esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 2 Related Work
### Common AE Generation Methods
#### 2.2.1 Fast Gradient Sign Method (FGSM) Attack  
The FGSM attack, proposed by [PERSON] et al. [37], is an optimal method of max norm constraint to craft perturbations of the original input. This attack fools the ML/DNN model to misclassify the input generated by increasing the direction of the gradient based on the gradient descent principle. The FGSM can be formulated as shown in Equation (2).  
\[x^{\prime}=x+\epsilon*sign(\bigtriangledown xJ(\theta,x,y)) \tag{2}\]  
This equation computes the perturbed input, \(x^{\prime}\), which is the original input (\(x\)) supplemented with a small perturbation (\(\epsilon\)) that is proportional to the sign of the gradient of the model's loss function (\(J(\theta,x,y)\)) with respect to the input features (\(x\)), where \(\theta\) is the model parameter and \(y\) is the true label of the data point. The sign function ensures that the perturbation is in the direction that maximizes the loss function, whereas the epsilon value controls the intensity of the perturbation. The FGSM method is computationally efficient and can be used to quickly generate AEs. However, it may not always be effective in generating strong AEs and can be easily defended from methods such as adversarial training.
# The DREAM Endstation at the Linac Coherent Light Source
## 4 Scientific Application
### Coulomb Explosion Imaging  
To acquire sufficient statistics, coincidence methods require the detection of a large number of events. This requirement is met by LCLS III and by high-repetition-rate optical laser systems supporting DREAM. The large data sets created by coincidence experiments at repetition rates of \(<\)100 kHz will require novel data science approaches to extract the desired structural information, which is also given at TMO.  
Several recent experiments have demonstrated that CEI with XFELs can image the position of each atom in a polyatomic molecule [11; 13]. If an ultra short X-ray pulse is used to efficiently ionize and fragment the polyatomic molecule within a timescale in the range from femto- to atto-seconds (faster than photo-chemical reactions), the CEI method may be an ideal probe for transient molecular structures. Based on the inverse relation between the internuclear distance and the Coulomb potential, the molecular structure before the explosion is encoded in the momentum distribution of the ion fragments, which directly reflects the equilibrium geometry of the molecule. Thus, CEI is a promising imaging technique and gives the extensively used expression \"molecular movie\" a new and extended meaning in terms of accessible particle correlations.
# A Molecular View on Biomaterials and Dental Stem Cells Interactions: Literature Review
## 1 Introduction  
The oral cavity has aroused particular interest as a source for obtaining mesenchymal stem cells (MSCs) because there are different oral tissues from which MSCs can be isolated. Dental tissue-derived mesenchymal stem cells (DT-MSCs) are easy to culture, as they can be obtained from a wide range of primary and permanent teeth without ethical controversy. This makes them a valuable and accessible source of autologous stem cells [1]. Most DT-MSCs are derived from the neural crest and can differentiate into multiple cell types, including epithelial cells, odontoblasts, osteoblasts, chondroblasts, adipocytes, neuronal cells, glial cells, and muscle cells. Thus, they are currently considered a promising resource for their therapeutic application in regenerative medicine. However, as in other types of stem cells, for its application to be successful, its proliferation and differentiation must be controlled in an environment that mimics in vivo conditions. Therefore, an artificial niche, such as biomaterials, is a fundamental strategy to exploit the therapeutic potential of DT-MSCs. Currently, in the design of biomaterials, characteristics, such as the physical chemistry of the material, its biological interaction, mechanical properties, specific biological functionalities, and shape or geometry at different scales, such as on the macro, micro, and nano levels, are considered during their cell-b biomaterial interactions [2]. However, thebiomaterial's chemical composition (natural or synthetic) also plays an important role in improving stem cells' in vitro biological response. In this sense, natural biomaterials offer the advantage of being biocompatible and biodegradable, while synthetic ones also allow the possibility of improving their chemical and physical properties for a specific application. In summary, research on biomaterials and DT-MSCs has progressed; however, challenges remain regarding the ideal characteristics of biomaterials and the molecular mechanisms involved in regulating behavior in DT-MSCs. Therefore, the aim of this literature review is to provide a molecular view of biomaterial-cell interaction to understand how signals from biomaterials act as potent regulators of the microenvironment in DT-MSCs. In the first part, the background on DT-MSCs is presented, with a specific focus on their biological characteristics. Next, the state of the art is analyzed with respect to the different chemical and physical characteristics of biomaterials and the molecular mechanisms involved during their interaction with DT-MSCs. In this sense, various in vitro and in vivo experimental studies are considered that report valuable information to understand the behavior and differentiation of these cells in biomaterials.
## 2 Literature Review
### Concurrency Control Protocols in Distributed Manufacturing  
In distributed manufacturing, concurrency control ensures that concurrent operations on shared resources do not conflict or corrupt state. Traditional concurrency control approaches, such as mutex locks, semaphores, and timestamp ordering, were designed for centralized computing and can lead to transaction blocking, causing priority inversion problems and degrading system schedulability [19]. A particularly severe issue is priority inversion, where a low-priority task holds a resource needed by a high-priority job, delaying critical operations and degrading real-time responsiveness.  
The Priority Ceiling Protocol minimizes the duration of priority inversions by assigning each resource a ceiling equal to the highest priority of tasks that may access it; when a task locks the resource, its priority is elevated to this ceiling, bounding worst-case blocking to a single critical section essential for real-time manufacturing [20]. The PriorityInheritance Protocol (PIP) tackles the same problem by temporarily raising the priority of any low-priority task that blocks a higher-priority one, enabling it to complete its critical section sooner and release the resource [21; 22]. Unlike the Priority Ceiling Protocol, PIP does not require a system-wide ceiling table; therefore, it is simpler to implement but offers only bounded rather than minimal blocking times. From Figure 2, we can see a three-thread scenario where, without Priority Inheritance locking, the low-priority thread holds the mutex and blocks the high-priority thread while a medium-priority thread preempts, causing an unbounded delay. With Priority Inheritance, the low-priority holder temporarily inherits the high priority, finishes its critical section sooner, and the inversion window is bounded. PIP works well when the number of shared resources is small, yet as resource counts and task chains grow, it can suffer from transitive priority boosts and increased schedulability analysis complexity.  
Stack Resource Policy (SRP) is another resource locking protocol for real-time systems, which prevents unbounded blocking by assigning each resource a \"ceiling\" equal to the highest priority of any task that may use it; a task may only begin when its priority exceeds all current ceilings, thereby bounding blocking times to at most one critical section [23]. In addition to these lock protocols, real-time systems often rely on deadline-driven schedulers. Earliest Deadline First (EDF) is a preemptive, dynamic-priority scheduling algorithm that always dispatches the task with the nearest absolute deadline. While EDF is optimal for minimizing deadline misses on a single processor under no shared-resource constraints, it provides no protection against priority inversion when tasks contend for locks. To introduce inversion-avoidance into EDF, we pair it with the SRP. EDF + SRP thus isolates the benefit of inversion-avoidance on top of an otherwise ideal dynamic scheduler.
## 2 Materials and Methods
### Experimental Seeds and Physical Properties  
Chinese cabbage (Beijingxin-III, MC \(\leq\) 7.0%), carrot (Huangjian-I, MC \(\leq\) 7.0%), sesame (Baizhi-II, MC \(\leq\) 8.0%), onion (Zhongling Red Rose, MC \(\leq\) 8.0%), cabbage (Jingfeng-I, MC \(\leq\) 7.0%), radish (Jiujinwang, MC \(\leq\) 8.0%), tomato (Laosharang, MC \(\leq\) 7.0%), pepper (Hangjiao-I, MC \(\leq\) 7.0%), and bok choi (Shanghaiqing 605, MC \(\leq\) 7.0%) seeds were selected as experimental seeds in this study. The seed varieties and their moisture content (MC) have been indicated in the parentheses following the seed types. The first six types of seeds were used for the seeding performance experiments, and the last three types of seeds were used for the efficient parameter optimization and experimental verification.  
For the establishment of the dataset required for network training, we measured four types of physical properties of the nine types of seeds, namely, the geometric mean diameter (\(D_{g}\)), sphericity (\(\phi\)), thousand-grains weight (\(m_{1000}\)), and kernel density (\(\rho_{8}\)). The length (\(L\)), width (\(W\)), and height (\(T\)) of the seeds were measured with an electronic vernier caliper (Yantai Greenery Tools Co., Ltd., Yantai, China); the data are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Seed Type** & \(L\)**/mm** & \(W\)**/mm** & \(T\)**/mm** \\ \hline Chinese cabbage & 3.44 \(\pm\) 0.25 & 2.61 \(\pm\) 0.21 & 2.01 \(\pm\) 0.25 \\ Carrot & 3.43 \(\pm\) 0.54 & 1.58 \(\pm\) 0.20 & 0.71 \(\pm\) 0.11 \\ Sesame & 2.97 \(\pm\) 0.20 & 2.03 \(\pm\) 0.18 & 1.43 \(\pm\) 0.20 \\ Onion & 3.08 \(\pm\) 0.16 & 1.85 \(\pm\) 0.14 & 0.87 \(\pm\) 0.08 \\ Cabbage & 2.04 \(\pm\) 0.16 & 1.75 \(\pm\) 0.13 & 1.52 \(\pm\) 0.20 \\ Radish & 1.94 \(\pm\) 0.14 & 1.73 \(\pm\) 0.12 & 1.54 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three-axis dimensions of the seeds.  
Figure 4: Function unit of the seed cleaning device: (**a**) outer side; (**b**) inner side.  
The thousand-grain weight of the seeds was measured with an electronic scale (Shenzhen CNW Electronics Co., Ltd., Shenzhen, China). The kernel density was determined by the pycnometer method, shown in Figure 5.  
The computational formulas for the seeds' physical properties are as follows:  
\[D_{\text{g}}=(L\cdot W\cdot T)^{\frac{1}{3}}, \tag{1}\]  
\[\phi=\frac{(L\cdot W\cdot T)^{\frac{1}{3}}}{L}, \tag{2}\]  
\[\rho_{\text{s}}=\frac{m_{1}\cdot\rho_{\text{w}}}{m_{2}-m_{3}+m_{1}}, \tag{3}\]  
where \(\rho_{\text{w}}\) is the density of the water; \(m_{1}\) is the weight of the seeds; \(m_{2}\) is the weight of the water and pycnometer; and \(m_{3}\) is the weight of the seeds, water, and pycnometer. Table 2 lists the physical properties of seed types.
## 4 Discussion  
The submitted contribution describes a model solution for assessing the planned construction of the TEN-T railway connection between the airport on the left side of the Danube River through the city center on the other side of the Danube to the Petrzalka  
Figure 21: (a) Simulated groundwater flow direction with GWL course, assuming the construction of the impermeable construction pits with five openings in the sealed wall, and (b) simulated course of the GWL difference. Detailed view in the section RWS Slovany–RWS Filiàka.  
Figure 22: (a) Simulated direction of groundwater flow direction with GWL course, assuming the construction of the impermeable construction pits with nine openings in the sealed wall, and (b) simulated course of the GWL difference. Detailed view in the section RWS Slovany–RWS Filiàka.  
region, near the area discussed in [35]. There are several sections of this connection which should be built in an underground tunnel along the Carpathian Mountain, as well as under the Danube River.  
Due to the fact that the construction route is carried out primarily in the aquifer layer of the rock environment under the Little Carpathians, a numerical model of groundwater flow was compiled using FEM, with the help of which we determined the impact of this construction on the development of the groundwater level regime in the given area, taking into account the influence of the effective precipitation listed in [36].  
The solution showed different results for the impacts to the groundwater level regime. On the left side of the Danube River after tunnel construction, above the rail corridor route, there will be an increase in GWL of 1.5-2.0 m for the average or maximum condition (Figure 20). Backwater can be eliminated by designing \"openings\" in the sealing wall, which we propose to be located in places where it is feasible or as implemented by the model [17]. The designed openings will allow groundwater to flow across the proposed railway track. The solution was performed in two variants, i.e., five openings 180 m apart or nine openings 100 m apart. The decrease in the groundwater level is shown in Figures 21 and 22. The difference between these two variants was not significant, but building more holes speaks in favor of safety. The results of the modeling showed that this proposal would reduce the water level by 1.3-1.6 m compared to the sealing wall without any measures. The groundwater level will be 5.7 to 6 m below the surface, which is an acceptable state, in our opinion [17].  
Completely different results were obtained on the right side of the Danube River in the Petrzalka region (Figure 19c). The analysis revealed that the construction of the tunnel would cause a minimal and localized increase in groundwater level by only a few centimeters. This finding remained consistent whether considering the average or maximum hydrological conditions. As a result, no remedial measures are necessary to regulate the the groundwater level during the construction of the TEN-T connection.
# A Hybrid Deep Learning Model for Protein-Protein Interactions Extraction from Biomedical Literature
## 4 Datasets and Experimental Setup
### Performance Comparison  
The performance of the proposed DL model is compared with the state-of-the-art non-hybrid models and hybrid models. Table 3 shows the comparison results.  
CNN-based models [24; 25; 26] and LSTM-based models [31; 32] have been applied for PPI extraction recently. However, there are many differences in the text preprocessing strategies (e.g., protein entities are generalized with special symbols or protein IDs, utilizing different tokenization and parsing tools, and utilizing filtering rules or not) and other experimental setups (e.g., hyperparameter settings and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Aimed** & **BioInfer** \\ \hline Max length of sentences & 133 & 95 \\ Max length of SDPs & 31 & 35 \\ Optimization algorithm & Adam \\ Learning rate & \(1\times 10^{-4}\) \\ Number of epochs & 100 \\ Training batch size & 128 \\ \hline
**Bidirectional LSTM network** & \\ Number of LSTM units & 100 \\ Dropout rate & 0.2 \\ Recurrent dropout rate & 0.2 \\ \hline
**CNN network** & \\ Window sizes & (3, 4, and 5) \\ Num. of filters on each window size & 100 \\ Dropout rate after each max pooling layer & 0.5 \\ Activation function in convolution layer & Relu \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting.  
evaluation metrics). These make it difficult to compare the performance of the DL models directly. In our experiments, we compare the non-hybrid models (1-5 in Table 3) and hybrid models (6-7 in Table 3) with the same experimental settings, except for the models and the inputs of the models.  
By comparing CNN-based models 1 with 2, and LSTM-based models 3 with 4 in Table 3, it can be found that the two embeddings (word-embedding and SDP embedding) input is able to improve the performance for both CNN- and LSTM-based models. This result demonstrates the effectiveness of integrating word embedding and SDP embedding for PPI extraction. The interaction verbs (e.g., \"affect\" and \"bind\") and dependency relation symbols (e.g., \"nsubj\" and \"dobj\") in the SDPs could provide useful information for classifying target protein pairs and extracting the relations.  
It is also observed that the LSTM model outperforms the CNN model in the two datasets. The bidirectional LSTM achieved the best performance among the non-hybrid models.  
As a hybrid model, we compare the proposed bidirectional LSTM+CNN model (7 in Table 3) with the hybrid CNN+bidirectional LSTM model (6 in Table 3), which is a variant of C-LSTM proposed by the authors of [34]. C-LSTM learns the sentence representation by combining CNN and LSTM, and has been shown to be superior to CNN and LSTM for some text classification tasks. The results show that the proposed bidirectional LSTM+CNN model outperforms the CNN+bidirectional LSTM model greatly for PPI extraction in both of the datasets.
# Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
###### Abstract  
Polyvinyl Butyral (PVB) laminated glass (LG) with varying numbers of layers is extensively utilized in building structures, and its complex mechanical properties, due to the presence of PVB, pose significant challenges. While comprehensive research has been conducted on the bending behavior of two-layer PVB laminated glass, studies focusing on three-layer variants remain limited. This study aims to investigate the bending behavior of three-layer PVB laminated glass under out-of-plane forces and to ascertain the effects of increasing the number of layers. Experimental studies were carried out on one-, two-, and three-layer PVB laminated glass plates subjected to such loads. During the loading process, the out-of-plane displacement and surface strain of the glass plates were monitored and analyzed. From these observations, load-deflection curves were constructed, allowing for the determination of deflection and strain distribution across the planes. The impact of the number of glass layers on stiffness and ultimate load capacity was examined. Additionally, another experiment was conducted to analyze the mechanical properties of the laminated material (PVB). The research results indicate that the tensile strain at the bottom layer of the laminated glass plate reaches its threshold under an out-of-plane load, leading to a brittle fracture in that layer. However, the remaining layers of PVB LG do not fail at this stage, enabling the plate to continue bearing the load. The average ultimate loads for three-layer, two-layer, and one-layer PVB laminated glass are 37.8 kN, 36 kN, and 24.7 kN, respectively, with a ratio of 1:0.95.0.65. The bending stiffness values for these are 2.77 kN/mm, 1.71 kN/mm, and 1.21 kN/mm, respectively, corresponding to a ratio of 1:0.62.04. The stiffness shows a nonlinear increase with the layer count, an effect attributed to the PVB's characteristics according to the analytical findings. The tensile behavior of the laminated materials demonstrates a bilinear characteristic when subjected to strain. The out-of-plane bending tests on glass plates reveal that when the strain rate is low, PVB behaves like a quasi-linear elastic material. Therefore, for design calculations of PVB LG, adopting an elastic modulus of 1.34 MPa for PVB is deemed reasonable. The findings of this study provide insights for research on laminated glass plates, which are instrumental in refining the calculation methods specified in relevant design standards. Concurrently, it offers guidance for selecting the appropriate number of PVB LG layers in engineering applications.  
laminated glass plate; out of plane bending; polyvinyl butyral 2024
## 4 Results
### Effect of the Different Features on the Model Performance  
After the optimum window size and hyperparameters were assessed, we investigated the effect of a different group of features on the model's prediction capability by designing five different cases. Table 5 summarizes the statistics of these cases for their corresponding feature combinations where the model hyperparameters are based on the best performing LSTM model with a window size of five days (see Table 4). We found that the optimum solution for SM prediction was achieved when all feature groups were combined, i.e., Case V, for training the LSTM model.  
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Hyperparameters** & **Tested** & **Selected** \\ \hline Hidden Layer & 1, 2, 3 & 2 \\ Unit Size & 32, 64, 128 & 32 \\ Learning Rate & 0.01, 0.001, 0.0001 & 0.001 \\ Activation Function & Adam, Adamax, SGD & Adamax \\ Epoch Number & 1000–1500 & 1000 \\ Dropout Rate & 0–0.5 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter ranges of LSTM model and selected values for the last five days window size.  
\begin{table}
\begin{tabular}{l r r r r r r r r} \hline \hline \multirow{2}{*}{**Window Size**} & \multicolumn{4}{c}{**Train**} & \multicolumn{4}{c}{**Test**} \\  & \(R^{2}\) & **RMSE** & **ubRMSE** & **MAE** & \(R^{2}\) & **RMSE** & **ubRMSE** & **MAE** \\ \hline
1 & 0.701 & 0.069 & 0.069 & 0.053 & 0.695 & 0.071 & 0.071 & 0.053 \\
5 & 0.922 & 0.035 & 0.035 & 0.026 & 0.871 & 0.046 & 0.045 & 0.033 \\
10 & 0.922 & 0.035 & 0.044 & 0.026 & 0.859 & 0.048 & 0.048 & 0.035 \\
## 5 Evaluation  
As previously detailed in Section 4, to evaluate the detectability of C3, we conducted tests across six specific scenarios--command execution (using _whoami_, _ping 8.8.8.8_, and a scheduled task via _schtasks_), file upload, and lateral movement (using _net use_)--as well as a random scenario, against four anonymized, widely-used EDR products. These EDR products were chosen due to the availability of free trial versions.  
The evaluation aimed to determine whether and to what extent these attack techniques could be detected by commonly available EDR solutions. The specific product names of the EDRs are omitted, as the focus of this work is on evaluating the general detection capabilities of a range of EDR products against this technique, rather than the specific detection performance of individual products. However, the names of the EDR products tested can be made available upon request to the authors.  
To evaluate the detectability of C3 by each EDR, we first installed the necessary components: the C3 framework (including the web server and workstation components) and then the EDR software on the target workstation. The evaluation involved initiating a connection from the Chrome browser to the C2 server every 10 s. If a command was available, the communication proceeded as described in Figure 1. Otherwise, the C2 server responded with \"No\". Each of the six test scenarios was executed for five minutes with randomized command selection, i.e., either a specific scenario URL or a \"No\" response from the C2 server, for a total of 15 min per EDR. A final 10-min mixed scenario was then conducted, where commands were randomly selected from any of the five test cases (command execution or file upload).  
Remarkably, the proposed scheme evaded detection by all tested EDR solutions across all six scenarios. This finding, based on the specific conditions of this test and the current EDR capabilities, suggests a significant vulnerability in a range of EDR products to this post exploitation technique. The fact that zero detections were recorded, despite the absence of code obfuscation (excluding the necessary Base64 encoding for file uploads), further underscores the potential impact of this approach. The code and detailed instructions for this proof-of-concept (PoC) are publicly available on GitHub v0.8.0 [4].