esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In fact, in a fully coordinated system, traffic authorities could even take over the vehicle in certain critical conditions, such as crossing specific intersections, in order to maximize the traffic flow efficiency. The focus of the present work is on this type of network. However, for vehicular networks to become feasible in practice, some issues remain open, such as security and performance, as e.g., network coverage and connectivity. Vehicular networks are characterized by the rapid movement of vehicles along the road network, making connectivity dependent on location. In this work, we go deeper into the study of the Delta Network [29]. The Delta Network characterizes the connectivity of the vehicular network through the percentage of travel time in which vehicles are connected to roadside units. In summary, this article reviews the concept of the Delta Network and extends its study through the presentation of a general heuristic based on the definition of _scores_ to identify areas of the road network that should receive coverage. The definition of the _score_ is based on the number of vehicles meeting coverage in relation to the distance traveled, and different _score_ definitions lead to different roadside units deployment strategies. After defining the general heuristic, we show how small changes in the _score_ computation can generate very distinct (and interesting) patterns of coverage, each one suited to a given scenario. In order to exemplify such behavior, we propose three deployment strategies based on simply changing the computation of _scores_. This work is organized as follows: Section 2 overviews a representative set of related work. Section 3 introduces the Delta Network, and discusses how to solve the allocation of roadside units for achieving one single performance target. Section 4 extends our analysis on the Delta Network in order to achieve global solutions using the concept of _scores_. Section 5 presents experiments illustrating aspects discussed in the previous sections. Section 6 concludes the work.
## 2 Temporary Attachment Methods with the EMI Technique  
The experimental setup for this study is shown in Figure 1, where the AD5933 evaluation board was connected to the computer with the software provided by the provider, Analog Devices Co., (Norwood, MA, USA). The software performed the EMI technique, which measured the electrical impedance of the PZT transducer (model 5A4E, purchased from Piezo.com, accessed on 13 October 2021, Woburn, MA, USA). Here, Equation (1) shows that electrical admittance (the inverse of impedance), \(Y(\omega)\) directly relates to the mechanical impedance of the structure, \(Z_{s}(\omega)\), and the attached PZT transducer, \(Z_{a}(\omega)\). This means that any changes in the structure will change its mechanical impedance, which can be detected by monitoring the electrical impedance of the attached PZT patch [14]. Rest of the variables in the equation \(I\), \(V\), \(\omega\), \(a\), \(\varepsilon_{33}^{T}\), \(\delta\), \(d_{3x}\), \(\overline{\gamma}_{xx}^{E}\) are the PZT output current, PZT input voltage, input frequency, geometric constant, dielectric constant, loss tangent, piezoelectric constant, and Young's modulus, respectively.  
\[Y(\omega)=i\omega a\Big{(}\varepsilon_{33}^{T}(1-i\delta)-\frac{Z_{s}(\omega) }{Z_{s}(\omega)+Z_{a}(\omega)}d_{3x}^{2}\overline{\gamma}_{xx}^{E}\Big{)} \tag{1}\]  
The AD5933 evaluation board can measure impedance up to 100 kHz with 500 data points where it is fully powered by the data cable. For this study, the new concept of using a roller device labeled 'Case 1' in Figure 1 was investigated. The top surface was 55 mm \(\times\) 30 mm which is wide enough for a 15 mm square PZT to be attached, as shown in the figure. The wheel located at the bottom, with a diameter of 30 mm, allowed the device to be moved freely on any surface. The performance of this 'roller device' was tested  
Figure 1: Photo of both temporary attachment devices with AD5933.  
in the next section to investigate the possibility of the proposed idea. Since the device was made of metal, the vibrations from the PZT transducer should be enough to travel to the bottom of the wheel to detect defects. To compare this idea with another temporary attachment method, a 15 mm \(\times\) 20 mm metal (3 mm thickness) with a 13 mm square PZT attached on top was used to evaluate the performance difference between the two cases. The major difference between the two cases was that the're-attachable device' of case 2 required one to use a double-sided tape to detect damage. This can be time-consuming for large composite structures. On the other hand, the roller device (case 1) just needed to be placed on top of the composite structure where the weight of the device should be enough to detect any changes in structure (such as debonding).
# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
## 5 Results and Discussion
### YOLOv5 Ensemble vs. Others Models  
To validate our proposed model against the state-of-the-art, we evaluated two YOLO ensembles (YOLOv5 and YOLOv8) against the faster R-CNN ensemble and the DCN+ model in the same way as for the individual YOLOv5l model and the ensemble. We set the confidence values of DCN+ to 64% and 76%, as explained in Section 4.2.3, and we compared it with the ensembles in dataset 2, as shown in Figure 8.  
We add the \(mAP_{50}\) metric where the classes of each prediction are taken into account. This metric allows us to compare the results with future work, but in our case, we have not used it individually, as both the AUC vs. IoU and the mAP value must be complemented with the false positive rate.  
When evaluating DCN+ with the confidence score at 64% on the false positive set, according to Table 4, we obtain a false positive ratio of 30%, while the ensemble maintains a ratio of 9%. In the same way, using the confidence score at 76%, the false positive rate is slightly better than the ensemble (we set this threshold to achieve this), but the recall and AUC metrics are worse than the ensemble.  
At first glance, DCN+ could be a candidate to replace one of the YOLOv5 models in the ensemble. However, due to its false positive rate and its CPU inference time, which more than four times slower than the ensemble, it is not feasible to use it in the ensemble for our damage detection problem.  
On the other hand, the YOLOv8 ensemble can also be considered as a possible candidate. We even consider this ensemble as an alternative to the YOLOv5 ensemble since,  
Figure 7: YOLOv 5l ensemble vs. YOLOv5 ensemble vs. YOLOv5 and YOLOv8 individually.  
due to its inference time and high mAP50 value, it can be useful in a scenario where it is allowed a false positive rate greater than 10%.  
On the faster R-CNN side, it achieves a good performance in the precision metric, but in the other metrics, its performance falls below the other models.
## Proposed EDOVE Scheme
### Preliminary Notations  
EDOVE uses the notations in the holding time computation and the forwarding algorithm, and these notations are briefly described below with reference to the EDOVE network scenario shown in Figure 4.  
* _Network size_\(|\mathcal{N}|\): is the total number of nodes \(\mathcal{N}\) with the acoustic communication capabilities that form the network, is termed as the network size.
* _The set of receiver nodes_\(\kappa_{S}\): consists the nodes that are within the transmission range of \(S\) and have depths less than the depth of \(S\): \[\kappa_{S}=\{j\in\mathcal{N}\mid z_{j}<z_{S}\wedge dist_{(j,S)}\leq t_{r}^{S}\}\] (1) where, \(dist_{j,s}\) is euclidean distance between node \(j\) and \(S\), and \(z_{j}\) and \(z_{S}\) are the depths of node \(j\) and \(S\), respectively. Figure 4 shows that the nodes \(1\) to \(5\) are the neighbors of \(S\) and \(\kappa_{S}=\{2,3,4\}\).
* _Next hop forwarders of node \(i\), \(\kappa_{i}^{NF}\)_: are the nodes that lie within node \(i\)'s transmission range and having depths less than the depth of \(i\): \[\kappa_{i}^{NF}=\{j\in\mathcal{N}\mid z_{j}<z_{i}\wedge dist_{(i,j)}\leq t_{r}^ {i}\}\] (2) where, \(z_{j}\) and \(z_{i}\) are the depths of next hop node \(j\) and _receiver_ node \(i\), respectively. The next hop forwarder of receiver \(4\), \(\kappa_{4}^{NF}=\{3,6,7,8\}\), as shown in Figure 4. However, \(\kappa_{3}^{NF}=\emptyset\).
* _Residual energy of node \(i\), \(E_{i}\)_: is the current available battery level of node \(i\).
* _Normalized depth variance of node \(i\), \(\delta_{i}\)_: is the normalized variance of the depth differences of all the nodes in \(\kappa_{i}^{NF}\).  
The above discussed notations are used by the EDOVE to compute holding time and the forwarding algorithm that are briefly described below.
## 1 Introduction
However, despite the advancements made by these newly proposed approaches, the main motivation leading this work is that all these studies assume that forecasts remain stable over time. However, suppliers often change their performance due to unexpected events, making initial predictions unreliable. Adopting updating mechanisms in these forecasts thus represents a fundamental necessity to capture real-life dynamics. However, as previously mentioned, one of the challenges of the problem is that updating forecasts related to supplier performances requires sending orders to suppliers, as this is the only way to acquire new delivery data. From this perspective, new approaches have not been built to balance the trade-off between the benefits of data acquisition and the costs related to this acquisition (i.e., the cost of sending orders to suppliers). In the new AI era, where data are the fuel [18; 19; 20], approaches to solving the multiperiod SSOA problem thus need to be rethought explicitly to tackle this new trade-off. Given the relevance of the topic and the lack of studies on this aspect, this study aims to offer two innovative contributions:  
1. The formulation of a new approach based on a parametrized DLM for solving the multiperiod SSOA problem, considering the trade-off between order emission costs and data acquisition benefits when forecasts related to the risk of non-punctual deliveries from suppliers are considered. 2. An empirical evaluation of the advantages that the proposed approach can offer compared to benchmark approaches, representing, respectively, a decision-maker with perfect knowledge of the future and two decision-makers where classical DLM models are implemented without balancing the trade-off between data acquisition benefits and order emission costs. The structure of this paper unfolds as follows: Section 2 provides an in-depth exploration of the research background, reviewing pertinent literature. Section 3 delineates the proposed approach, elucidating its constituent modules and expounding the experimental design employed to evaluate the efficacy of the approach. Section 4 presents the findings garnered from the experimental design. Subsequently, Section 5 discusses the results and encapsulates the conclusions drawn from this study.
## 4 Results
### Ablation Study with Other Datasets
#### 4.10.1 Ablation Dataset-1
**Comparison with different classifiers:** Table 7 details the classification performance of BSL features when used with various classifiers. Among the classifiers evaluated, the GCN stands out as the proposed method. Following the GCN, the CNN achieved the highest individual classification accuracy at 92% and an average accuracy of 83%. SVM and k-NN also showed commendable accuracies of 85% and 75%, respectively. LDA recorded the lowest performance, with a classification accuracy of 69%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & \
\begin{tabular}{c} **Accuracy (\%)** \\ **(Best Classification)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy (\%)** \\ **Average of All Subjects** \\ \end{tabular} \\ \hline AEC-c & \(86\pm 1.23\) & \(80\pm 2.47\) \\ ImCoh & \(89\pm 5.39\) & \(85\pm 6.28\) \\ PTE & \(92\pm 0.25\) & \(89\pm 0.16\) \\ Graphical LASSO & \(94\pm 1.26\) & \(90\pm 0.45\) \\ BSL (Delta) & \(86\pm 1.89\) & \(81\pm 5.29\) \\ BSL (Theta) & \(96\pm 3.42\) & \(90\pm 2.52\) \\ BSL (Alpha) & \(97\pm 1.29\) & \(92\pm 2.13\) \\ BSL (Beta) & \(93\pm 1.98\) & \(88\pm 1.23\) \\ BSL (Gamma) & \(91\pm 1.13\) & \(85\pm 2.19\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of functional-connectivity metrics using the proposed classification model (GCN) for ablation-dataset-1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cleaners** & **Best Subject** & **Average of All Subjects** \\ \hline LDA & \(69\pm 2.66\) & \(57\pm 0.59\) \\ SVM & \(85\pm 0.76\) & \(79\pm 1.96\) \\ CNN & \(92\pm 0.12\) & \(83\pm 2.21\) \\ k-NN & \(75\pm 0.80\) & \(70\pm 2.66\) \\ proposed & \(97\pm 1.29\) & \(92\pm 2.13\) \\ \hline \hline \
# Balanced Loss Function for Accurate Surface Defect Segmentation
## 2 Method
### Balanced Loss Function
#### 2.3.3 Label Confusion Suppression  
Our solution to the boundary imbalance problem has two steps: first, measure the level of label confusion at each point; then, suppress the loss of the points with label confusion.  
We propose an entropy-based measurement of label confusion. Entropy is a commonly used measurement of distribution variance. The entropy (using a natural constant as the base) of a discrete random variable \(X\in\{x_{1},x_{2},...,x_{N}\}\) is:  
\[H(X)=-\sum_{i=1}^{N}P(x_{i})\ln P(x_{i}) \tag{8}\]  
We use the label entropy as the measurement of local region label confusion. For a \(r\times r\) (\(r>1\) and is odd) square region centered at point \((i,j)\) in pixel annotation \(A\), which is denoted as \(local(A_{i,j})\in\mathbb{N}^{r\times r}\), the label entropy is:  
\[H(A_{i,j})=-\sum_{k=1}^{C}\frac{N_{i,j,k}}{r^{2}}\ln(\frac{N_{i,j,k}}{r^{2}}) \tag{9}\]  
where \(N_{i,j,k}\) is the number of occurrence of label \(k\) within \(local(A_{i,j})\). The higher the label entropy, the more the label confusion at point \((i,j)\). The maximum of the label entropy is \(ln(C)\).  
To suppress the points with high label confusion, we calculate a soft mask \(M_{confusion}\in\mathbb{R}^{W\times H}\) for loss computation, whose entries are:  
\[M_{confusion_{i,j}}=1-\frac{H(A_{i,j})}{ln(C)} \tag{10}\]  
\(M_{consution}\) is defined as a constant soft mask over the loss values to suppress label confusion. The higher the label confusion, which is measured by Equation (9), the lower the weight given by Equation (10). When multiple labels mingle in a local region and confuse the training at \((i,j)\), the confusion mask will suppress the loss value and reduce the noise on the overall loss value. Section 2.3.4 introduces the connection between \(M_{confusion}\) and other components.
## 4 Conclusions  
An in situ test and a series of laboratory experiments were implemented to investigate the bending behavior of DSPCs. On the basis of the experimental results, the following conclusions can be drawn:  
1. In the field test, with the increase of the loads, there was no relative displacement between the concrete and the casing, and the casing and the concrete body of the pile deformed as an assembly. Therefore, it was suitable to use the plane cross-section assumption in the calculation. However, a correction coefficient that related to the loading level needed to be considered in the calculation of the bending stiffness.
2. From the laboratory studies, the bending capacities of those specimens were similar, the slurry condition had limited influence on the moment capacity of the specimens in terms of elastic stiffness and ductility, and the slurry condition had a negative influence on the bond quality. The plane cross-section assumption was reasonable to use in the calculation of bending capacity at low bending moments, and the influence of the casing thickness should be considered in the calculation of the section stiffness of DSPCs.  
Conceptualization, X.L. and G.D.; methodology, X.L.; validation, X.L.; formal analysis, X.L.; investigation, X.Y.; resources, G.D.; data curation, Q.Y.; writing--original draft preparation, X.L.; writing--review and editing, W.Z. and F.Z.; visualization, G.D.; supervision, G.D.; project administration, G.D.; funding acquisition, G.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, project number 51878160, 52078128, the Natural Science Foundation of Jiangsu Province, project number BK20180155. The authors are grateful for their support.  
Not applicable.  
Figure 11: The changes of strain along pile section of different specimens: (**a**) B-1, (**b**) B-2, (**c**) B-3, (**d**) B-4, (**e**) B-5, and (**f**) B-6.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the nature of this research.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
## 2 Background
### Group Lasso  
The group LASSO algorithm is a regularization method used in linear regression and other statistical models to perform feature selection when the features are naturally grouped into subgroups or clusters. It extends the concept of the LASSO (least absolute shrinkage and selection operator) algorithm to account for the grouped structure of the features [22].  
In many datasets, features are organized into groups (e.g., genes or brain regions). The group LASSO method extends the LASSO method to select entire groups of relevant features instead of working with individual features.  
The group LASSO method imposes an L1 penalty on the L2 norm of the coefficient groups, forcing entire groups of features to be included or excluded simultaneously. This is a straightforward approach based on the idea of sparseness applied at the group level. Thus, this method encourages the selection of entire groups of features, rather than the individual selection of each feature [21; 23].  
The group LASSO method can be applied to the selection of groups of features, thus preserving the natural structure of the data (for example, selecting groups of relevant genes in a gene expression analysis).  
This method works very well when the features are naturally organized into groups and it is important to select or exclude all features in a group together.  
Mathematically, the cost function for the group LASSO method is defined as:  
\[\min_{\beta}\frac{1}{2}{\sum_{i=1}^{n}{\|y-X\beta\|_{2}^{2}+\lambda{\sum_{g=1} ^{G}}}}{\left\|{{{\beta_{g}}}}\right\|_{2}},\]  
where y = the response vector, X = the feature vector, \(\beta\) = the vector of the model coefficients, \(\beta_{g}\) = the coefficients for the group \(g\), \(\lambda\) = the regularization hyperplane, and G = the total number of groups.  
The way that the group LASSO algorithm works is that, if the \(\ell 2\) norm of a group's coefficients is non-zero, then all features in that group are selected, while the \(\ell 2\) norm penalty causes some groups to be completely excluded from the model (\(\beta_{g}\) = 0).  
The main advantages of the group LASSO algorithm derive from the model respecting the group structure of grouped datasets, which is useful when features have a natural relationship between them; from reducing the dimension of data by eliminating entire groups of irrelevant features, simplifying the model; and from the fact that the algorithm tends to be more stable than the simple LASSO method in the presence of strong correlations between features in the same group.  
The disadvantages of this method derive from the fact that the algorithm is based on the assumption of the existence of feature groups, and the efficiency of the method depends on the correct definition of the groups, as incorrectly defined groups can reduce its performance. Another important aspect is the choice of the optimal value of \(\lambda\), which is crucial and requires careful validation. The computational complexity of the group LASSO algorithm is higher than that of the simple LASSO algorithm, especially for large datasets.
## 4 Case Study
### System Description  
This case study is based on an LNG plant which operates in Central Chile. Figure 5 deploys the main phases of the entire process.  
In such plants there are 4 fundamental areas responsible for the operation, among which we can mention:  
1. Unloading area: This section is where the LNG is unloaded from the ship. This is carried out by means of five arms located on a pier. From here, the unloaded LNG is sent to the storage tanks and to the vaporization and pumping area. In addition, return gases to the methane are sent to the tanker/ship to maintain a pressure balance in the storage tanks. This dock has the capacity to receive ships with LNG storage of up to 265,000 cubic meters.
2. Storage area: LNG is pumped to three storage tanks, two of which have a storage capacity of 160 thousand cubic meters each. The third of these tanks has a storage capacity of 14,000 cubic meters of LNG. The total storage capacity of the LNG plant is 334,000 cubic meters.
3. Vaporization and pumping zone: in general terms, at this stage the gas in its liquid state is pumped from the storage tanks to the vaporization zone where the LNG is transformed into natural gas. Since this stage is the focus of our case study, both zones will be described in detail below: 1. Pumping Zone: It is composed of five high-pressure pumps which operate in partial redundancy configuration (model K/N: 3/5). 2. Vaporization Zone: It is made up of three open-rack vaporizers (ORV) and one Submerged Combustion Vaporizer (SCV), each contributing 33% of the systemic load arranged according to a K/N:3/4 configuration.
4. Tanker truck loading facility: The remaining fraction of LNG that does not pass through the vaporizers goes directly to the truck loading facility which is composed of four independent loading islands that store the LNG in tank trucks. These are responsible for supplying both industries and cities that are not connected to the pipeline network where it is regasified locally.
## 4 Results and Analysis  
In this section, we present the results and in-depth analysis of our proposed method for scene recognition. The experiments were conducted using MATLAB R2019b with the deep learning toolbox on a machine equipped with an Intel Core i5 CPU operating at 2.4 GHz, 24 MB RAM, and an NVIDIA GeForce GTX 1650 GPU with 16 MB memory. Our method involved novel approaches such as multimodal deep learning, feature selection, and fusion. We employed two distinct datasets: the scene classification dataset [28] and the AID dataset [29]. Each dataset's image dimensions are matched to its characteristics and needs. To balance accuracy and efficiency, a lower resolution is suitable for datasets with fewer classes and complexity. A greater resolution helps the model catch scene features in datasets with more different classes.