esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Differences in Locust Phenotypic Characteristics  
Locusts belong to the order Orthoptera, which has nymph and adult growth stages, where the nymph stage can be divided into several sub-stages. The purpose of our research is to realize the automatic identification of AM locusts (third-instar, fifth-instar, and adult stage), rice locusts (adult stage), and cotton locusts (adult stage). The body compositions of the research objects are consistent; however, there are significant differences in their external morphology [33], as Figure 2 shows.  
As shown in Table 2, the body size and wing length of adult cotton locusts are significantly larger than those of the other two species. Their body color is yellow-green, their front and back plates are angular and slightly protruding, while their antennae are filamentous. Second, compared to AM locusts, rice locusts have a smoother exterior and backplate, a squarer head, a more slender antennae, a pointed web, and a green or brown-green body-color (similar to the color of rice). Third, there are significant differences in the body length, wing tooth length, and antennae segment number for different instars of AM locusts. For the third-instar, there are approximately 20-21 antennae segments, the body length is 10-20 mm, the wing tooth length only reaches the first abdominal section, and the posterior wing teeth are slightly triangular. For the fifth-instar, there are approximately 24-25 antennae segments, the body length is 26-40 mm, the wing teeth are obviously developed, and the length covers the fourth to fifth abdominal segments. The adult body lengths show significant male and female differences, with a male body length of 35-42 mm and a female body length of 39-52 mm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Species** & **Instars** & **Training Set Size** & **Validation Set Size** & **Testing Set Size** \\ \hline
1 & AM Locust & 3 & 4000 & 1000 & 1000 \\
2 & AM Locust & 4 & 4000 & 1000 & 1000 \\
3 & AM Locust & adult & 4000 & 1000 & 1000 \\
4 & Rice Locust & adult & 4000 & 1000 & 1000 \\
5 & Cotton Locust & adult & 4000 & 1000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample data sets for classification experiments.  
Figure 2: Differences of external morphology among different species and instars. (**A**) presents AM locusts; (**B**) presents rice locusts; and (**C**) presents cotton locusts.  
In summary, the research objects in this paper have differences in body shape, shape, and color of wing teeth, and other aspects, which meet the requirements for automatic classification. Note that swarming (gregarious) AM locusts and scattered (dispersed) AM locusts often show different color characteristics. The sampled image data in this paper are all swarming AM locusts.
## 2 Fundamentals of Liquid Crystals for Microwave Applications  
An LC is a mesophase between isotropic liquids and crystalline solids. LC molecules are shaped like rods or disks, which tend to align in a certain direction, which is described by the time-averaged direction \(\overrightarrow{n}\) along the long axis of the LC molecule (Figure 1a) [20]. LCs are dielectric materials with anisotropic characteristics such as the relative dielectric anisotropy \(\Delta\varepsilon_{r}\) and dielectric loss tangent \(\tan\ \delta\). The relative dielectric anisotropy can be defined as:  
\[\Delta\varepsilon_{r}=\varepsilon_{r,\parallel}-\varepsilon_{r,\perp}, \tag{1}\]  
where \(\varepsilon_{r,\parallel}\) is the LC's effective parallel relative permittivity and \(\varepsilon_{r,\perp}\) is LC's effective perpendicular relative permittivity.  
The relative permittivity of LC depends on the angle between the director \(\overrightarrow{n}\) and the radio frequency (RF) field \(\vec{E_{RF}}\)[10,21]. By regulating the LC directors by external bias, we can change the effective dielectric constant of the structure continuously. When the RF field is perpendicular to the LC director, the RF field experiences a complex \(\varepsilon_{r,\perp}\) that corresponds to the short axis of the LC (Figure 1b). In contrast, when the RF field is parallel to the LC director, the RF field experiences the complex \(\varepsilon_{r,\parallel}\) that corresponds to the long axis of the LC (Figure 1c). Between these, relative LC permittivities can be obtained continuously by changing \(\overrightarrow{n}\) using the external field. This external field-controlled tuning of the relative permittivity with a maximum effective anisotropy can be used directly for continuous phase shifting [22]. The maximum differential phase shift of the LC phase shifter can be written as:  
\[\Delta\phi_{phase\ shift}=\frac{2\pi f}{c_{0}}I_{physical}\Big{(}\sqrt{ \varepsilon_{r,eff,\parallel}}-\sqrt{\varepsilon_{r,eff,\perp}}\Big{)}=\frac {2\pi f}{c_{0}}I_{physical}\Delta n_{eff}, \tag{2}\]  
where \(\varepsilon_{r,eff,\parallel}\) is effective \(\varepsilon_{r,\parallel}\) value of the whole RF device, \(\varepsilon_{r,eff,\perp}\) is the effective \(\varepsilon_{r,\perp}\) value of the whole RF device, \(\Delta n_{eff}\) is the effective anisotropy of the refraction index, \(f\) is the frequency of operation, \(L\) is the physical length of the phase shifter, and \(c_{0}\) is the speed of the light. The LC used in this study is ZOC-A001X from the JNC corporation; at 28 GHz, it has \(\varepsilon_{r,\perp}=2.47\), \(\varepsilon_{r,\parallel}=3.09\), tan \(\delta_{\perp}=0.015\), and tan \(\delta_{\parallel}=0.004\).
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 4 ML Techniques in Energy Prediction
### Reinforcement Learning (RL) Solutions  
RL is an ML-based approach that aims to control systems to maximize long-term performance measures [91]. It is influenced by behaviorist psychology and focuses on how computer programs behave to maximize cumulative rewards. In particular, in circumstances with ample training data and no prior knowledge, RL algorithms can develop effective control rules. However, RL algorithms face challenges, such as high computational costs, when determining the appropriate course of action, as they must evaluate all possible states. Common techniques used in RL include brute force, Monte Carlo methods, temporal difference, and value function approaches. Reinforcement learning includes computational approaches to complicated control, optimization, and adaptive behavior problems, as well as the study of how rational people modify their behavior in uncertain circumstances.  
In recent years, significant advances have been made in the field of reinforcement learning. For understanding and replicating adaptive decision-making based on incentives and penalties, it provides qualitative and quantitative models [92]. Reinforcement learning (RL) has been utilized in multiple capacities within the framework of smart buildings (SBs). In [93], they employed Q-learning to regulate lighting in buildings, identifying the ideal intervals for activating and deactivating lights to improve energy efficiency. They suggested a dynamic programming strategy that uses Q-learning, offering clients a more efficient, adaptable, and flexible means of generating optimal selections in SB situations. Furthermore, ref. [94] presented a temporal differential reinforcement learning method that uses explicit or implicit user feedback to autonomously discern user preferences for music and lighting configurations in smart buildings.
## 4 Discussion
### Path-Tracking Stability  
The standard deviation of the steady-state deviation is used as an index to evaluate the stability of path tracking. This index can reflect the transverse deviation dispersion degree of a sample car after entering the stable state and can thus reflect the stability degree of the sample car when driving at this stage. As shown in Figure 8 and Table 2, the standard deviation of the steady state deviations of the present method under three different initial deviations in the test were 1.3 cm, 0.9 cm and 1.6 cm, respectively, while the standard deviations under the fixed look-ahead distance were 2.1 cm, 1.5 cm, and 1.9 cm, respectively. The experimental data showed that the dispersion of the lateral deviation of the prototype vehicle was significantly less than that of the control group with the fixed look-ahead distance, so the path-tracking model with a variable forward distance had a better performance than the fixed look-ahead distance. In the small-deviation situation, the fuzzy controller was used to dynamically select a large look-ahead distance for path tracking, which could preferentially reduce over-regulation in the control process, avoid oscillation, and improve the stability of path tracking.
# Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand
## 3 Problem Formulation
### Prescribed Performance Control  
In order to ensure the tracking error of the system constrained in a predefined zone, PPC was proposed by [33]. this paper proposes a prescribed performance for nonlinear systems as follows:  
\[-v(k)<e(k)<v(k), \tag{9}\]  
where \(v(k)\) is a prescribed performance function, which is described in the following:  
\[\begin{array}{l}\
u(k+1)=(\
u(k)-\
u(\infty))(1-\operatorname{atan}(\varepsilon k )/(\pi/2))+\
u(\infty)\\ \lim\limits_{k\rightarrow\infty}\
u(k)=\
u(\infty),\end{array} \tag{10}\]  
where the initial value satisfies \(\
u(0)>\
u(\infty)>0\), and \(\varepsilon\) is a positive constant correlated to the convergence rate. To deal with constrained tracking error, a monotone increasing function \(\Omega(\mu(k))\) is designed to transform the constrained error \(e(k)\) into unconstrained error \(\mu(k)\) as follows  
\[e(k)=\
u(k)\Omega(\mu(k)). \tag{11}\]  
The characteristics of the monotone increasing function \(\Omega(\mu(k))\) are summarized as follows:  
* \(\Omega(\mu(k))\in(-1,1)\), for all \(\mu(k)\in R\).
* \(\lim\limits_{\mu(k)\rightarrow-\infty}\Omega(\mu(k))=-1\), \(\lim\limits_{\mu(k)\rightarrow+\infty}\Omega(\mu(k))=1\).  
Since \(\Omega(\mu(k))\) is increasing function and \(\
u(0)>\
u(\infty)>0\), the inverse transformation is given as:  
\[\mu(k)=\Omega^{-1}(\frac{e(k)}{\
u(k)}). \tag{12}\]  
If \(v(0)\) is determined appropriately to meet \(|e(0)|<\upsilon(0)\) and ensure \(\mu(k)\) limited, then \(\Omega(\mu(k))\in(-1,1)\) holds, following (8) holds.  
According to the above analysis, a strictly increasing function is proposed for the proposed control method:  
\[\Omega(\mu(k))=\frac{\mu(k)}{\sqrt{\kappa^{2}+\mu^{2}(k)}}, \tag{13}\]  
where \(\kappa>0\) is a design parameter. Hence, one has  
\[\mu(k)=\frac{\kappa e(k)}{\sqrt{\
u^{2}(k)-e^{2}(k)}}. \tag{14}\]
# Estimating the Memory Consumption of a Hardware IP Defragmentation Block
### Contribution of the Article  
The original contribution of the article includes:  
* carrying out an extensive design space exploration that yields robust confidence intervals for obtained performance metrics and confirms the original hypothesis about the performance of an IP reassembly block
* developing an original Monte Carlo validation method for estimating the performance of network protocol accelerators and, in particular, for an IP reassembly block.  
As noted in Section 2.5, none of the previous works on flow processing focused exclusively on IP fragments. Although this problem seems to be similar to tracking connections, it has its unique characteristics and challenges. The similarities and differences between connection (TCP, UDP) tracking and IP defragmentation are summarized in Table 1. The TCP reassembly function was presented for reference despite belonging to different problem domain. IP defragmentation has more similarities to the TCP reassembly than to connection tracking since both require buffering and maintenance of the state. Tracking the state of L4 connections is optional, and implementations based on the concept of a cache (e.g., Yamaki [14], Tanaka [15] in Table 2) often do not keep the connection state. The bandwidth criterion in Table 1 means the availability of the relevant traffic in network samples. This presents a unique challenge for this work, as IP fragments are considered an exception in the general Internet traffic. This situation, however, may be changed during DDOS attack, when the bandwidth of defragmented IP traffic might be significant.  
In summary, IP defragmentation is challenging due to the low bandwidth ratio in the publicly available traffic samples and due to the need to maintain a state. Furthermore, there is no concept of long-lived connections in the realm of IP fragments, while TCP flows may last for days. These differences present a sufficient rationale for a focus on IP defragmentation alone in a novel manner.
## 3 Results
### Electrical Conductivity Analysis for Different Number of Pulses  
In PEF-treated samples, the number of pulses had a significant impact on the measured conductivity (Figure 5a-d). Overall, the (relative) conductivity increased with the number of pulses. For 8/9 investigated NP, the most important relative conductivity changes were observed near 1000 Hz (Table 2). At 1000 Hz, relative conductivity increased from 470 \(\pm\) 263% to 1535 \(\pm\) 256% as the number of pulses increased from 10 to 70 (Figure 5c,d).  
Figure 4: ANOVA results at 1000 Hz highlighting significant differences in mean conductivity values with standard deviation (N = 6) for various pulse voltages (**a**), number of pulses (**b**), pulse widths (**c**), and pulse intervals (**d**) compared to the control group (“Ctrl”). Asterisks denote significance levels (* = \(p\) + 0.05, ** = \(p\) + 0.01, *** = \(p\) + 0.001, *** = \(p\) + 0.0001). ANOVA results for other frequencies with mean conductivity values are listed in Supplementary Tables S5–S12 and Supplementary Figures S6–S9, respectively.  
At 1 kHz, mean conductivity increased significantly from the control value of 0.030 \(\pm\) 0.006 S/m to 0.170 \(\pm\) 0.063 S/m after 10 pulses (\(p\) = 0.033). Very significant changes (\(p\) < 0.01) were observed for 15, 20, 50, and 60 pulses (Figure 4b). The highest mean conductivity was measured for 60 pulses (0.511 \(\pm\) 0.134 S/m, \(p\) = 0.004), while the most significant increase was observed at 70 pulses (0.491 \(\pm\) 0.083 S/m, \(p\) = 0.0004).  
Table 3 summarizes the conductivity values at 1 kHz; extended results are presented in Supplementary Tables S7 and S8.
# Evaluating the Robot Inclusivity of Buildings Based on Surface Unevenness
## 4 Experimental Validation
### Test Site 3: Connector Bridge Area  
The connector bridge is part of a network of outdoor corridors and terraces that link up different campus buildings and is exposed to weather. It contains two primary materials, areas with natural wood planing and areas with cementitious materials, and varying levels of surface roughness.  
The generated LiDAR map of the connector bridge and the path taken by the robot is seen in Figure 8a. The corresponding unevenness of the surface (\(U\)), measured using Algorithm 1, is plotted onto the map as depicted in Figure 8b. The resultant RIL-SU heatmap for this site is presented in Figure 8c.  
To validate the RIL-SU heatmap, three different zones with different RIL-SU values were selected in the site similar to the previous experiment. The selected zones are represented as 'L7', 'L8', and 'L9' in Figure 8b,c. The robot was made to pass through the selected locations at its nominal speed and also made to run multiple times around each zone. At location 'L7', the cementitious material holds enough friction between the wheels and the floor for the robot not to undergo slippage and produce any jerk. Hence, the unevenness of the surface is low, which is replicated in Figure 8b. While experimenting at L7, the Meerkat robot mapping and localization were precise, and there were no navigation issues as seen in Figure 9a, which indicates the robot's high inclusivity in this zone. This validates the High RIL in Figure 8c.  
Location 'L8' is chosen where there are wooden planks with an uneven gap in Figure 9b. Thus, a \(U\) value of around 1 was observed (see unevenness heatmap in Figure 8b). As a result of this moderately elevated \(U\), the RIL of that location was tagged as 'Moderate' (see Figure 8c). The Meerkat robot mapping and localization were precise, and there were no navigation issues, which indicates the robot's high inclusivity level in that zone. The robot laser scan initially did not show any drift, but after traversing through that zone multiple times over the period, there were observations of accumulated drift, leading to the medium inclusivity, which validates the RIL in Figure 8c.  
Figure 7: Robot navigation performance in different zones in the printing room during the validation phase. (**a**) Location 4, (**b**) Location 5, and (**c**) Location 6.  
Finally, at location 'L9' in Figure 9c, there is a slightly broken wooden plank, which makes the zone more uneven. The \(U\) in this zone is higher (see Figure 6b), and the inclusivity is tagged as low (see Figure 6c). The Meerkat robot laser scan drifts by a higher range once the robot tries to traverse across this zone (see Figure 9). It implies the robot's ineffectiveness in reaching its destination coordinates, indicating 'Low' inclusivity. The results are in agreement with the generated RIL-SU heatmap.
## 2 Responses of Single-Event Transients in the LDO  
### PLL Architecture  
At present, the commonly used PLL is based on charge pumps, and its overall topology is shown in Figure 1. In the PLL, the clock \(CLK_{OUT}\) generated by the VCO is compared with the reference clock \(CLK_{REF}\) after the frequency divider (DIV), and the CP sources or sinks charges for a controlled amount of time according to the comparison signal \(UP\) and \(DN\). The low-pass filter (LPF) responds to the pump current with voltage a jump, and \(V_{cont}\) controls the frequency of the signal generated by the VCO.  
Figure 1: Conventional charge pump phase-locked loop (CP-PLL) structure. \(R_{p}\) represents the equivalent parallel resistance of the inductor in LC voltage-controlled oscillato (VCO), and \(S_{el}\) controls the constant tank capacitors, \(C\).  
The source of VCO phase noise is electronic noise, power supply noise, and substrate noise; the phase noise directly determines the quality of the clock signal generated by the PLL [28]. The phase noise of the LC VCO can be expressed in Equation (1) [29]:  
\[S_{\phi n}(f)=(\frac{\sqrt{2}}{2}\gamma+1)\frac{\pi^{2}kT}{2R_{p}l_{tail}^{2}}( \frac{f_{0}}{2 Qf})^{2} \tag{1}\]  
where \(f_{0}\) is the VCO oscillation frequency, \(Q\) is the quality factor of the LC oscillator, \(k\) is the Boltzmann constant, and \(T\) is the temperature in Kelvin. Equation (1) shows that the oscillation frequency \(f_{0}\) is related to all capacitors in the LC VCO, including the switch-controlled constant capacitor \(C\), the switch-equivalent capacitor, the variable capacitor \(C_{var}\), and the drain-bulk capacitance of \(M_{1}\) and \(M_{2}\). When the power supply fluctuates, all variable capacitors, including the drain-bulk capacitance of \(M_{1}\) and \(M_{2}\), will be changed with the fluctuation of the power supply, thus changing the VCO output frequency \(f_{0}\), which affects the phase noise of the VCO. Simultaneously, the power supply fluctuation also changes the \(K_{VCO}\) of the VCO, which directly affects the magnitude of the VCO output signal.  
To suppress power-supply noise in VCOs, LDOs are commonly used in modern VCO designs. Any added circuit introduces extra sensitive nodes in the radiation environment; thus, SET-sensitive node analysis for the LDO is necessary. Since the LDO is used to suppress the power supply noise of the VCO and the SET-sensitive nodes in the LDO cannot be analyzed merely by considering the output voltage; therefore, the LDO and VCO need to be analyzed together.
## 1 Introduction
### Key Contributions of the Paper  
The fundamental objective of this article is to illustrate an in-depth overview and potential of 6G wireless communication to perform the abovementioned applications and also to overcome the challenges of massive connectivity and monitoring, secured communication, and the time-critical operations of smart grid communication. Moreover, the prospects of 6G wireless communication in terms of the KPIs for the abovementioned smart grid applications are described in this survey paper. The required KPIs for the considered smart grid applications by utilizing the novel technologies of 6G are discussed elaborately in this article. In addition, the challenges and future research pathways in terms of 6G for the considered smart grid applications are also explained extensively here. This paper mainly emphasizes the 6G-based smart grid applications for the smart city. The principal contributions of this paper are outlined as follows:  
* Section 2 is a description of the novel technologies of 6G for the respective KPI improvements for the considered smart grid applications.
* Section 3 represents the major applications of the smart grid that can be served by 6G wireless communication.
* Section 4 expresses the main challenges along with the potential future research pathways in the realm of 6G wireless communication for the considered smart grid applications.
* This study provides its conclusions in Section 5, and it also provides a concise overview of the key discoveries.
## 3 Results  
### Classification Algorithms  
To compare the effectiveness of different classification algorithms, a database was created from the descriptions of the 573 known postures, as shown in Table 3, and 903 postures which were not correct. Using this database, three algorithms were obtained that tested the sensitivity, specificity and accuracy of values. (Figures 6 and 7).  
The mean sensitivity for the first algorithm was 92.5%, while for the second it was 98.95% and for the third it was 96.5%. Table 5 demonstrates detailed statistical results for three algorithms. Figure 8 shows receiver operator characteristic (ROC) curve results for three algorithms.  
The mean intersection of sensitivity and specificity for the first algorithm was 75.7%, while for the second it was 94.1% and for the third it was 87.7%. The mean accuracy for the first algorithm was 76.6%, while for the second it was 94.9% and for the third it was 89.3%. The area under the ROC curves for the first algorithm was 0.862, while for the second it was 0.986 and for the third it was 0.966.  
Figure 6: Relationship between specificity, sensitivity, accuracy and threshold for: (**a**) Algorithm 1; and (**b**) Algorithm 2.  
Figure 7: Relationship between specificity, sensitivity, accuracy and threshold for Algorithm 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Mean Sensitivity, \%**} & \multicolumn{2}{c}{**Intersection of**} & \multirow{2}{*}{**Mean Accuracy, \%**} & \multirow{2}{*}{**Area under the ROC Curve**} \\  & & **Sensitivity and Specificity, \%** & & \\ \hline Total vector error (A1) & 92.5 & 75.7 & 76.6 & 0.862 \\ Total angle error (A2) & 98.95 & 94.1 & 94.9 & 0.986 \\ Multiplication of vector errors by angle errors (A3) & 96.5 & 87.7 & 89.3 & 0.966 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical results.