 MVA and a voltage ratio of 110 kV/10.5 kV. In Figure 7, Model 1 is depicted as a resonant grounding system, utilized for creating the training dataset. In Figure 7, Model 2 is utilized for generating the test set. Within Model 2, when the switch at the busbar was open, it represented a neutral ungrounded system (NUS), whereas when the switch was closed, it signified a neutral resonant grounding system (NES). In the resonant grounding systems, an overcompensation strategy was uniformly employed, with the compensation degree set to 10%.  
A dataset was constructed by simulating various fault scenarios across each line in the distribution grid models, encompassing different fault locations, initial fault angles, and fault grounding resistances. The specific scenarios are detailed in Table 4.  
In Table 4, 'Emanuel' refers to an arc high resistance model [25], illustrated in Figure 8, which comprises two diodes, two voltage sources, and two resistors connected in inverse parallel. This model was designed to mimic the random fluctuations in arc voltage and arc resistance observed in real-world situations, where \(U_{\text{P}}\), \(U_{\text{n}}\), \(R_{\text{P}}\), and \(R_{\text{n}}\) fluctuate randomly over time within a range of \(\pm 10\%\) of their set central values. By adjusting these parameters, simulations of diverse grounding medium conditions could be achieved. The specific parameters for this model are listed in Table 5.  
Based on the scenarios outlined in Tables 4 and 5, training, validation, and test datasets--namely, NES training set, NES test set, and NUS test set--were generated. Each of these datasets comprised 2520 fault sample groups, with each group containing data from four measurement points. Ultimately, the time-domain datasets were transformed into image datasets using the methodology outlined in Section 3.2.
## 3 Current State of Object Detection Algorithms
### Yolo
#### 3.2.5 YOLOv7  
YOLOv7 [43] is the latest advancement in object detection algorithms. YOLOv7 brings several important improvements that make it a more powerful tool for object detection. One of the major differences between YOLOv7 and YOLOv6 is the backbone network. YOLOv7 uses an Extended Efficient Layer Aggregation Network (E-ELAN) instead of the EfficientRep used in YOLOv6. The E-ELAN makes use of the cardinality of expand, shuffle, and merge operations to continually enhance the network's learning potential while preserving the gradient path. This leads to improved accuracy and speed in object detection tasks. Another improvement in YOLOv7 is the use of the FPN and PANet in the neck. This results in an FPN-PANet structure that combines the best of both networks. The primary head classifies objects, while the secondary heads train in the middle layers, leading to improved accuracy and faster detection times.  
In addition to the improved network architecture, YOLOv7 also utilizes Neural Architecture Search (NAS) instruments to optimize the scaling of models for deployment. This NAS-based approach increases scalability and robustness, as the NAS algorithm iteratively identifies the optimal scaling factors based on resolution, width, depth, and stage (number of feature pyramids). Finally, YOLOv7 also employs module-level re-parameterization, where different segments of the model are optimized using exclusive strategies. The gradient flow propagation channels determine which segments of the model require recalibration, leading to improved robustness and accuracy.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 5 Results and Comparative Analysis
### Scenarios with Heterogeneous Nodes  
The network's performance can be efficiently simulated and analyzed under a variety of conditions, including the effect of node mobility, network size, and the mix of fixed and mobile nodes. A realistic and representative heterogeneous network scenario can be created with the aid of the Random Waypoint Mobility Model for mobile nodes and the static placement of fixed nodes. This allows for the evaluation of crucial performance metrics and the creation of efficient communication strategies.  
Heterogeneous nodes are different types of nodes utilized in a network, with the number of mobile and fixed nodes varying across configurations [22]. This section describes the performance of heterogeneous networks with various combinations of mobile and fixed nodes. By gradually increasing the number of both node types, the analysis reveals how the network's scalability affects important performance measures. Adding a ground speed of 3 m/s to the Random Waypoint Mobility Model for the mobile nodes makes the simulated heterogeneous network more like real-life situations where things are both fixed and moving slowly. This lets us do more accurate performance tests and come up with better ways to communicate.  
In the first scenario, which involves 10 mobile nodes and 10 fixed nodes, we observe a small deployment that is particularly useful for localized applications or proof-of-concept studies. As the network scales to 40 mobile and 40 fixed nodes, and then to 100 mobile and 100 fixed nodes, the network's capacity and complexity increase significantly. This growth leads to changes in various performance metrics such as total load, throughput, latency, energy consumption, jitter, packet loss, and delivery ratio. The final scenario, featuring 250 mobile and 250 fixed nodes, represents a large-scale heterogeneous network akin to a smart city or IoT infrastructure. Here, the sheer number of devices introduces new challenges related to coordination, resource management, and maintaining quality of service.  
By evaluating this spectrum of network sizes, we can identify scaling trends, bottlenecks, and trade-offs among performance indicators. This understanding is vital for creating and optimizing heterogeneous communication systems that can adapt to the diverse needs of various applications and deployment contexts.  
In the experiment with 10 mobile and 10 fixed nodes, the role of fixed nodes as signal relays becomes crucial. Their effectiveness in improving overall network latency and throughput can be significant, as they facilitate more stable connections and reduce the distance that mobile nodes need to cover to reach one another. By acting as intermediaries, fixed nodes can help maintain a reliable communication path, thereby enhancing the overall performance of the network.
## 4 Discussion
### The FDA's Total Product Life Cycle (TPLC) Approach with HMMDs  
The FDA's Center for Devices and Radiological Health (CDRH) implemented the Total Product Life Cycle (TPLC) approach in 2019, ensuring continuous oversight of medical devices from development through to post-market use [21; 22]. Since then, gastrointestinal pathogen panels, including HMMD-based assays, have been under FDA post-market surveillance. A recent rise in error alarms reported through medical device surveillance systems, primarily related to false positives, suggests potential limitations in relying solely on HMMDs for infection surveillance (Figure 6) [23]. Given that HMMDs often lack confirmable isolates, and many laboratories depend entirely on HMMD results, public health surveillance based solely on molecular data may be insufficient. This study specifically evaluated _Salmonella_ detection with the Luminex xTAG GPP; therefore, findings should not be generalized to other HMMDs without further evaluation.  
Figure 4: Annual reported cases of _Salmonella_ to the KDCA and patients confirmed positive for _Salmonella_ via the xTAG Gastrointestinal Pathogen Panel at our hospital. There was a sharp increase in reported cases of _Salmonella_ to the KDCA in 2017, followed by a gradual increase in subsequent years. Similarly, SNUBH experienced a significant rise in the number of confirmed _Salmonella_ cases in 2017, with a continued upward trend observed in 2021 and 2022.  
Figure 5: Temperature changes and reported cases of _Salmonella_ to KDCA over the past nine years. (**A**) Monthly temperature changes from January 2015 to December 2023. There was no significant difference in average temperatures during the summer months from May to August. (**B**) Reported cases of _Salmonella_ to the KDCA from January 2015 to December 2023. Due to the nature of _Salmonella_ infections increasing with rising temperatures, there was an increase in reported cases during the summer months from May to August. Despite some fluctuations, there has been a general upward trend in the number of reported cases since 2017.
## 2 System Specification
### Rules
#### 2.2.2 Oscillations Rule for \(\mathit{TR1}\)
Table 1 introduces a couple of complementary situations that can cause an oscillation and that were not expected by the system. These two complementary situations are:  
* The node has now a negative error for the node degree, meaning that it has more neighbors than desired. In the previous iteration, the error was positive, meaning that it had fewer neighbors than desired. Furthermore, the change in the communication range was the minimal. Thus, the reasoner will decide to change the communication range again, as the current error is beyond expected limits. However, we can foresee that even the minimal change will put the node into the previous situation, when it had fewer neighbors than desired. Thus, there is a high chance that if the change is made, in the next iteration, the error on the node degree will be positive, causing an oscillation. Therefore, in this case, no change should be made. The complementary is also valid. * The node has now a negative error for the node degree, meaning that it has more neighbors than desired. In the previous iteration, the error was also negative, and no change was applied to the communication range. This means that in the previous iteration, the system decided not to update the communication range to prevent an oscillation (this is the only way to reach this situation). In this case, the reasoner will decide to change the communication range, as the current error is beyond the limits. However, if the previous iteration tried to prevent an oscillation and nothing has changed from it, then there is still no need to change the communication range. Additionally, again the complementary is also valid. The following rule has been defined as a result of the analysis of Table 1:  
\[ \begin{array}{l}\textbf{IF}\\ \{sign(e_{ND}(k))=sign(e_{ND}(k-1))\textbf{ AND }\Delta cr(k-1)=0\}\\ \textbf{OR}\\ \{e_{ND}(k)<-\xi_{ND}\textbf{ AND }e_{ND}(k-1)>\xi_{ND}\textbf{ AND }|\Delta cr(k-1)|=\Delta cr_{min}\}\\ \textbf{OR}\\ \{e_{ND}(k)>\xi_{ND}\textbf{ AND }e_{ND}(k-1)<-\xi_{ND}\textbf{ AND }|\Delta cr(k-1)|=\Delta cr_{min}\}\\ \textbf{THEN}\\ \textbf{DO NOTHING}\\ \textbf{ELSE}\\ \textbf{evaluate }ACTIVATION\&\textbf{FILE FOR }TR1\end{array}  \tag{16}\]  
The rule in Equation (16) must precede the rule in Equation (14).
###### Abstract  
The intelligent reflecting surface (IRS) is an emerging technology that can conquer visible light communication's (VLC) dependency on the line-of-sight (LoS) channel by offering additional non-light-of-sight (NLoS) communication links. In this paper, a newly proposed electro-tunable intelligent reflecting metasurface is deployed in dimmable single-input single-output (SISO) VLC systems. We aim to improve the bit error rate (BER) performance by jointly optimizing the transmit constellation and the reflectance of the IRS units. To this end, the optimization problem can be solved in two steps. The minimum distance of the received constellation is firstly maximized by a convex problem, which guarantees the minimum BER. Then, the transmit constellation and the synchronously-tunable reflectance of the IRS units that correspond to the optimal received constellation are determined with an iterative alternate optimization algorithm. Finally, the simulation results show the BER performance improvement and the dimming relaxation benefit of the tunable IRS-aided SISO VLC systems.  
visible light communication; intelligent reflecting surface; tunable reflectance; dimming; BER performance
# Design and Test of Vertical Axis Rotating Cutters for Cutting Corn Roots and Crown
## 1 Introduction
developed a bionic cutter for cutting straw based on the mandibles of grasshoppers [14]. The above studies demonstrated that bionic cutters could effectively reduce resistance and improve cutting efficiency during the process of cutting corn root-crowns and straws. The discrete element method (DEM) has become a well-established method for studying the interactions between agricultural materials and operational components. DEM is employed to explore the interplay among root-crown, cutters and soil. It is possible to accurately predict the dynamics of soil and root-crown. By employing DEM, the effect of various factors on root-crown and soil could be clarified, which could significantly reduce the cycle and cost of developing devices. DEM was employed to study the mechanisms of cutting sugarcane crown [15] and simulate the process of cutting and throwing the crown [16]. [PERSON] et al. used DEM to investigate the effects of different operational conditions on the rate of cleaning crowns based on the device integrated by the side cutter and the disk [17]. [PERSON] et al. employed DEM to investigate the effect of the forward speeds of the device on the power consumption of the device and the rate of cutting the root-crown [18]. [PERSON] et al. employed DEM to study the interaction between a wing-shaped device and a root-crown and determined the optimal operating conditions of the device [19]. The above research indicated that the DEM was suitable for simulating the interactions among tillage components, soil, and root-crown. The corn root-crown consists of a crown above ground and roots underground. The roots are fixed to the soil to form the soil-roots combination. Domestic and international studies have provided the method support for constructing the device-soil-root-crown model and studying the device of cutting the root-crown. This study aims to design and test the device integrated by the cutters imitating the outline and action of a cat's claw for cutting crown and the multi-curve cutters for cutting roots, which achieved simultaneous and efficient cutting of both corn roots and crown while minimizing soil disturbance and power consumption. DEM was employed to simulate the interaction among cutters, soil, and root-crowns. The effect of the operating parameters of the device on the indexes was investigated, and optimal working conditions for the device were obtained. This study would provide the technical support for designing the device for cutting root-crowns.
## 3 Results and Discussion
### VOC Analysis of Strawberry Juice and Wines
#### 3.5.1 VOCs Analyzed by HS-SPME-GC-MS
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Compounds** & \multicolumn{4}{c}{**Stavberry Wine**} & \multicolumn{4}{c}{**Stavberry Juice**} \\ \cline{2-9}
**(ug/L)** & **ZJ** & **TCL** & **TZ** & **BX** & **ZJ** & **TCL** & **TZ** & **BX** \\ \hline Alcohols & 8804.42 \(\pm\) 262.51 \({}^{\circ}\) & 674.08 \(\pm\) 435.05 \({}^{\circ}\) & 7616.55 \(\pm\) 315.01 \({}^{\circ}\) & 5681.23 \(\pm\) 1158.84 \({}^{\circ}\) & 388.66 \(\pm\) 183.0 \({}^{\circ}\) & 158.3 \(\pm\) 49.02 \({}^{\circ}\) & 412.68 \(\pm\) 104.44 \({}^{\circ}\) & 474.81 \(\pm\) 118.12 \({}^{\circ}\) \\ Esters & 3788.21 \(\pm\) 252.52 \({}^{\circ}\) & 2572.22 \(\pm\) 214.46 \({}^{\circ}\) & 3702.41 \(\pm\) 95.95 \({}^{\circ}\) & 2372.16 \(\pm\) 284.91 \({}^{\circ}\) & 648.66 \(\pm\) 328.24 \({}^{\circ}\) & 1347.72 \(\pm\) 711.5 \({}^{\circ}\) & 172.61 \(\pm\) 94.1 \({}^{\circ}\) & 1362.22 \(\pm\) 94.01 \({}^{\circ}\) \\ Acids & 1264.55 \(\pm\) 151.93 \({}^{\circ}\) & 923.67 \(\pm\) 260.59 \({}^{\circ}\) & 808.34 \(\pm\) 299.29 \({}^{\circ}\) & 1916.27 \(\pm\) 146.13 \({}^{\circ}\) & 495.64 \(\pm\) 66.09 \({}^{\circ}\) & 987.28 \(\pm\) 108.46 \({}^{\circ}\) & 650.04 \(\pm\) 121.3 \({}^{\circ}\) & 3571.82 \(\pm\) 102.61 \({}^{\circ}\) \\ Terpene & 1152.78 \(\pm\) 116.22 \({}^{\circ}\) & 874.52 \(\pm\) 17.81 \({}^{\circ}\) & 813.02 \(\pm\) 300.74 \({}^{\circ}\) & 815.71 \(\pm\) 29.38 \({}^{\circ}\) & 700.76 \(\pm\) 149.22 \({}^{\circ}\) & 607.76 \(\pm\) 168.5 \({}^{\circ}\) & 987.23 \(\pm\) 247.32 \({}^{\circ}\) & 3397.97 \(\pm\) 916.22 \({}^{\circ}\) \\ Phenyl compounds & 1628.16 \(\pm\) 541.18 \({}^{\circ}\) & 901.06 \(\pm\) 80.29 \({}^{\circ}\) & 919.86 \(\pm\) 644.3 \({}^{\circ}\) & 714.39 \(\pm\) 122.59 \({}^{\circ}\) & 156.47 \(\pm\) 17.69 \({}^{\circ}\) & 162.61 \(\pm\) 45.1 \({}^{\circ}\) & 141.28 \(\pm\) 3 \({}^{\circ}\) & 344.61 \({}^{\circ}\) \\ Others & 71.43 \(\pm\) 4.4 \({}^{\circ}\) \({}^{\circ}\) & 100.06 \(\pm\) 19.4 \({}^{\circ}\) & 97.76 \(\pm\) 7.57 \({}^{\circ}\) \({}^{\circ}\) & 215.87 \(\pm\) 5.41 \({}^{\circ}\) & 186.51 \(\pm\) 8.98 \({}^{\circ}\) & 161.29 \(\pm\) 60.14 \({}^{\circ}\) & 189.04 \(\pm\) 45.61 \({}^{\circ}\) & 1070.14 \(\pm\) 378.82 \({}^{\circ}\) \\ \hline \multicolumn{9}{c}{Mean \(\pm\) standard deviation (\(\sigma=3\)) followed by different letters within each row indicate significant differences (Duncan test, 95%). BC: Snow White; TCL: Sweet Charles; T2: Toughoughough; ZJ: Athaine. All volatile compounds were identified by computing the mass spectrometry data with the NIST20 library; -methyl-5-pentamid (0 mg/L) was utilized as internal standard and the quantitative analysis of identified volatile compounds was performed by computing the peak area with the thermal standard.} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Concentrations of different VOCs in different cultivars of strawberry juice and wines.
## 3 Sink-oriented Dynamic Location Service
### Sink Mobility and Data Forwarding Maintenance
#### 3.5.1 Automatic Operation  
The first rule is an automatic operation. When the sink moves out of the range of its current PA, within a predefined distance (e.g., a cell size), it selects another neighboring node as its Immediate Agent (IA) and sends the location of the IA to its PA. The sink continuously updates its instant location as its New Immediate Agent (NIA) so that future data are forwarded to the NIA. This process can be done by broadcasting a solicit message from the sink and then choosing the node that replies with the strongest signal-to-noise ratio. When the sink moves outside a predefined distance from its PA, it selects a New Primary Agent (NPA). If it only considers allowing the chain between the PA and the NPA as in ALS, the following quandary occurs. Even though the sink moves toward the source node, the sensed data from the source node takes a detour. This causes large energy consumption with a long data delivery when the source node repeatedly sends the data to the sink. In the SDLS, the previous distance from the source node to the last IA of the sink is compared with the current distance from the source node to the sink's NPA. If the NPA is less than the threshold (i.e., half of previous distance), the NPA is updated through the Old Primary Agent (OPA). Until the source node obtains the location of the NPA, the OPA can forward the data continuously to the mobile sink. In particular, when the amount of data increases, the data forwarding cost will significantly increase. The following algorithm 3-1 summaries the first rule.  
**Algorithm 3-1. Pseudo-code to automatic operation.**  
**Input**: distance between source node and NPA  
**Output**: shortest path in automatic operation  
**Parameter**:  
P.A: Primary Agent, NPA: New Primary Agent  
IA: Immediate Agent, NIA: New Immediate Agent  
_1_: distance between sink and PA (NPA)  
_1_: predefined distance (i.e. half of cell size)  
_1_: distance between PA and IA (NIA)  
_1_: predefined distance (i.e. three times of cell size)  
_1_: distance between source node and NPA via old PA  
_1_: predefined distance (i.e. two times of distance between source node and NPA)  
**Procedure**:  
01: initially startFlag = true;  
02: **do**
## 4 Experiments and Results Discussion
### Models Evaluation  
#### 4.2.1 Evaluation Principles  
To evaluate the performance of the proposed MDISE model in this paper, two commonly used machine learning models for spectral quantitative analysis, PLSR and SVR, as well as a standard 1D-CNN model, were used for comparative performance validation. The hyperparameters of these models were tested multiple times to find the optimal values. The PLSR model was set with a maximum of 500 iterations, and the optimal number of latent variables (LVs) was searched within the range of 1 to 20. The optimal number of LVs for the PLSR models of the 1 st and 2 nd filter channels were 16 and 15, respectively. The optimal hyperparameters for the SVR model were determined using grid search, and the best kernel function for the SVR model was the radial basis function (RBF) kernel. The standard 1D-CNN model was constructed by removing all SE modules from the MDISE model and replacing one MD module and two 1D Inception modules with three standard convolutional layers. After multiple tests, the optimal convolution kernel sizes for the three convolutional layers were 7 \(\times\) 1, 5 \(\times\) 1, and 3 \(\times\) 1, respectively. The number of convolution kernels for each convolutional layer was set to 128, consistent with the output channel numbers of the MDISE model's modules. After each convolutional layer, a BN layer, a ReLU activation function, and a 3 \(\times\) 1 max pooling layer were added, while retaining the global average pooling layer and a fully connected layer. The same loss function and training method as those used in the MDISE model were applied for training.  
The PLSR and SVR models were implemented using the Scikit-learn 1.1.1 library based on Python 3.9.12. The MDISE and standard 1D-CNN models were implemented using the PyTorch 1.11.0 deep learning framework based on Python 3.9.12. The models were trained  
Figure 11: CO\({}_{2}\) absorbance spectra collected by 2 nd filter channel at blackbody temperatures of (**a**) 353 K; (**b**) 373 K; and (**c**) 408 K.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Filter Channel** & **Blackbody Temperature/K** & **Average SNR/dB** \\ \hline \multirow{3}{*}{1 st} & 353 & 5.1694 \\  & 373 & 8.0653 \\  & 383 & 8.4099 \\ \hline \multirow{3}{*}{2 nd} & 353 & 4.6475 \\  & 373 & 8.4233 \\ \cline{1-1}  & 408 & 9.0912 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average SNR of CO\({}_{2}\) absorbance spectra collected by two filter channels at different blackbody temperatures.  
in an experimental environment with the operating system Windows 11, CPU i7-12700H @ 2.70 GHz, GPU NVIDIA GeForce RTX 3060, CUDA version 11.3, and CUDNN version 11.3.