esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
Vero cells were infected with 100 TCID\({}_{50}\) PRV for 1.5 h, and then compound 4 (5 and 2.5 \(\upmu\)g/mL) was added. After 24 h, the indirect immunofluorescence assay was used for virus detection. The nucleocapsid (DAPI, blue) and anti-PRV (Green) responses are shown. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Antiviral** & **Cytotoxicity** & & **Antiviral** & **Cytotoxicity** & \\
**Activity**\({}^{\text{a}}\) & \({}^{\text{b}}\) & **SI** & & **Activity**\({}^{\text{a}}\) & \({}^{\text{b}}\) & **SI** \\
**EC\({}_{50}\)/\(\upmu\)g/mL** & **IC\({}_{50}\)/\(\upmu\)g/mL** & & & **EC\({}_{50}\)/\(\upmu\)g/mL** & **IC\({}_{50}\)/\(\upmu\)g/mL** & \\ \hline
**1** & - & \textgreater{}20 & - & **6** & - & \textgreater{}20 & - \\
**2** & - & \textgreater{}20 & - & **7** & 6.46 \(\pm\) 1.38 & 1.68 \(\pm\) 0.13 & 0.26 \\
**3** & \(1.74\pm\) 0.25 & \textgreater{}20 & \textgreater{}11.5 & **8** & - & \textgreater{}20 & - \\
**4** & \textless{}0.31 & \textgreater{}20 & \textgreater{}64.5 & Ribavirin & 4.44 \(\pm\) 1.53 & \textgreater{}20 & \textgreater{}4.50 \\
**5** & - & \textgreater{}20 & & Acyclovir & 2.96 \(\pm\) 1.48 & \textgreater{}20 & \textgreater{}6.75 \\ \hline \end{tabular}  
* EC\({}_{50}\) is the concentration that the compounds inhibit virus-induced cytopathic effect by 50%. \({}^{\text{b}}\) CC\({}_{50}\) is the concentration that the compounds kill 50% of the normal cells.  
\end{table} 
Table 3: Antiviral activity and cytotoxicity of compounds **1**–8.
# A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image
## 2 Related Work
### Hard Example Mining in Object Detection  
There is an imbalance problem in object detection, as the number of negative samples in the training dataset is usually much larger than the number of positive samples. During the training process of the model aiming at minimizing the loss function, the large number of negative samples often causes excessive weighting, leading to the degradation of detection accuracy.  
In general, the detector has to constrain the loss of positive and negative samples in order to balance the positive and negative samples. In the two-stage detector, the number of negative samples is generally reduced by downsampling the negative samples RoI. In the one-stage detector, the weights of positive and negative samples are generally adjusted directly on the loss function.  
Besides the above general methods of balancing positive and negative samples, there are also some methods that specialize in mining hard samples. OHEM [10] is a classic method in hard example mining. OHEM emphasizes the mining of hard samples and does not distinguish between hard positive and hard negative samples. In addition, it selects samples each time as those with large losses without setting the proportion of positive and negative samples. Since OHEM selects hard samples based on the total loss, and the ratio of classification loss and regression loss varies during the training of the network, this loss is not completely reliable. To address the above problems, S-OHEM [20] proposes a distribution sampling method according to the losses of different training stages. This method divides the losses into four stages, and the scaling parameters of classification loss and regression loss are different for each stage. However, the hard example mining method described above cannot be directly applied to a one-stage target detector; therefore, we have proposed a Hard Example Mining method applicable to RetinaNet.
# Instantaneous Networking Service Availability for Disaster Recovery
###### Abstract
Distributed cloud services facilitate local users to communicate with each other, sharing information such as rescue route, map, and disaster status. The motivation of providing prompt and local networking service with LI-Clouds is based on the observation of communication features and population distribution in the emergency situation. First, it is observed that the majority of communication destinations of a user are in the nearby area [11]. Second, residents in the disaster situation tend to gather at the designated safe places such as shelters or evacuation centers [12; 13]. Hence, even in the absence of conventional global communication services, the prompt and local cloud services are expected to be effective in quickly increasing the availability of networking services [14]. The goal of this paper is to study the networking service availability within the process of the whole network recovery. Since partial and local recovery of the networking services also plays an important role in a disaster situation, it is necessary to examine the temporal and spatial aspects of the process of networking service recovery [14]. At first, we propose to use a novel metric, instant networking service availability (I-NSA), in order to measure the time-varying efficiency of service restoration from the user side. I-NSA quantifies the instant availability of the networking service as the possibility that a user can communicate with any other users. We then measure and prove the efficiency of the networking restoration process based on LI-Cloud using I-NSA. Since I-NSA involves multiple factors of network connection status, as well as distribution of users, our study based on I-NSA enables an explicit description of the overall restoration status, even including the local and partial recovery of user services in a disaster situation. The evaluation results show that the methods based on the LI-Cloud system significantly improve the overall service availability for users. Analyses of evaluation results on I-NSA prove the advantage of the LI-Cloud method. In addition to that, I-NSA is shown to be useful in assessing the restoration strategy of a network system.
# Enhancing Structural Performance of Short Fiber Reinforced Objects through Customized Tool-Path
## 2 Background  
### Anisotropy of FDM Parts  
Many experimental studies have shown that FDM printed parts exhibit anisotropic mechanical properties. [PERSON] et al. investigated the anisotropic mechanical properties of ABS parts built by FDM [17]. The mechanical properties of PEEK and PC fabricated by FDM were studied by [PERSON] et al. [19] and [PERSON] and [PERSON] [20], respectively. They proved that FDM induces a significant structural anisotropy for thermoplastic materials.  
There have been several computational methods to address this issue. [PERSON] et al. proposed a computational design methodology to apply different build orientations to each section of an FDM part based on its stress map [20]. [PERSON] et al. developed a worst-case analysis method assuming the material is orthotropic to find the structurally weak sections [21]. [PERSON] et al. reported the effect of infill pattern on the mechanical properties of FDM parts by FEA simulation [22].  
Besides, build orientation selection methods to control the anisotropy has been proposed. [PERSON] et al. investigated the effect of building direction by FEA modeling of FDM parts with  
Figure 1: The workflow of the design approach.  
orthotropic materials [23]. [PERSON] and [PERSON] developed a build orientation selection algorithm using the Tsai-Wu failure criterion [24]. However, their works did not try to find the build orientation to minimize internal stress. Similarly, [PERSON] and [PERSON] proposed a method to find the best build orientation for FDM parts [25]. This method accounted for structural anisotropy to stacking layers of extrusion, assuming those are laminate composites. They found that different level of structural performances was captured depending on build direction.
# Anticipatory Troubleshooting
## 6 Evaluation
### Long-Term Experiments
#### 6.3.2 Results: Fix-Replace Dilemma  
In the first set of experiments, we focused on the fix-replace dilemma, and thus we compared the baselines always-repair (AR) and always-fix (AF) with \(DR1\) and \(N\)-\(MDP_{FixRep}\).  
Figure 7 presents the long-term costs (y-axis) as a function of the punish factor. As expected, using the \(N\)-\(MDP_{FixRep}\) algorithm reduces the total troubleshooting cost. Moreover, the greater the granularity level (\(N\)), the lower the costs are, since the MDP's depth of the lookahead is greater and thus it is closer to find an optimal solution. Nevertheless, increasing \(N\) beyond a certain value does not provide significant advantage in terms of long-term troubleshooting costs. In fact, in our experiments it was never worthwhile to use \(N\) that was greater than 4.  
Figure 8 presents the long-term costs as a function of the cost ratio (the ratio between replacing and fixing costs). When the cost ratio is small, fixing is significantly cheaper than replacing, and thus the AF algorithm performed best. On the other hand, when the ratio is high, there is a small difference between the cost of fixing and replacing, but a new component is less likely to fail than a used component, and thus always-repair performs best. In both cases the \(N\)-\(MDP_{FixRep}\) algorithm achieves the same cost as fixing for small ratio and as replacing for high ratio. In between, the \(N\)-\(MDP_{FixRep}\) algorithm significantly reduces the cost compared to the baseline algorithms and the \(DR1\) algorithm.  
Figure 6: Two Weibull curves, one for a new component (upper) and one for a fixed component (lower).
## 4 Results and Discussion
### Comparison of EMD-LSTM and EEMD-LSTM  
As shown in Figure 10, the R\({}^{2}\) and KGE of EMD-LSTM are lower than those of the EEMD-LSTM decomposition-ensemble learning model. The lower performance statistics of the EMD-LSTM decomposition-ensemble learning model are caused mainly by the mode mixing problem in EMD.  
Figure 11 compares the model performance for the periodic component in terms of R\({}^{2}\) when varying the training data size: The model performance improves with the data capacity of the training set. The outperformance of EEMD-LSTM over EEMD-based static methods, including ELM and SVM, is not remarkable when the training dataset is smaller than 80%. This can be explained as follows: Compared to traditional models, more parameters must be tuned in the LSTM-based prediction model. Therefore, more input data are required to maintain the model performance.  
The case study from the Muyubao landslide shows that the hybrid EEMD-RNN decomposition-ensemble learning model is promising for accurate prediction of landslide displacement by combining the advantages of EEMD and RNN. The main advantages of the proposed EEMD-based RNN decomposition-ensemble learning model can be outlined as follows:  
The MIC-based input variable selection is a systematic process without any a priori expert knowledge, computationally inexpensive, and capable of describing the nonlinear relationships. The performance of prediction model is able to be improved by EEMD decomposition of complicated forecasting problems into several easier ones, and the temporal dependency in complicated monitoring data is captured by adopting a RNN approach, thereby improving the ability to model dynamic systems.  
Although the EEMD-RNN decomposition-ensemble learning model has potential for the accurate prediction of landslide displacement, it has inherent limitations associated with data-driven approaches, including lack of transparency and a requirement for large quantities of training data [53; 54].  
Figure 11: Comparison of model performance for periodic components in terms of R\({}^{2}\) when varying the training set size.
## 4 Methods
### VAE Dimensionality Reduction and Denoising  
In LBSNs, node features are typically high-dimensional and susceptible to noise interference due to factors such as the randomness of user behavior, measurement errors in location data, or inaccuracies during the network propagation process. Even after feature initialization, the node features may still contain a significant amount of noise, which can affect model performance. To address this issue, Variational Autoencoders as a generative model, can effectively perform dimensionality reduction while denoising. The architecture is illustrated in Figure 3.  
The basic framework of VAE consists of three parts: encoder, latent variables, and decoder. First, the encoder network \(q(z|x)\) maps the input data \(x\) to the latent space \(z\). Then, the decoder \(p(x|z)\) generates reconstructed data \(\hat{x}\) based on the latent variable \(z\). The entire process can be achieved by optimizing the variational lower bound. Through this approach, VAE retains the main features of the data while removing noise during dimensionality reduction.
# An Improved PIN Diode Model Design for a Tunable Frequency Selective Absorber
## 3 Principle of Analysis
### Distribution of Current and Electric Field  
Figure 9 shows the surface current distribution and electric field distribution of the tunable FSS absorber at 11 GHz under both absorption and reflection operating states. As shown in Figure 9a, when R = 150 \(\Omega\) and C = 0.1 pF, the surface current is mainly concentrated on the intermediate resistance, and the energy of the incident electromagnetic wave is converted into the surface induced current energy and absorbed by the resistance. In addition, the high frequency current can pass well through the parallel capacitor on the left, so the surface current of the capacitor is also very dense. The electric field distribution at this frequency is shown in Figure 9b. Due to the capacitive effect between the patches, the electric field distribution at the edge of the patch is denser than inside. Meanwhile, the electric field strength in the capacitor is greater than that in the resistor, then the tunable FSS absorber is in the absorbing state. From Figure 9c, it can be seen the current surface density on the resistor becomes sparse when R = 10 k\(\Omega\). As shown in Figure 9d, it is difficult for the current to pass through the resistor, and the resistor and capacitor both have a large electric field strength, and the potential difference between the patches becomes larger, making the electric field at the edge of the patch stronger than the field distribution in Figure 9b.  
Due to the mismatch between the equivalent impedance of the FSS layer and the free-space impedance, the tunable FSS absorber is in a reflective state.
# A Dermoscopic Inspired System for Localization and Malignancy Classification of Melanocytic Lesions
## 2 Methods
### Extraction and Classification of Features
#### 2.2.3 Shape Features  
The computation of the shape symmetry index is performed using the lesion mark. The lesion centroid is positioned at the centroid of the image by using the difference in centroid positions method showcased in (5). This is carried out since the lesions are not aligned with the center of the image.  
\[\Delta\{x,y\}=\left\{\left(C_{l}(x)-C_{L}(x)\right),\left(C_{l}(y)-C_{L}(y) \right)\right\} \tag{5}\]  
\(C_{l}(x,y)\) and \(C_{L}(x,y)\) indicate the image and lesion centroids respectively. The image is divided into two halves with respect to \(x\)-axis and \(y\)-axis as illustrated in Figure 6 to determine the asymmetry along \(x\) and \(y\) axis. The maximum possible asymmetry index of the lesion \(AI\) is given in (6).  
\[AI=max\big{\{}A_{x}=x_{1}\text{\textasciitifchar{12}}x_{2}\text{, }A_{y}=y_{1}\text{\textasciitifchar{12}}y_{2}\text{, }\big{\}}/2 \tag{6}\]  
where, \(x_{1}\) and \(x_{2}\) are the two halves with respect to the \(x\)-axis and \(y_{1}\) and \(y_{2}\) are the two halves with respect to the \(y\)-axis. Since, asymmetry quantifies malignancy the proposed method takes into account the maximum possible asymmetry to minimize the classification error.  
A multi-scale method termed as fractal dimension (FD) is used to quantify border irregularity. It is computed by dividing the image in small grids of size \(r\times r\) as given in (7) [9].  
\[log\frac{1}{N(r)}=fd\times\log(r)-\log(\lambda) \tag{7}\]  
\(N(r)\) gives the contour length, \(\lambda\) indicates the scaling constant. The circularity of the lesion is measured using the metric of Compactness (8) [24].  
\[CI=\frac{{P_{L}}^{2}}{4\pi A_{L}} \tag{8}\]  
\(P_{L}\) indicates the perimeter of the lesion and \(A_{L}\) indicates the area of the lesion.
## 3 Experimental Results and Discussions
### Algorithm for Determining Damaged DPF  
As explained in Section 3.1, the airflow mass rate, EGR actuator (duty ratio), fuel pressure, boost pressure, and VGT actuator (duty ratio) were measured by the GDS diagnostic as comparable signals to determine whether it is a damaged DPF. And it was observed  
Figure 15: Comparison of particle number density for 4 types of DPF sample. (**a**) Total particle number. (**b**) Particle number with nano-sized of 23 nm or less (decimal logarithm).  
that the maximum value of the three parameters (airflow mass, boost pressure, and VGT actuator) was distinctly different for each of the four DPF types (see Table 7).  
Based on these experimental results, the OBD-based algorithm was derived from the airflow mass, boost pressure, and VGT actuator control duty signals, as shown in Figure 16. Whether the DPF is damaged is determined primarily by comparing the maximum value of these three variables with the reference value. Therefore, X-ray scanning should be performed for a detailed inspection. If it is not satisfied whether the DPF is damaged, DPF is determined secondarily by determining whether the measured value for each of the three variables exceeds the given range. If the PM sensor is used as an auxiliary indicator for this algorithm, the accuracy of the algorithm decision can be improved based on the accumulated PM actually emitted.
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 1 Introduction
Thus,  
Figure 1: Typical and adopted ECC hierarchies. (**a**) A typical ECC hierarchy; (**b**) adopted ECC hierarchy in this article. there is always a tradeoff when choosing between security levels and the performance of the cryptographic algorithm/protocol. Furthermore, the polynomial basis we have used to achieve faster multiplications [12]. On the other hand, the Gaussian normal basis is significant when frequent squaring operations are required to be computed [16]. A projective coordinate system is selected as it needs lower inversion operations when compared to the affine coordinate system [14]. The deployment of cryptographic algorithms on software platforms (i.e., microcontrollers) provides higher flexibility with a decrease in performance as compared to hardware platforms (such as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs)). Moreover, software platforms also provide interoperability and portability features. Therefore, an ECDH crypto library is introduced in [29] where an ultra-low-power MSP430 microcontroller is used to implement the time and memory consumed cryptographic operations with limited resources. Some additional benefits to using ECC as software implementations are highlighted in [30]. Higher performance and maximum security can be achieved when cryptographic algorithms are deployed as hardware accelerators. Consequently, this work deals with the hardware implementation of the ECDH key-exchange protocol on a reconfigurable FPGA platform. The American National Institute of Standards and Technology (NIST) recommended different key lengths for a binary and prime field to use. The NIST-standardized prime fields are 192, 224, 256, 384 and 521. Similarly, the binary elliptic fields are 163, 233, 283, 409 and 571. Therefore, the purpose of this work is to provide an ECDH accelerator over \(GF(2^{m})\) with \(m=233\) for healthcare [1], automotive [3,4], smart cards [5,6], IoT [7, 8, 9, 10] and many other emerging applications that require the exchange of sensitive information on insecure public channels. To achieve 80 and 128-bit symmetric key security, the RSA requires 1024 and 3072-bits. For the equivalent security level, ECC requires only 163 and 283 bits. For additional details, we refer readers to [29]. Consequently, to achieve an 80-bit symmetric key security, we have selected a 233-bit key length of ECC to report area and performance results for the ECDH computation.
## 2 Related Works
In other words, the interaction from users is needed in the system to achieve its goal. Generally, users provide a preliminary prompt of shadow area to the computer. Hence, the use of human knowledge can significantly help the computer conduct shadow detection. In [32], users marked a shadow surface and its sunlit counterpart. The shadow area is modeled as a function of sunlit region. [PERSON] et al. [33] proposed a very simple interactive approach to detect shadow. Specifically, in this approach, users left a mark in shadow area, then employed region growing from this mark to detect the whole shadow region. The main limitation of these methods is the effort required from the user, especially in the analysis of big images datasets. However, most of the state-of-the-art algorithms refers to feature-based detection models, since it has been proven that features can efficiently aid in discriminating shadow and non-shadow zones. For example, [PERSON] et al. [34] combines color and edge features to develop a more sophisticated shadow detection system. However, experimental results showed that this approach was not suitable for complex images. [PERSON] et al. [35] trained a single region classifier using SVM with linear kernel and a pairwise classifier using SVM with RBF kernel to detect shadow, where the posterior performs better. Experimental results showed that the proposed non-adjacent region based approach achieved good performance and was robust to the interferences from adjacent pixels. [PERSON] et al. [36] used a physical model to find pairwise shadow region by employing logistic regression of Adaboost with 16-node decision trees. This method performed poorly when the surface was uneven. In [37] [PERSON] et al. proposed a method to learn some key features of the shadow boundaries by using a convolutional neural network (CNN) based framework. The local information of the shadow edge was captured by the developed CNN. The author modelled the interactions between shadow and bright areas by formulating a global optimization technique. Furthermore, the shadow areas were detected by least-square optimization. In [38] [PERSON] et al. proposed similarity constraint generative adversarialnetwork (scGAN) to detect shadow in a single image, extracting higher level relationships and global characteristics. In particular, the authors employed a shadow detector to enhance detection accuracy by combining the typical GAN loss with a data loss term. Experimental results showed that the classification error reduced significantly. [PERSON] et al. [39] based on the characteristic of human visual system designed a feature fusion and multiple dictionary learning for shadow detection of single image. In the present paper, we propose a novel adaptive direction-tracking filter which moves along the object boundaries and extracts relevant features, subsequently used for shadow detection in the BMI system.