esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Developmental Stage and Age Cohorts  
Children's interactions with social robots and how well they improve cognitive abilities are strongly influenced by their age. While older kids can work on more difficult problem-solving and programming exercises, younger kids might mostly concentrate on sensory exploration and simple interactions [58]. A wide range of cognitive, social, and emotional development occurs between the ages of 0 and 18. Children in this range go through different developmental phases, each of which is distinguished by particular emotional reactions, social interactions, and cognitive capacities.  
_Infants (0-1 years)_: Children at this age are mostly interested in developing their fundamental motor abilities and exploring their senses. Simple, sensory-based tasks like visual tracking or aural replies are probably what people would do when interacting with robots. For instance, research indicates that babies are attracted to robots that imitate human facial expressions and movements, which can aid in the development of their early senses and motor skills [59].  
_Toddlers (1-3 years)_: Toddlers start to interact with their surroundings more intricately and acquire language abilities. For this age group, robots could concentrate on language learning, fundamental social interactions, and basic problem-solving activities. For example, young children have been taught fundamental math and programming principles by robots such as BeeBot [29].  
_Preschoolers (3-6 years)_: Rapid cognitive growth, encompassing memory, imagination, and social cognition characterizes this stage. Through interactive games, role-playing, and narrative, robots can improve these abilities. For instance, NAO robots have been utilized to improve social skills and teach the fundamentals of music to this age range [43].  
_School-age children (6-12 years)_: This age group of kids gains sophisticated problem-solving abilities and logical thinking. Programming, critical thinking, and cooperative problem-solving are all skills that robots can impart. Research has indicated that robots such as mBot and LEGO Mindstorms are useful for improving cognitive abilities in this age range [48].  
_Adolescents (12-18 years)_: Abstract thought, identity development, and increasingly intricate social interactions are also part of this period. In addition to supporting social and emotional development, robots can be utilized to teach complex STEM ideas. As an illustration of how robots may assist with complicated cognitive and social activities, Pepper has been used to get teenagers talking about ecological sustainability [49].
# Energy Sources of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency
## Energy Sources of Mobile Robots
### Case Studies  
#### 3.4.1 Case Study: Powering of Robots for IoT and Metaverse Purposes  
Robots, as sets of mobile agents, are an important part of recognition systems or systems for exploration works, or group work. Communication between robots is required to provide a control layer that can prevent dead-lock situations and centralize path planning to avoid congestions in robot traffic. The collection of sensor data in a cloud environment is necessary to implement cloud computing and limit processing onboard each mobile unit. As data processing can consume computing resources, it will also consume a considerable amount of energy. Therefore, it is common to shift data conditioning and processing to stationary computers from mobile robots, limiting computing resources demand to a bare minimum and thereby enabling robots' self-sufficiency in the environment. The IoT (Internet of Things) facilitates the offloading of data processing from robots to stationary computers, providing common standardized interfaces and data protocols such as MQTT (Message Queuing Telemetry Transport) [14]. A balance must be found between onboard and offboard data processing; while the first option provides robot independence from infrastructure, it raises energy consumption, the complexity of the robot control system, and, therefore, the overall cost and weight of the robot. Offloading computation to the cloud, by comparison, limits the need for computing power and provides scalable computing resources for future upgrades of functions, but increases the demand for fast connectivity and, in the ultimate case, makes the robot incapable of doing anything when connection is unavailable. To meet the demand for data transfer and to limit latency, 5G and 6G mobile networks are required. In constrained environments, Wi-Fi or, on a small scale, Bluetooth can also be sufficient (see [17] for a review of the technology).  
Sensors and effectors for IoT are designed with low power consumption in mind and fast data interfaces. Nevertheless, the control system must plan and utilize power saving modes implemented in such sensors to achieve the lowest possible energy consumption. The power delivery system, on the other hand, must be ready to support burst demands for power when sensors and actuators are woken. For swarm robots or a group of working robots, the role of the cloud computing layer as a centralized planning system is large, as it can provide optimization of power consumption considering mission plans for all the robots in a group and, by utilizing robots' positioning data, limit the need for all-around sensing of each robot and other robot's direct recognition. Ambient Intelligence (AmI) and Affective Computing (AC) are the next step, and their power requirements need to be considered in the robot's power system design.
## 3 Methodology
### GC with Adaptive Scaling Scheme  
In each iteration, fixed and large numbers of initial populations result in a longer signal search time. As a result, the initial detection of the presence of a desired signal (the signal parameters have not been accurately acquired yet) allows us to decrease the number of initial populations in the next search. The \(\,(p\,\mathrm{+1})\,\mathrm{-th}\,\) iteration of initial population size is as follows:  
\[Z_{p+1}\,\mathrm{=}\,Z_{p}\,\mathrm{/}\,(Q_{max}^{(p+1)}\,\mathrm{/}\,Q_{max}^ {(p)}) \tag{13}\]  
where \(\,Q_{max}^{(p)}\,\mathrm{\in}\,\mathrm{max}\,\{Q_{1}^{(p)}\,\mathrm{,}Q_{2}^ {(p)}\,\mathrm{,}Q_{3}^{(p)}\,\mathrm{,}\mathrm{...}\,Q_{\mathrm{z}}^{(p)}\}\,\). When the \(\,Q_{max}^{(p)}\,\) exceeds the noise floor, GC method initiates two procedures. The first procedure is that when \(\,Q_{max}^{(p)}\,\) exceeds the set detection threshold \(\,\mathrm{v}\,\) (depicted in Section 2), it indicates that the desired signal has been acquired and the GC process terminates. The second procedure is when the \(\,Q_{max}^{(p)}\,\) is between the detection threshold and the initial detection threshold. In such case, the adaptive scaling scheme begins to control the number of initial population. The initial detection margin is set at \(\,1.4\,Q\,\) and the definition of \(\,Q\,\) is the same as that in Equation 7. The only difference is that \(\,Q\,\) is the fitness value acquired under the scenario of no desired signal. Meanwhile, the scaler also controls and adjusts the search space of Doppler frequency and code delay, which is depicted in the following:  
\[f_{c}\,\mathrm{-}\,u_{max,\mathrm{\textit{\textit{\textit{ \textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{ \textit{\textit{\textit{\textit{\textit{\textit{ \textit{ \textittextittextittextittextittextittextittextittextittextittextittextittextit     }}}}}}}}}}}}}}}^{\mathrm{\mathrm{}}}}\, \mathrm{\mathrm{\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{ \[u_{max,lo}^{(p+1)}=\begin{cases}f_{c}+\hat{f}_{d}^{(p)}-\alpha\big{\langle}u_{ max,lo}^{(p)}-f_{c}\big{|}/2\big{\rangle},&u_{max,lo}^{(p)}>u_{max,lo}^{(0)}\\ u_{max,lo}^{(0)},&\text{otherwise}\end{cases} \tag{17}\] \[v_{max,up}^{(p+1)}=\begin{cases}\hat{z}^{(p)}+\beta(v_{max,up}^{(p )}/2),&v_{max,up}^{(p)}<v_{max,up}^{(0)}\\ v_{max,up}^{(p+1)},&\text{otherwise}\end{cases}\] (18) \[v_{max,lo}^{(p+1)}=\begin{cases}\hat{z}^{(p)}-\beta(v_{max,lo}^{(p )}/2),&v_{max,lo}^{(p)}>v_{max,lo}^{(0)}\\ v_{max,lo}^{(0)},&\text{otherwise}\end{cases} \tag{19}\]  
where \(\alpha\) and \(\beta\) are reduced factors, which mainly control the convergence rate during the signal acquisition process.  
The adaptive scaling scheme adjusts the Doppler frequency and code delay boundaries with the increase of acquisition iteration. Thus, this adjustment results in faster convergence and more accurate values of the signal parameter.
# Application of Quality Function Deployment for Product Design Concept Selection
## 2 Literature Review
On the other hand, [PERSON] and [PERSON] suggested the rating method defines a set of intensity levels as a base to assess the performance of the alternatives in terms of each criterion [39]. In this study, we apply the AHP rating method to assess concept competitiveness and the AHP pair-wise comparison method to calculate the weighted value of the combined scores by reflecting experts' group opinions. DEA is a methodology to measure efficiency with multiple inputs and outputs. [PERSON], [PERSON], and [PERSON] states it is a non-parametric approach that does not require assumptions about the functional form of a production function and a priori information on the importance of inputs and outputs [40]. [PERSON] and [PERSON] (2013) developed the super efficiency model [41] to improve upon the two limitations of the DEA-CCR by [PERSON], [PERSON], and [PERSON] [42; 43] and the DEA-BCC by [PERSON], [PERSON], and [PERSON] [44; 45] models. The first limitation is that these models cannot rank all of the decision-making units (DMUs). Since these models do not calculate an efficiency score greater than one, they score all efficient DMUs equal to one--discussed by [PERSON] [15] and [PERSON] [9]. The second limitation is that these models do not indicate the DMUs that require improvement. The basic concept of the super efficiency model is to calculate the relative difference among the efficiency frontiers depending on the existence of DMUs. [PERSON] et al. [46; 47] suggested there are two types of super-DEA models, a radial model and a non-radial model. For the radial model, infeasibility can occur depending on the data structure without considering slacks. Infeasibility issues are critical in calculating efficiency. To solve this infeasibility, [PERSON] et al. (2019) suggested using a non-radial model, i.e., slacks-based measure of efficiency model (super-SBM) [48]. In this study, we apply a super-SBM to evaluate the design development efficiency and rank all design alternatives without infeasibility. We want to summarize all these approaches and define their advantages and limitations in use (Table 1). We notice from table above that each separate method has advantages and disadvantages in practical case; so, it is necessary to develop new method, which may combine all these approaches and reduce limitations.
## 3 Materials and Methods
### Weighted-Based Model-Optimization Algorithm  
Instead of evaluating the success of several models separately, general performance success can be achieved with a new single model that incorporates the capabilities of the successful models that created it by searching for the most appropriate weights through a weighting strategy algorithm. Moreover, more efficient mIoU values of the new model can be obtained from the backbone fusion of the models stated in Algorithm 1.  
Figure 3: ResNet34+Unet architecture and VGG19+FPN architecture fusions are shown as encoder–decoder flow. Input image patches are focused first layer.  
```
1.Input Data: test images \(\mathbf{D}=\{\textbf{(x_{1},x_{2},\ldots,xm)}\}\), i = 1,2, \(\ldots\),m and \(\mathbf{Y_{pred}}\)- Predictions of each base model
2.Initialization Set parameters for a dataset: Cnum-Class number of the dataset, Range_ofWeights = (0,1) Wofmodel = [\(\mathbf{w_{1}}\)/10., \(\mathbf{w_{2}}\)/10., \(\ldots\mathbf{w_{k}}\)/10.], where a weight array for each base model, \(\mathbf{k}\) corresponds to the finalist model number
3.Prediction Search for the best combination for Wofmodel that gives maximum IoU for each \(\mathbf{w_{i}}\) in Range_ofweights Compute the mean IoU values for each class: Wts_IoU = meanIoU(Cnum) Sum the predictions of all objects on the specified axis and find the maximum predictions Wts_Ensemble= tensordot(\(\mathbf{Y_{pred},Wofmodel}\))max Measure the metrics (AUC, IoU), and store them in the wts_ensemble_IoU wts_ensemble_IoU = GenerateMetrics(D, Wts_Ensemble) return wts_ensemble_IoUresults
```  
**Algorithm 1** Weighted-based model-optimization algorithm
# Reddit CrosspostNet--Studying Reddit Communities with Large-Scale Crosposst Graph Networks
## 4 Characterizing CrosspostNet
### Community Analysis
\begin{table}
\begin{tabular}{l l} \hline \hline
**Subreddit Group** & **Topic** \\ \hline r/Vilent\_Collection, r/Desinude\_daily, r/Desi\_Kama, r/Lust\_maal, r/indianEXnude, r/Fap\_r/DesiXvideo, & Pornography \\ r/premium\_Daily, r/Desii, r/Lustdesi, r/Desi\_Boners & & \\ \hline r/PremierLeague, r/LiverpoolFC, r/FantasyPL, r/Barca, r/reddevils, r/Gunners, r/coys, r/chelseafc & Sports \\ \hline r/Ladyboys, r/ShemalesParadise, r/ProneSpread, r/amateur\_shemales, r/Asian\_Ladyboys, & Pornography \\ r/StandingAssSpread, r/ThaiLadyboy & & \\ \hline r/GoNets, r/nbcatriclejerk, r/lakers, r/sivers, r/bostonceltics & Sports \\ \hline r/AnimeGirlsAndThongs, r/AnimeGirlsinjeans, r/officelady, r/somuchhentai, r/NikkeNSFW & Pornography \\ \hline r/GayKnots, r/gayyiff, r/deerbutt, r/gayfurryporn, r/femyiff & & Pornography \\ \hline r/ShingekiNoKyojin, r/attackontitan, r/itinafolk, r/yeagerbomb, r/AttackOnRetards & TV Series \\ \hline r/LaborPartyofAustralia, r/Australia\_r/PoliticsDownUnder, r/friendlyjordies & Politics \\ \hline r/amazingtits, r/LiveFreeCams, r/OnlyAmateurPorn, r/Solo\_Girls & Pornography \\ \hline r/IndieDev, r/IndieGaming, r/Unity2D, r/Unity3D & Games \\ \hline r/GolemSexy, r/BitedSizeSexy, r/ThinkTankSexy & & Pornography \\ \hline r/FeetLoversHeaven, r/feetpics, r/VerifiedFeet & & Pornography \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top subreddits (nodes) groups in the top 99.9 th percentile cosine similarity of node embedding with Node2 Vec. The majority of graph-similar subreddit groups are related to various types of pornography. Noticeably, there is a standing-out separate group of subreddits with Indian pornography (subreddits with \"Desi\" in their names). There are also groups about sports (especially football), politics, and amine. This is very consistent with previous findings on the text content similarity and embedding clustering [14], which also found that the biggest group of subreddits are related to pornography, games, politics, sports, and movies/TV series. The most noticeable niche groups concern aquarium and map-related subreddits, which also have high node embedding similarity in CrospostNet. These two groups were also noticeable in the previous work [14]. This, again, shows a relation between content similarity and crossposts. Furthermore, the consistency between content similarity and crosspost similarity suggests that crossposts could be used, in studies, instead of (or to cross-validate) content similarity.
# A Compact Sequentially Rotated Circularly Polarized Dielectric Resonator Antenna Array
## 3 Array Feeding Topology  
Figure 4 depicts the proposed feed network, which is composed of four curved quarter-wave transformer sections connected together in a successive order to formulate a four-port network. The array is formed as a sequential rotated architecture of series and parallel assembly to layout four output port networks. The quarter-wave segments were prototyped in a circular outline to decrease discontinuity. The feeding topology generates a phase shift of 90\({}^{\circ}\) and an equal power split for neighboring output ports. The signal magnitudes of four output ports that are fed to each resonating element are theoretically equal; however, there exists a phase difference of 90\({}^{\circ}\) between the two adjacent ports.  
The width of all the microstrip segments is a quarter-wavelength with different inherent impedances depending on the binomial quarter-wavelength transformer theory. The binomial theory was used to form the four output ports as an input impedance of 50 \(\Omega\) to match with 100 \(\Omega\) at the last segment. Once the characteristic impedances are found, the widths of the microstrip sections can be determined. Table 1 lists the corresponding impedance and width of each segment.  
The multi-segment quarter-wavelength transformer is employed to decrease the current attenuation along the array fed, and consequently, increase the coupled power to array radiating elements. Figure 5 reveals that reducing the input impedance leads to an increase in current transmission, resulting in a rise in the obtained power. It can be  
Figure 4: Configuration of the proposed array feed: (**a**) Front plane; (**b**) Bottom plane.  
Figure 3: E-field distributions within the rectangular DRA coupled to an ES.  
seen that sufficient current flows throughout the feeding network to reach all the radiating elements.  
Figure 6 shows the power allocation over the feeding network. It can be seen from the figure that the power takes place exactly above the coupling slots on the bottom plane, resulting in the maximum power being transferred to the DRs. This proves that the power nodes take place at the desired locations based on the calculated dimensions of the feeding array network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transformer Element** & **Width [mm]** & **Characteristic Impedance** \\  & & **[\(\Omega\)]** \\ \hline Z\({}_{0}\) & 1.898 & 50 \\ Z\({}_{1}\) & 1.77 & 52.21 \\ Z\({}_{2}\) & 1.32 & 62.09 \\ Z\({}_{3}\) & 0.796 & 80.52 \\ Z\({}_{4}\) & 0.539 & 95.75 \\ Z\({}_{\text{Load}}\) & 0.483 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Width and characteristic impedance of the transformers.  
Figure 5: Current flow through the feeding array network.  
Figure 6: Power distribution in the feeding array network.
## 2 Experimental Database
### Experimental Program  
#### 2.1.1 Test Scheme  
A total of 24 two-span continuous RC beams and 12 single-layer two-span continuous RC frames were designed and tested to investigate the moment redistribution at the negative moment regions. The rectangular cross-section of the continuous beams and frame beams were 180 mm \(\times\) 250 mm and 180 mm \(\times\) 300 mm, respectively. The length of each span of the continuous beams and frame beams are 4 m and 3 m, respectively. Owing to the limited number of test specimens, four main variables affecting the moment redistribution were considered in the design, namely, the neutral axis depth factor (_c/d_), the ratio of the tensile reinforcement ratio over the critical negative moment regions to the tensile reinforcement ratio over the positive moment regions (\(\rho_{\text{s1}}/\rho_{\text{s2}}\)), the yield strength of the reinforcement (\(f_{y}\)), and the concrete compressive strength (\(f_{c}\)'). All beams were designed to have a reserve capacity in the positive moment regions, which ensure that the internal forces transfer from the negative moment regions to the positive moment regions. A sufficient magnitude of stirrups was arranged in all beams to avoid shear failure, and the strong column-weak beam requirement was followed in the design of the frames. The mechanical properties of the tensile longitudinal reinforcements in the test specimens were measured by uniaxial tensile tests of the steel bars. The grades of the steel bars included HRB500 and HRB600 as specified in the Chinese code for design of concrete structures GB 50010-2010 [38]. Table 1 lists some important characteristics of the steel bars with various diameters: yield strength\(f_{y}\), yield strain \(\varepsilon_{\text{sy}}\), ultimate tensile strength\(f_{u}\), and ultimate strain \(\varepsilon_{\text{su}}\). It should be noted that, since the steel bars in the test beams and the test frames were in different batches, there were some slight differences in the performance of the corresponding steel bars. The concrete compressive strength\(f_{c}\) was obtained from the compression tests of the concrete cylinders, which were prepared and cured in the same manner as the test specimens. Tables 2 and 3 list the details of the test specimens. Figure 1 presents the reinforcement arrangement of the specimens.  
Figure 1: Reinforcement arrangement of: (**a**) continuous beams; and (**b**) continuous frames.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Specimens** & \(f_{\mathbf{c^{\prime}}}\) **(MPa)** & \multicolumn{4}{c}{**Mid-Support Region**} & \multicolumn{4}{c}{**Mid-Span Region**} & \multirow{2}{*}{\(\rho_{\mathbf{s1}}\)/\(\rho_{\mathbf{s2}}\)} & \multirow{2}{*}{_Ild_} \\
**No.** & & & & & & & & & \\ \cline{3-10}  & & \(cld\) & \(A_{\mathbf{s1}}\) **(mm\({}^{2}\))** & \(f_{\mathbf{y}}\) **(MPa)** & \(\omega_{\mathbf{sw1}}\) **(\%)** & \(A_{\mathbf{s2}}\) **(mm\({}^{2}\))** & \(f_{\mathbf{y}}\) **(MPa)** & \\ \hline L-A-1 & 38.4 & 0.09 & 157 & 680 & 0.44 & 402 & 638 & 0.39 & 19.07 \\ L-A-2 & 38.4 & 0.20 & 509 & 633 & 0.58 & 763 & 632 & 0.67 & 19
# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 3 Main Architecture
### Detector and Tracker  
During pedestrians' movements, head tracklets (including head bounding boxes) and full-body bounding boxes are generated by using a head tracker and a body detector, respectively. The design of the head tracker follows the TDB paradigm which consists of a head detector and a head tracker. The whole-body detector was built based on Faster RCNN [12]. Next, we will describe how our head tracker and body detector work in detail.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 1 Introduction
This model can reduce the maintenance costs of large solar power plants, decrease personnel costs and hazards, and obtain fault information more promptly, quickly addressing faults to enhance the stability, reliability, and safety of solar power plant operations, providing higher economic benefits for the solar power plants of the future. In this paper, MATLAB Simulink is used to model various states of solar modules in Section 2, with the simulated curves serving as training data for the model. Section 3 introduces the data preprocessing methods employed in this study, emphasizing the necessity of preprocessing the training data before inputting it into the model for training. In Section 4, a model is presented that is designed to simultaneously utilize curve features and curve images as input data, followed by an analysis of its performance on real field data.