esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of Local Discomfort Indexes and _Pmv_  
### Dissatisfied Percentage Related to Vertical Air Temperature Gradients  
Figure 9 demonstrates the vertical air temperature gradients and relative percentage of dissatisfied for the summer configurations. It shows that when the temperature gradient is \(\Delta T\) < 3 \({}^{\circ}\)C, the percentage of dissatisfied is \(PD_{v}\) < 4% which is considered as comfortable condition for occupants. According to Figure 9, the highest air temperature gradients along the z axis was associated with the minimal inlet air-flow rate, especially when solar radiation was accounted for (Case C). At the minimal flow rate of the fan coil, Stations 2 and 3 were the zones with the most discomfort in terms of \(PD_{v}\). The results indicate that, when the inlet air-flow rate was set at the maximum, the distribution of \(PD_{v}\) was more uniform, and the comfort condition was dominant in both the case with and that without solar radiation.  
Figure 8: Top view of the internal temperature profile obtained for winter heating with low input air velocity (Case E) and high input air velocity (Case F).  
Figure 9: Summer cooling condition. Vertical air temperature gradients (top subfigures) and relative percentage of dissatisfied (bottom subfigures) obtained for low input air velocity and no solar load (Case A), high input air velocity and no solar load (Case B), low input air velocity and solar load (Case C), high input air velocity and solar load (Case D).  
During winter condition, the situation was inverted. There were lower values of \(PD_{v}\) where the minimal air flow rate was accounted for, as shown in Figure 10. In fact, Case F presents values of \(\Delta T\) that ranged from 2 to 3 \({}^{\circ}\)C all over the office except for the zone where the fan coil was located. Case E, instead, had a \(\Delta T\) of 1 and 2 \({}^{\circ}\)C with a consequent \(PD_{v}<1\%\).
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
To overcome these challenges, researchers have explored array antennas, which are well-established for enhancing gain [11; 12]. For instance, ref. [13] proposed a 1 \(\times\) 4 MIMO antenna array for 5G terminals using a corporate feed with elements spaced less than 0.5\(\lambda\) apart, resulting in a directive radiation pattern and low sidelobe levels. However, its large size and semi-flexible substrate made it unsuitable for wearable applications. In [14], an array antenna with frequency-selective structures achieved a stable 12 dBi gain, but its rigid substrate and large volume posed limitations. A leaf-shaped 1 \(\times\) 4 corporate feed array in [2] achieved a gain improvement of up to 3.95 dB but lacked flexibility due to its rigid FR4 substrate. These studies reveal a gap in the development of high-gain, Sub-6 GHz flexible array antenna structures. To address the demand for high data rates and low latency, MIMO antennas have emerged as a promising solution. MIMO structures enhance the channel capacity by integrating multiple antenna elements without increasing the transmission power [15]. While several flexible MIMO antennas have been proposed for the 5G mid-band [16; 17; 18], there is a lack of flexible MIMO array antenna designs, particularly for Sub-6 GHz bands. Although [13] extended its 1 \(\times\) 4 array to a MIMO configuration, the design lacked multiband functionality, remained semi-flexible, and had a large volume. To bridge these gaps, a dual-band two-port Sub-6 GHz MIMO array antenna is proposed in this brief article. This design features a unique DGS that provides a minimum isolation of better than 22 dB across both operating bands (3.6-3.8 GHz and 5.65-5.95 GHz). The antenna exhibits high flexibility and compact dimensions of 57 \(\times\) 50 \(\times\) 0.1 mm\({}^{3}\), with a modified rectangular radiator incorporating L-strips and a reduced ground plane. In the first and second bands, it attains a radiation efficiency and peak gain of 5.2 dBi/92% and 7.7 dBi/91%, respectively. Additionally, the antenna satisfies the MIMO diversity metrics necessary for reliable communication and offers low SAR values. The resonance behavior of the antenna is analyzed using characteristic mode theory, and the design addresses the key limitations in current Sub-6 GHz antenna research.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 6 System Model
### Q-Learning on ns-3  
In order to execute the proposed solution, a new class was added to ns-3. It implements the Q-Learning (QL) algorithm, which will be described in Section 10. Its first version was introduced by the authors of [43].  
The simulation script includes both models described in Section 6.2. The EPC model enables the installation of applications in the UEs, which allows better control over the offered load and the appearance of hotspots during the simulation, considering the applications can be turned on and off at any given time.  
All calculations related to the QL algorithm are made during _runtime_, while the simulation is executed. The algorithm also operates in the system during _runtime_, which guarantees that the network is able to adapt dynamically.
## 3 Results
### Effect of Different Size of Alginate Spheres on DPCs Proliferation
We next investigated whether this convergence was dependent on a particular parameter. We selected several candidate parameters to investigate: input cell number, diameter, surface area and volume of alginate sphere. The number of live cells at day 90 was highly dependent on the volume of the alginate spheres (Figure 2L). We could conclude that the volume itself was a parameter strongly related to this convergence phenomenon given its relationship with cellular number, leading optimal density. The remaining cellular density in average after the convergence was 2892.14 (day 70), 2555.87 (day 80), 2235.86 (day 90) cells per mm\({}^{3}\) at the moderate size, 3012.32 (day 70), 2661.76 (day 80), 2506.64 (day 90) cells per mm\({}^{3}\) at the middle size and 4799.31 (day 70), 4253.93 (day 80), 3872.16 (day 90) cells per mm\({}^{3}\) at the small size respectively. The number of cells per mm\({}^{3}\) after convergence tended to increase as the volume of the sphere decreases. Figure 2: Short-term and long-term cultivation of dermal papilla alginate spheres in different sizes. **(A–D)** Different sizes of alginate spheres with human DPCs. **(A)** Small size (100–200 \(\upmu\)m). **(B)** Middle size (300–500 \(\upmu\)m) **(C)** Moderate size (500–1000 \(\upmu\)m) **(D)** Large size (more than 1500 \(\upmu\)m). **(E)** Live cell number in different sizes of alginate spheres. **(F)** The number of total cells included live and dead cells in different size of alginate spheres. **(G)** The ratio of live cells to total cells in different sizes of alginate spheres. **(H)** The distribution of cell numbers in different sizes of alginate spheres. **(I–K)** Live cell number in small-size, middle size and moderate size respectively. **(L)** Normalized live cell number with the volume of the alginate spheres in different sizes of spheres. (Cell number/mm\({}^{3}\)). Note: *: \(p\)-value <0.05, ***:**\(p\)-value <0.01, compared to day 0 (control). Red color: Small size (100–200 \(\upmu\)m), green color: Middle size (300–500 \(\upmu\)m), Blue color: Moderate size (500–1000 \(\upmu\)m). Scale bar: 100 \(\upmu\)m.
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 2 Fundamentals of QZS  
### Basic Principle of QZS  
The QZS system is composed of a positive stiffness structure and a negative stiffness structure through parallel connection. Positive stiffness refers to a material or structure's ability to resist elastic deformation under external forces, providing the primary load-carrying capacity in a QZS system. The opposite is true for negative stiffness, in which the direction between force and displacement is opposite in this type of structure and does not provide load-carrying capacity [18]. According to the basic principle of vibration isolation, the total stiffness of the parallel elastic element system is the sum of the elastic element stiffnesses, \(k=k_{1}+k_{2}\). As shown in Figure 2, in the negative stiffness region of the negative stiffness element, the total stiffness of the system is close to zero, and in the non-negative stiffness interval, the parallel system has a greater load-carrying capacity compared to a single positive stiffness element. Therefore, by reasonably selecting the positive and negative stiffness elements to isolate the system from low-frequency vibration in the neighborhood [x1, x2] of the desired static equilibrium position, it is possible to design a vibration isolation device with both high load-bearing capacity and low stiffness characteristics. Compared with the single elastic element system, it can play a vibration isolation effect in the ultra-low frequency, and through the appropriate structural design, it can enhance its energy dissipation characteristics to further improve the vibration isolation effect, which is the optimal choice for the current low-frequency vibration isolation.
## 5 Beam Profile  
The beam profile of the MIR beam after a Ge (\(t=0.5\) mm) filter measured with a pyroelectric camera (Pyrocam III, Spiricon) is shown in Figure 5(a). The shape of the beam was a ring and the angle of the cone was estimated to be about 3\({}^{\circ}\). The cone angle of the MIR beam can be explained by phase matching condition based on a straightforward integration of the FWM response over the beam overlap region [12]. The angle is similar to that of the previous experiment with argon [22] since the dispersions of argon and nitrogen are almost the same and the differences in the phase matching conditions between the two cases is negligible. Symmetry of the ring pattern becomes much better than the previous experiment [22]. In the previous experiment, a periscope was used to align the polarizations of the input pulses. In contrast, a dual wave plate was used in the current experiment. As a result, the mode matching between the two input pulses has improved and the symmetry of the beam became much better.  
As is the previous experiment [22], the generated MIR pulse has basically pure one-direction linear polarization (\(>\)40:1) in the entire cross-section of the beam as the input pulses, which was confirmed  
Figure 5: **(a)** Experimental radial intensity distribution 250 mm after the generation point (on CM2 in Figure 1(a)) of the MIR pulses; (**b**) The intensity distribution at the focal point of the MIR pulse focused with a concave mirror (\(r\) = 2 m).  
with a wire grid polarizer (NT62-774, Edmund Optics). This suggests that the generation process of the MIR pulse is different from Cherenkov emission through which THz pulses were generated in [47] with their polarization perpendicular to the emission cone surface. The \(\sim\)12 mm diameter beam was focused down to 0.9 mm (at \(1/e^{2}\)) with a concave mirror (\(r\) = 2 m), indicating a reasonable focusability for a ring-shaped spatial mode. The beam profile at the focal point is shown in Figure 5(b).
## 2 Related Work
In this part of the related work, the use of Blockchain technology in IoT device authentication is discussed. Blockchain technology is used to store device identification information to facilitate IoT device authentication using a distributed ledger [18]. Blockchain technology is also used in registering and authenticating IoT devices in smart city applications. In this research, an API gateway is developed that can be used by IoT devices and the network gateway to sign, identify, and authorize messages [19]. The authors in [20] advocated against using centralized third-party-based identification mechanisms for IoT devices and advised to use of identity-based self-authentication algorithms using Blockchain technology. The proposed authentication method provides a cross-domain access control-oriented authentication mechanisms. Device authentication in smart dust IoT systems is difficult because it includes a very large number of devices. Hence, researchers in [21] proposed a lightweight Blockchain scheme by reorganizing the linear block structure of the conventional Blockchain. This binary tree-structured lightweight Blockchain helps to reduce the device authentication time by an average of 10%. In one research study [22], the authors proposed cluster-based authentication for IoT devices using Blockchain technology. In this authentication method, IoT devices are locally grouped and one cluster head is assigned to each group that leads the authentication mechanism for its group members. In contrast to this research, our proposed mechanism deals with global authentication of geographically distant or multi-entity controlled located IoT devices. Researchers also used Blockchain technology with probabilistic models and random numbers to authenticate IoT devices [23]. In order to secure access to sensor data, a lightweight authentication architecture is proposed using private Blockchain technology [24]. In this method, a scalable and energy-efficient proof-of-authentication consensus algorithm is used. A cost-effective authentication mechanism using the modified Lamport-Merkle digital signature method is proposed for signature generation and verification in medical IoT for blockchain-based fog/cloud IoT network [25]. The study of related work shows that not enough work is carried out in group authentication using a decentralized approach, such as Blockchain technology. The existing group authentication protocols are proposed for the group of IoT devices that are controlled by a single entity and no framework is proposed for the scenario, such as a group of IoT devices controlled by different entities.
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 4 Scheduling for Adaptive Time-Triggered Systems
### Establishment of Multi-Schedule Graph
#### 4.5.1 Metascheduler
In addition, the precedence constraints ensure that all nodes obtain context information about \(c\) that requires this information. For this purpose, the successor nodes of schedule \(v\) are determined, which exhibit schedule differences compared to \(v\). In these schedules, we determine the endsystems where message transmissions or task executions are changed. These endsystems become context event recipients, and the path-index \(C_{0}\) must ensure that the context information about \(c\) is delivered to them. Formally, we introduce a helper function \(ES^{*}\) for determining whether an endsystem \(e\) is a sender or receiver of a message \(m\) in the schedule \(x\):  
\[ES^{*}(x,m,e)=\left\{ \begin{array}{ll}true&G[m]=(t_{1},t_{2})\\ &\wedge(A_{x}[t_{1}]=e\lor A_{x}[t_{2}]=e)\\ false&\text{otherwise}\end{array} \right. \tag{1}\]  
Based on this function, we can define the set of endsystems, which are recipients of context information:  
\[ \begin{array}{l} recipients=\left\{\ e\in\left\{1,\ldots,ne_{v}\right\}\ \right|\\ \exists m\text{ with}\left(P_{x}[m]\
eq P_{0}[m]\lor T_{x}[m]\
eq T_{0}[m]\right) \wedge ES^{*}(x,m,e)\\ \lor\ \exists t\text{ with}\left(A_{x}[t]\
eq A_{0}[t]\lor R_{x}[t]\
eq R_{0}[t]\right) \wedge A_{x}(t)=e\ \}\end{array}  \tag{2}\]  
An endsystem \(e\) is a recipient of context information \(c\) from a schedule if the schedule of a message or task is different in a predecessor node and the endsystem \(e\) is a sender or receiver of the message or the allocated endsystem of the task.
## 4 Model Development
### Bus Passenger Boarding Station Inference  
Nowadays, most studies focus on estimating the missing bus alighting station and time by integrating SC data, GPS data, scheduling plans, and others [31]. Different from these previous studies, this study only estimates the boarding stations at which the passengers swipe their smart cards. In practice, this is not difficult to obtain by associating the SC transaction timestamp, the bus vehicle GPS coordination and timestamp, and the static station coordinates.  
Normally, the GPS system sends the current timestamp and location in longitude and latitude to the operation center after the bus enters the station. Then, the passengers get on this bus and swipe their smart cards to pay. Finally, the bus vehicle departs from the station and the GPS system records and uploads the bus location and timestamp to the center. Therefore, the passengers' SC swiping time should be between the bus arrival time and departure time recorded by GPS system. However, some passengers swipe their cards when the bus leaves from the station because they forget it or there is no time to swipe during the morning and evening peak hours due to bus overloading. Figure 4 shows the relationship between the SC timestamp and bus arrival time.  
Figure 4: The sequence of card swiping time and bus arrival time.  
The Shenzhen ground-level bus GPS system dataset records two checkpoints at each station. The first point is the arrival time. Subsequently, it is necessary to estimate the passengers' boarding station and time because the SC system does not record this information. On one hand, if the passenger's SC touch time is between the bus \(n\) arrival time at station \(l\) and that at the adjacent station \(l+1\), one can assume the current station \(l\) is the boarding event. On the other hand, if the passenger's SC touch time is a little earlier than the bus \(n\) arrival time at station \(l\) but the time difference is less than the user-defined elastic time \(\delta\), it is also considered that the passenger takes this bus vehicle \(n\) at station \(l\). The passenger's boarding station in the two previous scenarios can be expressed as below:  
\[\begin{array}{l}l_{m,n,p}^{B}=l,\text{ if }t_{m,n,l}^{GPS}\leq t_{m,n,p}^{SC}<t_{m,n,l+1}^{GPS}\text{ or }\\ \left(t_{m,n,l}^{GPS}>t_{m,n,p}^{SC}\text{ and }t_{m,n,l}^{GPS}-t_{m,n,p}^{SC} \leq\delta\right)\end{array} \tag{2}\]  
where, \(l_{m,n,p}^{B}\) denotes the passenger's \(p\) boarding station while taking bus vehicle \(n\) belonging to route \(m\); \(t_{m,n,p}^{SC}\) means the SC swiping time of the passenger \(p\); \(t_{m,n,l}^{GPS}\) is the bus \(n\) arrival time at station \(l\) recorded by the GPS system; and \(\delta\) is the user-defined elastic time.
## 3 Results  
The average morning rMSSD was 52.43 \(\pm\) 16.77 ms (95% CI: 51.54-53.31 ms), while the average resting heart rate (HR) in the morning was 63.68 \(\pm\) 4.97 bpm (95% CI: 63.42-63.94 bpm). During the competition week, the rMSSD trend was, on average, 7.51 \(\pm\) 18.37% lower than the preceding 60-day average. On competition days, the average resting HR was 68.79 \(\pm\) 6.24 bpm, indicating a 3.25% increase from the overall average. The average rMSSD on competition days was 44.08 \(\pm\) 16.13 ms, 4.4% lower than the general daily average.  
The analysis indicated a statistically significant positive effect of sleep quality (Figure 1a) on rMSSD (_p_ < 0.001). The model considered fixed effects and random daily variations, allowing for a better understanding of the relationship between sleep quality and heart rate variability. However, the duration of sleep (Figure 1b) did not have a significant effect on rMSSD (_p_ = 0.267). The random effects parameters reveal substantial day-to-day variability in rMSSD (SD = 14.01), suggesting that additional daily factors influence heart rate variability.  
The model examined the menstrual cycle in relation to HRV and identified a statistically significant effect of the follicular phase on rMSSD (_p_ = 0.003). The analysis showed an increase in rMSSD during the follicular phase (Figure 2a). The average HRV (rMSSD) during the follicular phase was 53.76 \(\pm\) 29.37 ms compared to 51.05 \(\pm\) 27.90 ms in the lute phase. Substantial daily variability was observed in both baseline rMSSD values and the impact of the follicular phase on rMSSD.  
Illnesses were recorded within 60 days during a four-year monitoring period (Figure 2b). The average rMSSD during illness was 50.46 \(\pm\) 21.51 ms compared to \(52.50\pm 16.57\) ms when illness was absent. The model indicates that illness has no statistically significant effect on rMSSD (\(p\) > 0.05). The random effects parameters suggest substantial day-to-day variability in the baseline rMSSD and its relationship with illness.  
In this longitudinal case study, we analyzed the impact of the acute training load (ATL) on HRV over a competitive season (Figure 3). The model indicates that the ATL has no statistically significant effect on rMSSD (\(p\) = 0.94).  
Figure 1: (a) rMSSD measured in relation to various levels of sleep quality, as subjectively determined, and (b) rMSSD as measured for different sleep times, as objectively determined. * indicates \(p\) < 0.05, and ** indicates \(p\) < 0.001.  
Figure 3: Training load and HRV (rMSSD) trends over time.  
Figure 2: rMSSD measured during (a) the luteal and follicular phases and (b) during healthy and sick days. * indicates \(p\) < 0.05.  
The results further suggest that despite daily variability in the competition performance index, rMSSD1 is not a statistically significant predictor of performance (\(p\) = 0.82). Similarly, the rMSSD trend does not significantly predict performance (\(p\) = 0.70).  
On average, the HRV trend during competition weeks was 7.51 \(\pm\) 18.37% lower than the preceding 60-day average, while the HRV on competition days was 4.4% lower than the overall average.
## 4 Analysis and Results
### Human Response at Grid Scale
Hotspots in Zhongguancun, Chaoyang CBD and IT Park have a highly mixture of multiple types of residential, business, and education POIs. There is no significant difference in the proportions of the POIs in other hotspots. Figure 13a shows the binary relationship between the different levels of _HERI_ and rainfall amounts. We classify the _HERI_ and rainfall amount into three groups (high, medium, and low) using the Jenks  
Figure 12: Spatial distribution of the _HERI_ values (**a**); _HERI_ hotspot analysis results (**b**); Percentage of POI types in each HERI hotspot area (**c**). natural breaks, respectively. In total, there would be nine combinations between the different levels of _HERI_ and rainfall amounts. The HH combination (high _HERI_ and more rainfall) is located in suburbs, such as Fangshan District and Xi'erqi, which were hit by heavy rains and afflicted with serious rainstorm-induced damages and losses. The combination (HL) with a higher HERI and less rainfall is mainly found in densely populated areas, including the urban core area, Tongzhou District, CBD and the Beijing Capital Airport. The texts of the RRWP show that people in these areas complain that the rainstorms caused significant traffic jams and ruined their daily routine. Passengers trapped in the Beijing Capital Airport also published more RRWP due to the significant flight delays. The LH combination (low _HERI_ and high rainfall amount) is mainly in the sparsely populated regions, where few RRWP were posted due to the fewer number of the Weibo users. Figure 13b shows the POI types in the different combinations of the _HERI_ and rainfall amount levels. The areas with a higher _HERI_ value tend to have more transportation POIs, no matter what the rainfall amount is. By contrast, places with fewer RRWP and higher rainfall levels are less populous and with more green space. Figure 13: The ENRR values across our study area (**a**) and the proportions of different types of POIs within the areas with different ENRR values (**b**).