esa-sceva/satcom-chunk-collection huggingface dataset:
# Cascaded Nonlinear-Optical Loop Mirror-Based All-Optical PAM Regenerator
## 1 Introduction
The rest of the paper is organized as follows: the regeneration characteristics of a single NOLM is firstly described in Section 2, then, the noise reduction ratio (NRR) and the improvement of Q-factor for multi-level regeneration are defined for evaluating the performance of a PAM Regenerator in Section 3, and Section 4 presents the details of this kind of cascaded NOLM-based regeneration scheme for PAM-4 and PAM-8 signals. Finally, the conclusion is given in Section 5.
# Random Vibration Fatigue Analysis Using a Nonlinear Cumulative Damage Model
## 2 Theorical Background
### Random Vibration  
In the frequency domain, the random vibration behavior can be well represented with a PSD function. Random vibration allows products to resonate all of the time [18], and by its cyclic movement, it causes cumulative fatigue damage, which results in vibration stress that reduces the strength of the material to support it. Thus, due to the generated strain, the repetitive vibration stress produces the failure of the component [19]. In the analysis, we must consider that the product being vibrated responds to the input vibration as a function of the product's resonant frequency [18], where the amplification factor of the response \(Q\) is expressed as the transmissibility of the vibration amplitude at the resonance frequency, and it is given by,  
\[Q=\frac{f_{n}}{\Delta f} \tag{2}\]  
where \(f_{n}\) is the natural frequency in Hz, and it is determined either by Equation (3) or Equation (4),  
\[f_{n}=\frac{1}{2\pi}\sqrt{\frac{k}{m}} \tag{3}\]  
\[f_{n}=\frac{W_{n}}{2\pi} \tag{4}\]  
\(m\) is the mass. \(k\) is the material's stiffness and \(W_{n}\) is the natural frequency in Rad/s. Here, we highlight that the \(W_{n}\) value included in Equation (4) is determined according to the geometry, support and loads of the component under analysis. Once the component is submitted to the stress of random vibration PSD load, the generated fatigue damage is accumulated in a nonlinear way, as follows.
# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 3 System Model
### Data Sharing Model  
Vehicles periodically share traffic information and RSUs every 100 ms using wireless communication [6]. Furthermore, vehicles also share the current status of their computational resources in the form of a metric we define in this paper as computation availability time \(T_{ca}\). This is defined as the time after which the vehicle's computational resources will become available. Thus, it measures the busy time of a vehicle's computational resources and is calculated directly from the computational latency of the current task allocated to the vehicle. \(T_{ca}\) is given by the following equation:  
\[T_{ca}=\frac{WC_{b}}{C} \tag{1}\]  
where \(W\) measures the task size (given in bits), \(C_{b}\) is the number of cycles taken by the vehicle's CPU to compute one bit (given in cycles/bit) and \(C\) is the frequency of the vehicle's CPU (given in cycles/s).
## 2 Materials and Methods
### Method
\begin{split}&\lambda_{cond}\sum\limits_{i=0}^{S^{2}}\sum \limits_{j=0}^{B}\mathbbm{I}_{i}^{obj}\left[\left(x_{i}-\hat{x}_{i}\right)^{2} +\left(y_{i}-\hat{y}_{i}\right)^{2}\right]+\lambda_{cond}\sum\limits_{i=0}^{S ^{2}}\sum\limits_{j=0}^{B}\mathbbm{I}_{i}^{obj}\left[\left(\sqrt{w}_{i}-\sqrt {\hat{y}}_{i}\right)^{2}+\left(\sqrt{\hat{y}_{i}}-\sqrt{\hat{y}}_{i}\right)^{2 }\right]\\ &+\sum_{i=0}^{S^{2}}\sum_{j=0}^{B}\mathbbm{I}_{i}^{obj}\left(C_{ i}-\hat{C}_{i}\right)^{2}+\lambda_{noboj}\sum_{i=0}^{S^{2}}\sum_{j=0}^{B} \mathbbm{I}_{i}^{obj}\left(C_{i}-\hat{C}_{i}\right)^{2}+\sum_{i=0}^{S^{2}} \mathbbm{I}_{i}^{obj}\sum\limits_{c\in classes}(p_{i}(c)-\hat{p}_{i}(c))^{2} \end{split}  \tag{4}\]  
The YOLO model is designed in one conventional neural network (CNN) structure, as shown in Figure 4. The YOLO model was modeled on the Pascal VOC dataset. The front end of this CNN is a convolutional layer, followed by a fully connected layer. The convolutional layer extracts feature from the image, and the fully connected layer predicts class probabilities and bounding box coordinates. The YOLO neural network structure is derived from GoogLeNet, which is used for image classification. YOLO consists of a total of 24 convolutional layers and two fully connected layers. Instead of GoogLeNet's reception structure, YOLO used a combination of a \(1\times 1\) reduction layer and a \(3\times 3\) convolution layer. The combination of a \(1\times 1\) reduction layer and a \(3\times 3\) convolutional layer is said to replace the reception structure [12]. The overall structure of the YOLO model is as follows: the final output of this model is the prediction tensors of \(7\times 7\times 30\). YOLOV3 is almost similar to YOLOV2, but there are some differences. YOLOV3 changed its backbone architecture from Darknet-19 to Darknet-53. Darknet-53 adds even more layers to Darknet-19 by applying the proposed skip connection concept from ResNet. Table 3 shows that Darknet-53 has a high FPS, although its accuracy does not differ much from that of ResNet-101 and ResNet-152. YOLOV3 applies binary classification using a sigmoid for each class without using softmax for class prediction. YOLOV3 runs much faster when compared to other object detection model with similar performance. Figure 5  
Figure 4: YOLO Network. presents the results for the IoU threshold 0.5 criteria. To evaluate the performance of the detector on the COCO dataset, based on MS COCO mAP (mean average precision), RetinaNet is 40.8 and YOLOV3 is 33.0; that is, the result is lower in YOLOV3 [13]. Table 4 shows the average precision (AP) of detecting small, medium, and large images using various algorithms and backbones. The higher the AP, the more accurate it is for that variable. As shown in Table 4, YOLOV3 is 57.9 and RetinaNet 61.1 based on Pascal VOC mAP (average precision: 0.5); that is, it can be seen that the speed of YOLOV3 is faster [13]. In DRAS, YOLOV3 was used for the purpose of detecting accidents and sudden obstacles on the road that occurred between vehicles running at high speed in CCTV
## 3 Results
### Tensile Performance  
Tensile test results for the AW joints, the PWCR joints, the PWSHT joints, and the PWCR-SHT joints are shown in Figures 10-12. The UTS of the AW joints measures 209.1 MPa, which is 61.8% of the BM, with an EL of 6.3%. The fracture location is within  
Figure 8: The HAZ microstructure of the PWCR-SHT joints: (**a**) close to the FZ (HAZ1) and (**b**) away from the FZ (HAZ2).  
Figure 9: Hardness test results of the joints.  
the HAZ, a distance from the FZ. This region is characterized as a \"softened\" zone based on the hardness distribution. The fracture morphology within this \"softened\" zone, as shown in Figure 12a, exhibits equiaxed dimples of varying sizes with traces resembling \"chain waves\" inside the dimples. Some dimples contain granular particles and a small amount of smooth surface, indicative of ductile fracture. The UTS of the PWCR joints is 254 MPa, equivalent to 82.4% of the BM and an EL of 4.2%. The increase in tensile strength and decrease in elongation align with the strain-hardening effect induced by the CR. The fracture still occurs within the HAZ softened zone, as shown in Figure 12b. This region exhibits limited small equiaxed dimples, indicating ductile fracture characteristics. However, numerous smooth platform surfaces suggest brittle fracture traits, leading to a judgment of mixed ductile-brittle fracture. For the PWSHT joints, the UTS of 210.2 MPa is achieved, representing 68.2% of the BM and an EL of 14.0%. Consequently, the PWSHT process minimally impacts the tensile strength of the aluminum alloy joints but significantly enhances its ductility. The fracture occurs within the FZ, as shown in Figure 12c, revealing numerous closely spaced equiaxed dimples with consistent sizes. These dimples lack granular particles, indicating simultaneous and uniform growth during stretching, resulting in improved ductility. However, the dimples are smaller and shallower, which correlate with reduced strength. Finally, the PWCR-SHT joints exhibit a UTS of 197.2 MPa, equivalent to 63.9% of the BM, with the highest EL of 18.8%, surpassing the BM. The fracture occurs within the HAZ, as shown in Figure 12d, displaying numerous dimples with minimal particle content. These dimples are distinct and vary in depth, lacking smooth platform surfaces, signifying a ductile fracture. The PWCR-SHT process imparts superior ductility to the joints, surpassing that of the PWSHT joints.  
Figure 11: The tensile fracture position of joints: (**a**) the AW joint; (**b**) the PWCR joint; (**c**) the PWSHT joint; and (**d**) the PWCR-SHT joint.  
Figure 10: The tensile properties of the joints.
## 3 Materials and Methods
### UV Aging Test  
Selection and design of aging simulation systemsTo simulate the aging of asphalt in outdoor UV conditions, an asphalt rotary film oven and a UV aging simulation system were selected for indoor accelerated asphalt UV aging. A high-pressure mercury lamp of model GYL230-220V-1000W is selected as the UV light source, its spectral range was 320-450 nm, and the main peak was 365 nm. It is an ideal light source for aging of UV polymer compounds. The self-designed UV aging simulation system consisted of two parts. The first part was the UV aging case, which included the UV aging box, air cooling system, UV lamp set, and temperature monitoring system. The second part was the UV aging sample part, which included the UV aging sample box and gas exchange system (Figure 2). The UV aging simulation system is suitable for asphalt light, light-heat, and light-heat-oxygen aging tests, the temperature can be controlled within the range of 60 \({}^{\circ}\)C-130 \({}^{\circ}\)C, and the UV intensity can be controlled between 50 and 600 w/m\({}^{2}\).  
InstructionsAccording to the testing purpose and the design requirements, 15.4 g of asphalt after RTFOT aging was poured into an aging plate with a diameter of 140 mm, and the aging plate was placed in a 163 \({}^{\circ}\)C rotating film oven 10 min to make UV aging test specimen. The UV sample chamber could hold 6-8 ray asphalt samples. The UV sample box door covered the UV sample box. The box buckle was fastened, and the UV sample box was placed in the UV aging box so that the asphalt sample was facing the UV lamp set. The gas inlet of the ultraviolet sample box was connected to the gas valve, and the vacuum pump, the nitrogen outlet, and the negative pressure gauge were connected. The inlet and outlet valves of the UV sample box were opened, and the box was filled with nitrogen at a flow rate of 400 mL/min for 20 min. The exhaust valve was closed, the vacuum pump was opened, and the air pressure in the UV sample chamber was pumped to \(-\)0.1 MPa. The previous gas exchange step was repeated, and the inflation port and the exhaust port were closed. The UV aging test box door was closed, and the time switch was set between the total time control switch and the UV lamp group. After connecting the device power supply, the electric shock protector switch was closed to operate the device for the UV aging test. The main parameters of this test were a temperature range of 65-70 \({}^{\circ}\)C and an average irradiation intensity of 200 w/m\({}^{2}\).
### Cryptosystem  
A cryptosystem is one of the essential systems to provide security for data protection, which can be used to protect data from replay attacks [8]. A cryptosystem is the shortest form of a cryptographic system. A cryptosystem is a combination of various methods and algorithms that enable all information and communication to be transmitted to any node on a network in a secure form [9]. Whenever an attacker tries to access cloud data, he uses all sorts of internal and external attacking algorithms that can help to gain access to that data [10]. If the attacker gains access to the data, it is difficult to protect that data from the attacker. A cloud cryptosystem is a technique used to protect data from attackers [11]. When different techniques are used in this algorithm, the attacker cannot use the data even though he has access.  
The cloud cryptosystem consists of two techniques: encryption and decryption [12]. Whenever data is converted from a readable format to a non-readable format, various steps and techniques are used to convert the data [13]. A non-readable text is obtained; this process is called encryption [14]. Non-readable text is a cipher text that is impossible for both authentic and inauthentic users to understand or read [15]. Whenever data is converted from a non-readable form to a readable form, such a process is called decryption. Decryption is a process in which various steps convert cipher text to plain text [16].
# Effect of Compressive Strain Rate on Auxetic Foam
## 2 Methods
### Compression Testing  
Compression testing was parallel to the long cell axis of anisotropic (\(y\)-axis), and unconverted (z-axis) open cell foams, as these were the directions where the highest magnitudes of Poisson's ratio were expected [47,57,62]. The triaxial open cell foam was compressed in its \(z\)-axis. Samples were compressed by 12 mm (Bose ElectroForce 3200, 250 N load cell Figure 2), imparting 45 to 80% compression for respective 25 to 15 mm thick samples, with foam densification expected at ~80% compression [47,49,79]. Applied compression rates were 0.1, 1, 10, 50, 250, 1000, and 3250 mm/s, giving applied strain rates between ~0.05 and 200 s\({}^{-1}\). All samples were tested once at the lowest rate of 0.1 mm/s, then the sample from each group type with the median stress at 10% compression was selected for testing at the higher rates. The stiffer of the two anisotropic open cell foam samples was selected. The five selected samples were tested once at each of the higher rates, with at least 15 min between tests. For compression rates over 100 s\({}^{-1}\), the total displacement was as low as 40% of the intended displacement (of 12 mm) due to device safety features. Data logging frequency was 30 Hz for compression rates of 0.1 mm/s, 60 Hz for 1 mm/s, 1 kHz for 10 mm/s, and 5 kHz for 50 mm/s and above.  
Surfaces were prepared with a speckle pattern (Halfords Matt Black or White Primer) to allow 2D DIC (Figure 2). Tests were filmed (Proton, FASTCAM_SA3, 60K & Nikon, Nikkor 35 mm f/2D lens) at a frame rate that matched the sample rate of the test device, and with the camera's image plane parallel to the front face of the sample. Video footage was analysed (GOM Correlate, free 2D DIC software) to obtain lateral and axial engineering strains and strain rates in a target area (~5 mm high, 15 to 25 mm wide) in the centre of the samples (Figure 2a), avoiding the regions affected by contact with the compression plates [58].  
Engineering stress vs. strain was calculated from test device force vs. displacement data, and sample dimensions. Values for Young's modulus (E\({}_{z}\) for unconverted and triaxial foams, and E\({}_{y}\) anisotropic foam) and [PERSON]'s ratio (v\({}_{\mathrm{Zx}}\) or v\({}_{\mathrm{Yx}}\) as above) were obtained from the gradients of straight lines fitted to respective stress or lateral strain vs. axial strain between 0 and 2% compression, within the quasi-linear region for all foams.
## 4 WSN-SES/MB Framework
### Overview  
The WSN-SES/MB framework automatically synthesizes various simulation models through the structural representation and the network's environment configuration of a sensor network using our transformation process to save synthesizing time and cost. Our framework's transformation process is as follows  
\[SES\to Proposed\ Pruning\to PES\to Composition\ Tree+Environment\ Parameters+\ MB\to Simulation\ Model. \tag{4}\]  
In Equation (4), our proposed framework generates a simulation model using the entity structure, the configuration, the network environment parameters, and the atomic DEVS models through its transformation process. In the proposed framework, our pruning algorithm generates a PES tree from an SES representing the hierarchical structure of a sensor network. This PES contains a composition tree for the network structure and the environment parameters. Thus, the WSN-SES/MB synthesizes the composition tree and the parameters of the PES, and the atomic models in a model base, to generate a single simulation model.  
The WSN-SES/MB framework uses five phases for synthesizing the structures and the models as follows:  
* SES base: This base contains the entity structures of the WSNs. The entity structures show the components of the WSNs hierarchically in various trees;
* Proposed Pruning: Our pruning algorithm selects entities from an SES tree for generating a PES and configures a sensor network's environment parameters;
* PES base: This base includes pruned entity structures of the WSNs. Each entity structure contains the environment parameters;
* Model base: Atomic DEVS models defined in this base are entities corresponding to the leaf of the SES and PES trees. These atomic models represent the behavioral and procedural characteristics of the sensor network. The models are synthesized to generate the final simulation model;
* Simulation Model: This simulation model is the result of our framework, which is finally created by the structural representation, the network's parameters, and the behavioral models.  
Therefore, our WSN-SES/MB framework achieves time and cost efficiency while synthesizing the composition tree, the environment parameters and models using our transformation process. The proposed framework increases the diversity of synthesized simulation models and facilitates modeling simulation for WSNs.
## 3 Results and Discussion
### Effect of the Nominal Maximum Aggregate Size on the Cracking Resistance
#### 3.2.2 Displacement at 50% of Post-Peak Load (\(\text{d}_{0.5\text{PostFmax}}\))  
Figure 11 provides a notion of the mixtures' ductility, which decreases as temperature drops. As expected, PMB 45/80-65 mixtures present the highest values due to greater ductility that such binder provides to the mixtures.  
Analyzing the obtained multi-linear regression equation, it can be observed that the displacement at 50% of post-peak load is strongly correlated to the test temperature (T) asphalt binder type (BG) and NMAS, Equation (7).  
\[d_{0.5 PostFmax}=-0.363+0.047\times T+0.023\times BG-0.008\times NMAS \tag{7}\]  
Based on the obtained results and comparing the resulting equations for fine fraction study, Equation (4), and NMAS study, Equation (7), it can be concluded that the fine fraction content exerts a stronger influence on the ductility of the mixture than the NMAS. This can be explained by the higher correlation coefficient of the fine content compared to the NMAS.
## 3 Proposal
### Word Graph Construction  
This step uses a word graph to represent the relationships among a set of word groups \(W_{i}\) corresponding to domain strings in training dataset \(X_{i}^{*}\). A word graph \(G_{i}\) is simply a weighted undirected graph with a vertex set and an edge set. Each vertex corresponds to a word in set \(W_{i}\). The edge weights indicate the frequencies of co-occurrence of words in set \(W_{i}\), where co-occurrence means that the words belong to the same word group. Note that words with similar character strings share the same vertex. We use the Levenshtein ratio for similarity calculations and unweighted centroid clustering with threshold \(th_{\alpha}\) for word aggregation. Figure 3 shows an example of a constructed word graph using the set of three-word groups [[kyutech, local, domain], {kyutech, local, net}, {mpi, domains}]. Due to the similarity of domain and domains, the two words share a vertex.  
Dynamically generated malicious domain names and human-generated benign domain names exhibit clear differences in their word co-occurrence frequencies, and the words that illuminate these distinctions most effectively play the central role in a word graph. Specifically, words that have low centrality in word graph \(G_{0}\), which is constructed from benign dataset \(X_{0}\), and that have high centrality in another word graph are effective for identifying malicious domains. Based on these findings, we first define conjunction as a process that uses two-word graphs. Specifically, conjunction is a process of reconstructing word graph \(G_{ij}\) from word group sets \(W_{i}\) and \(W_{j}\) into word graphs \(G_{i}\) and \(G_{j}\).  
Figure 4 shows an example of the conjunction process \(G_{ij}\) for word graphs \(G_{i}\) and \(G_{j}\). Here, the vertices in white represent words in word graph \(G_{i}\), the vertices in black represent words in word graph \(G_{j}\), and the vertices in gray represent words that are common to both. Note that the weights of edges are updated with the conjunction process \(G_{ij}\). We then associate the following importance value with an arbitrary group \(w\) comprising words \(w_{1},\cdots,w_{j},\cdots,w_{n}\):  
\[\mathcal{S}_{i}(w)=\sum_{w_{j}\in w}|w_{j}|\big{(}\,\mathcal{C}_{0:i}(w_{j})- \mathcal{C}_{0}(w_{j})\,\big{)}\]  
Here, \(|w_{j}|\) denotes the length of word \(w_{j}\). \(\mathcal{C}_{0}(w_{j})\) and \(\mathcal{C}_{0:i}(w_{j})\) are functions that derive the centrality of word \(w_{j}\) in word graphs \(G_{0}\) and \(G_{0:i}\). Accordingly, this value means that the change in the centrality of word group \(w\) resulting from the conjunction of word graph \(G_{0}\) is constructed from benign dataset \(X_{0}\) and that \(G_{i}\) is constructed from malicious dataset \(X_{i}\). To compute the values, we use the PageRank centrality, which can be used even if a word graph is undirected and disconnected.