esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Agent Deep-Q Network-Based Cache Replacement Policy for Content Delivery Networks
## 4 Formulation of Markov Decision Process Problem
### Reward Function  
To maximize the cache hit ratio of an edge server, a reward function is designed to encourage the server to cache content efficiently. The agent is given the reward immediately after taking an action. Hence, our reward function is designed based on short-term feedback. We define the reward for transitioning from state \(\mathbf{s}_{t}\) to \(\mathbf{s}_{t+1}\) by taking action \(a\) as  
\[r(s_{t},a_{t},s_{t+1})=\lambda L(s_{t},a_{t},s_{t+1})+\eta(1- \lambda)P(s_{t},a_{t},s_{t+1}), \tag{3}\]  
where \(L(s_{t},a_{t},s_{t+1})\) is the reward term and \(P(s_{t},a_{t},s_{t+1})\) is the penalty term. \(\lambda\) is a weight parameter that balances the reward and penalty terms. Moreover, \(\eta=1\) if \(a_{t}\) causes a cache miss; otherwise, \(\eta=0\). The reward term \(L(s_{t},a_{t},s_{t+1})\) is defined as the weighted sum of hit counts in each cache slot of the \(i\)th edge server and is written as  
\[L(s_{t},a_{t},s_{t+1})=\sum_{k=1}^{C}w_{k}(HC_{k}(t+1)-HC_{k}(t)), \tag{4}\]  
where \(HC_{k}(t)\) is the cumulative hit count of the \(k\)th cached slot at the \(t\)th decision epoch. We employ a weight parameter \(w_{k}\) to highlight the differences among cache slots. For instance, we can increase the weight for newly cached content to emphasize the latest selected actions. The penalty term is implemented to impose a reduction on the total reward when the action taken causes a cache miss, thereby signifying that it was not an optimal action. Furthermore, it is defined such that the penalty decreases as the cumulative hit count increases between two decision epochs. Then, the term \(P(s_{t},a_{t},s_{t+1})\) in (3) can be written as  
\[P(s_{t},a_{t},s_{t+1})=\beta+\frac{\rho}{L(s_{t},a_{t},s_{t+1}) ^{\kappa}+1}, \tag{5}\]  
where \(\beta\), \(\rho\), and \(\kappa\) are hyperparameters of the penalty terms satisfying \(\beta\leq 0\), \(\rho<0\), and \(\kappa\geq 1\).
## 3 A proposed Cyberattack Prevention Framework and Hypothesis Development  
### Perceived Severity  
According to [64] [PERSON] et al. (2017), integration changes bureaucratic structures, increasing the functional quality of cyberattacks and requiring appropriate flexibility inside the company to stop them. The perceived severity of a cyberattack as perceived by an enterprise is not addressed [61; 64; 65]. In terms of picking from the response obtained through back excitation, perceived severity is measured [67]. One can ask someone how they feel about the possibility of falling victim to cyberattacks, and responses such as \"not stressed\" or \"very restless\" are indications of how terrified they are of the threat. The perceived severity of the serious repercussions affects the propensity for a prospective cyberattack behavior. The unpleasant repercussions a person identifies with an event or result, such as a cyberattack behavior, are referred to as perceived severity. These effects may be related to a potential future occurrence, a present condition, such as a cyberattack issue, or both [65]. Although it goes by a more generic name, the idea of severity as a significant behavioral factor has been discussed in a variety of theories and across numerous academic areas [61; 64; 65]. Although the concept of severity might be viewed as an illustration of negative utility and negative valence, the phrase itself seems to have its origins in the Protection Motivation Theory. Some scholars have concluded that vulnerability, severity, and efficacy should multiply together so that if any one of these three factors has a value of zero, motivation will be zero [61; 64; 65]. Therefore, a hypothesis related to perceived severity can put forward as:  
**H:** _Perceived severity has a positive, significant relationship with cyberattack prevention._
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 4 Security Analysis and Performance Evaluation
### Performance Evaluation
#### 4.2.3 Data and Application Rate  
The data and application rate parameter refers to the total number of successfully sent vehicle data in a unit of time (in bit per second for data rate, and in the packet for application rate), which shows the transmission rate effectiveness in vehicular networks. It also represents an average traffic flow rate where the higher value indicates a better system performance [36]. All vehicles attempt to continuously route 64 byte application packets at a rate of 2.048 Kbps in our scenario. Figure 6 shows the application data receiving rate with the overall average throughput remaining at 140 Kbps, whereas Figure 7 shows the receiving rate at an average rate of 25 packets per second. We consider the application packet as well as the blockchain packet to evaluate the performance of application traffic in our system.
# The Size Effect of Shear Bands in Dense Sands--A Discrete Element Analysis
## 3 The Observation of the Size Effect
### Less Size Effect in the Angle of Shear Bands  
Less size effect was found on the angle of shear bands. The angles of different shear bands were mainly between 50 and 54\({}^{\circ}\). As the isotropic confining stress increased, the angle of the shear band decreased to some extent (Figure 6).  
There are three main theories about the angle of shear bands: Mohr-Coulomb theory, [PERSON] theory, and [PERSON] theory [6,7,34]  
\[\begin{array}{l}\theta=\frac{\pi}{4}+\frac{\varphi}{2}\qquad\text{(Mohr-Coulomb)} \\ \theta=\frac{\pi}{4}+\frac{\varphi}{2}\qquad\text{(Roscoe)}\\ \theta=\frac{\pi}{4}+\frac{\varphi}{4}\quad\text{(Arthus)}\end{array} \tag{4}\]  
where \(\varphi\) is the friction angle, and \(\psi\) is the dilation angle obtained by  
\[\sin\psi=\frac{\frac{\mathrm{d}\varepsilon_{\mathrm{z}}}{\mathrm{d} \varepsilon_{1}}}{\frac{\mathrm{d}\varepsilon_{2}}{\mathrm{d}\varepsilon_{1} }-2} \tag{5}\]  
where \(\mathrm{d}\varepsilon_{\mathrm{y}}/\mathrm{d}\varepsilon_{1}\) was calculated from the deformation inside the shear band.  
It was calculated that \(\psi=14.96^{\circ}\); hence, the estimations of the Mohr-Coulomb, the Roscoe, and the Arthur formulas were 57.15\({}^{\circ}\), 52.48\({}^{\circ}\), and 54.8\({}^{\circ}\), respectively. Our  
Figure 5: Stress–strain curves and development of volumetric strain of tests under different confining stress: (**a**) 100 kPa; (**b**) 300 kPa; (**c**) 500 kPa; (**d**) 700 kPa; (**e**) 900 kPa.  
Figure 6: The influence of isotropic confining stress on the angle of the shear band.  
results were more consistent with the Roscoe type. Meanwhile, the size of the specimen had less impact on the angle of the shear band, because different-sized specimens led to similar results.
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 5 Conclusions  
In this paper, a fog-based CDN framework was proposed. The framework provides an autonomous technique for offloading content to nearby fog nodes, utilizing the fog nodes as CDN nodes. This will provide the end user with a better QoS and less latency. Our framework consists of five main components: a Fog Registry (_FR_), Fog Service Locator (_FSL_), event channel, Fog Worker (_FW_), and Fog Browser. These components are deployed at different layers of the fog strata. The _FR_, _FSL_, and event channel are deployed at the cloud layer. The _FW_ is installed on the fog nodes at the fog layer. The Fog Browser is installed and used at the edge layer. The end user can make use of this framework using the fog browser as an extension, a plugin to an existing browser. One of the key advantages of our proposed framework is that it does not require any special setup to the network. Our proposed framework respects the privacy of the content, as service engineers control the resources that can be offloaded in the service registration process.  
Our experiments were conducted using well-known websites providing different types of static content, which were chosen based on their ranking. We had three experimental scenarios to evaluate the latency enhancement achieved by our proposed framework. These experiments showed that our proposed framework can reduce latency by 85% and enhance website user experience. As a topic of future work, we will consider the number of edge devices served by a fog node in the resource offloading process. We will also incorporate mechanisms to compute other fog node aspects in the Fog Registry which can improve content offloading strategies. Furthermore, we will evaluate our enhanced version of the framework against other metrics, such as the energy consumption of the fog nodes and integrate our framework with other fog-based energy efficient frameworks such as the one proposed in [19].  
**Author Contributions:** Conceptualization, A.H.L; Data curation, Z.T.F.; Investigation, A.H.L; Methodology, H.M.F.; Project administration, Z.T.F.; Software, A.H.I; Supervision, H.M.F.; Validation, H.M.F.; Writing-original draft, A.H.I.; Writing-review & editing, Z.T.F. and H.M.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Fog-based CDN latency (multiple fog nodes) vs. cloud-based CDN latency.
###### Abstract  
The segmentation results of brain magnetic resonance imaging (MRI) have important guiding significance for subsequent clinical diagnosis and treatment. However, brain MRI segmentation is a complex and challenging problem due to the inevitable noise or intensity inhomogeneity. A novel robust clustering with local contextual information (RC_LCI) model was used in this study which accurately segmented brain MRI corrupted by noise and intensity inhomogeneity. For pixels in the neighborhood of the central pixel, a weighting scheme combining local contextual information was used to generate the corresponding anisotropic weight to update the current central pixel and thus remove noisy pixels. Then, a multiplicative framework consisting of the product of a real image and a bias field could effectively segment brain MRI and estimate the bias field. Further, a linear combination of basis functions was introduced to guarantee the bias field properties. Therefore, compared with state-of-the-art models, the segmentation result obtained by RC_LCI was increased by 0.195 \(\pm\) 0.125 in terms of the Jaccard similarity coefficient. Both visual experimental results and quantitative evaluation demonstrate the superiority of RC_LCI on real and synthetic images.  
Keywords:image segmentation; brain MRI; intensity inhomogeneity; bias field correction; weighting scheme +
Footnote †: journal: Medical Imaging
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 5 Current Anti-Phishing Methodologies and Techniques
### Technical Anti-Phishing Techniques
#### 5.2.3 Visual Similarity Detection  
In this method of phishing detection, the similarity between a suspicious site and a database of legitimate website features (including logos, icons, screenshots, and document-oriented models) is computed. If the similarity score is a above a threshold, then this suggests that the suspicious site is being mimicked. This is useful as attackers will often copy legitimate sites to fool their targets into parting with their credentials. However, this method is far from infallible as it can be easily bypassed by the phisher if they simply slightly adjust some visual elements without effecting the overall look or content of their mimicked page.
# A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data
## 3 Results
### Change of Center of Gravity of APPI  
According to the public perception results of air pollution from 2012 to 2017 in Section 3.2.2, the air pollution perception index (APPI) and its geographical location information are used to build the center of the gravity model of public perception according to Equation (10). The results are shown in Figure 5.  
The red five-pointed star in Figure 5 represents the center of Shandong Province, and the blue points represent the center of gravity of APPI in different years. The ellipses in different colors represent the directional distribution of APPI in different years. The green line and pink column in the statistical chart respectively indicate the distance and azimuth angle change between the center of gravity of APPI and the center of Shandong Province.  
Figure 5 suggests that the general trend of the public's air pollution perception is in the southwest of Shandong Province from 2012 to 2017. There is little difference in the distance and direction of the center of gravity of public perception of air pollution over  
Figure 5: Change of center of gravity of APPI (blue points) from 2012 to 2017.  
the years, the mean offset distance is about 17 km (SD = 2.39), and the mean offset angle is about 232\({}^{\circ}\) (SD = 11.09) per year in Shandong Province.  
It may be that the northern and eastern regions of Shandong Province are mostly coastal cities. First of all, coastal cities generally have relatively strong winds, and the air circulates quickly. Secondly, seawater will absorb some of the air's impurities due to the sea and have a specific effect of purifying the air. Moreover, coastal cities generally have more vegetation than inland cities, which can improve the air quality. So coastal cities' air quality is usually better than inland towns, and the public is more concerned about air pollution. The public's perception of air pollution is relatively low in the southwestern region of Shandong's center of gravity, cities such as Jinan, Laiwu, Zibo, Zaozhuang, Linyi, and other towns dominated by industry, mining, and enterprises [31]. The discharge of three industrial wastes, such as exhaust gas, wastewater, solid waste, will pollute the atmosphere and directly endangers human health. Therefore, the public has a stronger perception of air pollution.  
Moreover, in recent years, the Shandong public's perception of air pollution has not been very different. It may be due to some inherent air pollution problems related to industrial development that have not been resolved. Specific issues and reasons need to be further developed with the help of refined data.
## 4 Results
### AD Definition as a Data Preprocessing Method (Substudy 1)
#### 4.1.2 Effects of X-outliers on the Model Performance
In the above observations and analyses, it is important to keep in mind that once outliers with anomaly scores above a given threshold are removed from the dataset, new values for the anomaly scores can be calculated based on the remaining data. These new values are not necessarily below the given threshold, meaning that points that were not outliers become outliers. For example, Figure 6(b) shows the new standardized residuals versus the new anomaly scores after removal of the points with initially anomaly scores above 200 kJ/mol (see Figure 6(a)). It can be observed that the new anomaly scores of some points (points in red in Figure 6(b)), display an increase in comparison with their initial anomaly score (points in red in Figure 6(a)). These red points belong to molecules from various chemical families, but mostly from the families of nitrogen and halogen compounds. These molecules become outliers with respect to the data after the elimination of the initial outliers, probably also because their structure becomes even less represented among the remaining data. Furthermore, the elimination of the X-outliers seems to reduce the range of the anomaly scores, and this also modifies the range of standardized residuals without necessarily improving it. This phenomenon is clear evidence of the direct dependence of the notion of outlier and the composition of the complete dataset, which renders the detection and treatment of outliers particularly complex. Figure 6: Comparison of Lasso model performance in predicting the **enthalpy** under different proportions of X-outliers removed from the preprocessed data via the three tested methods on the (**a**) train and (**b**) test datasets.
# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 3 Materials and Methods
### Classification Architectures
#### 3.2.2 Long Short-Term Memory (LSTM)  
A long short-term memory, or LSTM, is a type of recurrent neural network model proposed by [PERSON] and [PERSON] in 1997 [PERSON] and [PERSON] (1997) capable of learning longer sequences of data by reducing the gradient vanishing problem. The LSTM architecture consists of recursively connected subnetworks known as memory cell blocks. Each block contains self-connected memory cells and multiplicative units that learn to open and close access to the constant error stream, allowing LSTM memory cells to store and access information for long periods. In addition, there are forget gates within an LSTM network, which provide continuous instructions for writing, reading, and reset operations for the cells.
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
###### Abstract  
Speech and singing voice discrimination is an important task in the speech processing area given that each type of voice requires different information retrieval and signal processing techniques. This discrimination task is hard even for humans depending on the length of voice segments. In this article, we present an automatic speech and singing voice classification method using pitch parameters derived from musical note information and \(f_{0}\) stability analysis. We applied our method to a database containing speech and a capella singing and compared the results with other discrimination techniques based on information derived from pitch and spectral envelope. Our method obtains good results discriminating both voice types, is efficient, has good generalisation capabilities and is computationally fast. In the process, we have also created a note detection algorithm with parametric control of the characteristics of the notes it detects. We compared the agreement of this algorithm with a state-of-the-art note detection algorithm and performed an experiment that proves that speech and singing discrimination parameters can represent generic information about the music style of the singing voice.  
audio segmentation; voice discrimination; singing voice; pitch +
Footnote †: This paper is an extended version of our paper published in IberSPEECH2018.
## 2 Theory and Method
### Kinematics Modelling  
The schematic diagram of the pitching mechanism is shown in Figure 4. One end of the hydraulic cylinder is hinged at point O, and its piston rod and pulley module are hinged at point A; \(s_{4}\) represents the stroke of the hydraulic cylinder; O\({}_{4}\) is the center of the circular slot; and \(r_{4}\) is the radius of rotation of the circular slot. Adjustment of the table's pitch angle is achieved by controlling the movement of pulleys B and C in the circular arc groove c\({}_{4}\). The T-slot platform's center point, O\({}_{\rm c}\), is situated on the line O\({}_{4}\), A, at a distance \(l_{4}\) from O\({}_{4}\); \(\theta_{4}\) indicates the starting angle of the pitching mechanism; the hydraulic cylinder stroke in the starting state is \(s_{4\rm m}\); and the pitch angle is 0 rad. When the stroke of the hydraulic cylinder is \(s_{4}\), the table rotates around the \(x_{4}\)-axis of the coordinate system in which O\({}_{4}\) is located, O\({}_{\rm c}\) moves to O\({}_{\rm c}\)\({}^{\prime}\), and the angle \(\hat{x}_{4}\) between O\({}_{4}\)O\({}_{\rm c}\)\({}^{\prime}\) and O\({}_{4}\)O\({}_{\rm c}\) is the pitch angle of the table.  
By analyzing the geometrical relations of the moving mechanism, a positive kinematic model of the pitch layer can be obtained as  
\[\hat{x}_{4}=\arccos\left(\frac{r_{4}^{2}+m_{4}^{2}-s_{4}^{2}}{2r_{4}m_{4}} \right)-\theta_{4\rm m} \tag{1}\]  
Figure 3: Mechanism of HLTST.  
where \(\theta_{\rm 4m}=\arccos\left(\frac{r_{4}^{2}+m_{4}^{2}-s_{5}^{2}}{2r_{4}m_{4}}\right)\) is the starting angle of the pitch mechanism. Since the roll layer has the same kinematic mechanism as the pitch layer, the positive kinematic model for the roll layer can be obtained as  
\[\hat{y}_{5}=\arccos\left(\frac{r_{5}^{2}+m_{5}^{2}-s_{5}^{2}}{2r_{5}m_{5}} \right)-\theta_{\rm 5m} \tag{2}\]  
where \(\theta_{\rm 5m}=\arccos\left(\frac{r_{5}^{2}+m_{5}^{2}-s_{5}^{2}}{2r_{5}m_{5}}\right)\) is the starting angle of the roll mechanism.
## 4 Materials and Methods
### CsAFSs Recombinant Expression and Enzyme Assay
#### 4.4.3 E. coli Expression of CsAFS  
We transformed the expression vector into _E. coli_ Rosetta cells (Novagen, Madison, WI, USA) for protein expression. Firstly, transformed Rosetta cells with a recombinant vector (100 mL) were incubated at 37 \({}^{\circ}\)C and 200 rpm until the OD\({}_{600}\) was 0.5. Next, 0.1 mM IPTG was added to the bacterial fluid, which was then kept at 18 \({}^{\circ}\)C and 200 rpm for 16 h, and finally cells were collected after centrifugation at 10,000\(\times\)\(g\) for 5 min at 4 \({}^{\circ}\)C. After resuspending the precipitate in 25 mM, pH 7.4 Tris-HCl buffer, the mixture was disrupted by sonication for 15 min and then centrifuged at 10,000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C. The supernatant was collected and filtered. Ni-NTA bind resin (GE Healthcare, Uppsala, Sweden) was used to purify the supernatant, and the partially purified protein was then purified through a PD-10 desalting column (GE Healthcare, Buckinghamshire, UK). The protein was finally eluted with Tris-HCl (25 mM, pH 7.4) for further study.
## 5 Evaluation
### Experimental Result
#### 5.2.4 Result  
We applied our proposed method to the other 10 indexed faults in Table 3. As a result, we could find all the causes of the faults injected by reviewing the presented fault candidates. The results of the experiment are summarized in Table 5. As a result of the fault injection experiment, we could find the cause of the fault by only checking 5.77% of the updated symbols on average.  
Figure 12: Fault Candidates of  
The proposed method is applied not only to our test bed but also to the commercial BCM seatbelt warning test. The BCM used in the experiment is an OSEK/VDX-based OS and is an SPC5604B BOLERO [27] -based ECU. The experiment dumped 29.4 kB of memory in the same 10 ms cycle as the system main task [9]. In this experiment, 1019 addresses were updated between the total 29.4 kB of allocated memory. Among them, 87 candidate symbols are localized and the localization rate is 8.54%. The previous method suggests fault candidates as the suspect region within the updated memory region rather than the address level. Therefore, the reduction rates (size of fault candidate region/size of the updated memory region) of fault candidates on average were about 22.42% (2 kB/8.7 kB) and about 19.21% (4.7 kB/24 kB). This shows a significant performance improvement when compared to the existing result [9].  
As a result, the proposed method was localized at an average of 5.77% in the test bed and 8.54% in the commercial BCM. Experiments were performed on two types of ECUs running different OS that conform to the OSEC/VDX standard. The MC9S12X is 16-bit and the SPC5604 is a 32-bit micro controller, which differs in terms of core family [24; 26]. Nevertheless, when we experimented with the proposed fault localization process, we could derive the fault candidates, including the cause of fault, by analyzing memory usage. This shows that the proposed method is applicable to ECUs based on OSEK/VDX. In other words, the memory usage related to the failed operation can be presented as debugging information through the memory usage in the normal operation for finding the fault occurring in the HiL test environment without the debugging tool or the source code. However, the proposed method has limitations in the case of a signal that processes the continuous values like analog signals, when the update does not have a significant meaning.