esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
The following abbreviations are used in this manuscript:  
SAS \[\text{smart antenna systems}\] MAC \[\text{medium access control}\] OMNI \[\text{ommid directional}\] DMAC \[\text{directional medium access control}\] RR-MAC \[\text{round-robin MAC}\] ABT-RRMAC \[\text{adaptive beamforming round-robin medium access control}\] DA-MAC \[\text{deafness-aware medium access control}\] MANET \[\text{mobile ad hoc network}\] VANET \[\text{vehicular ad hoc network}\] SDMA \[\text{spatial division multiple access}\] DRTS \[\text{directional request to send}\] DCTS \[\text{directional clear to send}\] DNAV \[\text{directional network allocator vector}\] CPU \[\text{central Processing Unit}\] SRL \[\text{short Retry Limit}\] PDR \[\text{packet delivery ratio}\] AODV \[\text{ad hoc on demand distance vector}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Explanation** \\ \hline \(Q\) & term referred to the queue size \\ \(N\) & number of nodes \\ \(N_{s}\) & number of sectors \\ \(w_{Q}\) & queue weight \\ \(\overline{W_{Q}}\) & queue weight vector \\ \(S_{i}\) & i-th sector \\ \(T_{i}\) & i-th sector time \\ \(T_{s}\) & sector time \\ \(\alpha_{i}\) & i-th sector angle \\ \(\underline{size_{q_{i}}}\) & i-th sector queue size \\ \(\overline{T}\) & mean beamforming time \\ \(\lambda\) & wavelength \\ \(s\) & seconds \\ \(m\) & meters \\ \(mps\) & meters per seconds \\ \(J\) & joules \\ \(dBm\) & decibel-milliwatts \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of symbols used in this manuscript.
# Particle Formation Mechanism of TiCl\({}_{4}\) Hydrolysis to Prepare Nano TiO\({}_{2}\)
## 2 TiCl\({}_{4}\) Hydrolysis Process and Mathematical Model  
### Process Flow of TiCl\({}_{4}\) Hydrolysis to Prepare TiO\({}_{2}\)  
In the user interface of the software, an appropriate engineering environment such as solid processing, electrolyte, or gasoline splitting can be selected, and a file with a.bkp suffix is created. Subsequently, it is necessary to identify whether the reactants and products are conventional substances. If so, they can be selected directly from the material library; otherwise, the product needs to be customized. Following this, the SOLIDS physical property method is employed to iteratively estimate and correct the physical parameters of the substance, such as enthalpy and entropy, until the initial operation is performed correctly [17]. Next, the preparation process is established along with the creation of the reaction equation. During this stage, the required parameters for each module are determined and obtained through experimentation, including reaction order, activation energy, nucleation rate constant, growth rate constant, and fitting coefficient,among others. A computational model that aligns with particle nucleation and growth behavior is determined through screening, comparison, and customization [18]. Finally, the particle size distribution (PSD) data are exported and compared with experimental results for verification. If the validation results are satisfactory, reaction parameters can be adjusted for the sensitivity analysis to predict the particle size distribution and mass fraction under varying operational conditions, with the aim of obtaining smaller and more concentrated nanoparticles. Concurrently, the nucleation and growth behavior of particles are recorded for future reference and analysis.  
The TiCl\({}_{4}\) hydrolysis process in Aspen Plus is composed of three main unit operations: hydrolysis, solid-liquid separation, and powder drying, as depicted in Figure 1. Initially, a specific volume ratio of V\({}_{\text{TiCl}}\), V\({}_{\text{H2O}}\), and V\({}_{\text{C2H5 OH}}\) is added to the reactor R01 and stirred at room temperature for 30 min. Subsequently, the mixed solution is introduced into the hydrolysis reactor R02, where the hydrolysis temperature is adjusted as per the requirement. In different constant temperature reactors R02, C\({}_{6}\)H\({}_{15}\)NO\({}_{3}\) is added dropwise at a rate of 0.2 mL/min under continuous stirring. Following a designated reaction period, the suspension is left to settle for 12 h. It is then passed through L02 into the multi-stage washing device CCD01 and the centrifugal filter CF01 for centrifugal separation. Post-separation, the filter residue S02 is transferred to a continuous simple dryer DRYER01 for drying, resulting in the final product, TiO\({}_{2}\).
## 5 Discussion
### Alternative Approach to Define Development Working Time Coefficient  
In the current section, an alternative way to simplify the system of working-time balance equations is presented. Let us keep Assumptions 1-3 from Section 4.5, replacing the fourth assumption with a linear relationship between the PWT of the development team, \(W_{D}=\sum\limits_{m\in T_{D}}W^{m}\), and the NDE, \(E\):  
\[W_{D}=\kappa\,E, \tag{28}\]  
where \(\kappa>1\) is an alternative way to define the DWTC (which is originally introduced in (8)). This implies the following system of working-time balance equations:  
\[\left\{\begin{array}{lcl}\kappa E&=&\sum\limits_{m\in T_{D}}W^{m},\\ W^{m}&=&\phi^{m}L,\,m\,\in T,\\ E&=&\sum\limits_{m\in T_{D}}\rho^{m}D^{m},\end{array}\right. \tag{29}\]  
an expression for NDC:  
\[C=\frac{L}{\kappa}\sum\limits_{m\in T_{D}}\phi^{m}, \tag{30}\]  
and the estimated project duration:  
\[L\geq\frac{\kappa E}{\sum\limits_{m\in T_{D}}\phi^{m}}. \tag{31}\]  
As can be noticed, (30) and (31) are quite similar to (13) and (15), respectively. The only difference is that (30) and (31) rely on the development FTE, \(\sum\limits_{m\in T_{D}}\phi^{m}\), while (13) and (15) are based on the ND-FTE (which, in turn, takes into account productivity coefficients \(\rho^{m}\) of each development role \(m\in T_{D}\)). From this standpoint, (28) results in even simpler estimation formulas in comparison to the ones following from (8).  
To choose between the two approaches for a particular case, it is recommended that one take into account such factors as the necessity of considering the PCs and the availability of historical data to define the DWTC (in particular, (8) requires more granular historical project data than (28)).
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
## 3 Results
### Simulation of Sensor Asynchrony and Delay  
Under simulation scenario 1, assuming stable and effective measurement information from all sensors within the INS/GPS integrated navigation system, we established various asynchronous and delay conditions between the GPS measurement frequency and the INS output frequency. This aimed to assess the factor graph algorithm's capability in managing sensor asynchrony and delay. The term \"asynchronous\" denotes the discrepancy in output  
Figure 9: Position error of KF and FG with dynamic changes in GPS effectiveness.  
frequencies between INS and GPS. We assumed a time delay in GPS measurements and configured delay times as dt = 0.1 s, dt = 0.3 s, and dt = 0.5 s, respectively. We subsequently compared these results with the integrated navigation of FG and KF without delay (dt = 0 s). The simulation results are shown in Figure 10 and Table 1.  
From Figure 10 and Table 1, it is evident that the positional error increases to a certain extent in the presence of asynchrony and delay. This occurs primarily because the system output frequency surpasses the GPS measurement frequency, compelling reliance solely on INS without GPS measurements. Notably, the FG algorithm exhibits a lesser sensitivity to delay times, sustaining comparable accuracy to the KF algorithm even in the absence of delay measurements. The FG algorithm requires no adjustment for sensor asynchrony and delay. Varied delay measurements merely alter the sequencing of corresponding cost functions in deriving global cost functions, without affecting the optimization results. Instead, sensor measurement values received must be systematically denoted as factor nodes, progressively connected to corresponding variable nodes in the factor graph. Subsequently, the incremental inference algorithm is employed to deduce the factor graph, yielding the optimal estimate of the navigation state.
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 3 Human Activity Signatures Measured by Using a SISO FMCW Radar System  
An FMCW radar modulates its transmit signal frequency to detect the radial range of a target. The electromagnetic signal transmitted by the FMCW radar interacts with stationary and non-stationary objects present in the radar's range, thus altering the amplitude, phase, and frequency of the transmitted signal. After applying suitable radar signal preprocessing techniques, the phase and frequency variations of the backscattered signal provide the range and micro-Doppler information of the target (see Section 2). As part of the preprocessing, the multipath components originating from objects relatively stationary to the radar system are filtered out, thereby making the radar system merely sensitive to the radial component of the object's motion. Therefore, detecting and processing a scatterer's motion perpendicular to the radar's boresight becomes a major challenge. For a human body under observation, the radar signals are reflected off the human body segments. Generally, human body segments can be modeled as a cluster of point scatterers [46]. The TV radial range and micro-Doppler signature caused by such moving body segments can be measured from the backscattered radar signals.  
In this section, we primarily focus on the impact of the direction of human activity on the TV radial velocity distribution \(p_{ij}(v,t)\) of a SISO radar system, where \(i,j=1\). To highlight the limitations of the SISO radar system deployed in an indoor environment, we first describe the measurement setup. Secondly, we illustrate three different experimental scenarios in which the human activities are performed and analyzed. Thirdly, we show the radial velocity distribution \(p_{11}(v,t)\) and mean radial velocity \(v_{11}(t)\) derived from measurements of a SISO radar system. Finally, the implications of different directions of human activities on the performance of an RF-based step counting algorithm are discussed for the considered SISO radar system.
## 3 The Use of the Lozi Map in Electrical Devices, Memristors, and Computers
### Multiple Access Communication Based on Control of Special Chaotic Trajectories  
In 2000, Dmitriev et al. [45] discussed the principle of multiple access in satellite communication systems or cellular telephony based on the fine structure of chaotic attractors and using control of special chaotic trajectories. They demonstrated the experimental verification of the proposed approach for asynchronous packet data transmission. In their approach, they considered that a chaotic attractor can be treated as a number of countable sets of special trajectories consisting of unstable periodic orbits (UPO) and transitions between these orbits. The instability of the periodic orbits and transient trajectories between them gives rise to irregular chaotic behavior. They used the set of the unstable \"skeleton\" periodic trajectories, constituting the structure of the strange attractor (or a part of this set), as a \"reservoir\" of potential codes for multi-user communication systems. They observed that the multitude of codes from a certain \"reservoir\" for communications is practically infinite, i.e., the number of users that can be provided with individual code sets is unlimited.  
The initial information carrier (in the absence of the transmitted message) is the chaotic oscillations produced by the dynamic system. The \"user\" codes are the fragments of the entire complex motion; thus, each user's \"own\" code does not disturb the system behavior, and alternating the \"owned\" code for a certain user and others' orbits does not change the structure of the total signal in the channel. The signal in the channel is the \"information for everybody\", containing pieces of chaotic trajectory, \"owned\" unstable cycles (orbits) and codes belonging to \"others\". This means that the proposed method for the transmitted message formation additionally has the useful property of confidentiality. As both the position and duration of the code packages in the channel can be arbitrary, a sequential asynchronous stream of cycle-codes is realized.  
As an example of the realization of their method, they considered twenty period-16 (UPO) of the Lozi map (3) for \(a=1.7\) and \(b=0.5\). They displayed the switching between them in the Figure 2 of [45] and showed from this diagram that the forming of all successive cycles (repeating ten times) is practically instantaneous. Improving on their initial method, they remarked that unstable periodic orbits can be utilized for encoding the entire transmitted information as well as to attribute it to this or that group of users, i.e., playing the role of \"chaotic markers\". The idea of using the system of unstable periodic orbits as markers was applied to the problem of asynchronous packet transmission of data from several users through a single common communication channel.  
They concluded that the generation and control of UPO may be realized in the relatively high-frequency bands provided by modern digital methods.  
They later carried out a similar study with the Lozi map (3) for \(a=1.4\) and \(b=0.3\) in [46].
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 2 Background  
### Routing Security in IoT  
IoT layers are susceptible to various security attacks [30], including node capturing, denial of service [31], fake node or Sybil attack [32], replay attack [33], side-channel attack [34], and routing threats in data forwarding process [35]. The network layer is mainly prone to several attacks, including the routing attacks that are more disruptive to the IoT networks. Mostly in IoT applications, routing is performed using RPL protocol, which is vulnerable to WSN-inherited attacks and RPL-specific attacks. IoT is gaining prominence in multiple application areas, where security, especially networks and routing security, is one of the key issues. Furthermore, RPL is designed specifically for IoT networks for the fulfilment of routing requirements with efficient consumption of resources.  
RPL is a standard introduced by IETF ROLL working group, initially specified in RFC 6550 in 2012. There have been a few updates after RFC 6550. A recent update that leverages RFC 6550 for its routing operations is specified in RFC 9010 in April 2021. Additionally, there have been several RFCs released by IETF to expand the original RFC 6550. Some advanced and related documents include the routing metrics (RFC 6551), OF0 (RFC 6552), MRHOF (RFC 6719), the optimization of parent node selection [36], and security threat analysis for RPL (RFC 7416). In addition to the above, a specification on the design guidelines for routing metrics composition by IETF was introduced. Moreover, several active Internet-drafts for RPL observations and extensions, provided at [[https://datatracker.ietf.org/doc/active/#roll](https://datatracker.ietf.org/doc/active/#roll)]([https://datatracker.ietf.org/doc/active/#roll](https://datatracker.ietf.org/doc/active/#roll)) (accessed on 22 May 2022), are intended for discussions and highlighting issues to the specified standard for up-to-date research work and possible improvements in the original RPL standard. Since its standardization, several studies have pointed RPL's limitations related to its efficiency, applicability, and security. Researchers have proposed various extensions for the enhancement of the standard RPL specifications from implementation and operation point of view. This research work focuses only on the security attacks, such as Blackhole and Rank attacks, which have a high impact in RPL. The domain and focus of currently active IETF drafts vary from this research. Similarly, related to the security aspect, there has not been any implementation of RPL security features in any of the operating systems or platforms [37].
## 3 SDN, NFV, and SFC: An Overview  
In service provider networks, the process of creating, deleting, modifying, and steering traffic in SFCs is carried out efficiently by using SDN and NFV technologies [48]. These technologies are the key networking paradigms that are at the core of the frameworks surveyed in our study. In this section, we describe these technologies as they relate to NFV/SFC implementation frameworks in service provider network environments, thus showing their interrelation in the operations of next-generation networks. Even though the chaining of hardware middle-boxes is possible, the use of NFV makes it much easier  
Figure 4: Gi-LAN in mobile networks.  
and cheaper [49]. Thus, SDN is employed for orchestrating virtual network functions by providing a centralised logical control and the creation of service chains.
# Exploring Safety-Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts
## 5 Numerical Verification
### Numerical Investigation on the IDM with Bidirectional Information  
The spacing bidirectional IDM is subject to numerical experiments to validate the findings. The parameters used in the experiments are identical to those in Table 5. Figures 14-16 demonstrate the vehicle speed profiles when \(\gamma_{x}\)= 0.4, 0 and \(-\)0.4, respectively. Figure 17 depicts the minimum TTC value among 1 st-10 th vehicles. These simulation outcomes are consistent with the previous simulation results obtained using [PERSON]'s model. Specifically, utilizing spacing information with the IP effect can effectively reduce collision risk and enhance platoon stability. On the contrary, incorporating spacing information with the OP effect can increase collision risk and worsen platoon stability.  
Figure 12: Vehicle speed profile under the bidirectional Helly’s model when \(\gamma_{\
u}=-0.4:\) (**a**) 1 st-10 th vehicles, (**b**) 10 th–20 th vehicles.  
Figure 13: Minimum TTC variation with respect to \(\gamma_{\
u}\) in the bidirectional Helly’s model.  
Numerical experiments are further conducted using the velocity difference bidirectional IDM model. Figures 18-20 depict the vehicle speed profiles when \(\gamma_{v}\)= \(-1.5\), \(0\) and \(1.5\), respectively. Figure 21 displays the minimum TTC value. The simulation results are in line with the previous findings obtained using [PERSON]'s model. Specifically, using velocity difference information with the IP effect can reduce collision risk but worsen platoon stability, whereas velocity difference information with the OP effect can improve platoon stability but increase collision risk.  
Figure 16: Vehicle speed profile under the bidirectional IDM when \(\gamma_{x}=-0.4:(\textbf{a})\) 1 st–10 th vehicles, (**b**) 10 th–20 th vehicles.  
Figure 14: Vehicle speed profile under the bidirectional IDM when \(\gamma_{x}=0.4:(\textbf{a})\) 1 st–10 th vehicles, (**b**) 10 th-20 th vehicles.  
Figure 15: Vehicle speed profile under the bidirectional IDM when \(\gamma_{x}=0:(\textbf{a})\) 1 st–10 th vehicles, (**b**) 10 th–20 th vehicles.  
Figure 19: Vehicle speed profile under the bidirectional IDM when \(\gamma_{\rm{\tau}}=0:\) (**a**) 1 st–10 th vehicles, (**b**) 10 th–20 th vehicles.  
Figure 17: Minimum TTC variation with respect to \(\gamma_{x}\) in the bidirectional IDM.  
In conclusion, the numerical analyses conducted on both linear and nonlinear models verify the findings presented in Tables 3 and 4. The use of spacing information can enhance both platoon string stability and safety. However, the use of velocity difference information can only enhance either safety or stability.
## 2 Related Work
### Stance Detection
#### 2.1.1 Machine Learning Methods for Stance Detection  
[PERSON] et al. [12] proposed an SVM-based supervision system that uses vocabulary, emotion, semantics, and potential frame semantic features to perform stance detection on specific targets in the SemEval-2016 dataset. [PERSON] et al. [13] used a two-stage method based on SVM to detect stances in tweets. First, they determined whether the tweet was neutral or non-neutral. If it is a neutral tweet, it was deleted. If it was non-neutral, it was passed to the next stage, which classified the tweet as positive or negative.  
[PERSON] et al. [14] used stacked classification and syntactic features to perform stance detection on SemEval 2016 Task6.The model demonstrated a slight performance improvement. [PERSON] et al. [15] proposed training logistic regression (LR) models and auto-encoding bag-of-words to be labeled for stance detection on the [PERSON] dataset. [PERSON] et al. [16] used [PERSON]'s labeled data and [PERSON]'s unlabeled data and used a rule-based algorithm to help detect the SemVEval 2016 stance task.  
[PERSON] et al. [17] introduced a purely unsupervised author interaction topic viewpoint model (AITV), which is more inclined to \"heterogeneity\" rather than \"homogeneity\" when identifying the nature of the author's online debate interaction. Furthermore, it was conducted in six corpora involving four different controversial issues extracted from two online debate forums. The results show that AITV's stance detection ability is better than other models, even under the unsupervised model. [PERSON] et al. [18] proposed a stance model for detecting controversial topics. First, they created clusters among users, and then no domain or topic-level knowledge was required to specify related stances (tags) or perform actual tasks Mark. Their research results show that when implementing a clustering algorithm, using forwarding as a feature provides the best performance score. Supervised methods are exceeded when using short text and SVM models. Their findings are considered to be a big motivation for the future use of unsupervised methods for stance detection.  
[PERSON] et al. [19] proposed an unsupervised method for specific target stance detection in a polarized context, especially in Turkish politics. The model is mainly based on a convolutional neural network-based multilingual universal sentence encoder. Then, they used the uniform manifold approximation and projection (UMAP) algorithm and hierarchical density-based clustering (HDB-SCAN) to cluster the projected user vectors.