esa-sceva/satcom-chunk-collection huggingface dataset:
# Grid Anchor Lane Detection Based on Attribute Correlation
## 4 Experimental Results
### Results
#### 4.2.3 Qualitative Results  
Figure 9 presents qualitative comparison results of our method, along with RESA, UFLDv2, and BezierLane on the CULane dataset. As shown, the UFLDv2 model, which uses hybrid anchors, exhibits unstable performance in crowd scenes due to the lack of continuity in anchor predictions. Both BezierLane, based on parameterized curves, and UFLDv2, using line-shaped anchors, struggle to fit lane lines with large curvatures in curved scenarios. Our model can accurately locate lane lines in normal, crowd, curve, and night scenarios, demonstrating its overall robustness. The detection model, implemented using the PyTorch framework, achieves a detection speed of 94 FPS on an NVIDIA GeForce RTX 2080 Ti GPU.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **Backbone** & **F1** & **Acc** & **FP** & **FN** \\ \hline ERF-E2E [38] & ERFNet & 96.25 & 96.02 & 3.21 & 4.28 \\ PINet [29] & Hourglass & 87.10 & 97.62 & 15.30 & 10.50 \\ ConvGRUs [45] & ConvGRU & 90.97 & **97.98** & 13.26 & 4.38 \\ UFLD [39] & ResNet34 & 88.02 & 95.86 & 18.91 & 3.75 \\ UFLDv2 [7] & ResNet34 & 96.22 & 95.56 & **3.18** & 4.37 \\ CLD [40] & RESA & 94.99 & 96.17 & 5.50 & 4.50 \\ CBGA [42] & ResNet34 & 95.93 & 96.10 & 18.80 & 3.61 \\ GAAC (ours) & ResNet18 & 96.43 & 96.18 & 3.86 & 3.27 \\ GAAC (ours) & ResNet34 & **96.47** & 96.27 & 4.13. & 2.91 \\ \hline \end{tabular} The comparative data are cited from other studies. Boldfaced data indicate that the data rank first in the corresponding metric.  
\end{table}
Table 5: _Cont._  
Figure 9: Visualization results from the CULane dataset with RESA, UFLDv2, BezierLane, and our method. In the image, the purple, green, red, and blue lines represent lane lines.
# Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology
## 3 Methodology
### Sensors
#### 3.1.3 Dht11  
The DHT11 sensor is used to measure temperature and humidity, which are two very important parameters for assessing air quality. This sensor, known for its low cost and very high accuracy, serves as a digital temperature and humidity sensor [18]. The sensor is shown in Figure 5.
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 3 Results and Discussion  
Section 3.1 demonstrates the performance of the algorithm based on the actual measurement data that contains both the undamaged and damaged condition in the thrusters. The performance in classification is then compared with the reference methods in Section 3.2. The results and additional research recommendations are further discussed in Section 3.3.
# General Total Least Squares Theory for Geodetic Coordinate Transformations
## 2 Adaptation of the Transformation Models to the Constrained/Unconstrained TLS Problem
### Adaptation of the Transformation Models to the Stochastic Model of the TLS Problem  
In Sections 2.2 and 2.3, we showed that the four kinds of transformation models can be formulated using a constrained or unconstrained EIV model. In order to adapt the stochastic part of the EIV model, we propagate the covariance matrix of the observed coordinates in both the source and target systems to the covariance matrix of the EIV model for all 2D cases:  
\[D(\mathbf{e})=\sigma_{0}^{2}\mathbf{Q}=\Bigg{[}\begin{array}{cc}\mathbf{F}_ {2D}&0\\ 0&\mathbf{I}\end{array}\Bigg{]}D\Big{(}\Big{[}\begin{array}{cc}\mathbf{x}_ {s}^{T}&\mathbf{y}_{s}^{T}&\mathbf{x}_{t}^{T}&\mathbf{y}_{t}^{T}\end{array} \Big{]}^{T}\Big{)}\Bigg{[}\begin{array}{cc}\mathbf{F}_{2D}&0\\ 0&\mathbf{I}\end{array}\Bigg{]}^{T} \tag{16}\]  
where D denotes the dispersion operator, and the Jacobian matrix for 2D is:  
\[\mathbf{F}_{2D}=\frac{\partial vec(\mathbf{A})}{\partial\Big{[}\begin{array} []{cc}\mathbf{x}_{s}^{T}&\mathbf{y}_{s}^{T}\end{array}\Big{]}}=\frac{\partial vec \Big{(}\Big{[}\mathbf{I}_{2}\otimes\Big{[}\begin{array}{cc}\mathbf{x}_{s} &\mathbf{y}_{s}\end{array}\Big{]},\mathbf{I}_{2}\otimes 1\Big{]}\Big{)}}{ \partial\Big{[}\begin{array}{cc}\mathbf{x}_{s}^{T}&\mathbf{y}_{s}^{T}\end{array} \Big{]}}.\]  
Similarly, we give the stochastic model of the EIV model for all kinds of 3D transformations:  
\[D(\mathbf{e})=\sigma_{0}^{2}\mathbf{Q}=\Bigg{[}\begin{array}{cc}\mathbf{F}_ {3D}&0\\ 0&\mathbf{I}\end{array}\Bigg{]}D\Big{(}\Big{[}\begin{array}{cc}\mathbf{x}_ {s}^{T}&\mathbf{y}_{s}^{T}&\mathbf{z}_{s}^{T}&\mathbf{x}_{t}^{T}&\mathbf{y}_{t}^ {T}&\mathbf{z}_{t}^{T}\end{array}\Big{]}^{T}\Bigg{)}\Bigg{[}\begin{array}{ cc}\mathbf{F}_{3D}&0\\ 0&\mathbf{I}\end{array}\Bigg{]}^{T} \tag{17}\]  
where D denotes the dispersion operator, and the Jacobian matrix for 3D is:  
\[\mathbf{F}_{3D}=\frac{\partial vec(\mathbf{A})}{\partial\Big{[}\begin{array}{ cc}\mathbf{x}_{s}^{T}&\mathbf{y}_{s}^{T}&\mathbf{z}_{s}^{T}\end{array}\Big{]}}=\frac{ \partial vec\Big{(}\Big{[}\mathbf{I}_{3}\otimes\Big{[}\begin{array}{cc} \mathbf{x}_{s}&\mathbf{y}_{s}&\mathbf{z}_{s}\end{array}\Big{]},\mathbf{I}_{3} \otimes 1\Big{]}\Big{)}}{\partial\Big{[}\begin{array}{cc}\mathbf{x}_{s}^{T}& \mathbf{y}_{s}^{T}&\mathbf{z}_{s}^{T}\end{array}\Big{]}}. \tag{18}\]
## 4 Experiments and Results  
Our experiments present the PrimeNet as a shallow multiscale neural structure mainly to obtain the input's prime discriminative characteristics. We also present the backpropagation free layer-wise gradient update procedure within PrimeNet, which combines with the other traditional layers and allows the weight update alternatively with the traditional backpropagation method. To evaluate the performance of the representation learning algorithm, we used PrimeNet as a feature extractor on various benchmarked datasets and evaluated the performance of linear models fitted on top of these features. Moreover, we visualize and compare the feature representations generated by PrimeNet and ResNet models using t-Distributed Stochastic Neighbor Embedding (t-SNE) [33] feature visualizations. Finally, we presented the result analysis by comparing with different considered baseline performances.
## 4 Discussion
### Comparison between Normal Walking and Ekso-Assisted Walking  
Regardless of the type of assistance, remarkable and significant differences in muscle activation timings were observed in the knee flexor muscles (ST and BF), confirming what was previously observed by [21] for the Ekso and by [43] for the Mindwalker exoskeletons. Specifically, the hamstrings muscles stabilize the body during the stance phase and flex the knee, having a peak of activation during the middle/end of the swing phase [44]; thus, the physiological timing of activation for these muscles is at the beginning and at the end of the gait cycle [45]. However, during the Ekso walking, we found a peak of activation between 60-80% of the gait cycle i.e., at the end of the terminal stance/beginning of the swing phase.  
This result was confirmed by the synergies analysis. The muscle synergy S4, which is dominated by the ST and BF muscles, had completely opposite activation patterns when walking without the Ekso with respect to all the walking modalities with the Ekso: S4 was active mainly between 60-80% of the gait cycle instead of at the beginning (\(<\)20%) and at the end of the gait cycle (\(>\)80%).  
Since this pattern was always observed when subjects walked with the Ekso, as well as in the absence of assistance, the change in the activation timing of these muscles might depend on the mechanical structure of the device itself, and not on the type or amount of assistance. More specifically, this abnormal activation might be due to the reduced mobility of the ankle joint of the exoskeleton, which as in many other devices, is semi-rigid and unpowered. This could create an overloading of knee flexors muscles in the push-off phase. The mechanical design of the ankle joint can also explain the reduction in amplitude during the push-off phase in the Ekso walking for the muscle TA, GM, and SOL; a similar observation was also recently reported by [22].  
The single muscle analysis did not highlight differences in the remaining muscles, as in [18], while the muscle synergies analysis highlighted that, when walking with the Ekso, unlike in NW, the synergy S3--dominated by VM, RF, and GLM--anticipated the peak of activation by 10% of the gait cycle, while the synergy S2--dominated by the TA--had the peak of activation delayed.  
These observations, as explained in the previous point, highlighted that healthy subjects not exposed to prolonged training were inclined to activate the muscles in all the exoskeleton conditions, even when the device provided the user with total assistance.
## 4 Experiment
### Data Description  
In Figure 5, the accelerated degradation experiments are taken in the PRONOSTIA platform [42]. Three parts, such as the rotating part, the degradation part, and the measurement part, are included in the platform. The degradation part is used to accelerate the bearing degradation. This platform of the PHM2012 dataset is composed of two high-frequency accelerometers of type 3035B DYTRAN placed horizontally and vertically on each bearing to pick up the horizontal and vertical vibration signals. The accelerometer is a single axis with a range of 50 g, and the response frequency is 0.34 to 10,000 Hz. Two acceleration sensors are mounted on the vertical and horizontal axes in order to measure the bearing's vibration signal. Compared to the vertical vibration signal, the horizontal vibration signal has a higher and more noticeable amplitude. As a result, the experiment solely uses the horizontal vibration signal. The sampling frequency is 25.6 kHz, and the signal is sampled for 0.1 s every 10 s. The experiments contain 17 run-to-failure bearings. All the datasets are shown in Table 1. The raw vibration acceleration signal of the test bearing1_3 is shown in Figure 6. The signal to the left of FOT is the healthy bearing signal, and the one to the right of FOT is the fault signal. The overall time domain waveform shows that the vibration acceleration amplitude is relatively low in the front, starts to gradually increase after the early failure point FOT, and increases dramatically after the failure stage. The lower left figure shows the signal in the normal phase, with lower amplitude and a smoother signal. The lower right figure shows the signal in the failure phase, and the vibration amplitude is larger than the normal state, which can be seen due to the shock component caused by the failure.  
Figure 4: The structure of the Wiener–LSTM model with PSO optimization. The signals measured by sensors are \(x_{1},x_{2},x_{3},\ldots x_{t}\) at time \(1,2,3,\ldots t\). The corresponding RUL is recorded as \(r_{1},r_{2},r_{3},\ldots r_{t}\). The predicted RUL \(\alpha_{1},\alpha_{2},\alpha_{3},\ldots\alpha_{t}\) is predicted by the LSTM network (\(\phi\)). The Wiener process \(Y_{t}\) of the predicted RUL is introduced to control the uncertainty of the predicted result. \(W_{t}\) denotes the standard Wiener process. \(c\) is the parameter used to control the uncertainty propagation rate. The Wiener process can be introduced into LSTM in the back propagation of the LSTM network.  
Figure 5: The PRONOSTIA platform for bearing accelerated degradation tests. The rotating part is driven by an AC motor. Acceleration sensors are placed on the tested bearing both on the horizontal axis and vertical axis. Cylinder pressure is placed to accelerate the degradation of the tested bearing.  
Figure 6: Time domain waveform and the Failure Occurrence Time (FOT) of the raw signal. The middle one is the time domain waveform of test bearing1_3. The signal to the **left** of FOT is the healthy bearing signal, and the one to the **right** of FOT is the fault signal.
# Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning
## 4 The Proposed Algorithm
### Generation of Clusters  
After calculating the similarity between all pairs of data points, the AP algorithm was used to cluster the data points into sub-areas for each robot. The AP algorithm works by passing messages between data points, which indicate their preference for a particular cluster. These messages are updated iteratively until a consensus is reached on the number of clusters and which points belong to which cluster.  
Upon the conclusion of the iterative message-passing phase, the AP algorithm proceeds towards the establishment of sub-areas (Algorithm 3). This crucial stage determines the most suitable exemplar for each cluster--a data point that accrues the maximum preference value when both availability and responsibility are taken into account. Consequently, each data point (cell) is assigned to the exemplar of its respective cluster.  
Figure 4: Two points (denoted with blue color) will always have the same similarity matrix with each other. In practice, this means that going from point p\({}_{1}\) to point p\({}_{2}\) has the same cost as going from point p\({}_{2}\) to p\({}_{1}\).  
A key attribute of the proposed algorithm lies in its capacity to autonomously ascertain the optimal number of clusters (sub-areas), without any need for user-defined inputs. This ability to self-regulate cluster formation ensures flexibility and adaptability, which is particularly beneficial in complex real-world applications.  
The final output of the algorithm is a list of cluster labels for each data point in the initial environment, indicating the respective robot assignment for each cell (Figure 5). This clustered set of data points serves as the foundation for subsequent stages of the proposed model, leading to the final path planning for the robots.
# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 3 The Proposed Hybrid Deep Learning Approach
### BERT-Based Encoder for Image Segmentation Dataset
#### 3.2.2 Feature Mapping by ResNet  
The BERT-based encoder extracts features, which are then mapped in a form usable for disease classification through a Residual Network (ResNet). For maintaining high performance in deep networks without suffering from the 'vanishing gradient' problem, ResNet is chosen with its identity mappings or skip connections are used. The residual block operation can be defined as:  
\[F(x)=x+F(x,\{W_{i}\}) \tag{6}\]where \(x\) is the input to the residual block and \(F(x,\{W_{i}\})\) represents the residual function involving convolutional operations parameterized by weights \(W_{i}\).  
The segmentation mask \(M\) and the original image \(I_{\text{seg}}\) are combined and fed into the ResNet architecture to map these features into a higher-dimensional space suitable for classification. The feature mapping through ResNet can be represented as:  
\[F_{\text{ResNet}}=\text{ResNet}\big{(}M\oplus I_{\text{seg}}\big{)} \tag{7}\]  
where \(F_{\text{ResNet}}\) is the feature map produced by ResNet, and \(\oplus\) denotes the concatenation operation.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 3 Methods
### KNN Model
#### 3.2.5 Model Training and Hyperparameter Tuning  
After performing all these steps on the images, the normalized HOG features are flattened and added to a 'data' list. The corresponding labels for the images are added to the 'labels' list. Finally, the 'data' and 'labels' lists are converted to numpy arrays. The  
Figure 6: Data steps for our KNN model.  
'data' array is reshaped, maintaining the number of samples (rows) and arranging the HOG features into a 2D array with the appropriate number of columns. This structured format is crucial to train and evaluate the KNN model.  
Having prepared the images and extracted their HOG features, we found the best parameters for the KNN model. We began by dividing the dataset into training and testing sets, using a 30-70% split to ensure that the model's performance could be evaluated on previously unseen data. Following this, we conducted a randomized search combined with cross-validation to find the optimal hyperparameters for the model.  
To carry out this process, we constructed a parameter grid containing various combinations of the number of neighbors (K values ranging from 1 to 100), weights (uniform or distance), and distance metrics (Euclidean, Manhattan, and Minkowski). Using cross-validation, we then instantiated a RandomizedSearchCV object to search for the best hyperparameters. To enhance the search efficiency, we set the number of iterations to 50 and employed 10-fold cross-validation. The search process was further expedited by running it in parallel, utilizing all available CPU cores.  
Because KNN is already a high computational time model, we decided to create two identical models, one with a 64 \(\times\) 64 resized image and one with 32 \(\times\) 32, to compare computational time and accuracy trade-offs.
# Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules
## 4 Experiment
### Ablation Experiments
#### 4.5.9 Summary and Analysis of Ablation Experiment  
In short, the performance of the model is improved when the submodules of the model are added to various combined models. Eighteen sets of ablation experiments show the validity of each submodule of the model. The CLUENER2020 dataset contains ten categories, and each category represents a domain. The Weibo dataset contains four categories, and each category is labeled with two markers, explicit entities (NAM), and generic entities (NOM). The CLUENER2020 dataset focuses on distinguishing between multiple categories, and the Weibo dataset has a lower requirement for distinguishing between categories.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CLUENER2020**} & \multicolumn{3}{c}{**Weibo**} \\ \hline
**Compare Models** & \(\mathbf{P\%}\) & \(\mathbf{R\%}\) & \(\mathbf{F1\%}\) & \(\mathbf{P\%}\) & \(\mathbf{R\%}\) & \(\mathbf{F1\%}\) \\ \hline BERT-ATT-BILTAR-GlobalPointer & **80.485** & 81.270 & **80.848** & **79.227** & **72.720** & **75.751** \\ BERT-ATT-BILTAR-GlobalPointer & **80.485** & 81.270 & **80.848** & **79.227** & **72.720** & **75.751** \\ BERT-ATT-BILTAR-GlobalPointer & 79.000 & **81.729** & 80.317 & 76.921 & 68.068 & 72.169 \\ (Concatenate) & 79.180 & 81.819 & 80.447 & 68.813 & 72.035 & 70.206 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments of summing and splicing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CLUENER2020**} & \multicolumn{3}{c}{**Weibo**} \\ \hline
**Compare Models** & \(\mathbf{P\%}\) & \(\mathbf{R\%}\) & \(\mathbf{F1\%}\) & \(\mathbf{P\%}\) & \(\mathbf{R\%}\) & \(\mathbf{F1\%}\) \\ \hline (2,2) & **80.485** & 81.270 & **80.848** & **79.227** & **72.720** & **75.751** \\ (1,1) & 77.796 & **83.386** & 80.449 & 74.571 & 67.582 & 70.797 \\ (1) & 79.958 & 81.264 & 80.575 & 74.966 & 68.155 & 71.368 \\ (3,3) & 79.710 & 80.856 & 80.232 & 74.155 & 68.776 & 71.348 \\ (4,4) & 79.180 & 81.819 & 80.447 & 68.813 & 72.035 & 70.206 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experiments where the encoder of the TIME module changes the number of fully connected layers.  
gories. But the Weibo dataset requires the model to recognize whether entities are explicitly or generically referred to. The difficulties of the two datasets are different, so in the same ablation experiment, the model has different enhancement effects on the two datasets.
## 5 Experiments and Results
### Adjustable Parameters Test  
We also developed adjustable parameter tests between algorithms to identify which parameters are the best. Thus, for the J48 algorithm, we tested the parameter that determines the use of pruning in the decision tree. In the KNN algorithm, we evaluated the K parameters and similarity calculation. The kernel and cost parameters were tested on the SVM algorithm. Finally, for the MLP algorithm, we tested the parameters of the maximum amount of iterations, learning rate, momentum, and the number of neurons in the hidden layer. The PRISM and NB algorithms do not have adjustable parameters, because of this, no tests were performed with them. The tests were performed with the three artificial datasets Target, Period, and Setting by verifying the classification accuracy metric and using cross-validation with a value of 10. Table 6 presents the adjustable parameter test results for each algorithm.  
In Table 6, we identify the best adjustable parameters for each classification algorithm. Thus, these parameters were used in the experiments presented in the following subsections.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 5 Options for a BSI-Compliant Integration of EMS into Smart Metering Systems
### Partial Integration Using the SMGW Solely for the Collection of Measurements  
Options that make use of SMGWs for collecting meter measurements but bypass the transparent data tunnel to communicate with a remote EMS are omitted in this paper, since they are similar to _Options 1a-c_. All of the EMS integration options with a remote EMS introduced above, require the involvement of an _active EMP_, i.e., an EMP with a valid BSI-issued X.509 certificate. An active EMP, according to Bundesamt fur Sicherheit in der Informationstechnik [9], needs a security concept, and additionally an ISO/IEC 27001 certification. When bypassing the data tunnel, non of this is needed. However, while this may reduce efforts, for example for the EMS provider, it reduces the security of the transport channel, hence resulting in potential drawbacks in particular for the consumer.
## 4 Discussion
### Comparative Analysis  
To compare the best performing models retrieved from each of the three different techniques studied (ANN, SVM and SVM+SA) in forecasting cooling and heating energy demand, a weekly load profile corresponding to the test subset was created in order to evaluate the aptitude of those models in fulfilling their goal.  
Figure 23 presents weekly load profiles for ANN_C2, SVM_C5, and SVM+SA_C5 and reference for the cooling period.  
By analyzing Figure 23, model SVM+SA_C5 provides the most accurate of the load profiles when compared to the reference.  
The weekly load profiles regarding heating energy demand for the best performing models in each machine learning technique studied are presented in Figure 24.  
Figure 21: Cooling energy demand error distribution for the test period using SVM+SA.  
Figure 22: Heating energy demand error distribution for the test period using SVM+SA.  
Analyzing the weekly load profiles for heating energy demand forecast (Figure 24), the most precise model is ANN_H4. Especially when analyzing the peaks registered in the morning and afternoon from Monday to Friday, model ANN_H4 is the model that best fits the reference profile.