esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Asymmetric Link Based Protocol  
Asymmetric links can drastically degrade the performance of low power WSNs. That is why some researchers have dealt with this asymmetric link in the network layer. [PERSON] et al. introduced Asymmetric Geographic Forwarding (A-GF) [37] where they discovered asymmetric links by broadcasting beacons in the network. They increased the stability of the asymmetry by maintaining neighbor tables. Finally, they proposed an efficient route for data delivery. AsymRp (Asymmetric convergecast Routing Protocol) was proposed by [PERSON] et al. [38], who used two-hop neighbor information to select the common and intermediate neighbors for data and explicit acknowledgement transfer. However, using two-hop information in routing table increases the complexity. Moreover, due to memory constraint, holding a large number of information in table in a dense network has been a challenging task.  
ASYM-MAC [30] works over RI-MAC considering duty cycling. ASYM-MAC combines sender and receiver initiated MAC protocol. By default, system operates on receiver initiated mode. When sender cannot get beacon from its receiver, it waits for 't' time and then switches to sender initiated mode where it sends its data followed by a long preamble. However, when sender gets beacon from receiver but sender cannot send data due to link asymmetry, this protocol works very poorly. Figure 2a,b shows the working mechanism of ASYM-MAC. ASYM-MAC has two modes of operation: R-mode and T-mode. R-mode is the default mode in which a receiver-initiated MAC is used. ASYM-MAC switches its mode to T-mode when it fails to receive a probing packet from the intended receiver. This switching is triggered by a timeout that occurs after a certain period of time elapses without receiving the probing packet. In T-mode, the transmitter sends a series of small preamble packets to notify the receiver of the pending data transmission. Once the receiver captures the signal of on-going transmission of preamble packets, it also receives data from the sender.  
In [39], [PERSON] et al. tried to solve the asymmetric link problem by using LQI and a dual tree topology. They used link quality estimators to construct the dual tree, where one is a collection tree and another is a dissemination tree. ERPPro [40] performs energy-efficient reverse path routing for an asymmetric link. This protocol uses residual energy and link quality information to select a legitimate reverse path. [PERSON] et al. proposed a Dynamic Hierarchical Agglomerative Clustering-based protocol by considering asymmetric links [41]. Besides WSNs, some other researchers [42; 43; 44] have dealt with  
Figure 2: Working mechanism of ASYM-MAC protocol: (**a**) operation of the ASYM-MAC protocol; and (**b**) a collision scenario.  
asymmetric links in underwater sensor networks and wireless mesh networks. [PERSON] et al. observed the link asymmetry problem in duty cycled Industrial Wireless Sensor Networks [45]. They showed that, when the link asymmetry increases, the nodes sleep less, hence, lifetime decreases.
# Address Space Layout Randomization Next Generation
## 5 ASLR Weaknesses
### Correlation Weakness  
Attacks launched to bypass ASLR are becoming more and more sophisticated; for instance, instead of attacking an object directly, the attacker can first de-randomize an object with low entropy, and then use it to de-randomize the target object (the object which contains the required gadgets or data). The idea that an object's memory address leak can be used to exploit another one was first demonstrated by [PERSON] et al. [27] through the offset2 lib weakness. In that case, the executable was de-randomized using a byte-for-byte attack [37] and then the libraries zone was calculated, resulting in a very fast bypass of ASLR. This weakness was analyzed by [PERSON], [PERSON] at al. [38], and referred as EffH (Effective Entropy).  
In Linux and PaX, the heap and the executable are separated from each other by a random value. A leak in the heap area does not only compromises the heap, but it also reveals information about the executable, because there is less entropy distance from the executable to the heap (correlation entropy) than the absolute entropy of the executable. Huge pages and the objects in the mmap_area are also correlated. Since huge pages have the largest alignment, they have the lowest entropy, and attackers can build correlation attacks by de-randomizing huge pages first and then later the libraries zones. For example, in PaX i386, instead of attacking the libraries directly (16 bits), attackers can de-randomize huge pages (6 bits) and later de-randomize the libraries zone from the huge page zones (10 bits). This two-step attack takes \(1088=2^{10}+2^{6}\approx 2^{10}\) attempts, instead of the 65,536 = \(2^{16}\) tries required to directly attack the library.  
All mmapped objects are located together in the mmap_area, which results in total correlation between them. From a security point of view, this is a weak design. MILS (Multiple Independent Levels of Security/Safety) [39] criteria state that objects of different criticality levels must be isolated. Google Chrome, for instance, is aware of this security issue and has addressed it by implementing some form of user-land ASLR to map JIT (just-in-time compiled code) objects in its own zone, which is separated from the default mmap_area. Note that JIT objects are an appealing target for attackers [40].  
Figure 14: Statistical distribution of mmapped (libraries, thread stacks, anonymous maps, etc.) objects using PaX security mode.
# Improving Human Ground Control Performance in Unmanned Aerial Systems
## 4 The Study
### Method  
#### 4.1.1 Participants  
A total of 40 students from the University of Salerno were recruited to participate in this experiment. There were 25 male and 15 female participants. Their average age was 23.7 years. They were all volunteers and were UAS drivers at amateur level.
## 1 Introduction
### Motivation  
Existing task-offloading schemes and frameworks in IoD fog computing rely on stationary fog nodes (base stations) or mobile fog nodes (drones). The first category allows for higher storage and computing capacity, which helps reduce processing delays, while the second category achieves reduced transmission delays since the fog UAVs are near the edge nodes. Nevertheless, fog BS-based platforms are not suitable for real-time applications due to higher transmission delays, and the UAV-Fog method is limited in applications that require higher processing capacity due to the limited resources of fog UAVs. Based on the aforementioned gaps in the literature, we take advantage of both fog BS and fog UAV elements by proposing a hybrid task-offloading system for IoD fog computing. Moreover, our platform employs a heuristic genetic algorithm to find the optimal offloading strategy that minimizes both transmission and computing delays.
## 3 Study Area and Data Used  
### Study Area  
Dak Lak Province (12\({}^{\circ}\)9'45\" to 13\({}^{\circ}\)25'06\" north latitude; 107\({}^{\circ}\)28'57\" to 108\({}^{\circ}\)59'37\" east longitude) is located in the central highlands of Vietnam (Figure 1). The province covers about 13,085 km\({}^{2}\), which accounts for 3.9% of the area of Vietnam. The topography of the study area is predominantly mountainous. The elevation in the area varies from 400 m to 2442 m (Chu Yang Sin Peak). A flat stretch of highland is in the middle part of the province, covering about 50% of the area. The climate of the province varies from hot under an elevation (El) of 400 m, humid at 400 m (El), and cold over 800 m (El). There are two main seasons: dry (November to April) and rainy (May to October) with 90% annual rainfall.  
One-third of Dak Lak Province is covered by basalt rock and associated soil, which are favorable for developing rubber, coffee, and pepper plantations. About 660 million m\({}^{3}\) of water is needed for 264,000 ha of coffee area, while availability of the surface water is only 250 million m\({}^{3}\). During the dry months, the estimated amount of groundwater exploited for irrigation is about 500,000 m\({}^{3}\)/day. Due to climate change and El Nino-effect changes of the rainfall pattern in the central highland region, more dependency on the groundwater resources for the socio-economic development of the province is required. Therefore, the balance requirement needs to be met by groundwater through proper groundwater management. Thus, groundwater potential mapping is required, using advanced technology for the groundwater assessment and identifying an area suitable for the recharge.  
Figure 1: Location map of the study area (Dak Lak Province).
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 1 Introduction
\(\bullet\) We also present a macro-scale MC experiment, using pH as the information carrier combined with Manchester pulses, that was implemented for this study. This experiment aimed to validate the reliability of the TCH codes in an MC environment. This paper is organized as follows. Section 2 describes the system model and the characteristics of the TCH transmitter and receiver. Section 3 presents the results of the simulated and experimental system. Finally, Section 4 summarizes the main conclusions of the paper.
# A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
## 2 Related Works
### Fuzzy Based Strategies for Network Slicing  
In recent times, decisions concerning matters pertaining to 5G mobile networks have been made using fuzzy logic and rule bases. In the paper [22], the authors developed an algorithm for an information security management system that was based on soft computing. They also implemented a prototype of an intrusion detection system (IDS) for a software-defined network. This IDS prototype consisted of a module for collecting and processing statistics as well as a fuzzy rules for making decisions. According to ref. [23], it is possible to manage user mobility using a dynamic fuzzy Q-Learning algorithm. System learning is used to generate fuzzy rules from the lack of existing fuzzy rules in order to achieve a balance between the signaling costs incurred by handover and the user experience affected by the call drop ratio. A study by ref. [24] looked at the implementation of fuzzy systems to address the problems of 5G mobile network resource optimization and the paradigm of SDN architecture development. The authors first discussed the various terms needed to understand 5G technologies, and then went on to analyze the feasibility of implementing fuzzy systems to telecommunications, particularly 5G technology and SDN architectures development. Resource allocation algorithm FUZZRA has been proposed in Ref. [25] by using fuzzy logic. According to FUZZRA, fuzzy logic rules are formulated based on multiple input variables and are used to allocate resources to various UEs. The parameters of the fuzzy system can be dynamically adjusted based on the network status to ensure optimal resource utilization and high QoS. The proposed strategy significantly increases resource utilization, improves information dissemination between various UEs, and increases network throughput. As a consequence of this, the service level agreement (SLA) parameter will be utilized as an input parameter for admission control in 5G wireless networks.  
As recently as ref. [26] implemented a Fuzzy-based scheme to deal with user SLA issues, a new user was attempted to be connected to an appropriate slice using a set of slices that matched the required SLA. Reliability (Re), Availability (Av), Latency (La), and Traffic load are all taken into consideration as input parameters by the proposed scheme, which included the introduction of two models for an admission control mechanism. Ref. [27] presented a fuzzy-based scheme for evaluating Slice Priority (SP), taking into consideration the three parameters: Slice Traffic Volume (STV), Slice Interference from Other Slices (SIOS), and Slice Connectivity (SC). The findings of the scheme concluded that each of the considered parameters has its own unique impact on the SP. The SP parameter is increased when both STV and SC are moving in the positive direction; however, when SIOS is moving in the positive direction, the SP parameter is decreased.
## 3 The Proposed Model
### Data Pre-Processing  
During this case, min-max normalized has been executed for scaling the data s to unit variance. It is commonly utilized to compute the similarity degree among points. Assume that \(A\) is data, which is mapped from the dataset range from \(A_{min}\) to \(A_{max}\), employing in Equation (1):  
\[A_{normalized}=\frac{A-A_{min}}{A_{max}\ -A_{min}} \tag{1}\]  
The employ of min-max normalization ensures that the feature was exacted as to similar scales.
# Wind Loads on a PV Array
## 3 Results and Discussion
### The Lift Coefficient  
\(C_{L}\) was calculated by integrating \(\Delta C_{p}\) (differential pressure between upper and lower surface). The variation in \(C_{L}\) with \(\alpha\) and \(\beta\) is shown in Figure 10. The value of \(C_{L}\) was negative for \(\beta\leq\) 75\({}^{\circ}\). The lowest value for \(C_{L}\) was observed for \(\alpha\) = 30\({}^{\circ}\) and \(\beta\) = 45\({}^{\circ}\). This is similar to the results of [PERSON] et al. [21] for an inclined panel, for which \(L/\mathcal{W}=2\). The value of \(C_{L}\) was relatively small for \(\beta\) = 90\({}^{\circ}\), and it was positive for \(\beta\geq\) 105\({}^{\circ}\), which represents a downward force. The critical wind loads on the inclined panels occurred for lower values of \(\beta\); hence, the effect of \(\alpha\) on \(C_{L}\) is only shown for \(\beta\) = 0\({}^{\circ}\)-60\({}^{\circ}\) in Figure 11. For an inclined panel, for which \(L/\mathcal{W}=2\), the value of \(C_{L}\) for \(\beta\leq\) 75\({}^{\circ}\) decreased linearly with \(\alpha\) (\(\leq\)30\({}^{\circ}\)), following an increase for \(\alpha\) = 50\({}^{\circ}\). At high values of \(\alpha\) (= 60\({}^{\circ}\)-80\({}^{\circ}\)), \(C_{L}\) remained approximately constant for \(\alpha\) = 30\({}^{\circ}\) and 40\({}^{\circ}\)[21]. For \(\beta\) of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\), for which \(L/\mathcal{W}=0.19\), the variation of \(C_{L}\) with \(\alpha\) was U-shaped. The lowest value of \(C_{L}\) for \(\beta\) = 0\({}^{\circ}\) and 30\({}^{\circ}\) was observed at \(\alpha\) = 20\({}^{\circ}\). For \(\beta\) = 45\({}^{\circ}\) and 60\({}^{\circ}\), it respectively corresponded to \(\alpha\) = 30\({}^{\circ}\) and 40\({}^{\circ}\). For high values of \(\alpha\) (= 60\({}^{\circ}\)-80\({}^{\circ}\)), \(\beta\) had a less significant effect on the amplitude of \(C_{L}\). Therefore, for an inclined array at lower values of \(\alpha\) (\(\leq\)30\({}^{\circ}\)) and \(\beta\) (\(\leq\)45\({}^{\circ}\)), there were lower values in \(C_{L}\), which was critical to the safe design of the system.  
Figure 12 shows the effect of \(\beta\) on \(C_{L}\) for \(\alpha\) of 0\({}^{\circ}\)-80\({}^{\circ}\). For \(\beta\)\(\leq\) 30\({}^{\circ}\), the value of \(C_{L}\) decreased as \(The wind load on each inclined panel was of interest. For \(\alpha=30^{\circ}\), the variation in \(C_{L}\) with \(\beta\) is shown in Figure 13. For P1 (the left-most panel), the value of \(C_{L}\) increased linearly as \(\beta\) increased. The variation in \(C_{L}\) with \(\beta\) for P2 and P3 was similar to that for P1. However, there was a sudden increase at \(\beta=60^{\circ}\)-\(75^{\circ}\). For P5-P9, the value of \(C_{L}\) decreased initially, following an increase. The lowest value for \(C_{L}\), which decreased from P5 to P9, was observed for \(\beta=30^{\circ}\)-\(45^{\circ}\). For \(\beta\geq 90^{\circ}\), the variation in \(C_{L}\) for P1-P9 was less significant. This demonstrates that the wind loads on the inclined array were unsymmetrical at lower \(\beta\) (\(\leq\)60\({}^{\circ}\)).
# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
## 3 Methods and Proposed Approach
### Deep-Learning Algorithms
#### 3.6.1 Long Short-Term Memory  
LSTM is a recurrent neural network used for the classification and prediction of time series data. It can process complete sequences of data instead of single data points. Typical recurrent neural networks (RNNs) are capable of processing current input data by using previous information. However, they have a lag in remembering long-term dependencies due to vanishing gradients, which is resolved by the introduction of LSTM [32]. This consists of three parts, i.e., the input gate, forget gate, and output gate.  
The first part of LSTM decides whether the information from the previous timestamp is useful or not. The second part attempts to learn new information from the input. Furthermore, the third part passes the information from the current to the next timestamp. Like typical RNN, LSTM also has a hidden state of timestamps in addition to a cell state. The hidden state is known as short-term memory, while the cell state is known as long-term memory [33].
# Hiding Stealth Optical CDMA Signals in Public BPSK Channels for Optical Wireless Communication
###### Abstract  
A new optical steganography scheme is proposed that transmits a stealth optical code-division multiple-access (OCDMA) signal through a public binary phase-shift keying (BPSK) channel. Polarization beam splitters and arrayed waveguide gratings are used to implement a spectral-polarization coding (SPC) system with an incoherent optical source. We employ a Walsh-Hadamard code as the signature code of the user who wants to transmit stealth information using the system. A free space optical link applied to this system maintains the polarization states of light during propagation. The secret data are extracted using correlation detection and balanced subtraction in the OCDMA decoder of the intended receiver, and the other signal from the public channel is reduced by the OCDMA decoder. At the demodulator of the public channel, BPSK demodulation eliminates the stealth signal so that the public channel is not affected by the stealth signal. The two signals cannot interfere with each other. The results of this study show that our proposed optical steganography system is highly secure. The stealth signal can be favorably hidden in the public channel when the average source power of the stealth signal, public noise, and public signal are \(-5\), \(-3\), and \(0\) dBm, respectively.  
optical steganography; optical code-division multiple-access (OCDMA); free space optics (FSO); chirped fiber Bragg grating (CFBG) Article
## 2 Problem Formulation and Preliminaries
### Dropout Compensate ILC Method  
To achieve the control objective (7), we propose the ILC law to compensate the loss of packets.  
\[u_{i,k+1}(t)=u_{i,k}(t)+\Gamma_{i}\!\left\{\sum_{j\in N_{i}}a_{i,j}[\delta_{i,k} (t+1)-\overline{\delta}_{j,k}^{i}(t+1)]+\varphi_{i}\varepsilon_{i,k}(t+1)\right\} \tag{10}\]  
When there are packet losses during multiple agents' communication, we utilize the previous iterative data to compensate this iterative data. In (10), \(\overline{\delta}_{j,k}(t+1)\) is data which the \(i\)th agent received from the \(j\)th agent  
\[\overline{\delta}_{j,k}^{i}(t+1)=\xi_{k,ij}\delta_{j,k}(t+1)+[1-\xi_{k,ij}] \delta_{j,k-1}(t+1) \tag{11}\]  
where \(\xi_{k,ij}\) is mentioned above. In practice, we can utilize a register to store each iterative data. If there is no packet loss, the next iterative data replace the old data. Otherwise, the old data are retained.  
**Remark 4**.: _In order to make the multi-agent system closer to the practical engineering applications, \(\xi_{k,ij}\) could be different for different agents. That means the multi-agent system is heterogeneous in this paper._  
Since \(\Delta_{i,k}(z)=D_{i}(z)-Y_{i,k}(z)\), (6) can be rewritten as  
\[E_{i,k}(z)=R(z)+\Delta_{i,k}(z) \tag{12}\]  
By combining the Z-transform of (10) with (12), we get  
\[U_{i,k+1}(z)=U_{i,k}(z)+z\Gamma_{i}\!\left\{\sum_{j\in N_{i}}a_{i,j}[E_{i,k}(z )-\overline{E}_{j,k}(z)]+\varphi_{i}E_{i,k}(z)\right\} \tag{13}\]  
where \(\overline{E}_{j,k}(z)=\xi_{k,ij}E_{j,k}(z)-(1-\xi_{k,ij})E_{j,k-1}(z)\). According to Equations (3), (11)-(13) and Assumption 2, we have  
\[\begin{array}{c}E_{i,k+1}(z)-E_{i,k}(z)\\ =-Y_{i,k+1}(z)+Y_{i,k}(z)\\ =G_{i}(z)[U_{i,k}(z)-U_{i,k+1}(z)]\\ =-zG_{i}(z)\Gamma_{i}\!\left\{\sum\limits_{j\in N_{i}}a_{i,j}[E_{i,k}(z)- \overline{E}_{j,k}(z)]+\varphi_{i}E_{i,k}(z)\right\}\\ =-zG_{i}(z)\Gamma_{i}\!\left\{\sum\limits_{j\in N_{i}}a_{i,j}[E_{i,k}(z)- \xi_{k,ij}E_{j,k}(z)-(1-\xi_{k,ij})E_{j,k-1}(z)]+\varphi_{i}E_{i,k}(z)\right\} \end{array} \tag{14}\]  
Let \(H_{i}(z)=G_{i}(z)\Gamma_{i}\), \(H(z)=diag[H_{1}(z),H_{2}(z)\cdots H_{n}(z)]\), \(H_{i}(z)\in\mathbb{C}\), \(H(z)\in\mathbb{C}^{n\times n}\). Let \(E_{k}(z)=\left[E_{1,k}(z),E_{2,k}(z),\cdots,E_{n,k}(z)\right]^{T}\), \(E_{k}(z)\in\mathbb{C}^{n\times 1}\), then (14) can be rewritten as  
\[E_{k+1}(z)-E_{k}(z)=-zH(z)[(\mathcal{L}_{\xi}+\Phi)E_{k}(z)-\mathcal{A}_{\xi} E_{k-1}(z)] \tag{15}\]  
where \(\mathcal{L}_{\xi}=[l_{ij,\xi}]\in\mathbb{R}^{n\times n}\) and \(\mathcal{A}_{\xi}=[a_{ij,\xi}]\in\mathbb{R}^{n\times n}\),  
\[\begin{array}{c}l_{ij,\xi}=\left\{\begin{array}{c}-a_{i,j}\xi_{k,ij},\ j \text{ }\text{The relationship among the \((k-1)\)th, the \(k\)th, and the \((k+1)\)th system error is shown in (15). Our goal is to converge the system error when the number of iterations tends to infinity. Thus, (15) will be further analyzed in the iterative domain.
## 2 Materials and Methods
### Validation and Performance Evaluation
#### 2.5.3 Image Quality  
Image quality was evaluated through simulations of two phantoms. First a planar grid of sources containing three \(4\times 4\) arrays of spheres filled with \({}^{22}\)Na embedded within a tank of water. The diameters of the spheres were set to 0.75, 1.0, and 1.25 mm (corresponding center-to-center distances  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of block rings & 1 \\ Detector blocks per ring & 10 \\ Scintillator material & LYSO \\ Crystals per block & \(24\times 24=576\) \\ Axial FOV & 50 mm \\ Transaxial FOV & 100 mm \\ Number of image planes & 109 \\ Coincidence time window & 4.0 ns \\ Energy window & 150–650 keV \\ Energy resolution & 11.7\% \\ Detector block entrance area & \(50\times 50\) mm\({}^{2}\) \\ Crystal size (thickness) & \(2\times 2\times 10\) mm\({}^{3}\) \\ Detector ring diameter & 168 mm \\ Photodetector & SiPM \\ Array size & \(12\times 12\) \\ Pixel pitch & 4.2 mm \\ Light guide size & \(50\times 50\times 3\) mm\({}^{3}\) \\ Reflector material & BaSO4 \\ Thickness & 0.1 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical characteristics and dimensions of the preclinical positron emission tomography (PET) scanner used for the validation of GEANT4 Monte Carlo simulations.  
were 1.5, 2.0, and 2.5 mm). Second, a cylindrical phantom (50 mm length and 30 mm diameter) was simulated to evaluate Anger Logic and MLP positioning algorithms.