esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
## 4 A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
### Request Association Cache
#### 4.3.2 Request Strength Threshold to Separate Strong and Weak Request Data  
We propose a request strength threshold to dynamically separate strong and weak request data. We have two situations to adjust the request strength threshold. The first situation is to increase the request strength threshold. When the LRU request information will be removed from SB2, we will check whether the request's starting LPN exists in the strong request list. If the request's starting LPN is in the strong request list, it means that the request is considered as a strong request but it is removed because it has not been used for a long time. The reason is that the request strength threshold is so low that the request is easy to be regarded as the strong request. Therefore, we think that the request strength threshold for being considered the strong request should be increased. In the first situation, a raising factor Tup will be increased by 1.  
On the other hand, the second situation is to decrease the request strength threshold. When a weak request's content is hit in WT1, a reduction factor Tdown will be increased by 1. The reason is that the request strength threshold is so high that the request is easy to be regarded as the weak request but it might be considered as the strong request better. In the second situation, we think that the request strength threshold being considered the strong request should be decreased.  
When the request association analyzer collects the hot association pairs and will separate the strong and weak request lists, we can compare Tup and Tdown to dynamically determine the request strength threshold. As shown in Figure 9, the request strength threshold will be adjusted based on \(Threshold=Threshold-p\times 5\%\), where \(p\) (whose initial value is 0) is a variable as the adjustment weight and the initial \(Threshold\) is 100%. If Tup is greater than Tdown, \(p\) is increased by 1, and will be reset to 0 once if Tdown is greater than Tup. Similarly, if Tdown is greater than Tup each time, \(p\) is decreased by 1, and will be reset to 0 once if Tup is greater than Tdown.
# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 5 Simulation  
Modelling and simulation are well-established tools for analysis, design, acquisition and training in the automotive domain. Despite the heterogeneity of subsystems and disciplines involved in the development of an autonomous vehicle [9], there are many simulation methods that together allow covering of the entire development process. V-model and its variants have become the most common process models adopted in the automotive industry, guiding the development of systems on a variety of refinement levels with a multiple-stage validation and testing process [47]. By applying virtual simulation technologies at different abstraction levels, several \"X\"-in-the-Loop (XIL) (where \"X\" refers to any type of test included in the development process) testing setups can be performed: Model-In-the-Loop (MIL), Software-In-the-Loop (SIL), and Hardware-in-the-Loop (HIL). Moreover, ISO 26262 itself does have some limited guidance on the use of simulation in verification activities. The safety process of ISO 26262 is based on a safety V-model. As such, it is not straightforward to match with an agile development process, which is the natural choice for AV development. Section 6, \"Product Development at the Software level\", provides some directions on verification of the software architecture design and recommends \"simulation of dynamic parts of the design\". Section 6 further notes that \"software integration testing can be executed in different environments\", and lists as examples MIL tests, SIL tests, processor-in-the-loop tests, and HIL tests.  
Although the number of tools for the simulation of autonomous vehicles has increased in recent years, it is very difficult to select the best tool for a specific development. Features like open-source, multi-platform, personalization and documentation are desirable in all simulators. Furthermore, regarding the simulation of such robotic systems, one must take into account all the aspects of physical implementation to further simplify the transition from virtual- to real-world scenarios.  
The Agent paradigm has been introduced as well, as a way to address some of the current issues in autonomous-based driver behavior regarding its distribution capabilities, computing efficiency and scalability [48; 49]. However, these are not found in all the simulation tools. Some of the solutions provide a series of toolchains to address the end-to-end process of an autonomous vehicle; others only contemplate some of the subsystems or functionalities (Advanced Driver Assistance Systems (ADAS), Simultaneous Localization And Mapping (SLAM), Driving, traffic, etc.). Most simulation tools are compatible with programming languages such as C/C++, Perl, Python, Java, LabVIEW, URBI or MATLAB.  
Below we present the different approaches that can be taken into account to select a simulator for autonomous vehicles (vehicle tests, robotics, game engine, specific development), focusing on the perception subsystem.
# Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks
## 2 An Example of Installation of Wireless Sensor Network  
Before diving into the depths of this paper, it is very important to know the architecture of the Wireless Sensor Network system, its main components, and their utilities. As we have stated in the introduction chapter, this description consists of three main components:  
(1) Sensor node: it is a small device (shown in Figure 1 and also in Figure 2) and inexpensive with limited resources of battery, also with limited resources of computation power, which are deployed in an area to monitor and control the environment. This device possesses the capacity to sense, to gather sensor information from the environment, process data, and communicate with other nodes. (For more information, please refer to [6]).  
Some different types of sensor modules are pressure, humidity, temperature. Ordinarily, these small devices are attached to the connection port area of the sensor node device during configuration.  
(2) Wireless Co-ordinator: To receive data from wireless sensors, the wireless coordinator is essential (See Figures 3 and 4 for the connection). It helps with the successful communication of a wireless system. It has a long-range transmission distance of up to 1 km.  
Figure 1: Wireless sensor node device [7].  
Figure 2: Different types of wireless sensor modules [7].  
(3) Programmable Logic Controller (PLC) or **Human-Machine Interface (HMI)**: PLC is an industrial computer control system that continuously controls the state of input devices and makes decisions based upon a conventional program to monitor the state of output devices. (For more information, please see [8]).  
A Human-Machine Interface (HMI) is defined as a device or component of an industrial control system (ICS) or software application that facilitates humans to engage and interact with machines. (For more information, please see [9]).  
The configuration of the wireless sensor network installation is as follows:
## 4 Environmental Parameters Measurement and Discussion of Results
### Impact of Both Turbulence and Thermal Factors on UWOC  
#### 4.2.1 Temperature Rise Experiment  
The annual average temperature of seawater in every area is different [17]. When the water temperature changes, the refractive water index also changes, which leads to the laser beam propagation path shift and will affect the optical signal reception. In this experiment, the UWOC system performance is tested when the temperature rises or falls. The architecture is shown in Figure 4.  
In this experiment, the water was heated from room temperature 25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 45 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, and the water temperature was kept constant. The received optical power was measured by the optical power measurement. The results of water temperature and optical loss are shown in Figure 5a. The total loss of signal light increases with the  
Figure 4: The experimental heating setup of underwater wireless optical communication (UWOC).  
Figure 3: Transmission distance of 6 m (**a**) bit error rate box plot of the motor pump is on/off, and (**b**) bit error rate against received power when the motor pump is on/off.  
increase of water temperature after 6 m of transmission. The transmission loss is 5.18 dB at 35 \({}^{\circ}\)C. When the water temperature is 50 \({}^{\circ}\)C, the total transmission loss is 8.93 dB. The difference in optical signal loss is 0.42 dB when the temperature rises to 15 \({}^{\circ}\)C. Therefore, when the water temperature rises, the optical transmission loss is more significant than at room temperature. Next, the PD is used to receive the signal and measure the bit error rate, as shown in Figure 5b. It can be seen that the fluctuation of BER is more severe with the increase of water temperature up to 50 \({}^{\circ}\)C compared with 25 \({}^{\circ}\)C. The reasons may be attributed to the water molecules being more active at a higher temperature. In addition, the temperature distribution is nonuniform in the water tank and the optical propagation path. Prior work [18] demonstrated, lower scintillation index, higher signal-to-noise ratio, and lower BER at 25 \({}^{\circ}\)C water temperature. Simultaneously, the eye diagrams shown in Figure 6a,b indicates that the system has better performance at 25 \({}^{\circ}\)C water temperature compared to 50 \({}^{\circ}\)C, with larger eye-opening and less time jitter. Nevertheless, the power penalty is little. It means that water temperature variation has little influence on UWOC performance.  
Figure 5: Transmission distance of 6 m (**a**) water versus optical loss, and (**b**) received power against the bit error rate under different temperatures.  
Figure 6: Eye diagram for a transmission distance of 6 m at (**a**) water temperature of 25 \({}^{\circ}\)C, and (**b**) water temperature 50 \({}^{\circ}\)C.
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 5 Networks in the Design Phase
### Camacho Net  
The Camacho Net project was initiated by an award from the IEEE Humanitarian Technology Challenge (HTC) in 2011. This ICTD project focused on providing broadband links between health posts in the Altiplano of Bolivia northeast of La Paz, Bolivia, along the eastern bank of Lake Titicaca. The area is one of the most remote and sparsely populated in the world. The surface of Lake Titicaca lies at an elevation of 3800 m. The mountains farther east peak at well above 6000 m.  
The initial system was configured in 2014 and it links health posts in Chaguaya, Carbuco, and Challapata. However, the funds for the link to the regional hospital in Escoma and the northern port town of Puerto Acosto were not released in 2014. CamachoNet is a new name, for this previous HTC project is expected to be initiated in January 2018 with funds from IEEE. The network is planned to include the Escoma hospital, as well as health posts in Puerto Acosto and Humanata. The Chaguaya, Carbuco, and Challapata network is still functioning and has been credited as being a primary reason for several lifesaving surgeries on accident victims that could not have taken place without dedicated communications between health workers.  
There is a local cell phone carrier in the region. Broadband can be purchased from the carrier, but the prices are exorbitant and no price moderation is possible in the near future, in part due to the scarcity of population in the region. A private subscription network could be devised that does not compete with the present carrier. A for-profit company was formed from the University volunteers from La Paz who constructed the original network in tight collaboration with the communities in question. Agreements with the communities are being updated and new agreements with local entrepreneurs who have ideas for services that are not in direct competition with carriers are also being developed.
# BGP Neighbor Trust Establishment Mechanism Based on the Bargaining Game
## 2 Related Work  
To date, there have been many studies on BGP security, which are mainly divided into BGP security extension and abnormal route detection. The main research results in BGP security extension are Secure BGP (S-BGP) [3], secure origin BGP (soBGP) [4], and pretty security (psBGP) [5]. The most complete and representative work is S-BGP. S-BGPprotocol uses digital certificates and digital signatures to verify the credibility of routing information. Although these solutions can effectively guarantee BGP security, they have not been implemented on the Internet due to difficulties in deployment.  
Anomaly detection is one of the methods to protect BGP route security. The core work of anomaly detection is to diagnose and analyze the characteristics of abnormal behavior on the network, and then identify the abnormal behavior and information and send an alarm to the victim. The main research results in this field are Prefix Hijack Alert System (PHAS) [21] and iSPY [22]. Although anomaly detection can detect incorrect routes from route information, it cannot prevent malicious ASes from declaring untrusted routes again. The detection result also depends on the attack feature extraction algorithm and route data set, and there will be certain errors.  
Simultaneously, more and more researchers have proposed methods to solve BGP security problems from the perspective of identifying trusted ASes. It is a feasible method besides security extension and anomaly detection. One study [10] shows that the reputation mechanism has an incentive effect, effectively reducing the propagation speed of false information and inhibiting deceptive behavior. The inter-domain routing system has the conditions to establish a reputation mechanism. [PERSON] et al. [11] proposed a distributed reputation protocol for cooperation between ASes. The key idea is to simulate the trust relationship in the real world, where an AS can selectively receive information collected from neighbors. [PERSON] et al. [12] proposed the AS reputation system, ASwatch, which can identify a malicious AS by monitoring the credibility of its behavior. Experimental results show that ASwatch can detect 93% of malicious ASes, and the false alarm rate is only 5%. [PERSON] [13] proposed a neighbor watch method, where ASes form a trusted group and monitor abnormal ASes by exchanging information and querying abnormal results. Literature [14] proposed the AS-TRUST mechanism. This analyzes the collected update messages and forms different types of feedback, and then uses the Bayes algorithm to calculate the reputation of a global AS.  
Inter-domain trust technology is a lightweight solution with good implementation capability. At the same time, it can incentivize legitimate ASes to punish malicious ASes and improve overall inter-domain security. In recent years, it has received increasing attention from researchers
# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 4 Background
### Amodal Completion  
Amodal completion is the natural ability of humans to discern the physical objects in the environment even if they are occluded. Our environment contains more partially visible or temporarily occluded objects than fully visible ones. Hence, the input to our visual system is mostly incomplete and segmented. Yet, we innately and effortlessly imagine the invisible parts of the object in our mind and perceive the object as complete [1]. For instance, if we only see a half of stripped legs in the zoo, we can tell that there is a zebra in that territory.  
As natural and seamless this task is for humans, for computers it is challenging yet essential. This is because the performance of most computer vision-related real-world applications drop when objects are occluded. For example, in autonomous driving, the vehicle must be able to recognize and identify the complete contour of the objects in the scene to avoid accidents and drive safely.  
Our environment is complex, cluttered, and dynamic. An object may be behind one or more other objects, or an object may hide one or more other objects. Thus, possible occlusion patterns between objects are endless. Therefore, the shape and appearance of occluded objects are unbounded.  
Whenever a visual system requires de-occlusion, there are three sub-tasks involved in the process (Figure 2). Firstly, inferring the complete segmentation mask of the partially visible objects, including the hidden region. Secondly, predicting and reconstructing the RGB content of the occluded area based on the visible parts of the object and/or the image. Often, these two sub-tasks require the result of the third sub-task, which determines the depth order of the objects and the relationship between them, i.e., which object isthe occluder and which one is the occludee. Several of the existing works address these sub-tasks simultaneously.  
Designing and training a model that could perform any/all of the above-mentioned sub-processes presents several challenges. In the following section, we explore the existing works in the literature wherein a GAN architecture is implemented to address those obstacles.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 4 Experimental Analysis
### Case Study 2: Noise Resistance Experiment under Different Levels of Noise  
The noise resistance performance of the proposed WMSCCN algorithm is tested in this section. In the choice of noise, many researchers choose Gaussian white noise to simulate noise pollution in industrial environments [16; 22; 35] and further test the anti-noise performance of the algorithm. Therefore, in this section Gaussian white noise is used for the noise immunity experiments. Specifically, different levels of Gaussian white noise are injected into the test set to simulate the noise pollution in industrial environments. It is worth noting that the training set in this experiment still uses the original signal.  
SNR is a standard to measure the noise level in a signal. Its formula is as follows. Larger SNR values indicate a better signal quality and lower noise levels in the signal; otherwise, it is the opposite. When the SNR value is 0, the signal and noise power are equal.  
noise, which significantly reduces the fault recognition accuracy of all the algorithms. Among them, the ANN + FFT algorithm has the worst recognition performance--only 33.56%. The SVM + FFT algorithm has a better recognition performance of 67.82%. WMSCCN-AdaBN uses domain adaptive technology, so it can better identify the fault characteristics in the strong noise environment; it finally reaches an accuracy of 92.30%, far exceeding other algorithms. When the SNR = \(-2\) dB, as the noise weakens, the accuracy of the algorithm improves significantly. The proposed WMSCCN algorithm has the biggest improvement; compared with the \(-4\) dB case, it improves the accuracy by 24.48%. When the SNR = 0 dB, the power of the original signal is the same as the noise. At this time, the algorithms with a diagnostic accuracy exceeding 90% include SVM + FFT, WMSCCN, and WMSCCN-AdaBN; the diagnostic accuracy of the WMSCCN-AdaBN algorithm is still the highest, at 98.39%. When the SNR = 10 dB, the power of the noise is much smaller than the signal power, and the diagnostic accuracy of all the algorithms exceeds 97%. As can be seen from the figure, WMSCCN-AdaBN can maintain a 92.3% diagnostic accuracy in a strong noise environment with a SNR = \(-4\) dB, showing a very strong anti-noise ability. When the SNR exceeds 4 dB, the accuracy reaches 100%, indicating that the proposed algorithm has a very good accuracy at low noise levels.
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
## 2 Related Work
### The Authentication Scheme for Mobile Money in Uganda  
The current authentication scheme for mobile money uses a 2 FA scheme where a subscriber must have a registered SIM card and mobile money PIN to perform a transaction. The SIM card has a unique phone number that is used as the account number for mobile money. The scheme has two main phases, namely, the enrolment phase and the authentication phase.  
1. The Enrolment Phase The enrolment phase involves obtaining subscriber's details, including biodata, fingerprint, passport photo, national identification number (NIN) of the national ID, or valid passport and provisionally saving the information in the MNO's mobile money database. The NIN or passport number is verified from the national identification and registration authority's (NIRA) database. If the NIN matches with the one in the NIRA database, the subscriber is allowed to set the mobile money PIN. The subscriber's information is updated and saved in the database, and a confirmation message is sent to the subscriber for successful mobile money registration. If the NIN or passport number provided by the subscriber does not exist in the NIRA database, they are allowed to retry three times. If all the three attempts fail, a confirmation message is sent to the subscriber to try again. Figure 2 shows the data flow diagram for the mobile money enrolment phase.  
2. The Authentication Phase During the authentication phase, to send money to an MTN Uganda mobile user, the sender sticks to the following steps: (1) dial \"165#, (2) select send money from the menu, (3) choose to send money to a mobile user, (4) enter recipient's phone number, and (5) enter amount. The amount entered is compared with the available electronic balance in the sender's account. If the amount is less than the amount entered, it terminates the transaction; otherwise, the sender continues. Then, (6) enter the reason for sending, and (7) confirm by entering the PIN Code. The application server then authenticates the PIN. If the PIN is correct, the money is sent, and (8) the sender receives a confirmation message; otherwise, they are allowed to retry for the maximum of three times. Note that the confirmation message contains details about the amount sent, the recipient's name and phone number, the fee charged for sending the money, the reason for sending the money, the available balance in the sender's account, and the transaction ID. Figure 3 shows the data flow diagram for a mobile money authentication phase.  
Figure 2: Mobile money enrolment phase.