esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Highly Sensitive Temperature Microsensors for Localized Measurements
## 2 Materials and Methods  
### Design and Sensing Principle of the Temperature Microsensor  
The temperature microsensor is a RTD with a meander shape. Its sensing principle is based on a metal resistor with a positive temperature coefficient (PTC) in which the electrical resistance increases with the increase of the temperature [23,24].  
The geometry of the RTD studied consist of meander-shaped windings (5-24) of a 200 nm platinum thin film with a line width and spacing of 10 \(\upmu\)m. The width was fixed to 400 \(\upmu\)m and the length is variable as a function of the windings number. Table 1 shows the dimensions of the several temperature microsensors studied and Figure 2 schematically depicts their meander structure. These microsensors were designed in a four-wire configuration, in which the excitation is driven through the external pads and the electrical potential is measured in the inner pads. This configuration allows reading the electrical potential with high accuracy without interference from the lead wires used for the sensor excitation [25].
# Online Foot Location Planning for Gait Transitioning Using Model Predictive Control
## 5 Results
### Walk with a Fixed Gait  
The UPMPC can also be used for walking with a fixed gait. Let the gait be trotting and the foot location planning method be UPMPC. In this gait the quadruped robot accelerates from stepping in place to 1.6 m/s. Each speed increase is 0.1 m/s with time interval of 30 gait periods (12.6 s). The roll, pitch and yaw changes during this process are shown in Figure 11.  
The quadruped robot has a small spike at each acceleration and the amplitude of the oscillation increases as the speed increases. Yaw has deviated from the stable value. However, it basically oscillates back and forth around the stable value. The peak values of posture has not yet exceeded \(\pm\)0.03 rad (1.72 deg) which is so small that it can be ignored. With the same control method, the use of UPMPC also allows the quadruped robot to  
Figure 11: (**a**) The change of roll in trottting. (**b**) The change of pitch in trottting. (**c**) The change of yaw in trottting.  
Figure 10: The figure shows the force changes in gait transitioning between flight trotting and trotting at 1.2 m/s. The numbers 1, 2, 3, 4 and 5 in (**a**,**c**), indicate gait transitioning occuring. The horizontal coordinate of these numbers in the diagram represents the exact time of the transitioning. The transitioning process is: trotting (0–1.4 s (number 1)), flight trotting (1.4–4.26 s (number 2)), trotting (4.26–8.08 s (number 3)), flight trotting (8.08–10.94 s (number 4)), trotting (10.94–14.11 s (number 5)) and flight trotting (14.11-final). The numbers 1, 2, 3, 4 and 5 in (**b**,**d**) denote the peak values of the corresponding forward forces or lateral forces after the gait transitioning. Furthermore, the number 6 indicates the mean of the absolute value of the corresponding forces over the whole time. (**a**) Change in forward force at 1.2 m/s gait transitioning. (**b**) Comparison of the peak values of forward force after transitioning and mean of the absolute values of forward force in the whole time for the two methods at 1.2 m/s. (**c**) Change in lateral force at 1.2 m/s gait transitioning. (**d**) Comparison of the peak values of lateral force after transitioning and mean of the absolute values of lateral force in the whole time for the two methods at 1.2 m/s.  
advance at a uniform speed and accelerate steadily. Therefore, UPMPC can be applied not only in the process of gait transitioning, but also in the planning of foot location under fixed gait.
## 4 Feeder Selection with Reinforcement Learning
### Routing with Q-Learning  
The agent updates the Q-values based on the combined cost computed by the measured packet-loss ratio and round trip time (RTT) delays as given in (2). At time \(ft\), the agent observes the state of the environment \(s^{ft}\), selects an action (feeder link) \(a^{ft}\) based on the policy and receives an immediate payoff \(cost^{ft}\), observes the subsequent state \(y^{ft}\), and adjusts its Q-values based on a learning rate \(\alpha\):  
\[Q^{ft}(x,a)=\begin{cases}(1-\alpha)Q^{ft-1}(x,a)+\alpha(cost^{ft}+\gamma\min_{a }Q(y^{ft},b)),\text{where, }x=x^{ft},a=a^{ft}.\\ Q^{ft-1}(x,a),\text{ otherwise.}\end{cases} \tag{6}\]  
where the constant \(\gamma\) is a learning rate and \(\alpha\) is an averaging constant. The feeder link selection uses an \(\epsilon\)-_greedy_ approach, where a feeder link is selected at random with probability of \(\epsilon\). Otherwise the feeder link with the lowest Q-value is selected, since the Q-values represent costs. By selecting the cost as the payoff, the agent tries to minimize both the average latency and packet-loss ratio at every state. The Q-Learning approach is described in Algorithm 4.  
\(ft>>mt>>dt\)  
```
1:Initialize:
2:\(0<\alpha,\gamma,\epsilon<1\)
3:while true do
4:\(delays=[d_{1}\), \(d_{2}\),..., \(d_{N}]\), where \(N\) is Number of links \(\triangleright\) Repeat every \(ft\)
5:\(pkt\_loss\_rate=[pkt\_loss\_rate_{1}\), \(pkt\_loss\_rate_{2}\),..., \(pkt\_loss\_rate_{N}]\)
6:for\(i=1\) to N do
7:\(cost_{i}\gets k\)\(pkt\_loss\_rate_{i}+(1-pkt\_loss\_rate_{i})\)\(d_{i}\)
8:endfor
9:\(minqval\gets min(qvals)\)
10:for\(i=1\) to N do
11:\(qvals_{i}\gets qvals_{i}+\alpha\)\((cost_{i}+\gamma\minqval-qvals_{i})\)
12:endfor
13:if\(random.random()<\epsilon)\)then
14:\(bestindex\gets random.randint(0,N-1)\)
15:else
16:\(bestindex\leftarrow\text{argmin}\left\{qvals\right\}\)
17:endif
18:return\((bestind)\)
19:endwhile
```  
**Algorithm 4** Latency and Packet-loss Optimized Q-Learning Link Selection
## 4 Discussion  
The primary aim of this study was to evaluate the amount of RE benefit amateur athletes can realistically expect when switching running gear from their traditional training shoes to CPRSs and to compare this benefit across CPRS models from different manufactures for the same cohort. As expected, all models of CPRS significantly reduced mean ECT when compared to traditional training shoes with percentage differences from \(-1.52\%\) to \(-2.80\%\). These effects are similar in magnitude to previously reported findings of CPRS/AFT effects when compared to regular running shoes [32] and racing flats [22; 23; 24]. Interestingly, all CPRSs tested showed an improvement of more than \(1.5\%\) in RE, a level not achieved by several other current manufacturers [31; 43; 44]. Based on mean group-level RE improvement, the CPRS models rank as follows (in the order of worst-performing to best-performing): Puma Fast-R Nitro Elite (\(-1.52\%\) ECT), Saucopy Endorphin Pro 3 (\(-2.31\%\) ECT), Mizuno Wave Rebellion Pro (\(-2.57\%\) ECT), and Hoka Rocket X2 (\(-2.80\%\) ECT). Notably, regarding pairwise comparisons, only the differences between Hoka Rocket X2 and Puma Fast-R Nitro Elite were statistically significant. When considering the percentage of runners experiencing RE benefits of a given magnitude, however, rankings change and substantial differences between CPRS models become apparent. At \(1\%\) improvement, the Saucony Endorphin Pro 3 performed best with \(89.5\%\) of runners experiencing at least this amount of ECT reduction, closely matching values recently reported for the Nike Vaporly \(4\%\)[32]. In contrast, reductions of at least \(1\%\) ECT were induced in \(73.7\%\) of runners for HOK, \(63.2\%\) for MIZ, and in \(52.6\%\) for PUM, indicating increasingly higher numbers of non-responders. Conversely, at \(4\%\) improvement, MIZ and HOK performed similarly well with \(36.8\%\) and \(31.6\%\) of runners reducing their ECT by this amount. Both CPRS models exceed values reported for the Nike Vaporly \(4\%\)[32]. In contrast, SAU and PUM were substantially less beneficial, providing (at least) \(4\%\) of ECT reduction only to \(15.8\%\) and \(10.5\%\) of runners, respectively. A comparison of the structural parameters of the various CPRS models, as outlined in Table 1, with the corresponding experimental RE results reveals some noteworthy insights. The PUM model is the only CPRS model to utilize solely EVA foam in the heel section, with the remaining models employing either PEBA or a combination of EVA and PEBA (e.g., the MIZ) in this region of the shoe. A recent study has indicated that footwear with PEBA foam incorporated into the midsole results in a lower ECT than footwear with EVA foam midsoles [45]. It may thus be reasonably deduced that the structural distinctiveness of the PUM shoes, which feature PEBA foam only in the forefoot section and EVA in the heel section, might have had a detrimental effect on the potential for RE improvement, given
## 2 Delaunay Triangular Mesh  
### Delaunay Theory  
In the context of a given set of discrete points, as p = {p1, p2,..., pn}, a Delaunay triangular section can be obtained by connecting any two points with a straight line, ensuring that line segments connect all points and that no two line segments intersect at any location other than the vertex. The three vertices of a triangle can determine its circumcircle. If this circumcircle contains no additional points, considering it is empty, it is classified as a Delaunay triangle. Conversely, if it locates the remaining vertices of the triangle within the circumcircle, it is considered non-empty and classified as a non-Delaunay triangle.  
The determination of whether a triangle is a Delaunay triangle requires the aid of the Delaunay criterion: (1) The empty circle property: the Delaunay triangulation net is unique (no four points can be co-circular), and no other points can exist within the outer circle of any triangle in the Delaunay triangulation net; (2) Maximizing the minimum angle property: the minimum angle is the largest among the possible triangles formed by the collection of scatter points' triangular dissections. During the dissection process, these two criteria minimize the likelihood of narrow and irregular triangles appearing, and the mesh pattern generated is unique for the same set of discrete points.
## 3 Results and Analysis
### Evaluation of Cooling Energy Performance
#### 3.1.3 Cooling Coil Loads  
Based on the results from Section 3.1.2, the cooling coil loads were anticipated to decrease owing to the reduced MA temperatures. Section 3.1.3 describes the evaluation of the cooling coil loads by varying MLSP. Figure 6 shows the cooling coil loads for each case, calculated using the following equation:  
\[q=\rho\times Q\times C_{p}\times(t_{MA}-t_{SA}), \tag{1}\]  
where \(q\) is the cooling coil load, \(\rho\) is the air density, \(Q\) is the air flow rate, \(C_{p}\) is the specific heat capacity, \(t_{MA}\) is the MA temperature, and \(t_{SA}\) is the supply air temperature.  
The cooling coil loads for Cases #2 to #7 decreased by 3.9%, 7.3%, 10.0%, 11.2%, 11.2%, and 10.7%, respectively, compared to Case #1. This reduction was primarily due to the decreased temperature differential between the MA and supply air temperatures. Case #5 exhibited the largest reduction in cooling coil loads, owing to the effective use of maximum OA intakes under available conditions. These results indicate that the cooling coil loads for Cases #2 to #7 were reduced relative to Case #1, driven by the diminished difference between the MA and supply air temperatures.  
Figure 5: MA conditions by varying MLSP: (**a**) MA dry-bulb temperature; (**b**) MA absolute humidity.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Simulation Cases** & **Case \#1** & **Case \#2** & **Case \#3** & **Case \#4** & **Case \#5** & **Case \#6** & **Case \#7** \\ \hline Average MA & 18.13 & 17.95 & 17.80 & 17.64 & 17.55 & 17.56 & 17.58 \\ dry-bulb temperature [\({}^{\circ}\)C] & & (0.18\(\downarrow\)) & (0.33\(\downarrow\)) & (0.49\(\downarrow\)) & (0.58\(\downarrow\)) & (0.57\(\downarrow\)) & (0.55\(\downarrow\)) \\ \hline Average MA absolute & 10.12 & 10.08 & 9.95 & 9.77 & 9.70 & 9.64 & 9.63 \\ humidity [kg/kg\({}^{\circ}\times 10^{3}\)] & & (0.04\(\downarrow\)) & (0.17
# LoRa Architecture for V2X Communication: An Experimental Evaluation with Vehicles on the Move
## 3 Related Works in LoRa V2X and Applications
Moreover, most of these research works consider WAN architecture which doesn't allow direct V2V or V2I communication, rather data is routed through the gateway to the destination. It hampers the overall latency of the communication making it unsuitable for real-time ITS applications like accident avoidance. The proposed research work aims to address these challenges by developing a D2D based reliable and low power LoRa architecture and evaluate it with vehicles on the move under practical scenarios with the dynamic environment at a higher speed of the vehicles.
# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
## 4 Characterizing Heavy Rain Event  
### Outage Pattern  
The U.S. Department of Energy (DOE) tracked the outage distribution for each power outage caused by major hurricane events [31]. This research reported that the number of outage customers rose within a small number of hours of the event. It suggests that the power outage caused by weather events may be observed to be a general pattern.  
Therefore, we can expect that the outage distribution caused by heavy rain may follow a general pattern, similar to hurricane events. Figure 1 illustrates the normalized damage distribution for several heavy rain events. These heavy rain event durations and the number of damages were normalized into between 0 and 1 using its maximum value. Therefore, different heavy rain event durations and the number of damages can be compared over the same axis range. Consequently, Figure 1 shows that the peak number of damages often occurred close to the end of the heavy rain event time. In the case of a hurricane, the system experiences a strong weather attack early in the event. Conversely, heavy rain consistently hits the system with a relatively weak attack, causing the peak outage to occur near the end of the event. Therefore, it can be concluded that the power outage events may occur due to prolonged stress caused by accumulated rainfall under heavy-rain conditions.
## 2 Materials and Methods  
### Materials  
DOPC and DPPC were obtained from Avanti Polar Lipids (Alabaster, AL, USA). PEG-PE was obtained from Funakoshi Co., Ltd. (Tokyo, Japan). PLLA (_Mw_ = 5000, 10,000, 20,000, and 50,000) was purchased from Wako Pure Chemical (Hiroshima, Japan). PMMA (_Mw_ = 400,000 and 500,000), PSf (_Mw_ = 75,000), and PVP K90 (_Mw_ = 1,200,000) (supplied by Nacalai Tesque) were gifted by TORAY Co. Ltd. (Shiga, Japan).  
A\(\beta\) with forty amino acid residues was purchased from the Peptide Institute (Osaka, Japan); its sequence was as follows: H\({}_{2}\)N-Asp-Ala-Glu-Phe-Arg-His-Asp-Ser-Gly-Tyr-Glu-Val-His-His-Gln-Lys-Leu-Val-Phe-Phe-Ala-Glu-Asp-Val-Gly-Ser-Asn-Lys-Gly-Ala-Ile-Gly-Leu-Met-Val-Gly-Gly-Val-Val-COOH. Thioflavin T (ThT) for monitoring fibrillization was obtained from Dojindo (Kumamoto, Japan). All other reagents used were of analytical grade.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 4 Hyperbranched Polymer Toughening
### Current Research
#### 4.2.3 Hyperbranched Polysiloxanes  
Hyperbranched polysiloxanes (HBPSs) are a type of polymer material characterized by a three-dimensional network structure composed of silicon, oxygen, and organic groups, synthesized through specific polymerization reactions [87]. This structure endows HBPS with unique physicochemical properties, such as excellent thermal stability, superior me chanical performance, and low surface energy. In the context of toughening epoxy resins, HBPS has garnered attention for its effective toughening capabilities [88].  
[PERSON] et al. [89] synthesized an epoxy-terminated hyperbranched polysiloxane (EPTS-12) and incorporated it as a toughening agent into epoxy resins. Both at room temperature and low temperatures, the tensile strength and elongation at break of the epoxy resin increased with the addition of EPTS-12, initially rising and then declining. When the EPTS-12 content was 5 wt%, the maximum tensile strengths were 89.55 MPa at room temperature and 116.68 MPa at \(-\)72 \({}^{\circ}\)C, while the maximum elongations at break were 14.19% and 16.06%, respectively. These values represent increases of 35.66% and 74.19% compared to the unmodified resin.  
[PERSON] et al. [90] utilized the A2 + B3 \"one-pot\" method to synthesize a novel silicon-containing hyperbranched polysiloxane (HAHBP) for the modification of epoxy resins (Figure 6A). The study demonstrated that, compared to unmodified resins, the addition of HAHBPs significantly improved the mechanical properties of the epoxy resin. Specifically, as shown in Figure 6B, incorporating 3%, 8%, 16%, 20%, and 30% HAHBPs resulted in increases of 2.6%, 12.0%, 13.0%, 25.7%, and 13.0% in flexural strength, respectively. Additionally, the impact strength of the epoxy resin increased progressively with the amount of HAHBPs added, with an 8% HAHBP content improving the impact strength by 70%.  
Figure 6: (**A**) (**a**) Synthesis of HAHBPs and (**b**) Schematic diagram of preparation process of HAHBP  
modified E51 epoxy resin; (**B**) Effect of HAHBP dosage on bending strength and impact strength of E51-DDM/HAHBPs resin system. Reproduced from Ref. [90] with permission of John Wiley and Sons.
## 4 Application Studies
### Comparison with Resilience Index (RI)  
The newly proposed indexes are compared with the widely applied resilience index (RI) for verification. Figures 11 and 12 show the comparison between the value of the criticality indexes and RI for Network 1 and 2, respectively. RI is plotted on a reverse scale to achieve a more comprehensible comparison with the criticality indexes since the value of RI closer to 1 shows a better resilience (less criticality), while a lower RI value indicates less resilience (more criticality). Note that RI measures the excess pressure, thus it can be lower than 0 if the available pressures are lower than the required pressure.  
It can be seen from Figures 11 and 12 that the proposed criticality indexes behave in similar patterns with RI. More importantly, they identically show peaks for the pipes with higher criticality. Note that RI has been widely applied as a representative metric for measuring WDN resilience for normal and abnormal operating conditions. The same manner of responses verifies that the proposed indexes can apply for component-based criticality assessment in WDNs. Furthermore, the criticality indexes intend to quantify four different aspects of WDN functionality, while RI only considers the hydraulic aspect. Having more parameters to consider, the proposed criticality indexes can efficiently identify the critical pipes.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 5 Formation Flying Nanosatellite Bus
### Subsystems Design
#### 5.2.6 Thermal Control Subsystem (TCS)  
Passive thermal control was applied to each satellite using an anodized aluminum frame and a black-colored FR-4 PCB. The battery board includes heaters for heat dissipation, which maintain the temperature of the battery cells above 0 \({}^{\circ}\)C. The on-orbit thermal transient simulation was conducted using an NX10.0 Space Thermal System. Figure 20 shows the exterior temperature contours for the hottest and coldest cases with seasonal eclipse variations. The temperature ranges summarized in Table 11 are within the operating temperature range, which have thermal margins above 10 \({}^{\circ}\)C, whereas the deactivated components are within the survival temperature.
## 2 Channel Reliability Modeling for 5G Communications
### Probability of Reliable Circuit Breaker Operation  
As a typical switch of the distribution network, the circuit breaker will break the branch in time once the fault is found, which is consistent with the function of the traditional power grid circuit breaker, and can act without the control center to issue commands. However, the transmission between the circuit breaker detection, calculation, and action modules can also fail, resulting in switch action failure; such failures should be categorized as secondary equipment failures, so the probability of reliable action of the circuit breaker itself is \(p_{br}\), which is the probability that the switch, being in a normal state, will not fail to act.
## 3 Proposed Method
### Loss Function
Its losses are as follows:  
\[L_{offset}=\frac{1}{k}\sum_{i=1}^{\hat{W}}\sum_{j=1}^{\hat{H}}SmoothL_{1}( \hat{O}_{\hat{S}ij}^{c},\hat{O}_{\hat{p}ij}) \tag{13}\]  
Among them, \(\hat{O}_{\hat{S}ij}^{c}\) represents the ground-truth offset value \(O_{\hat{S}ij}\) after scaling at the \((i,j)\) point and \(\hat{O}_{\hat{p}ij}\) represents the predicted offset value \(O_{\hat{p}ij}\) after scaling at the \((i,j)\) point. Therefore, \(\hat{O}_{\hat{S}ij}^{c}=\frac{O_{\hat{p}}}{s}\), \(\hat{O}_{\hat{p}ij}^{c}=\frac{O_{\hat{p}}}{s}\). For the scale prediction branch, similar to the center offset branch, we also used SmoothL1 loss, except that we took the actual size logarithm, which makes the training process easier to proceed. Its loss is as follows:  
\[L_{scale}=\frac{1}{n}\sum_{k=1}^{n}SmoothL_{1}(\log\hat{S}_{\hat{S}ij}^{c}, \log\hat{S}_{\hat{p}ij}) \tag{14}\]  
where, \(\hat{S}_{\hat{S}ij}^{c}\) represents the scaled \(S_{\hat{S}ij}\) which represents ground truth of the object size at \((i,j)\) point, and \(\hat{S}_{\hat{p}ij}\) represents the scaled \(S_{\hat{p}ij}\) which represents the object size predicted by the model. Thus \(\hat{S}_{\hat{S}ij}^{c}=\frac{S_{\hat{S}ij}}{s}\) and \(\hat{S}_{\hat{p}ij}^{c}=\frac{S_{\hat{p}ij}}{s}\). \(\log S_{\hat{S}ij}^{c}\) and \(\log S_{\hat{p}ij}^{c}\) are used because logarithm loss makes the training process smoother and easier to train. The final detection loss is composed of the above three items which are center loss, offset loss and scale loss. The total loss formula is as follows:  
\[L_{det}=\lambda_{c}L_{center}+\lambda_{o}L_{offset}+\lambda_{s}L_{scale} \tag{15}\]  
In which, the hyperparameter \(\lambda_{c}\) is set to 0.1, \(\lambda_{o}\) is set to 1 and \(\lambda_{s}\) is set to 10.
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 4 Optimizations of the Coil Position Parameters
### Optimization of Coil Turn Number  
A higher quality factor (\(Q_{3}\)) of the external data coil can further improve the SIR as discussed in Section 3.2. Higher \(Q_{3}\) could be obtained by more turns of the external data coil. However, higher \(Q_{3}\) also induces a narrower bandwidth (\(BW=f_{\rm ul}/Q_{3}\)) of the uplink. Therefore, the turn number of the external data coil should be optimized to have a higher quality factor, but at the same time satisfy the requirement of the bandwidth.  
We manufactured a set of external data coils with different numbers of turns. Figure 7 shows the measured quality factor and its bandwidth. The quality factors are measured by using a network analyzer (E5071, Keysight, Santa Rosa, CA, USA) at the uplink carrier frequency (\(f_{\rm ul}~{}=~{}500\) kHz). It can be seen that more turns of the coil contributes to higher quality factor. The bandwidth requirement for our intraocular sensor system is \(BW=50\) kHz to achieve the maximum 50 kbps data rate. Thus, the specified target \(Q_{3}\) for the external data coil is \(Q_{3}=f_{\rm ul}/BW=10\). To derive the target \(Q_{3}\), the number of turns is selected to be 25 according to Figure 7.  
Figure 6: (a) Calculated coupling coefficient \(k_{23}\) as a function of \(d\) versus different \(d_{12}\); (b) calculated coupling coefficient \(k_{12}\) versus different \(d_{12}\) when the coil separation \(d\) is 20 mm.  
Figure 7: Measured quality factor of the external data coil and its bandwidth on the 500-kHz carrier versus the number of coil turns.