esa-sceva/satcom-chunk-collection huggingface dataset:
# Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles
## 1 Introduction
### Breast Density  
Breast density describes the amount of fibrous and glandular tissue in a breast compared with the amount of fatty tissue. The breast density is assigned to one of four classes in the mammogram report based on the ACR BI-RADS standard. In class A, the breasts are almost entirely fatty. A few areas of dense tissue scattered within the breasts indicate class B. In class C, the breasts are heterogeneously dense. Finally, in class D, breasts are extremely dense [8; 15]. Breast density plays a significant role in detecting breast cancer and on the risk of developing breast cancer. The clinicians must identify breast density from a mammogram for each patient and write it in their reports. Usually, dense breasts--i.e., breasts categorized into class C or D--are more likely to be affected by breast cancer [16]. In [17], the authors studied the relationship between mammographic densities and breast cancer risk. The results showed that the ratio of positive cancer cases for the different ACR classes were as follows: D (13.7%), C (3.3%), B (2.7%), and A (2.2%). Table 1 illustrates the difference between ACR classes.  
\begin{table}
\begin{tabular}{c c c c} \hline
**ACR Class** & **Feature** & **Tissues Proportion** & **Example** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Breast density classes.  
Breast density is important and must be specified in breast screening medical reports. Determining breast density can be challenging and mostly subjective, especially if they have been affected by treatment such as chemotherapy [18].
# Time-Domain Aeroelasticity Analysis by a Tightly Coupled Fluid-Structure Interaction Methodology
## 7 Conclusions  
A tightly coupled FSI methodology based on computational fluid dynamics (CFD) and computational structural dynamics (CSD) is developed for aeroelasticity analysis in the time domain. Preconditioned N-S governing equations are used as flow solver and tightly coupled with structural model by the pseudo time-stepping temporal discretization method. A modified mesh deformation method on reduced control points RBF is utilized to update the fluid volume mesh, and an RBF based mapping algorithm is employed for data exchange on the interaction interface.  
The flutter boundary of the Isogai wing Case A model is analyzed. An S-shaped flutter boundary with a dip near the Mach number of 0.85 is obtained, and the simulation results are in good agreement with those of other simulation results. The LCO response of the case is observed, with a higher reduced velocity index than the flutter speed index, the pitching and plunging displacements come to equi-amplitude oscillation finally and the vibrational amplitudes are independent of the initial movements. The responses of different initial movements in both two coordinates reach the same orbit respectively in phase portraits.  
The flutter boundary of AGARD 445.6 wing is predicted. Results show that the methodology provides reasonable flutter speed indexes in the subsonic and the transonic regions, for the supersonic cases, the simulation results are overpredicted. However, some improvement is achieved by our method compared with other simulation results from  
Figure 11: Flutter frequency for the AGARD 445.6 wing.  
previous studies. The methodology predicts the flutter frequency ratios well even in the supersonic region as well as in subsonic and transonic regimes, while simulation results computed by other three methods still have larger differences with the experiment.  
The simulation results show that the tightly coupled FSI methodology with preconditioned N-S equations and CSD models can predict the aeroelasticity in the time domain accurately, and can also reveal the nonlinear characteristics of the aeroelastic system.  
Conceptualization, Z.L., G.Y., X.N.; methodology, Z.L., X.N.; software, X.N., Z.L., G.Z.; validation, X.N., Z.L.; investigation, Z.L., X.N.; writing--original draft preparation, Z.L., X.N.; writing--review and editing, Z.L., X.N., G.Z.; supervision, G.Y., G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation, grant number No.11702298.  
The authors declare no conflict of interest.
# Anomaly Detection Method Considering PLC Control Logic Structure for ICS Cyber Threat Detection
## 4 Experiment and Evaluation
### Dataset Description  
The dataset used in the experiment is the PLC control logic Ladder Logic Bombs (LLB) dataset [21]. LLB is a broad category of attack types that focus on manipulating PLC control logic, like Stuxnet. The dataset consists of 30 normal control logic samples and 30 malicious control logic samples. The study claims to be the first to construct a PLC control logic dataset. For this reason, the LLB dataset is the only available dataset for this experiment. The control logic dataset is provided in LD and ST formats. The dataset was created in the OpenPLC [22] environment. Normal control logic is a program that models a water tank filling system. The malicious control logic was designed by incorporating LLB attack patterns. A total of 26 variations of malicious samples were generated by applying different attack techniques, such as comparison operations, water level detection, and specific component modifications. These variations are classified into two main types: (1) modifications in the placement of function blocks containing malicious code within the LD code or changes in the connection methods between LD components, and (2) replacement of function blocks containing logic bombs with other similar function blocks that serve the same purpose. While the proposed LLB attacks were tested on a water tank filling system, they can also be applied to other ICS environments utilizing PLCs.  
For the experiment, we converted it into IL format using Open PLC. In this experiment, the additional code introduced for LLB was labeled as anomalous. The training dataset consists of 30 normal control logic samples, comprising 714 lines of IL code. The test dataset consists of 30 malicious control logic samples, comprising 1260 lines of IL code. The goal of this experiment is to identify malicious code embedded within the anomalous control logic. This malicious code represents previously unknown threats.
# Routing in Mobile Wireless Sensor Networks: A Leader-Based Approach+
## 4 Evaluation
### Experiment Description  
We have carried out extensive experiments to evaluate the performance in terms of message delivery rate (We define message delivery rate as the number of CONTENT messages delivered to the sink divided by the total number of messages transmitted in the WSN.) and end-to-end delay.  
Our main concerns in the evaluation are scalability and resiliency to node mobility. Henceforth, the relevant configuration parameters to feed the evaluations have been network size (number of nodes) and node mobility (maximum velocity).  
We have defined a constant node density for all the tests, thus the network size determines the simulation area.  
Every node of our simulations (including the sink node) is assumed to move according to a Random Walk mobility model [29]. A random direction and velocity, between 0 and the maximum velocity, is generated every second. This movement is divided in five sub-movements, i.e., one every 200 milliseconds, in order to effectively make a smooth movement.  
A summary of the configuration parameters used for the experiments is shown in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Buffer discharge time (seconds) & 20 \\ Number of nodes & 8, 16, 32, 64, 128, 256 \\ Node density (nodes per square meter) & 0.003 \\ Transmission range, \(r\) (meters) & 50 \\ Maximum node velocity (meters per seconds) & 5, 10, 15, 20, 25 \\ Message generation rate per node (messages per second) & 4 (for most simulations), 10 \\ Packet size (bytes) & 10 for AODV and LBR1; 100 for RASeR and LBR2 \\ Simulated interval (seconds) & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Preliminary evaluations of the algorithms LBR1 and LBR2 showed that timeout values should be slightly higher than the heartbeat period, which is 0.2 s. We do not define bounds in buffer size. Furthermore, results reported in [11] show that the best performances are obtained when messages are not acknowledged by the receiver.  
In the original work of RASEeR [21], a transmission range of 250 m is used, which we consider unrealistic for the kind of technology we used. Instead, in our simulations, we have assigned a common range for all the protocols.  
Finally, in order to optimise the performance of the protocols, we have adjusted the length of the packet for each protocol. In the cases of LBR1 and AODV, we send a single content packet of 10 bytes, whereas, for LBR2 and RASEeR, we send a list of ten messages increasing the packet size to 100 bytes.
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 2 Modal Parameters Identification
### Structural Gain Evaluation  
In order to evaluate the structural gain, the ratio between the maximum of primary structure acceleration \(\dot{y}_{max,i,j}\) and proof-mass acceleration \(\dot{x}_{max,i,j}\) is computed for each \(i\)-th mode and each \(j\)-th actuator. Starting from the transfer function between the acceleration measured at the actuator location \(\tilde{Y}(s)\) and the transmitted force \(F_{T}(s)\), defined in the Laplace domain:  
\[\frac{\tilde{Y}(s)}{F_
## 3 Results
### Evaluation of Classification Model  
The classification model's performance is depicted through the ROC curve in Figure 4, showcasing the model's ability to discriminate between classes effectively. Table 4 details the area under the curve (AUC) for each class across all folds, with a mean AUC of 0.953. This high level of AUC across varying degrees indicated the model's consistent and reliable classification capability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** & **Mean** \\ \hline \(0^{\circ}\) & 0.978 & 0.971 & 0.972 & 0.975 & 0.984 & 0.976 \\ \(5^{\circ}\) & 0.968 & 0.965 & 0.965 & 0.964 & 0.969 & 0.966 \\ \(10^{\circ}\) & 0.956 & 0.940 & 0.964 & 0.955 & 0.959 & 0.955 \\ \(15^{\circ}\) & 0.946 & 0.935 & 0.961 & 0.959 & 0.955 & 0.951 \\ \hline \hline \end{tabular}
\end{table}
Table 4: AUC for each angular classification at each fold.  
Figure 4: ROC curve for each fold. ROC curves represent different angle classes ranging from \(0^{\circ}\) to \(60^{\circ}\) in \(5^{\circ}\) increments. The color of each ROC curve changes gradually from cool to warm colors, with \(0^{\circ}\) represented by blue and \(60^{\circ}\) represented by red.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **MSE \({}^{\text{a}}\) (degree\({}^{\text{2}}\))** & **RMSE \({}^{\text{b}}\) (degree)** & **Correlation Coefficient (r)** \\ \hline fold 1 & 11.146 & 3.339 & 0.985 \\ fold 2 & 20.035 & 4.476 & 0.970 \\ fold 3 & 11.175 & 3.343 & 0.983 \\ fold 4 & 12.982 & 3.603 & 0.980 \\ fold 5 & 18.830 & 4.339 & 0.985 \\ \hline mean & 14.833 & 3.820 & 0.981 \\ \hline \hline \end{tabular} \({}^{\text{a}}\) Mean squared error; \({}^{\text{b}}\) root mean squared error.  
\end{table}
Table 3: Summary of regression model performance metrics across five folds.
# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 4 Methodology
### Two-Way TransTag Regularization  
Generally, users have diverse interests and items are of diverse characteristics. The user's preference for a target item occurs when some features of the item exactly match the user's interest in some aspect. In social tagging systems, these user-item interactions are hidden in the tagging behavior data. Nevertheless, the diverse semantic information cannot be well modeled by GCN. For this purpose, we design a two-way TransTag regularization function to capture diverse user interests or item characteristics.  
The TransTag regularization function is inspired by TransE [57], a common method for knowledge graph embedding, which models relationships as translations in the embedding space. The rationale behind the TransTag regularization function is that the user-tag-item triplet \((u,t,i)\) indicates that user \(u\) prefers item \(i\) from the aspect of tag \(t\). Hence, the item embedding \(e_{i}\) should be a nearest neighbor of \(e_{u}+e_{t}\) in the underlying embedding space, i.e., \(e_{u}+e_{t}\approx e_{i}\). It is worth mentioning that the one-way TransTag regularization function has been successfully applied in recent tag-aware recommendations [14; 26].  
Note that a user prefers items based on diverse interests represented by tags, and an item with diverse characteristics is also oriented towards appropriate users. It will result in insufficient expression ability only considering one-way translation \(u\to i\) or \(i\to u\)[24]. Therefore, we conduct a two-way TransTag regularization function. Given a user-tag-item triplet \((u,t,i)\), the distance function in our model is defined as:  
\[\begin{array}{l}d_{u\to i}(u,t,i)=\|e_{u}+e_{ut}-e_{i}\|,\\ d_{i\to u}(i,t,u)=\|e_{i}+e_{it}-e_{u}\|,\end{array} \tag{13}\]  
where \(e_{u}\) and \(e_{ut}\) are the respective final representations of user \(u\) and tag \(t\) learned from the user-tag graph, while \(e_{i}\) and \(e_{it}\) are the respective final item and tag representations learned from the item-tag graph.  
Based on the distance function, we adopt a margin based pairwise ranking loss defined as:  
\[\begin{split} L_{E}=\sum_{(u,t,i)\in S^{+},(u^{{}^{\prime}},t,i^{{}^{ \prime}})\in S^{-}}\text{ReLU}(d_{u\to i}(u,t,i)-d_{u\to i}(u^{{}^{\prime}},t,i^{{}^ {\prime}})+m)\\ +\text{ReLU}(d_{i\to u}(i,t,u)-d_{u\to i}(i^{{}^{\prime}},t,u^{{}^{ \prime}})+m),\end{split} \tag{14}\]  
where \(m\) is the margin parameter, \((u,t,i)\in S^{+}\) denotes the positive sample, and \((u^{{}^{\prime}},t,i^{{}^{\prime}})\) indicates the negative sample that is generated by randomly replacing either the user or item node in \((u,t,i)\). By minimizing the loss function \(L_{E}\), we bridge the information gap between users and items based on tags.
# Design and Experimental Study of the General Mechanical Pneumatic Combined Seed Metering Device
## 3 Key Component Parameter Design and Analysis
### Structure Design of Type Hole and Seeding Strip  
Under the identical conditions, the more the number of holes in the seed feeding drum, the greater the seed metering rate of the seed metering device would be, whereas the number of holes was limited by the diameter of the feeding drum and the distance between the holes. Thus, it yields:  
\[\Delta l\geq 2d_{max} \tag{2}\]  
where \(d_{max}\) denotes the maximum diameter of the seed, mm. Taking the maximum diameter of soybean seeds as the basis for designing structural parameters, the average diameter of soybean seeds was 5.1-7.3 mm [22]. With the combination of (1) and (2), the number of circumferential holes of the seed metering device is expressed as \(Z=12\), and the number of selected holes was three rows.  
When the seed metering device is working, to prevent the seeds from clogging the holes, and to facilitate the seed filling and the unloading, the orifice should be maximally wide, and the hole bottom diameter should ensure that the seeds with the smallest particle size do not leak into the feeding drum. After the seed cleaning, there was a seed attached to the bottom of the hole, with the equation [23; 24]:  
\[\left\{\begin{array}{c}L_{1}\geq d_{max}+(1~{}\sim~{}1.5)\\ L_{2}\leq d_{min}\\ H\geq d_{max}/2\end{array}\right\} \tag{3}\]  
where \(L_{1}\) denotes the top diameter of the hole, mm; \(L_{2}\) represents the bottom diameter of the hole, mm; \(H\) is the depth of the hole, mm; \(d_{min}\) is the minimum seed size, mm.  
Given the physical characteristics of seeds, the average diameter of soybean seeds was 5.1-7.3 mm; the average diameter of rapeseed was 1.5-2.5 mm [25]. After substituting Equation (3), the top diameter of soybean seeding strip hole was set to 12 mm. The top diameter of the seeding strip hole was 5.4 mm.  
The cone angle of the hole could be a vital structural parameter of the seed metering device. With the increase in the cone angle, the pressing effect of the cleaning airflow on the seeds would be reduced, and the cleaning effect on the seeds would be improved. Given the existing research experience [26], since the seed metering device should minimize the missed seeding under the normal seeding, the design hole cone angle \(\theta\) of this study did not exceed \(50^{\circ}\) (Figure 2). Set the soybean seed filling hole depth to 17 mm, take the soybean seed size as the structural parameter design basis, and use the soybean hole depth as the thickness of the seeding bar, and the bottom circle diameter of the hole is 4.6 mm, as presented in the Figure 2:For rapeseeds and other small and medium-sized seeding strips, a conical hole was added at the lower end of the hole with the same taper as the unloading pores of the drum. Then, the seeding performance of the seeder would not be affected after the seed strip was replaced. Designed with non-perforated seed strips, the seed strip could be replaced to change the seed grain spacing without changing the drum speed.
## 2 Materials and Methods
### Algorithm for Determining the Correction of Track Axis Coordinates  
The proposed algorithm is fully analytical, relying on explicit equations to compute corrected track axis coordinates. In this measurement scheme, two primary receivers--mounted above the bogie pivot pins--nominally define the track axis. However, as noted earlier, each of the six antenna positions carries random uncertainty due to varying measurement quality factors. Consequently, the track axis coordinates are derived using all six GNSS observations to mitigate individual receiver errors.  
The core of the algorithm is the definition of logical conditions that determine whether the computed inter-antenna distances at each epoch satisfy predefined quality thresholds. These conditions are evaluated independently for each set of instantaneous antenna positions, with threshold values chosen by the analyst. Given that modern GNSS techniques--using RTK/RTN corrections or precise SP3 orbit post-processing--can achieve centimeter-level accuracy, this study adopts a quality tolerance of 1 to 5 cm.  
In general, the quality conditions defined subsequently involve logical comparisons between the instantaneous antenna configurations and a reference model of control distances (\(L_{1}\) through \(L_{15}\)).
# Photoconductive Semiconductor Switches: Materials, Physics, and Applications
## 3 SiC PCSS Devices
The acceptor level (0/ - transition) arises when the neutral V accepts an additional electron, for instance, from an ionized donor, becoming negatively charged. Conversely, the donor level (0/ + transition) is observed when the neutral vanadium state donates an electron, becoming positively charged. The acceptor level is consistently positioned higher in energy relative to the valence band. Figure 5 illustrates the band diagram for SiC, where the two transition levels are represented by horizontal black lines, and the three charge states are shown as discrete vertical columns. Additionally, the figure depicts scenarios involving co-doping with either donors or acceptors. The photogeneration pathways in Figure 5 are represented by diagonal arrows indicating transitions between different energy levels [24; 32]. When V is the sole dopant in SiC (left diagram), it resides in its neutral charge state, allowing photogeneration transitions to both negative and positive charge states, as indicated by the green arrows. The transition to the negative charge state is accompanied by a dashed arrow, representing the generation of a hole in the valence band, while an electron is promoted from the valence band to the acceptor level. In the case where V is compensated by shallow donors such as N (Figure 5b), it resides in the negative charge state due to an electron transition to a lower energy state (red arrow) and can return to its neutral state only through electron emission (green arrow). When V is compensated by shallow acceptors such as Al or B (Figure 5c), it remains in the positive charge state due to electron transition to a lower energy state (red arrow) and can only return to its neutral state by emitting a hole (green arrow). The processes of hole generation and annihilation are again denoted by dashed arrows. This behavior is further complicated by the fact that V, like most transition metals, retains some of its atomistic properties in the form of excited atomic states, even when embedded in a host lattice. During illumination, these states create additional transition pathways, as indicated by the orange arrows. These pathways can be detected in the absorption spectrum but are not believed to generate a photocurrent [29; 30; 32; 33; 34; 35].
# A Survey on Deep Learning in 3D CAD Reconstruction
## 6 CAD Representation
### Sequence Representation  
Sequence representation represents the generation or editing process of a CAD model as a series of sequential operations. Similar to text sequences in natural language processing, sequence representation records geometric operations, construction steps, and parameters as a time series, and the model can generate CAD models by learning these sequences [15; 49; 81]. Specifically, the construction process of CAD models usually involves various geometric operations, such as drawing straight lines, drawing arcs, rotating, extruding, etc. Each operation has its specific parameters, such as the starting point, end point, radius, angle, etc., of the operation. Sequence representation records these geometric operations in the order of execution to form a set of operation instructions. In this way, each instruction (or element in the sequence) represents a specific geometric operation and its corresponding parameters as seen, for example, in the recently proposed dataset DeepCAD [10] of large CAD command sequence models.  
Similar to sequence representation are CAD procedural modeling and the macro parameter method (MPA). CAD procedural modeling automatically constructs geometric models through rules, scripts, or parameter instructions and is a model generation method. It emphasizes the automatic construction of models based on rules and parameters [106]. The macro parameter method (MPA) is a technical path to achieving semantic preservation and the exchange of CAD models, emphasizing the consistency of models across different platforms [107]. CAD procedural modeling is a modeling idea and method, whereas the MPA is an engineering solution for cross-platform semantic modeling conversion. In comparison, sequence representation is a form of data representation. It is a way to express the \"construction history\" of CAD models, which facilitates neural network learning and modeling, and is suitable for model generation and reconstruction tasks.