esa-sceva/satcom-chunk-collection huggingface dataset:
# An Application of Elastic-Net Regularized Linear Inverse Problem in Seismic Data Inversion
## 5 Discussions  
As in most inversion methods, the set of good regularization parameters is very important to obtain a satisfying solution in seismic inversion. In this paper, we dealt with elastic-net regularization. It is, in fact, the efficient combination of sparse regularization and dense regularization. Hence, the effect of elastic-net regularization is determined by two parameters: \(\lambda_{1}\) and \(\lambda_{2}\). As usual, the method most widely used to pick suitable regularization parameters is the L-curve criterion [9; 14; 39]. The best regularization parameter is closest to the corner of the L-curve. However, it is not suitable for ENRI. During the inversion procedure, the regularization parameters of elastic-net act as a tradeoff between the sparsity and density of the reflectivity series. Hence, the choice of the regularization parameters depends on the actual geological setting of underground formations and the aim of seismic inversion.  
If the underground formations are main thick, or the aim of inversion is to invert the main stratigraphic sequence interface of the underground formation, the value of \(\lambda_{1}\) needs to be large. In this case, it is, in fact, equivalent to sparse-spike inversion. On the other hand, if the underground formations contain a thin interbed, which is important for reservoir characterization, we need to relatively increase the value of \(\lambda_{2}\). One can argue that one is able to tune these regularization parameters, which is necessarily the case since there is now more flexibility in the elastic-net mathematical model. However, the flexibility increase provided by the regularization parameters of elastic net should be effectively taken advantage of. It is also one of the aims of the above discussions. Of course, the flexibility must match the actual geological setting. The point we want to express is that one has the flexibility to choose the appropriate regularization parameters according to the actual geological setting of underground formations and the aim of seismic inversion when they perform ENRI.  
To achieve this goal, we use quality control to determine these regularization parameters in this paper. It needs available actual well logs. In quality control, we think the actual well logs are the \"answer\" to the inversion of near-well seismic traces. The actual well logs represent the actual geological setting of underground formations. The best value of regularization parameters is determined by doing quality control at well locations. That is, adjust the value of regularization parameters, obtain the inversion result from the near well seismic traces for each set of regularization parameters, and choose the one whose inversion result has the best match with the well log. Then, the chosen regularization parameters are adopted when we perform inversion for other seismic traces.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 3 Results and Discussion
### Verification  
During the daytime, airborne visible light sensors captured many oil films at sea (Figure 14), but the cruise time of radar image data acquisition was during the nighttime. We validated the feasibility of our method by using images captured by shipborne infrared sensors in Figure 14. In the nighttime, thermal infrared images oil films have somewhat lower grey values than water in thermal infrared images [9; 14]. In Figure 15, the position of the oil film in the infrared image is the same as the position in the radar image, and therefore, our method is feasible.  
\begin{table}
\begin{tabular}{c c c} \hline
**Figure ID** & **Number of Oil Film Pixels** & **Oil Film Area (m2)** \\ \hline a & 3579 & 26,340.82 \\ b & 3889 & 21,100.92 \\ c & 3155 & 23,220.25 \\ d & 2142 & 5811.03 \\ \hline \end{tabular}
\end{table}
Table 2: Statistics of the oil film area.  
Figure 13: Location of the oil spill shown in the electronic chart.
# An Understanding of the Vulnerability of Datasets to Disparate Membership Inference Attacks
###### Abstract
Membership inference attacks seek to determine the inclusion of specific records within the training dataset of a model and can result in privacy-infringement issues, such as the discovery of personally identifiable information (PII) and personal health information (PHI) as well as identification of proprietary or confidential information. This effort focuses on membership inference attacks and, in particular, explores disparate membership inference attacks. Disparate membership inference attacks differ from general membership inference attacks in that they focus on attacking individual classes instead of the entire dataset as a whole. As discussed in more detail in _Introduction: Previous Work_, recent efforts have shown increased attack success when targeting more vulnerable subgroups instead of the entire dataset. These studies found minority subsets of data to be more vulnerable to attack, even after models were trained with fairness constraints and differential privacy, unless these were applied to an extent that sacrificed the accuracy of the model. This increased vulnerability to attack of minority subsets of datasets can prove troublesome for both privacy and competition. Typically, smaller subsets of data within a dataset are less represented because they are harder to obtain. In the case of health classification algorithms, these could be observations of patients with rare diseases. In the case of commercial competition, these could be examples of rare findings within a manufacturing or marketing dataset of key competitive advantages. In either of these cases, the discovery of that information by an adversary can prove detrimental to the organizations and individuals involved, whether through loss of privacy, profit, or competitive advantage.
# Fusion Classification Method Based on Audiovisual Information Processing
## 4 Experimental Result and Analysis
### Introduction of Experimental Platform and Evaluation Indicators  
#### 4.2.1 Evaluation Metrics  
In order to effectively explain the effectiveness of our proposed object classification method, we introduce the evaluation indicators of _Accuracy_, Recall, Precision, \(F1\) score, and \(G-Mean\). The specific evaluation index formulas such as Equations (12)-(16):  
\[Accuracy(A)=\frac{TP+TN}{TP+TN+FP+FN} \tag{12}\]  
\[Recall(R)=\frac{TP}{FN+TP} \tag{13}\]  
\[Precision(P)=\frac{TP}{FP+TP} \tag{14}\]  
\[F1=\frac{2\times P\times R}{P+R} \tag{15}\]  
\[G-Mean=\sqrt{\frac{TP}{TP+FN}\times\frac{TN}{TN+FP}} \tag{16}\]  
The values _TP_, _TN_, _FP_, and _FN_ represent true positive, true negative, false positive and false negative, respectively. Using accuracy alone as a measure of the standard is insufficient, especially when dealing with unbalanced datasets. Therefore, class-based performance measurement is obtained by incorporating additional standards.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 3 Materials and Methods
### Generate the Canonical Image
#### 3.3.3 The Construction and Function of the Deformation Field  
The Deformation Field is one of the key constituents of the CoDeF method. Its main function is to forge a pixel-level correspondence between video frames and the canonical field. This mapping relationship serves a dual purpose: it unifies the content representation of video frames and lays a solid foundation for the propagation of edits. Through the construction and optimization of the Deformation Field, the content within video frames can be effectively aggregated into the static canonical field. Simultaneously, the edited content can be efficiently propagated back to each frame, thereby achieving temporally-consistent video editing effects.  
The deformation field \(D(X,t)\) is a three-dimensional mapping function that characterizes the spatial relationship between video frames and the canonical image. Its function is to translate the coordinates of a pixel \(x\) in a video frame and the time frame index \(t\) into the corresponding coordinates \(x^{\prime}\) in the canonical image. It is defined as:  
\[D:\mathbb{R}^{2}\times\mathbb{R}\rightarrow\mathbb{R}^{2} \tag{5}\]  
The input is \((X,t)\), where \(X=(x_{x},x_{y})\in\mathbb{R}^{2}\) represents the pixel coordinates in a video frame, and \(t\in\mathbb{R}\) represents the time-index of the frame. The output is \(x^{\prime}=(x^{\prime}_{x},x^{\prime}_{y})\in\mathbb{R}^{2}\), which represents the pixel coordinates in the canonical image.  
In the implementation of the deformation field, the first step is to perform feature extraction on the pixel coordinates \((X,t)\) of the video frames. In order to efficiently represent the spatial and temporal information at different resolutions, a multi-resolution hash encoder is used:  
\[\gamma_{3D}(x,t)=HashEncoder(x,t) \tag{6}\]  
From the high-dimensional feature space to the specific coordinate transformation, learning needs to be carried out through a small multi-layer perceptron (MLP). Specifically, the MLP takes the encoded feature \(\gamma_{3D}(X,t)\) as input and outputs the pixel coordinates \(x^{\prime}\) in the canonical image:  
\[D(X,t)=MLP(\gamma_{3D}(X,t)) \tag{7}\]  
Here, the structure of the MLP is relatively simple, but its learning ability is sufficient to adapt to the nonlinear transformation relationship between the video frame and the canonical field.
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 3 Principles and Methods
### Change Result Generation
Let Y be the true value, \(p_{t}\) the model's predicted probability for the correct category, \(L_{fl}\) the focal loss, \(L_{dl}\) the dice loss, and \(L_{total}\) the total loss. The overall loss function for the coastal zone change detection task is as follows:  
\[L_{fl}(P_{i},\mathrm{Y})=-(1-p_{t})^{\gamma}\log(p_{t}) \tag{19}\]  
\[L_{dl}(P_{i},\mathrm{Y})=1-\frac{|P_{i}\cap\mathrm{Y}|}{|P_{i}|+|\mathrm{Y}|} \tag{20}\]  
\[L_{total}=\sum_{i=1}^{3}\left(L_{fl}(P_{i},\mathrm{Y})+L_{dl}(P_{t},\mathrm{ Y})\right) \tag{21}\]  
where \(\gamma\) is the adjustment factor for focal loss, used to adjust the model's focus on change samples (set to 0.5 in our case). Focal loss dynamically reduces the contribution of easily classified samples by introducing an adjustment factor. For easily classified non-change samples, \(p_{t}\) is close to 1, making \((1-p_{t})^{\gamma}\) close to 0 and reducing their loss weights. Similarly, for hard-to-classify change samples, \(p_{t}\) is smaller than for the non-change samples, making \((1-p_{t})^{\gamma}\) larger, which increases the loss weight for the change samples. Dice loss, on the other hand, measures the similarity by calculating the proportion of overlap between the predicted results and true labels, which is nearly unaffected by class imbalance. In fact, dice loss effectively measures the performance of minority classes through the overlap ratio. Figure 8: Schematic diagram of the foreground feature map.
# Towards a Vectorial Approach to Predict Beef Farm Performance
## 2 Materials and Methods  
### Aim and Scope  
The model that is currently used estimates the number of calves born alive produced per cow per year [13; 14; 15]. It is a classic statistical model, formulated based on zootechnical hypotheses, and it incorporates two variables extracted from the information of the single farm: the average _calving interval (intp)_ and the average calves _mortality at birth (m)_, i.e., perinatal mortality:  
\[Y_{p}=\frac{365}{intp}\Big{(}1-\frac{m}{100}\Big{)}. \tag{1}\]  
Calving and mortality detected on the farm at birth are combined through a model that provides the calf quota as a performance measure. However, it is reductive to measure breeding performance by observing only fertility and maternal conditions. As previously exposed in [12; 13], gains and losses in farms are not exclusively related to the calving but are often deeply influenced by the calf development after the first 24 h following the birth. The calf, on its side, goes through evolutionary stages that depend on its own condition. The phases immediately following birth, i.e., the intake of colostrum and the healthiness of the environment in which it lives, are of paramount importance. The physiological development process of the animal reaches completion in 60 days after birth. Calf mortality is also an important cause of economic damages in Piemontese cattle farms: For the farmer, it represents the loss of the economic value of the calf and the reduction in both the herd's genetic potential and size. It is straightforward that the gestational phase alone is not exhaustive. The breeding performance should be modeled also considering neonatal mortality, outlining the calf's ability to survive, and the sources of stress such as congenital calf's defects, eventually compromising the immune response and the growth rate, as well as environmental and food conditions, that affect the quality of life of the newborn.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 4 Results--Sea Surface Temperature Comparison
### Changes in SST Quality Levels
To date, no consideration of an uncertainty on the quality level assignment within SST datasets has been made.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Proposed Scheme for Consumer  
In push-based data forwarding, we assume that all the vehicles are consumer vehicles, and that the critical data content is crucial for them. Therefore, the critical data packet is forwarded to the CH of that cluster without receiving any request or interest packet. Every member vehicle of the cluster contains a MAC Table and cluster information such as CH and Gateways (GW). Upon receiving the critical data packet, the CH broadcasts the critical data packet to all the member vehicles in that cluster. If the intermediate vehicle is a GW, it forwards it to the GW vehicle of its neighboring cluster. If the consumer vehicle is only a member vehicle of that cluster, then it drops the data packet and does not forward it to any vehicle in its vicinity. Algorithm 2 explains the data forwarding procedures followed by the CH and intermediate vehicles as well as their roles during critical conditions.
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 4 Data-Driven Network Latency Processing
### Data Smoothing Preprocessing  
As shown in Figure 4, we found that the latency of the network fluctuated dramatically, even under normal traffic loads for the vast majority of the time. It brings great difficulties to the estimation and prediction of network latency. Therefore, we try to use some methods to smooth the data so that we can proceed to the next step. Traditional windowing smoothing technology cannot handle network latency well, so we combined with the application requirements, the upper limit of the latency that determines the type of auxiliary service that the VPP can participate in, and use the upper envelope of the network latency to smooth the data. As shown in Figure 6, we intercepted the continuously effective part of the acquired network latency and filtered the upper network latency envelope of the uplink and downlink, respectively. In this figure, the results of envelope filtering for uplink and downlink latency data are obtained by using the Hilbert transform [27]. From the partial displays, the envelope can capture the abnormal location of the network latency well, while ignoring the fluctuation of the network under normal conditions in order to predict the network latency. After taking the upper envelope, the delay of the network can be expressed as  
\[D=\{d_{1},d_{2},\ldots,d_{N}\}. \tag{6}\]
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 4 Simulations and Result Analysis
### Mars Aerocapture Mission  
For the Mars aerocapture mission, we start with the simulations in a nominal case. The nominal results are shown in Figure 9. It can be seen that the spacecraft's velocity drops from 5900 m/s to about 3900 m/s through aerocapture. The bank angle initially changes approximately in the form of a saturation function. However, due to Mars' thin atmosphere, the bank angle eventually saturates to 0 to provide sufficient upward lift. The bank reversal happens in about 230 s. In this case, the required total \(\Delta V\) is 54.47 m/s, and the target apoapsis altitude and orbit inclination are well met. Based on the nominal results, we further test the method's performance through Monte Carlo simulation.
## 4 Empirical Study
### Data Description and Preprocessing
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Category** & **Features** & **Explanation** \\ \hline
1 & Age & continuous variable \\
2 & Truck & Education Background & 1: High School and Above, 0. others \\
3 & Driver & Credit in Port & 0: Low, 1: Medium, 2: High \\
4 & & Region & 0: Native, 1: Nonnative \\ \hline
5 & & Cargo Type & 0-3: top 4 of largest amount of cargo processed in \\  & & & port, 4: others \\
6 & Bulk Cargo & Trade Type & 0: domestic trade, 1: foreign trade \\
7 & Import or Export & 0: import, 1: export \\
8 & & Customs Supervision & 0: unsupervised, 1: supervised \\
9 & & Cargo Price & continuous variable \\ \hline
10 & & Truck Manufacturer & 0-3: top 4 of largest number of trucks in port, \\
11 & & Year of Manufacture & 4: others \\
12 & Delivery & Registration Region & 0: province inside, 1: others \\
13 & Truck & Truck Category & 0-3: top 4 of most common truck registered, 4: others \\
14 & & Reformed or Not & 0: reformed, 1: unreformed \\
15 & & Ownership & 0: individual, 1: company \\
16 & & Times of Filing & 1: three times and above, 0. others \\
17 & & Blacklist & 1: ever, 0: never \\ \hline
18 & Cargo & Yard Category & 0: outdoor yard, 1: warehouse, 2: others \\ \hline
19 & Storage & Specialized Yard & 0: non-specialized, 1: specialized \\
20 & Yard & Cargo Volume & continuous variable \\ \hline
21 & & Censor Level & 0-3: classified based on the volume of business \\
22 & & Contract Category & 0: for a single vessel, 1: long-term, 2: others \\
23 & & Means of Payments & 0: after operation completion, 1: monthly payment, 2: \\ \hline
24 & & Arrival time & 0: 6:00-1359, 1: 14:00-21:59, 2: others \\
25 & & Leave time & 0: 6:00-1359, 1: 14:00-21:59, 2: others \\
26 & & Operation Period & 0: 2 hours and less, 1: 4 hours and above, 2: others \\
27 & & Operation Period & continuous variable \\  & & Deviation & continuous variable \\
28 & Operational & Operation Period Fluctuation & continuous variable \\
29 & Delivery & Cargo Weight & continuous variable \\  & & Deviation & continuous variable \\
30 & & Cargo Weight Fluctuation & continuous variable \\
31 & & Weightridge of Empty Cargo Truck & serial number of weightbridges \\
32 & & Weightridge of Heavy Cargo Truck & serial number of weightbridges \\
33 & & Arrival Date & 0: work day, 1: holiday \\
34 & & Shift Category & 0: three shifts, 1: two shifts \\
35 & & Number of Workers & continuous variable, number of workers in operation \\
36 & & Number of Machineries & continuous variable, number of machineries \\ \hline
37 & & Weather & 0: rain, 1: no rain \\
38 & Others & Busy Degree & continuous variable, operational amount per hour \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relevant elements of fraud risk on bulk cargo theft in port. After data preprocessing, which includes data cleaning, data balancing using SMOTE method and data discretization using MDLP method, we were left with 9796 records in structure storage with 38 features in addition to the response feature, i.e., normal of risk delivery.
# AR Presentation of Team Members' Performance and Inner Status to Their Leader: A Comparative Study
## 3 Visualization Techniques
* **Success** shows if a team member succeeded or failed their assigned tasks. The textual style presents success and failure as integer values and the ratio of success/failure as a percentage. Instead of using numbers, the graphical style presents a disc that fills radially with the success percentage. * **Score** shows how well a team member completed their tasks. Each task is rated on a scale from 0 to 1 inclusive (see Section 4.2 for more details). The component displays acumulative score since the beginning of the scenario (the sum of all the individual task scores) and the average score per task. The textual style presents the current score as a real number and the score per task as a numeric percentage. The graphical style only presents the score per task percentage as a disc that fills radially. * **Stress** shares a team member's _inner status_ summarized under the _simulated stress_ term. The textual style presents a history of stress levels an operator has been going through and the current level and value of their _simulated stress_. The written value of the current _simulated stress_ is colored in accordance with the stress level (Low--Green; Medium--Yellow; High--Red, see Section 4.2 for more details). The graphical style presents a 2D graph with time as the x-axis and _simulated stress_ as the y-axis. The line drawn on this graph shows the stress level recorded at that time. Figure 1: Awareness components with a textual presentation. Figure 2: Awareness components with a graphical presentation. The organization of components within panels was refined through pilot experiments, where valuable feedback was obtained. Three main categories emerged based on this feedback: the present and future workload of a team member (comprising Task Queue, Current Task Completion, and All Tasks Completion components), their past performance (including Task History, Portion Of Time Working, Success, and Score components), and their past and current _inner status_ (represented by the Stress component). To effectively organize and present this information, we opted for a structured approach. Instead of having everything plotted in one unique panel, we created three dedicated panels, each tailored to one of these categories. The final placement and design of these components aimed to achieve two key objectives. First, it sought to minimize overlap between panels of different team members. Second, the design was crafted with a focus on improving the overall readability of all components.