esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Stance Detection Method by Fusing Sentiment Features
## 3 Methodology
### Sentiment Feature
#### 3.2.1 Sentiment Lexicon Construction  
The model requires a comprehensive lexicon to work well. In this paper, an open-source sentiment lexicon known as Hownet is supplemented with the manual inclusion of new words to construct a sentiment lexicon. The lexicon consists of five sections, as shown in Table 2. A positive lexicon includes words that express joy, support, and other positive sentiments. A negative lexicon includes words that express disappointment and anger, representing negative sentiments. The adverp lexicon consists of adverbs that modify the degree of sentiments in other words. Negation words include words that express opposite meanings, and they hold a special position in sentiment analysis as they can reverse the polarity of a sentence's sentiment. Some conjunctions and prepositions have minimal impact on expressing sentiments and can be included in the stop-word list, as ignoring them can improve the efficiency of sentence analysis.
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 2 Methods  
Implantable antennas are designed to operate within the human body's tissue, necessitating their design and simulation within a human tissue model for a more precise performance assessment. The initial design and simulation setup were conducted using simulation software, and herein we continue to employ this software to establish a three-layer human tissue model, as depicted in Figure 1. The model consists of skin, fat, and muscle layers from top to bottom, with the designed antenna implanted 2 mm beneath the simulated fat tissue within the muscle tissue.  
The dielectric properties of different human tissues [16] at 2.45 GHz are presented in Table 1. Dielectric properties of different human tissues.  
The implantable antenna designed in this study was subjected to actual measurements within porcine tissue, with the following explanation for the choice of pork for experimentation. While the tissue structures of most mammals share similarities, the dimensions and compositions of these layers vary across different species, making not every mammalian tissue suitable for human body simulation. Swine tissue exhibits dimensional similarities with human tissue [17], and pork is readily available with tissue structures in common pork cuts closely resembling the three-layer human tissue model we designed for the simulation. In [17], the authors employed the coaxial probe method, in conjunction with a Keysight Network Analyzer (model N5225A, Keysight Technologies, Santa Rosa, CA, USA) to measure the dielectric constant and conductivity of pork tissue. The results demonstrated consistency in dielectric properties between pork and human tissues. Therefore, pork tissue is highly suitable for experimental testing of antenna performance, leading to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tissue** & **Relative Dielectric Constant (\(\epsilon_{r}\))** & \
\begin{tabular}{c} **Relative Permeability** \\ **[\(\sigma\) (S/m)]** \\ \end{tabular} &
\begin{tabular}{c} **Loss Angle** \\ **Tangent** \\ \end{tabular} \\ \hline Skin & 38.1 & 1.43 & 0.283 \\ Fat & 5.28 & 0.1 & 0.145 \\ Muscle & 52.7 & 1.74 & 0.242 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dielectric properties of different human tissues.  
Figure 1: Three layer tissue model of the human body.  
decision to conduct experiments within pork tissue. The experimental setup is illustrated in Figure 2.
## 2 Electromagnetic Interference for Stack-Chips and Antenna  
3D stack-chips are becoming common due to the increasing complexity of circuits and the requirement of small outline for an integrated circuit. Various factors can affect their reliability such as the stress induced from TSV [4], thermal dissipation effectiveness in the dense and 3D structure of the chips [5]. One of the advantages of using 3D stack-chip is the low latency of the interconnections for high-speed operation [6]. However, such high-speed operation can also induce electromagnetic field that can couple to the dices above and below, due to their proximity, resulting in serious electromagnetic interference (EMI) between the dices. [PERSON] et al. first examined such issue using electromagnetic computation method apply to integrated circuit.  
They found that near field electromagnetic measurement is ineffective to identify the hot spots of electromagnetic emission within the stacked dies, and they thus developed a computational method to identify the electromagnetic emission hot spots in integrated circuits with experimental verification. They found that if the stack distance between dices is too small, the coupled magnetic field strength can be as high as 16 times as compared with an unstacked IC. Thus, a minimum distance between the stack must be maintained. The method they developed can be carried out at the IC design stage before fabrication, enabling optimization of the stack distance as well as circuit design for reducing the EMI, and eliminating the significant cost of fabrication owing to improper design.  
Using the method developed, they also identified the key dimensional parameters of a miniaturized single element of T-shaped MIMO antenna which is commonly used today for data communication. They found an optimized antenna with 1 GHz improvement in the bandwidth due to the improved return loss that provides new opportunities for the antenna's utilization in different applications. This optimized antenna has lower current distribution that gives lower power dissipation, and its 1 m sphere EMI was also reduced.  
These are examples of using computational power to ensure good performances and lower power dissipation, which in turn can lead to better durability.
# Comparative Analysis of Performance in the High-Bar vs. Low-Bar Squat
## 4 Discussion
Furthermore, although an acoustic signal was given before the participant began the upward movement from the parallel position, causing a pause of 1-2 s, the duration of this pause was not measured. For these reasons, our results should be considered with caution and considering the limitations mentioned above. Several studies have employed different research designs to investigate squat techniques. For instance, some studies have used cross-sectional designs to compare muscle activation and performance metrics between high-bar and low-bar squats, while others have utilized longitudinal designs to assess the effects of training interventions over time. These differences in research design can influence the outcomes and interpretations of the studies. For example, cross-sectional studies may provide a snapshot of performance differences but may not capture long-term adaptations to different squat techniques [13, 22]. From the practical point of view, the main contribution of this investigation was to show the different weights lifted in 1 RM between the low-bar technique vs. the high bar. This approach can help coaches and athletes distinguish between them, because the use of one technique or another means differences in maximum force and weight. Moreover, coaches who quantify the relative intensity through velocity-based training (VBT) should know the speed at which the load is lifted with each technique. Studies with highly trained athletes may yield different findings than those obtained from recreational lifters; this should be taken into account when interpreting the obtained results.
## 2 Problem Description  
### Theoretical Foundations of CPPS State Estimation  
State estimation entails the examination of instrument measurement data within a SCADA system [21; 22], enabling the deduction of the operational state of the power system. This paper addresses the issue of state estimation in CPPS, encompassing a model characterized by n-dimensional measurement vectors and m-dimensional system state vectors. The procedure involves the analysis, processing, and amalgamation of measurement data to derive a precise estimate of the system's state. State estimation plays a pivotal role in power system operation and monitoring, facilitating the timely identification of anomalies by operators, aiding in decision making and fault diagnosis, and affording a comprehensive comprehension of the system's operational condition [21]. By consistently enhancing and optimizing the algorithms and methodologies used for state estimation, the accuracy and robustness of the power system's state can be augmented, subsequently elevating the security and reliability of the system. The state estimation problem can be formulated as follows:  
\[\mathbf{z}=\mathbf{H}\mathbf{x}+\mathbf{e}, \tag{1}\]  
where \(\mathbf{z}=(\mathbf{z}_{1},\mathbf{z}_{2},\dots,\mathbf{z}_{\mathbf{n}})^{T}\) represents the n-dimensional measurement vector of the system, \(\mathbf{H}\) represents the Jacobi matrix, \(\mathbf{x}=(\mathbf{x}_{1},\mathbf{x}_{2},\dots,\mathbf{x}_{\mathbf{m}})^{T}\) represents the system state vector, and \(\mathbf{e}=(\mathbf{e}_{1},\mathbf{e}_{2},\dots,\mathbf{e}_{\mathbf{n}})^{T}\) represents the measurement error.  
In the detection process, the traditional residual comparison method based on BDD plays a significant role. This method discerns the presence of subpar measurements--those that could be either faulty or compromised by malicious attacks--by contrasting the 2-norm of measurement residuals against a predetermined threshold. When the residual value surpasses this established threshold, the detector signals the occurrence of an attack. This technique offers an effective approach to identifying attacks and uncovering anomalies that might potentially jeopardize the system. Consequently, the BDD method relying on residual comparison has emerged as a pivotal element in fortifying the system's security. Through the continuous enhancement and optimization of this detection methodology, the system's attack detection capability can be further elevated, consequently reinforcing the assurance of system security and reliability. The detection approach through residual comparison can be formulated as follows:  
\[R=\|\mathbf{z}-\mathbf{H}\mathbf{x}\|_{2}^{2}\geq\tau, \tag{2}\]  
where \(R\) represents the 2-norm of residuals, and \(\tau\) represents the residual test threshold set by the CPPS.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 7 Evaluation and Discussion
### Proof of Concept Demonstration of Data Plane Forwarding
#### 7.1.2 Computational Overhead  
The dynamic tunnel set-up and the data traffic encryption will increase the need for computational power. The massive amount of encrypted channels can both slow down the link performance and overloading data-center resources. To offload the service plane with encryption processes, a possible  
Figure 26: NSH packet capture.  
solution is to utilise a common encryption component to lower the resource consumption in a data-center. It is expected that a programmable data plane [29] and distributed containers [30] will be more available on enterprise switches and routers. Both of these alternatives can possibly make encryption services run more efficiently.
## 2 Mathematical Models
### MSV Model  
Semivariance, also known as downside variance, is a statistical measure of the dispersion of returns for an asset or portfolio that specifically considers deviations below a defined target value. This metric is particularly relevant for evaluating the downside risk or the potential for significant losses in investments, as it disregards positive volatility (returns above the target), unlike standard variance. This risk measure is as follows.  
Let \(\Re_{ti}\) represent the return of the \(i\)-th security during period \(t\), and let \(k\) denote the investment's acceptable return threshold for the portfolio (target). Then, semivariance is defined as follows:  
\[\textit{semivariance}=\frac{1}{T}\sum\limits_{t=1}^{T}\left[\left(\sum \limits_{i=1}^{n}\Re_{ti}\varepsilon_{i}-k\right)^{-}\right]^{2}, \tag{8}\]  
where  
\[\left(\sum\limits_{i=1}^{n}\Re_{i}\varepsilon_{i}-k\right)^{-}=\begin{cases} \sum\limits_{i=1}^{n}\Re_{i}\varepsilon_{i}-k&\sum\limits_{i=1}^{n}\Re_{i} \varepsilon_{i}<k.\\ \\ 0&\sum\limits_{i=1}^{n}\Re_{i}\varepsilon_{i}\geq k.\end{cases}\]  
So, the MSV model is as follows:  
\[\textit{min}\frac{1}{T}\sum\limits_{t=1}^{T}\left[\left(\sum \limits_{i=1}^{n}\Re_{ti}\varepsilon_{i}-k\right)^{-}\right]^{2}\] \[s.t\sum\limits_{i=1}^{M}\varepsilon_{i}E(\Re_{i})\geq E(\Re_{p}), \tag{9}\] \[\sum\limits_{i=1}^{M}\varepsilon_{i}=1,\] \[\varepsilon_{i}\geq 0,i=1,...,M,\]  
where \(\Re_{p}\) is the portfolio return and \(\varepsilon_{i}\) is the weight of \(i\)-th stock in the portfolio.  
The adoption of semivariance as a risk measure, particularly in financial analysis and portfolio management, stems from the understanding that investors are often more concerned with the risk of losses than with the upside volatility. Consequently, semivariance can provide a more nuanced and pertinent assessment of the risk associated with unfavorable scenarios, informing more conservative investment decision-making.
## 1 Introduction
Increased co-activation of the _rectus femoris_ and _biceps femoris_ is considered a protective mechanism for knee stability [6,18]. Concerning the ankle, no studies have been identified that specifically examine co-contraction in Taekwondo athletes according to their experience level. From what has been presented in the previous paragraphs, it is evident that very little literature exists on co-contraction in Taekwondo athletes as a function of experience level or competitive category [6,11], with no studies existing on the ankle in the antagonist pair ofthe soleus and tibialis anterior. This joint, along with the foot, plays a fundamental role in executing kicks in Taekwondo, serving as the primary point of contact with the opponent and enabling efficient force transfer generated by the leg muscles [16]. Furthermore, additional considerations must be addressed. Firstly, analyzing combat sports requires specific methodological considerations due to their explosive nature. In this regard, careful attention must be paid to sEMG challenges, such as movement artifacts, signal processing accuracy, and factors like skin impedance, adipose tissue, and body composition, all of which can impact signal reliability and quality [19; 20]. Low-fat-mass percentages, adequate muscle mass, and the relationship between body composition and inter-limb asymmetry are significantly associated with performance in male and female Taekwondo athletes [21; 22]; therefore, these variables should be considered in sEMG analyses. Another important aspect is that most previous studies focused on reducing motor gesture variability. [PERSON] et al. [23] suggested that more than 10 repetitions of kicks should be assessed to obtain reliable data on muscle function. [PERSON] et al. [8] analyzed 20 consecutive kicks but reconstructed the sEMG signal by averaging them. Similarly, [PERSON] et al. [14] used 15 kicks and reconstructed the signal by interpolation based on the movement cycle. These studies often relied on averaged or reconstructed sEMG signals from consecutive kicks without thoroughly examining trends in peak EMG activity [8; 14]. [PERSON] et al. [18] were the first to study the effect of repeated kicking actions on the performance of the roundhouse kick in karate. No studies on sEMG have been found that seek to identify changes in peak EMG trends during consecutive kicks in Taekwondo athletes. In this context and given the lack of research on changes in peak EMG activity during consecutive kicks in Taekwondo athletes, the present study evaluated differences in muscle co-contraction and peak EMG activity between novice and advanced Taekwondo athletes during consecutive roundhouse (_handal chagui_) kicks. It also aimed to explore the influence of body composition and experience level on these parameters.
## 1 Introduction
These RDAC systems are generally installed with a desiccant wheel assembly with a high moisture absorption ability in the air-handling unit (AHU) [18]. In an RDAC system, the desiccant wheel is used to enhance the dehumidification performance of a traditional cooling coil. The operation concept of the RDAC system which was used in our study involves a differed enthalpy wheel and a heat-regenerated wheel. The desiccant wheel can absorb water vapor from the air downstream of the cooling coil and then add it back into the air upstream of the coil where the coil removes water through condensation. When the desiccant wheel rotates into the mixed air stream, the desiccant can release water vapor into the air due to it being exposed at a much lower relative humidity (typically 40% to 60%). The characteristics of the desiccant let the wheel be regenerated at low temperatures and do not require the second regeneration air stream. Thus, RDAC systems can save cooling and reheat energy [16]. Medical needs impose strict requirements for uninterrupted and specialized 24-h air conditioning, making hospitals among the most energy-intensive buildings. Furthermore, as ORs have specific air quality requirements, their ventilation conditions are even more stringent, making them among the most energy-intensive areas within hospitals [19; 20; 21]. According to the US Energy InformationAdministration (EIA), HVAC systems account for 30% of the total energy consumption in commercial buildings [22]. The air conditioning systems provide relatively high airflow to hospital ORs and then the energy consumption of ORs increases [23]. To date, few studies have assessed the energy consumption of different dehumidification air conditioning systems, or the indoor thermal-hygrometric conditions and air quality characteristics of ORs at rest, while using different dehumidification systems. Therefore, in this study, we evaluated the energy consumption of the different dehumidification air conditioning systems used in Taiwanese ORs and evaluated the air quality of ORs at rest.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### Tethered UAV  
This system offers longer flight times and support for heavier payloads. It can also deploy a very high-capacity communications channel if fiber-optic networking is used. The key component of this system is the tether, which provides power and data to the UAV from a GCS [28]. T-UAV technology overcomes two limitations of UAVs: reduced flight time and the lack of a reliable backhaul link. This is achieved by a dual link between the GCS and the T-UAV, providing power and data efficiently and securely. The configuration of the T-UAV system consists of three essential components: (i) the UAV, (ii) the GCS, and (iii) the tether or cable. The GCS is located in a strategic area, either on the ground or on a roof, and provides the connection to the central network, which is responsible for data processing and the power resource provided by a source. The tether is used to transmit power and data from the GCS to the UAV, as shown in Figure 4[29].  
Figure 3: Redranging UAV batteries using laser or solar energy.  
Although a T-UAV can fly uninterrupted for days, its mobility is limited to a specific range because of the tether's length. This length depends on the height of surrounding buildings and the angle of inclination of the tether, which must be kept below a specific value to avoid entanglement or accidents. In a real environment, these constraints or factors are reflected as shown in Figure 5. In addition, the line of sight of the T-UAV is conditioned by these two factors.  
Currently, several companies have been working on the development of T-UAVs for commercial use. In their research and development, they have reached a particular flight time and tether length, the details of which are presented in Table 2. In addition, in [28], it is determined that the tilt angle that the tether must have to be safe varies between 10 degrees for suburban areas and 31 degrees for urban areas, with a tether length of 150 m.  
T-UAVs can carry a payload of 3.5 to 6.2 kg, which includes all the processing equipment [29]. In addition to providing power and network connectivity, the GCS could also control the length of the cable to adjust coverage or deal with incidents.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Company Names** & **Tether Length** & **Flight Time** \\ \hline
**Equinox Systems** & 150 m & 30 days \\ \hline
**Arial Insights** & 120 m & Multiple operation \\ \hline
**Elistair** & 80 m & More than 10 h \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of T-UAV systems designed by different companies [28].  
Figure 4: Diagram of a T-UAV system [29].  
Figure 5: Tethered UAV system: coverage, cable length, and tilt angle, based on [28].
## 3 Design Concept
### Choice of Actuator Type  
A special type of spring is chosen as a passive element for the KARE-1 based on the following observations. When the KARE-1 is in the FSSC configuration, the spring force should be equal to the body weight. Since this body weight is constant over different sitting heights, the spring force should be also constant. While a usual type of spring would change force production as sitting height changes, a preloaded gas spring shows small variation over a wide range change in its stroke, as in Figure 3b. Also, a sudden drop in force production at zero stroke is also beneficial to remove interference with leg motion during walking.  
The spring force is approximated by:  
\[F_{s}(L_{s})=\left\{\begin{array}{cc}F_{o}+k\left(L-L_{s}\right)^{n}&\left( \varepsilon\;L>L_{s}\right)\\ \left[F_{o}+k*\left(L-L_{s}\right)^{n}\right]*\frac{L-L_{s}}{\left[L-L_{s} \varepsilon\right]}^{n}&\left(\varepsilon\;L\leq L_{s}<L\right)\\ 0&\left(L\leq L_{s}\right)\end{array}\right. \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Torso** & **Thigh** & **Shank** & **Foot** \\ \hline Mass & 25 kg & 7.5 kg & 4.5 kg & 3 kg \\ Length & 0.90 m & 0.43 m & 0.42 m & - \\ COM & 0.45 m & 0.22 m & 0.21 m & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: A Simple 4-Link Model for Simulation.  
Figure 4: Simulation of standing-to-sitting motion, and vice versa. Simulation result on torque-angular velocity curve computed for knee joint actuation without (**a**) and with (**b**) the extended link mechanism, animated trajectory of saddle stance (**c**) and deep sitting. (**d**) Contour plot of the additional reduction ratio created by the extended link mechanism with respect to the active angle and design variable (**e**).  
where \(F_{o}=380\) N is the preloaded spring force, \(k=2960\) N/m is a nonlinear spring constant, and \(n=1.25\) is the power constant with which the output spring force best fits experimental results. An additional constant, \(\varepsilon=0.99\), defines the stroke length at which the spring force starts to build abruptly from an uncompressed state.