esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Methodology of Computing Data Similarity  
Figure 2 shows the basic outline of the methodology of data similarity, which starts with data collection, followed by data preparation, data similarity measure, and finally the target application. In the first step, the raw data are collected and used to create the target dataset. In the data preparation step, the target dataset is cleaned and preprocessed to obtain consistent data. It also involves a transformation of the data into a feature matrix in which rows correspond to data items and columns to features. Generally, the representation of a feature matrix depends on the type of data and the application domain. It can be, for example, based on the bag-of-words model, vector space model, etc.  
Having obtained the features matrix, the data similarities can be computed from it. In this step, a similarity measure is applied to compute the similarity of every pair of items by computing the similarity between the vectors space models that represent this pair of items in the feature matrix. The output of this step is a similarity matrix in which each value represents the degree of similarity between a particular pair of items. In data mining, computing the similarity of vectors is a common operation with many choices available. The common choices are cosine similarity, the Pearson correlation coefficient and Euclidean distance (transformed into a similarity measure by subtraction). In the case of binary data, similarity measures such as Pearson, Sokal, and Jaccard measures are applicable [14]. It is worthwhile mentioning that the choice of data similarity measure is domain-specific, and therefore it is difficult to give a verdict on a question such as which measure is better or worse in a general setting. After the computed similarity matrix is obtained, it can be used by the target applications. In this step, the similarity matrix is processed by other computational algorithms, particularly ML algorithms such as clustering algorithms and dimensionality reduction algorithms. Examples of target applications include adaptive learning systems and automatic recommendations of activities. In addition, the computed similarity matrix provides useful and actionable insights for the practitioners and decision-makers as well. In the following section the details of applying the above-described methodology to compute the similarity between the PEOs of engineering academic programs is given.
## 3 Information and Communication Technology Solutions for Precision Agriculture
### Multi-Purpose Platforms
#### 3.4.2 Cyber-Physical Systems
In addition to this, various data (e.g., working path, time, and GPS quality) recorded by the transplanting robot can be used for providing details about the cultivation history, thus contributing to a more credible food safety system [98]. [PERSON] et al. present an autonomous robot for strawberry harvesting. A novel obstacle-separation algorithm was proposed to overcome the difficulties faced by other similar solutions in harvesting in cluttered and unstructured environments. Using this algorithm, a robotic griper can actively push surrounding obstacles aside, leading to increased harvesting efficiency. An integration and control algorithm was used for continuously harvesting strawberries along polytunnels [99]. [PERSON] et al. demonstrate an automatic weed control system utilizing autonomous vehicles and GPS technologies. The specific system addresses the problem of farmers having to remove weeds by hand within rows of their precision seeded crops. An autonomous tractor using GPS satellite navigation was field-tested, and simulation technologies were used for the assessment of the tilled area as well as for the quantification of the system error [100]. UAVs can be used in conjunction with UGVs, providing important benefits to PA. [PERSON] et al. demonstrate the main concepts of the Flourish Project, which combines the capabilities of multi-copter UAVs with multi-purpose agricultural UGVs. One of the proposed uses refers to the creation of crop property maps using UAVs, multi-spectral imaging, and 3D reconstruction technologies. This solution facilitates crop management decision-support. Complementing the aforementioned solution, UGVs can be used for plant classification as well as for real-time crop-from-weed differentiating [101].
## 3 Results
### The C-Configuration  
Since the tracks of each active module are concurrently driven by a single motor, the Crawler Unit cannot turn using a skid-steering technique, i.e., driving the tracks to spin in opposite directions. With this design layout, a possible way of inducing a turning motion on the Crawler Unit is by rotating the modules with the yaw joints, while the active tracks spin, as shown in Figure 9. In particular, the Crawler Unit assumed C-configurations, whose shapes were determined by the desired yaw angles. Moving the dual axis joystick from the center to the left/right directions, the operator determined how loose or sharp the Crawler Unit final configuration was and, consequently, the circular trajectory followed by the system. The curvature radius \(\rho\) depends on the lengths of the modules and on the yaw angles according to the following relation:  
\[\rho=\frac{l_{1}+l_{2}\cos q}{\sin q} \tag{1}\]  
where \(q=q_{3}=q_{4}=q_{8}\) represents the relative yaw angles between modules, \(l_{1}=l_{c}/2+l_{l}+l_{\rho}\) is the equivalent central module length, and \(l_{2}=l_{f}/2=l_{r}/2\) is the front module half length. It is worth noticing that this formula holds true, provided that all the modules rotate by the same angle. According to the system dimensions, the minimum curvature radius of version one of the Crawler Unit is \(\rho=0.45\,\mathrm{m}\), when the yaw joints reach their limit angles \(\pm 0.87\) [rad]. Version one of the Crawler achieved an estimated angular velocity \(\omega=0.1\) [rad/s] and rotated by about \(1.57\) [rad] in \(t=15\,\mathrm{s}\), with all yaw joints at their maximum allowed rotation of \(0.87\) [rad].  
The same experiments were performed on version two of the Crawler Unit, as shown by Figure 10. Adopting the same configuration of version one, the performance of the new prototype slightly downgraded and the curvature radius resulted \(\rho=0.6\,\mathrm{m}\). This behavior was not surprising, because version two has longer modules than version one, i.e., the length \(l_{1}\) is increased. In addition, the yaw joints have narrower limit angles of \(\pm 0.7\) [rad]. The data analysis demonstrated that the active tracks of version two achieved an average linear velocity \(v=0.0608\,\mathrm{[m/s]}\) with standard deviation \(\sigma=0.002\,\mathrm{[m/s]}\) and a theoretical angular velocity \(\omega_{t}=0.10\) [rad/s]. Accordingly, the robot rotated by about \(1.57\) [rad] in \(t=17\,\mathrm{s}\), achieving an estimated angular velocity \(\omega_{e}=0.09\,\mathrm{[rad/s]}\), when all yaw joints were at their limit angles.  
Figure 8: Forward motion sequence. The Crawler Unit version two travels \(1\,\mathrm{m}\) forward from the starting position, on the left, toward the finish line, on the right. The robot achieves an average speed of \(v=0.1\,\mathrm{[m/s]}\).
# Area-Efficient Early-Termination Technique for Belief-Propagation Polar Decoders
## 3 Proposed Method
We assumed the information set represented by \(A\) to be [4; 6; 7; 8], and the frozen set represented by \(A^{c}\) to be \(\left\{1,2,3,5\right\}\) without loss of generality. As shown in Figure 6, the circuitry for the frozen sets is completely eliminated, and the adders for the information set are also eliminated. Therefore, the proposed early termination provides an area-efficient solution without any degradation in system or error-correcting performance. Furthermore, the proposed early-termination algorithm alleviates the disadvantages of information-BER-based [16] and frozen-BER-based [17] methods. Due to the use of the windowing approach, information-BER-based [16] and frozen-BER-based [17] methods terminate the decoding process when there are no changes on information or frozen bit channels within the window size, respectively. From the perspective of hardware complexity, information-BER-based [16] and frozen-BER-based [17] algorithms require several registers to store and check the current state within the window size, but the proposed method does not require any registers by not employing the windowing approach. From the perspective of system performance, information-BER-based [16] and frozen-BER-based [17] methods have a slight degradation on the decoding throughput compared with the proposed method, since an infinitely long window size is not feasible. For a more in-depth discussion, we will provide various practical experimental results in Section 4.
## 3 Test Results and Analyses  
### Slope Deformation  
Figure 2 shows the variation of the vertical displacement on the top surface of the slope with the increase of the surcharge load. It can be seen that, with the increase of the surcharge, the vertical displacement of the slope increased almost linearly and gradually accelerated with a fast rate irrespective of the rainfall. However, the rainfall had a significant impact on the slope stability. The instability critical surcharge was determined by referring to the graphing method proposed by [PERSON] (2013). The backfill slopes had instability critical surcharges of 149 kPa, 125 kPa, 117 kPa, and 136 kPa in the tests T-1 to T-4, respectively. It can be found that the stability of the backfill slope decreases with the increase of the rainfall intensity and duration as other factors are consistent.  
Figure 3 displays the variations of the lateral displacement of the backfill slope at the monitoring points L1 and L2 with the vertical displacement on the top surface of the slope. The lateral displacement of the backfill slope increased rapidly with the increase of the vertical displacement. After reaching a certain value, it remained stable with the increase of the vertical displacement.
# Research on Rollover Stability and an Anti-Rollover Warning System for the Vibroseis Truck
## 3 Study on the Warning Index of Rollover for the Vibroseis Truck
### Rollover Warning Index for Vibroseis Truck  
Through the analysis of rollover stability for vibroseis trucks, some parameters such as steering speed and steering angle appeared to have a decisive influence on the stability of vibroseis trucks. Therefore, based on the existing vehicle warning index and the rollover stability of the vibroseis truck analyzed before, a new warning index for the vibroseis truck was established, based on the absolute value of the quotient of the vehicle speed \(v\) and the critical steering speed \(u_{2}\) in the slope steering process. It can allow a multistage evaluation of dangerous situations involving the vehicle.  
\[A=\left|\frac{v}{u_{2}}\right|=\left|\frac{v}{\sqrt{\left(\frac{\cos{\varphi} \cdot B}{2H}-\sin{\varphi}\right)gR}}\right| \tag{12}\]  
As shown in Figure 10, this multistage warning index range is divided into 4 levels. The evaluation index is named \(A\), and with an increase in its value, the rollover stability of the vibroseis truck gradually decreases. When the index ranges from 0 to 0.6, the alarm level is defined as level 1, called the safe level. The sound-light alarm wave for this level is green. The system sets off a blue sound wave in the range from 0.6 to 0.9 to produce a primary alert to the driver, and this alarming level is defined as level 2. In the range from 0.9 to 1, the third alarm level is activated. The system issues a yellow sound-light alarm to further alert the driver. Finally, when the value of the index \(A\) is beyond 1, the fourth alarm level is defined, and a red sound-light alarm wave is issued, which shows that the vehicle is undergoing a rollover accident.  
This warning index for the vibroseis truck uses a multistage contrast judgment method of vehicle speed \(v\) and critical steering speed \(u_{2}\) and can issue different types of sound-light alarm according to the different levels of security. At the same time, the index can not only  
Figure 10: Schematic diagram of the anti-rollover warning system.  
accurately judge the dangerous state in the slope steering process in real time, but also judge the stability of the flat road steering process.
## 1 Introduction
UWB filters based on stub-loaded multimode resonators were implemented in [25; 26; 27; 28; 29; 30; 31; 32]. These resonators utilize stubs to introduce additional resonant modes, enabling the design of compact filters with desirable UWB characteristics. The stopband characteristics of UWB filters have been studied using techniques such as step impedance resonators (SIRs) [33; 34] and multimode resonators (MMRs) [35]. However, some of the reported designs have larger circuit dimensions, which may pose challenges in practical implementations. To address the circuit dimension problem, UWB notch filters based on defected ground structures (DGSs) and defected microstrip structures (DMSs) were proposed in [36; 37; 38; 39]. However, achieving both sharp selectivity and a wide bandwidth simultaneously proved difficult with these techniques. Overall, it remains a challenging task for microwave researchers to design UWB bandpass filters with notch bands that have simple topologies and wideband characteristics while maintaining good selectivity. It is worth noting that the techniques mentioned in the literature provide valuable insights into the development of UWB filters, but researchers continue to explore new approaches to overcome the challenges and achieve the desired filter performance. In this regard, a SWB-BPF (super wideband bandpass filter) with a range of 2.5 GHz to 16.8 GHz and five transmission poles using a simple UTL has been developed in this study. Later, a quarter-wavelength resonator with a T-shaped structure is coupled to the SWB filter to suppress unwanted signals for 4G/5G operators for LTE backhaul and X-band downlink satellite communication applications. The proposed wideband filter topology also covered the basic requirement of the ultra-wideband BPF defined by the Federal Communication Commission (FCC), i.e., 3.1 GHz to 10.6 GHz. The filter footprint is about 22.5 \(\times\) 12 mm, with an absolute bandwidth of 14.3 GHz. Finally, the presented filter was designed and analysed in a 3D EM software, HFSS-15 (High-Frequency Simulator Software) [40], then fabricated on a low-loss PCB. After optimization, the design verifiesits attractive features, such as controllable bandwidths and stopbands, a flat group delay, multiple transmission poles, a good return loss, a good roll-off rate, and a wide passband response.
## 3 Background and Previous Related Work  
### Smart Cities  
Smart cities have recently generated a lot of attention. The idea of having a smart city comprising smart sectors such as smart population, smart administration, smart transportation, smart agriculture, smart grid, smart education, and smart infrastructure is unique but challenging [32]. Due to the attention given to the notion of a smart city, it has been defined in various ways in [33]. Others have presented the current concept of smart city and its future directions [34]. In [35], an extensive overview of smart cities is presented covering a wide range of topics including research aims, research challenges, potential scenarios, and potential project areas [14]. Functional specifications that needed to be known about smart cities have been discussed in [36]. Since the quality of life for each citizen is the most important factor, designing and planning the smart services within a smart city must be completed in a way that could facilitate people's lives due to the adoption of information and communication technology (ICT) [37]. In [38], numerous potential and commercial benefits associated with the smart economy and the connection between the economy and citizens are explored. In [39], it is stated that a smart government in smart cities is effective only if it offers their residents city services, channels, smart mobile services, and network integration. Environment quality such as air quality, water, trees, waste management, and infrastructure has been discussed in [40; 41; 42]. While using all these smart services, enormous amount of data are produced and have to be managed. Big data and the paradigm of the Internet of Things (IoT) or CPS might raise some challenges such as security, and privacy issues. In [43], protecting user confidentiality in blockchain-based IoT systems is discussed. The paper focuses on privacy issues presenting examples and cases. In [13], the potential of adopting DLT in smart cities and their applications is investigated. In addition, some blockchain paradigms that could be applied to a smart city are discussed.  
NEOM  
Recently, a new smart city has been introduced to the whole word under the name of \"The Line\" as part of the huge project of the government of Saudi Arabia's Neom mega city. The city is built over the desert from the ground up as a smart city taking into account all aspects to provide the optimum quality of life. The city will include a smart infrastructure as well as smart supply chain and logistic services. The city is designed with smart energy systems [44; 45].
# Effectiveness of Driving Simulators for Drivers' Training: A Systematic Review
## 1 Introduction  
### Simulators in Context  
Since their appearance in the 1940s, simulators have been gaining ground and consolidating themselves as \"efficient\" and risk-respectful tools for driver training under controlled risk conditions. In the beginning, given the large outlay involved, only the Administrations were able to commission the development of simulators, and the United States Army was one of the first sponsors.  
In the 1960s and 1970s, with the advent of the first digital computers and computer graphics, the U.S. Army commissioned the development of the first \"full mission\" simulator in history [1]. It was an aircraft flight simulator for training pilots that reproduced both the cockpit and a virtual scenario in which the aircraft flew. It was qualified as \"full mission\" because it allowed pilots to be trained from takeoff to landing.  
With the passing of the years and the great advances in the technological field (microprocessors, screens, projection, controllers, etc.), costs grew lower and, in addition to the Administrations, large companies began to develop their own simulators. This encouraged the appearance of simulators for all types of vehicles: flight [2], ship [3], submarine [4; 5],car [6; 7], truck [8; 9], etc. As an example of this expansion, in the 1970s, there had already been 28 driving simulators developed worldwide [10].  
Initially, simulators were used only for training in areas or situations that were either too dangerous for users to practice in real life or were much more expensive to reproduce in real life than the investment needed to develop a simulator. Today, this concept has expanded, with entertainment being one of the main niches in the field of simulation. On the other hand, there are also simulators intended exclusively for scientific use.
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
## 2 Background
### Overview of Sidelink Technology  
Sidelink is a cellular technology that offers direct communication between devices whether or not using cellular BS assistance. The 3 GPP has been exploring sidelink technology since its Release 12 [17]. Specially dedicated physical layer channels have been designed to support device discovery, synchronization, and sidelink transmission. These channels include the Physical Sidelink Shared Channel (PSSCH), the Physical Sidelink Control Channel (PSCCH), the Physical Sidelink Broadcast Channel (PSBCH), and Sidelink Control Information (SCI) [18]. There is a time-frequency resource structure dividing sidelink channels which consists of contiguous RBs in the frequency domain and 1 ms subframes in the time domain RBs. RBs in the same subframe are collectively referred to as a subchannel (shown in Figure 3) Depending on the setup, there may be a different amount of RBs in the subchannel. The scheduling of time-frequency resources from the RBs consists of recurring sequences of hyper frames which are known as the Scheduling Assignment (SA) or PSCCH period. The SA period is used for direct communication in both sidelink Modes 1 and 2 [18,19]. PSCCHs are controlled by each SA period which is carried by the PSSCH. The SCI carried by the PSCCH contains information on the modulation as well as the coding scheme, the RBs employed, and the resource reserve period. A packet intended for PSSCH transmission is contained in a Transport Block (TB).  
Figure 2: The time-frequency resource structure for 5G-V2X [16].  
In Modes 3 and 4, within the same subframe, the TB transmission may occupy adjacent or non-adjacent RBs while the SA is broadcast utilizing specified RBs in the subchannel. Resource conflicts can arise when many devices occasionally choose the same resources from the common pool [19; 20]. These choices are therefore coordinated using appropriate collision resolution techniques. Each node may select the resources for the time-frequency to employ in a semi-persistent scheduling mechanism that is currently in use. The competing nodes must employ a sensing-based algorithm to find the least frequent and time-used resources, which the algorithm does not offer [21; 22].  
Two essential features of sidelink technology, device detection and resource allocation have undergone extensive regulatory requirements and academic research [23; 24]. The discovery refers to the capability of using the sidelink (PC5) radio interface to find another device that is nearby. User Equipment (UE)s can accomplish this independently, or it could be done at the network's core level. It is possible to expand the definition of proximity to take into account elements that UE pairs experience on the radio channel, such as signal quality, throughput, delay and network load [7; 25].
# GTP-Bound N-Ras Conformational States and Substrates Are Modulated by Membrane and Point Mutation
## 2 Results
### Influence of Membrane on the N-Ras Conformational (Sub)States  
The presence of the membrane leads to smaller fluctuations in state 1, especially in the SI region, due to the attachment of the proteins' \(\alpha\)-helices 2 and 4 to the membrane, along with the increased stability of the P-loop. While significant in the dynamics of state 1, the membrane's presence does not seem to affect the ratio of time spent by N-Ras in state 1 during our simulations (see Figure 2A,B(d)).  
Substate 2\({}_{OY}\) is barely present (\(\sim\)6%) when membrane-bound for N-Ras WT as compared to the \(\sim\)20% in solution only (i.e., without the membrane). In contrast, the presence of the membrane increases the stability of substate 2\({}_{OX}\) as being the dominant substate for N-Ras WT (\(\sim\)55% as opposed to \(\sim\)25% in solution only). Although present in a small and similar percentage (\(\sim\)2%) as in the absence of the membrane, substate 2\({}_{OX}\) maintains its utmost importance due to its correspondence to the GAP-binding state [43].  
These results suggest that the membrane plays a crucial role in modulating both the N-Ras dynamics of states and substates, and (mostly) the distributions of substate 2\({}_{OX}\) and substate 2\({}_{OY}\) sampled during our simulations (see Figure 2A vs. Figure 2B).