esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Management for a Power-Split Plug-In Hybrid Electric Vehicle Based on Reinforcement Learning
## 2 PHEV Powertrain Model
### Battery Model  
To analyze the power relationship of the battery, a simplified battery model is presented here, which consists of an internal resistor and an open circuit voltage source, and the corresponding calculation equations of the battery model can be described as:\[\left\{\begin{array}{l}P_{bat}=OCV\cdot i_{bat}-i_{bat}{}^{2}R_{int}\\ i_{bat}=\frac{OCV-\sqrt{OCV^{2}-4R_{int}P_{bat}}}{2R_{\rm int}}\\ SOC=SOC_{init}-\frac{1}{C_{bat}}\int_{0}^{t}i_{bat}dt\end{array}\right. \tag{8}\]  
where \(OCV\) denotes the battery open circuit voltage, \(i_{bat}\) is the battery current, \(R_{int}\) is the battery internal resistance, \(C_{bat}\) is the battery capacity, \(SOC\) is the battery SOC and \(SOC_{init}\) is its initial value. Detailed battery parameters varying with SOC are shown in Figure 2. It can be found that \(R_{int}\) decreases from 0.1403 ohm to 0.09 ohm and \(OCV\) ranges from 165 V to 219.7 V.  
From the above analysis, we can find that if the battery power is predetermined, the energy distribution strategy inside the vehicle can be achieved. By this manner, the control strategy distributions can be ascertained by the battery power. In order to ensure safety of all components and consider their power limitations and performance extension, some constraint conditions are imposed:  
\[\left\{\begin{array}{l}P_{bat\_{\min}}\leq P_{bat}\leq P_{bat\_{\max}}\\ P_{mot1\_{\min}}\leq P_{mot1}\leq P_{mot1\_{\max}}\\ P_{mot2\_{\min}}\leq P_{mot2}\leq P_{mot2\_{\max}}\\ P_{eng\_{\min}}\leq P_{eng}\leq P_{eng\_{\max}}\\ P_{req\_{\min}}\leq P_{req}\leq P_{req\_{\max}}\\ SOC_{\min}\leq SOC\leq SOC_{\max}\end{array}\right. \tag{9}\]  
where parameters with subscripts min and max mean their corresponding minimum and maximum values, respectively. In the next step, the RL based strategy is introduced to achieve the energy management of the PHEV.
## 3 The Analytic Technique of Carleman Regularization
### The Simplest Transformations of the First-Kind Singular Integral Equations  
In any correct mathematical model, input and output variables have to be dimensionless. Firstly we normalize the input parameters of coplanar strips system \(x_{n}=x_{n0}+a_{n}t,\quad z_{n0}\), (\(n=1,\ldots,N\)) multiplying them by the wave number \(k\) (see Figure 1). So, starting from the intermediate one-dimensional mathematical model, presented by Formulas (10) and (11), we obtain new mathematical model in the form of the first-kind singular integral equations' system:  
\[\begin{array}{c}\sum_{n=1}^{N}\int_{-1}^{1}j_{n}(t)\cdot H_{0}^{(1)}(sq_{mn} (\tau,t))\cdot dt=M_{m}(\mu,\stackrel{{\rightarrow}}{{q}})\cdot \exp(iq_{1}a_{m}\tau),\\ |\tau|<1,m=1,\ldots,N.\end{array} \tag{12}\]  
Here, new unknown functions are defined by the formula: \(j_{n}(t)=a_{n}\psi_{n}(x_{n0}-a_{n}t)\), new arguments of kernel functions \(sq_{mn}(\tau,t)=\sqrt{\left(k(x_{n0}-x_{m0})+\alpha_{m}\tau-a_{n}t\right)^{2}+ k^{2}(z_{n0}-z_{m0})^{2}}\) and known constants of the right-hand side functions \(M_{m}(\mu,\stackrel{{\rightarrow}}{{q}})=-4i\cdot\exp(ik(q_{1}x _{m0}+q_{3}z_{m0}))\cdot\left(1-q_{2}\mu\right)^{-1}\). In the case of diagonal kernel functions (\(n=m\)) the root \(sq_{mn}(\tau,t)=\alpha_{n}|\tau-t|\) and we can use the following series representation:  
\[H_{0}^{(1)}(sq_{mn}(\tau,t))=\frac{2i}{\pi}\Biggl{\{}\ln|\tau-t|+\ln\frac{ \gamma\alpha_{n}}{2i}+\sum_{m=1}^{\infty}\frac{(-1)^{m}}{\left(m!\right)^{2}} \biggl{(}\frac{\alpha_{n}(\tau-t)}{2}\biggr{)}^{2m}\Biggl{[}\ln\frac{\gamma \alpha_{n}|\tau-t|}{2i}-\sum_{\kappa=1}^{m}\frac{1}{\kappa}\Biggr{]}\Biggr{\}} \tag{13}\]  
Here, the value of \(ln\gamma\) means the Euler constant, which is irrational number: \(ln\gamma=0.557215664\ldots\). That series representation of diagonal kernel functions gives us possibility to separate logarithmic singularity in the simplest form. So, multiplying by a corresponding constant, we obtain the first-kind system of log-singular integral equations:  
\[\begin{array}{c}\int_{-1}^{1}j_{n}(t)\ln|\tau-t|dt+\sum_{m=1}^{N}\int_{-1}^ {1}j_{m}(t)R_{nm}(\tau,t)dt=\frac{\pi}{2i}M_{n}(\mu,\stackrel{{ \rightarrow}}{{q}})\cdot\exp(iq_{1}a_{n}\tau),\\ |\tau|<1,\,n=1,\ldots,N.\end{array} \tag{14}\]  
In that first-kind system new regular kernel's functions are presented by the following expressions:  
\[R_{nm}(\tau,t)=\pi\cdot H_{0}^{(1)}(sq_{mn}(\tau,t))/2i-\ln|\tau-t|,R_{nm}( \tau,t)=\pi\cdot H_{0}^{(1)}(sq_{mn}(\tau,t))/2i. \tag{15}\]  
The new mathematical model is more correct, but has some deficiencies and needs to be improved further. To do this, we can transform it to the second-kind integral equations set by the Carleman regularization technique. That regularization technique is similar to the case of flat gratings with finite number of impedance zero-thickness strips, presented in the article [6].
## 2 Background
### Cyber Security Framework in Organizations
#### 2.4.4 The Framework for Attacks (MITRE)  
The acronym MITRE denotes the Massachusetts Institute of Technology's Center for Research and Engineering, Adversarial Tactics Techniques, and Common Knowledge [36]. The MITRE attack methodology provides a comprehensive overview of how cyberattackers gain entry into networks, navigate laterally, elevate access privileges, and circumvent secu  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Sensors** & **Vulnerability** & **Security Threats/Attacks** \\ \hline GPS [58] & Unsecure channel & MIMT/Replay attack \\  & Lack of permission & Privacy attack \\ Bluetooth [59] & Vulnerability in LMP/LLP & Hijacking attacks/blue \\  & A cheap antenna can increase & sniffing or snigging attacks \\  & the attack distance & Range extension attack \\ Gyroscope and & Have no access control & Side-channel attacks, tap \\ accelerometer [60] & mechanism/Permissions & logger attacks, tap prints \\ Gyroscope, accelerometer, & & attacks, eavesdropping attacks \\ and magnetometer [61] & Lack of fingerprinting technique & Sensor ID attacks \\ NFC [62] & Unsecure communication & Eavesdropping attacks \\  & URL/URI deceiving & Spoofing attack \\  & Unauthenticated NFC device & Tag replacement and tag \\ Microphone [61; 63; 64] & Unsecure environment & Eavesdropping attacks \\  & Unsecure smartphone password & Acoustic side-channel attack \\ Camera and microphone & The preinstalled camera app & Eavesdropping attacks \\ [61; 63; 64] & might grant other apps, camera, and microphone access & and spyware \\  & Without users’ consent, photos & Eavesdropping attacks \\  & and videos can be taken & and spyware \\  & Lack of privacy, location can & Privacy attack \\  & be tracked & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loopholes of Mobile Sensors.  
rity measures, all within a single resource [69]. This approach is employed by cyber security researchers to identify and classify cyber attacks and assess the risk to the organization. The assumptions of MITRE ATT&CK are analogous to those of other cyber security frameworks.  
In contrast to other cyber security solutions, MITRE ATT&CK operates under the assumption that a breach will inevitably occur and adopts an attacker-centric approach to address this issue [70]. Twelve potential tactics or objectives of an attacker are outlined, which include initial entry, execution, persistence, privilege escalation, defense evasion, credential access, discovery, lateral movement, collection, command and control, exfiltration, and impact [71]. Additionally, numerous attacking techniques, such as drive-by compromise, spear phishing, and rootkit attacks are mentioned [36; 72].
# Method of Multi-Angle Transmission Radiowave Tomography of Dielectric Objects
###### Abstract  
A method for solving the inverse problem for reconstructing the spatial distribution of dielectric permittivity from the results of multi-angle transmission broadband radiosounding is proposed. The method is based on inverse wave propagation. The average refractive index of the medium along the wave trajectory is calculated by comparing the results of the calculation of the time delay of the inverse signal in the entire sounding region and the forward propagation time in a homogeneous medium. This method takes into account diffraction effects in solving a direct problem, which allows one to obtain a resolution in the order of a wavelength. The combination of time delays obtained at different probing angles allows the restoration of the distribution of the refractive index in the medium. The paper presents the results of the numerical simulation of this method. The novelty of the proposed approach compared to the conventional back-projection algorithm is that ray approximation is not applied. Instead of the absorption coefficient (used in X-ray tomography), a time delay is considered, which is restored in the entire probed region. The developed method can be widely used in radiowave tomography or microwave tomography for remote non-destructive testing, diagnostics for the internal structures of inhomogeneous media and the restoration of the shapes of opaque objects based on multisensor sensing.  
transmission tomography; radiowave tomography; multi-angle sounding; refraction index; backward wave propagation; cross borehole radar +
Footnote †: journal: Applied Physics Letters  
_Article_  
Method of Multi-Angle Transmission Radiowave Tomography of Dielectric Objects  
[PERSON] and [PERSON]
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
###### Abstract  
The paper discusses the operation of wireless sensor networks consisting of nodes based on the IEEE 802.15.4 ZigBee transceiver operating in the presence of Rayleigh or Nakagami-\(m\) fading, additive white Gaussian noise (AWGN) and interference. Analytical expressions for bit error rate (BER) for the IEEE 802.15.4 network were obtained when Rayleigh and Nakagami-\(m\) fading were processed with a selection-combining (SC) and maximum-ratio-combining (MRC) combiner and when AWGN and interference were present. Analytical results are presented graphically in the form of BER change, depending on the signal-to-noise ratio (SNR) for different values of network parameters: fading parameter (\(m\)), number of branches of MRC combiner (\(L\)), modulation parameter (\(M\)), and spreading sequence lengths (\(L_{c}\)). Graphical results are presented for the system when there are no combiners and when the SC and MRC combiners are included. Numerical results were confirmed by Monte Carlo simulation. Analysis of the SC and MRC combiner application impact on the performance improvement of the IEEE 802.15.4 network and the received signal was performed.  
IEEE 802.15.4; Rayleigh fading channel; Nakagami-\(m\) fading channel; bit error rate (BER); selection combining (SC); maximum-ratio combining (MRC) Article  
Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
## 3 Materials and Methods
### Fault Model  
Faults have become common in the context of the IoT-FoG networks. As a result, some Fog access nodes may experience partial or whole faults during task-offloading activities, based on their minimum energy levels after the execution of multiple tasks within a particular period. In this proposed method, fault identification depends on the residual energy that remains in the Fog access nodes during task-processing. The number of faulty nodes within a specified period and the fault rate are identified based on the residual energy in the fog access nodes, which can be determined by continuously monitoring energy levels via fog control nodes. As a result, the expected number of faults during a specific time interval, from (0 to t), can be obtained using the following relation:\[\mathrm{N}(\mathrm{F})=\int_{0}^{t}\mu_{f}(t)dt \tag{13}\]  
where \(\mu_{f}(t)\) is the mean fault rate of the Fog nodes, and the probability of fault occurrence (\(\beta\)) in the Fog nodes during the period of task-offloading, obtained using the total number of faults that occurred \(\mathrm{T}(\beta)\) within a specified time, with a range of \(0\leq\mathrm{P}(\beta)\leq 1\), is given as follows:  
\[\mathrm{P}(\beta|\mu_{f})=\frac{\mu_{f}^{\beta}}{\mathrm{T}(\beta)}\mathrm{e} ^{-\mu_{f}} \tag{14}\]
## 3 Improved Q-Learning Algorithm for Better Management of Channel Utilization in UWCNs
### Description of the Q-Learning Formula  
* Present Q-value \(Q(s,a)\): This represents an approximation of the projected benefits of action in state s.
* Learning rate \(\alpha\): This indicates the degree to which recently learned knowledge supersedes previously learned information. When the value is 1, the agent only considers the most recent information; when the value is 0, the agent learns nothing at all.  
* Discount factor (\(\gamma\)): This establishes how important rewards in the future are. When the value is near 1, the agent will aim for long-term high rewards, but when the value is close to 0, the agent becomes short-sighted and only considers immediate benefits.
* Reward \(R(s,a)\): Instant reward obtained after the change from state s to \(s^{\prime}\) as a result of action \(a\).
* Maximum reward in the future \(\max a^{\prime}Q(s^{\prime},a^{\prime})\): The highest reward attainable from the newly created state \(s^{\prime}\), considering every action \(a^{\prime}\) that might be taken.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 2 Background and Related Works
### Trust Management for FANET  
As mentioned in the previous section, the concepts of trust management, interaction-based trust, and recommendation-based trust have been largely studied in the literature for different mobile environments. However, only few solution have been proposed for FANET environment. Furthermore, to the best of our knowledge, none of the previous work have studied the trust establishment for FANET over Named Data Networking.  
[PERSON] et al. [22] analyzed the requirements for efficient UAV communication. They identified the similarities and differences between MANETs and FANETs, and discussed various trust-based protocols and management schemes that can be used in FANET. However, this work should be improved to consider different mobility patterns, propagation models, and energy restrictions before its implementation. Furthermore, [PERSON] et al. [23] presented a trust-based connectivity analysis between to FANETs' nodes. Although, these two networks nodes are within the range of each other, a link is considered existent only if the estimated trust value is higher than a predefined threshold. Numerical results show that the proposed scheme is effective in ensuring a reliable data delivery path. The main limitation of this scheme is that it works only in high UAVs density cases and requires an important trust establishment delay due to the required learning phase.  
In our previous work [24], we proposed a trust management architecture distinguishing the intentional and unintentional misbehavior in FANETs. The main idea is to evaluate for all neighboring UAVs: the buffer occupancy, remaining energy, and mobility patterns which are the main reasons of the unintentional misbehavior. Thus, it enables to make a more accurate decision regarding the UAVs behavior. However, this proposal works only for the IP-based classical networks and not for Information Centric Networks (ICNs).  
In [25], the authors proposed a fuzzy classification trust model (FCTM) for FANETs. The proposed scheme is based on node's behavior and collaboration in the network operations. They also used Quality of Service (QoS) and social parameters to enhance the trust evaluation of each UAV. However, same as the previous work, this work is an entity-centric approach and do not work for ICNs.
# Incorporating Word Significance into Aspect-Level Sentiment Analysis
## 2 Related Works
### Attention Mechanism in Aspect-Level Sentiment Analysis  
Inspired by visual attention, researchers in various domains such as machine translation [34], question answering [35; 36] and machine comprehension [37] have successfully used attention to achieve performance gains. When used in conjunction with neural networks, the attention mechanism is used to do a soft selection over hidden representations and automatically learn the most important context works for a specific target word in a sentence [8; 9]. Due to numerous influential papers in attention research [34; 36; 38], there is a wide array of Long Short Term Memory(LSTM) attention based architectures which use aggregated attention scores to aggregate contextual features for prediction. [PERSON] et al. [10] model the aspect word dependent left and right context and concatenate both as the final representation before prediction. [PERSON] et al. [7] then introduce the concept of modeling aspect words with context words by concatenating aspect word embeddings to each context word, before using an LSTM and attention to generate the final representation. However these models only consider word level attention without taking the overall sentence meaning into consideration, making it difficult to make correct predictions when related words are far apart in long sentences.  
[PERSON] et al. [5] bridge this gap by separately modeling aspect word attention to extract mutual features of aspect and context words at sentence level though the IAN model. They use two attention based LSTMs to represent aspect and context words, then use the hidden context states to calculate attentions towards aspect words using a pooling operation and vice versa, thus capturing important parts in both the context and aspect words interaction. [PERSON] et al. [6] make modifications by forgoing the pooling operation as it ignores interactions between word-pairs through the AOA-LSTM model. These models generate mutual attentions to concentrate on important aspect and context words at sentence level, however they do not explicitly take the distance between aspect and context words into consideration. [PERSON] et al. [25] therefore enhance LSTM hidden states with position encodings before applying interactive attention, while [PERSON] et al. [11] introduce position weightings to transformation LSTM outputs before applying a single convolution to extract important features. [PERSON] et al. [24] also use a position weighted memory module in conjunction with sentence level attention to capture aspect global attention and context attention to simultaneously capture the order and correlations of words. However, these methods assume that a context word closer to an aspect word has higher importance, which is not always the case. Our model presents novelty decay and incremental interpretation as an alternatives to position weighted models, with attention being guided by word significance.
# Damping of Liquid Sloshing Using Hydrophobic Walls in the Off-Impulse Regime
## 1 Introduction
Through a series of experiments, they demonstrated that the effect of one to four layers of solid foam proportionally reduced the amplitude of the natural frequency at the free surface of a rectangular tank. They concluded that the resulting energy dissipation of sloshing is due to friction between neighboring spheres. The works mentioned above are only a small part of the vast literature available on techniques to minimize sloshing-induced loads; however, most existing research involves specialized engineering work that can be very costly, especially for large containers. Recently, Ref. [13] reported experiments performed in two identical containers under different conditions; in the first configuration the container was coated with a hydrophobic solution, while in the other it was left uncoated. They performed measurements using different modes of excitation related to the natural frequency of the tank and three different water levels (low, medium, and high). Their analysis was conducted for long excitation times. In the case of the intermediate level, the wall sloshing pressure load was reduced by up to 20%, while no significant reduction was observed for the low and high levels. They concluded that hydrophobic surfaces lead to a reduction in load sloshing on the container walls along with reduced wave breaking. Based on the results of [13], we present here an experimental study of sloshing in two configurations: with uncoated containers and with containers in which the walls are coated with hydrophobic oil. As described in Section 2, all containers were filled with water to the 50% level, and eight different values for the aspect ratio of the containers were considered. We describe how video processing allows extraction of the water-free surface as a function of time. Moreover, the containers were subject to different two regimes: a periodically forced regime, leading to results reported in Section 3, and a damping regime after external forcing was suddenly stopped, leading to the results reported in Section 4. The main objective of the study was to compare the sloshing damping time of water in containers with hydrophobic and non-hydrophobic walls. Our results were compared with those from the work of [13]. Our conclusions and a corresponding discussion are provided in Section 6.
# A Cooperative Communication Protocol for QoS Provisioning in IEEE 802.11p/Wave Vehicular Networks
## 6 Conclusions  
In this paper, we have proposed a cooperative communication protocol to improve the link connectivity in the WAVE standard. In particular, we have studied the limitations in the WAVE standard for the congestion and the shadow zone between neighboring RSUs and have proposed the cooperative communication scheme to address those limitations. The proposed scheme has been demonstrated to notably improve the network performance even when a low presence of infrastructure results in shadow zones between areas of coverage. The proposed scheme also has been designed according to the intricacies and special characteristics of the WAVE standard.  
The proposed scheme provides better performance than the existing algorithm. However, when service applications provided by the RSU increase, the number of SCHs for service applications increases, and the available resources for the relay communication are also reduced. As service applications provided to automobiles increase, the use of the proposed algorithm becomes more difficult. However, since the proposed algorithm can transfer data without the contention in reserved resources, collisions by access of multiple OBUs do not occur. Therefore, the proposed algorithm can provide a high throughput, a high delivery success ratio, and a low delay regardless of the number or speed of the vehicle. In the future, we will introduce cognitive radio algorithms and develop a relay communication protocol that operates in an environment where various service applications are provided. We will also develop OBU and RSU to perform field tests for the proposed algorithm.  
Figure 18: Delay as a function of the number of vehicles.  
Our simulation results show that the WAVE standard may extend the area of coverage using multi-hop communications, and the proposed scheme can reduce performance degradation due to frequent link disconnection caused by frequent vehicle speed changes. The proposed scheme can also provide the QoS since OBUs can transmit data frames without the contention. In the proposed scheme, the RSU can choose the best relay based on different parameters, such as relay reliability and link duration.  
The main contributions of [PERSON]. ([PERSON]) were to create the main ideas and execute performance evaluations by simulation. [PERSON]. ([PERSON]) and [PERSON]. ([PERSON]) contributed to the simulation. And [PERSON] works as the advisor of [PERSON]. ([PERSON]) to discuss, create, and advise the main ideas and performance evaluations together.  
This work was supported by the Incheon National University International Research Grant in 2014.  
The authors declare no conflict of interest.