esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Consistency of Precise Products from Different Analysis Centers
#### 3.3.4 BDS
As for the SISRE difference between the real-time and post-processed products, it changes from 4.8 to 5.4 cm for BDS-2 MEO satellites and from 6.8 to 7.4 cm for BDS-3 MEO satellites, respectively. Compared with GPS satellites (MEO satellites), except for the consistency results between GFZ and WHU precise products, the SISRE difference among different ACs of BDS-2 and BDS-3 MEO satellites is expanded by a factor of two to three times. It should be noticed that there are two manufacturers for the BDS-3 satellites. One is the China Academy of Space Technology (CAST), and the other one is the Shanghai Engineering Center for Microsatellites (SECM). The CAST and SECM satellites (used for the analysis of product consistency) are equipped with two different types of atomic clocks, namely rubidium clocks and hydrogen masers, which has an impact on the quality of onboard clock stability, and thus also affects the SISRE statistics and the quality of position determination. In addition, the two types of BDS-3 satellites also have different construction and dimensions. For further analysis, the consistency results shown in Table 6 between GFZ and WHU for BDS-3 MEO satellites are re-computed by dividing them into two groups of satellites, namely CAST and SECM satellites. The results indicate that the statistics are 2.6, 5.4 and 5.1 cm, 0.11 ns and 2.8 cm for CAST satellites in terms of radial, along-track and cross-track orbit difference, clock difference and SISRE difference, respectively, while the corresponding statistics are increased to 2.5, 6.4 and 5.6 cm, 0.14 ns and 3.4 cm for SECM satellites, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multirow{2}{*}{**ACs**} & \multicolumn{4}{c}{**ACs (for BDS-2 MEO)**} & \multicolumn{4}{c}{**ACs (for BDS-3 MEO)**} \\ \cline{3-8}  & & **GFZ** & **WHU** & **CNES** & **GFZ** & **WHU** & **CNES** \\ \hline \multirow{4}{*}{Radial (cm)} & ESA & 3.8 & 4.1 & 3.9 & 4.5 & 4.7 & 6.5 \\  & GFZ & – & 2.2 & 3.4 & – & 2.6 & 5.0 \\  & WHU & – & – & 3.6 & – & 5.3 \\ \hline \multirow{2}{*}{Along-Track (cm)} & ESA & 3.5 & 5.6 & 9.8 & 4.2 & 6.5 & 15.3 \\  & GFZ & – & 5.3 & 10.2 & – & 5.8 & 15.4 \\  & WHU & – & – & 10.9 & – & – & 16.4 \\ \hline \multirow{4}{*}{Cross-Track (cm)} & ESA & 4.1 & 5.5 & 7.1 & 3.9 & 5.9 & 10.4 \\  & GFZ & – & 4.8 & 7.6 & – & 5.3 & 10.5 \\  & WHU & – & – & 8.2 & – & – & 11.2 \\ \hline \multirow{4}{*}{Clock (ns)} & ESA & 0.05 & 0.07 & 0.16 & 0.11 & 0.14 & 0.27 \\  & GFZ & – & 0.08 & 0.15 & – & 0.12 & 0.26 \\  & WHU & – & – & 0.16 & – & – & 0.26 \\ \hline \multirow{4}{*}{SISRE (cm)} & ESA & 3.6 & 4.1 & 5.4 & 3.2 & 3.7 & 7.4 \\  & GFZ & – & 2.6 & 4.8 & – & 3.2 & 6.8 \\ \cline{1-1}  & WHU & – & – & 5.0 & – & 7.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMSs of epoch-wise differences of precise satellite orbit and clock corrections among different ACs as well as SISRE statistics for BDS-2 MEO and BDS-3 MEO satellites.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 5 SA-Based 5G V2X Services
### System Support
#### 5.1.1 Enhancements Support  
The technical report (_TR 22.886 Rel-15_) identifies various use cases and potential requirements for eV2X and aV2X services. The extended technical report (_TR 22.886 Rel-16_) complements the stated use cases and offers various new items targeting QoS due to the exposure of 5G communication technology. These updated changes are QoS change for remote driving applications; QoS list to support V2X critical applications; QoS estimation for different V2X applications; QoS aspect for extended sensors; corrections to use cases; and clarification on details of estimation information for different V2X applications. This implies that the V2X user experience of service is supported by QoS to better serve the needs of the V2X application. The V2X applications still need improved safety and non-safety services. Therefore, the latter version of (_TR 22.886_) analyzes use cases focusing on the interaction between the 3 GPP system and the V2X application. In addition, another important use case is considering interoperability with non-3 GPP V2X technologies.  
The release 17 (_TR 21.917_) specifies NR PC5 V2X communication with respect to power efficiency for UE's. It also adds support for QoS-aware NR-PC5 pedestrian UE power efficiency. It builds on the specification (_TS 23.287_) of PCS Discontinuous Reception (DRX) operations. NR-based unicast, groupcast, or broadcast mode communications on PC5, i.e., PC5 DRX operations are now enabled with pedestrian UE power saving.
## 4 Results
### Evaluation Metrics
#### 4.2.2 Volumetric Overlap Error (VOE)  
The Volumetric Overlap Error (VOE) is used to evaluate segmentation accuracy by measuring the discrepancy between the predicted segmentation and the ground truth. It is defined as the complement of the Jaccard Index (JI), which quantifies the overlap between two sets. The VOE is calculated as follows:  
\[VOE(GT,MS)=1-\frac{|GT|\cap SR|}{|GT|\cup SR|}=1-\frac{TP}{TP+FP+FN} \tag{7}\]
## 2 Materials and Methods
### UV Shielding Performance of PVA and Composite Films  
The coating suspensions were prepared by a mixing powder of OA-ZnO NPs or ZPPs (0.03 g, 1\({}^{\circ}\)wt of total volume) with a 6% PVA solution (3 mL), using magnetic stirrer, for 4 h. The suspension mixture (wet film thickness of 100 \(\upmu\)m) was cast onto the glass slide (22 \(\times\) 22 mm\({}^{2}\)) using a laboratory film applicator and allowed to dry under ambient conditions. The absorption and transmittance of the pristine PVA and composite films on the glass slides was determined using UV-visible spectrophotometer (UV-2600 Shimadzu, Kyoto, Japan) thrice. The scanning rate was 190 nm min\({}^{-1}\).
## 2 CoAP Overview
### Base CoAP
If this option is not included in any CoAP response, it can be assumed that the response will be fresh for 60 s and thus will not be queried again by a cache within this time frame. When using confirmable messages CoAP tries to achieve reliability by using a simple stop-and-wait retransmission with exponential back-off. By default the initial back-off is set to a random time between 2 and 3 s. This means that if a reply to a confirmable packet is not received within the initial back-off time, the CoAP sender will double the initial back-off time and retransmit the packet. If a reply to the first retransmission is not received, CoAP will again double the back-off time and retry the transmission until MAX_RETRANSMIT (by default 4) is reached. If no reply is received after expiring of the back-off time of the last retransmission, the client will be notified about the error condition. The IETF CoRE working group considers constrained RESTful environments as an extension of the current web architecture. The group envisions that CoAP will complement HTTP and that CoAP will be used not only between constrained devices and between servers and devices in the constrained environment, but also between servers and devices across the Internet [9]. An important requirement of the CoRE working group is to ensure a simple mapping between HTTP and CoAP so that the protocols can be proxied transparently. Thus proxies and/or gateways play a central role in the constrained environments architecture. These proxies have to be able to communicate between the Internet protocol stack and the constrained environments protocol stack and to translate between them as needed.
# Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM
## 5 Discussion  
In this paper, a diagnosis method of inter-turn short-circuit fault in PMSM where sparse representation is used to extract features and SVM is used for classification is studied. Previous studies on sparse representation mostly used vibration signals to diagnose mechanical faults of induction motors or gearboxes. In this work, current signals and vibration signals are combined to diagnose electrical faults in PMSM by sparse representation and SVM. Experiments indicate that the chosen features and the proposed method are feasible.  
However, among the features, as can be seen from the experimental results, compared with the features of vibration signal, the features of the current are not so obvious, and it is not powerful enough to distinguish between normal and fault signals just by them, which may be due to the selection of the dictionary or the optimization algorithm. Therefore, the result of current signal processed by sparse  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **\(F1\)** & **\(F2\)** & **\(F3\)** & **\(F4\)** & **Result** \\ \hline
1 & 35.9105 & 22.6089 & 0.2055 & 0.1654 & \(1\) \\
2 & 50.9903 & 45.8897 & 0.4094 & 0.2895 & \(-1\) \\
3 & 53.3843 & 31.3187 & 0.3106 & 0.1755 & \(1\) \\
4 & 67.5021 & 45.5020 & 0.4165 & 0.4091 & \(-1\) \\
5 & 52.2853 & 34.1310 & 0.2317 & 0.1591 & \(1\) \\
6 & 73.4088 & 34.3846 & 0.6328 & 0.3905 & \(-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results of support vector machine (SVM).  
Figure 13: Sample points and support vectors by \(F3\) and \(F4\).  
representation algorithm needs to be improved. In the future, dictionary learning algorithms should also be studied, in order to be more suitable for the feature extraction of motor signals. In addition, more complicated conditions and more PMSM faults need to be considered.
# Analysis and Experimental Research of a Multilayer Linear Piezoelectric Actuator
## 4 Results and Discussion
### Temperature Characteristics  
Figure 15 shows the surface temperature change of the piezo-vibrator. The temperature was measured at the following settings and conditions:  
(1) The apparatus used was an infrared thermometer (Optris CT-LT, Optris GmbH, Berlin, Germany).  
(2) The temperature was measured when the mover was in locked-rotor for 60 s.  
(3) The ambient temperature was 20 \({}^{\circ}\)C.  
(4) The voltage applied to the piezo-vibrator was \(V_{1}=\left(A_{\mathrm{pp}}/2\right)\) sin \(\left(2\pi ft\right)\), where \(A_{\mathrm{pp}}\) was set to be 100, 90, 80 V\({}_{\mathrm{pp}}\), respectively, and \(f\) was set to 53 kHz.  
The result shows that the vibrator generated lots of heat which caused a high temperature on the vibrator surface. The surface temperature then tended to become steady. With the decreasing of the input voltage, the heat generation was reduced, and when the input voltage was decreased to 80 V\({}_{\mathrm{pp}}\), the heat generation was reduced more drastically. When an alternating voltage (\(V_{1}\)) is applied to the multilayer piezoelectric ceramic, the internal ferroelectric domains will turn over continuously. If we applied the voltage to make the ferroelectric domains turn over discontinuously, the heat generation may be less; thus, experiments with offset voltage applied were conducted. The offset signal was set to be \(V_{2}=\left(A_{\mathrm{pp}}/2\right)\sin\left(2\pi ft\right)+A_{\mathrm{pp}}/2\). The actuator studied can run smoothly using the offset voltage signal and the temperature change decreased compared to the non-offset voltage signal; however, the decrease was not obvious.  
The heat productivity and temperature rise bring many disadvantages, such as frequency drift which causes instability of actuators, efficiency loss, and aging of piezoelectric ceramic and epoxy, which decreases the lifespan of the actuators. The main reasons for the high temperature rise of this multilayer piezoelectric ceramic vibration can be concluded as follows: because of the multilayer configuration, the mechanical quality factor \(Q_{\mathrm{m}}\) decreases to cause an increase in the mechanical loss, and the dielectric loss increases. Both the losses transform into heat and cause the increase of the temperature. Meanwhile, since the thickness of the layers of the piezoelectric ceramic cannot be fabricated exactly the same, the different thicknesses lead to different deformations along the length and width directions, which causes internal friction between layers.  
To reduce the temperature rise, a high \(Q_{\mathrm{m}}\) piezoelectric ceramic needs to be developed and the thickness uniformity of the piezo-layers should be improved.
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 4 Task Offload Optimization Algorithm
### Problem Conversion
The objective function \(P1\) is converted to  
\[ \begin{array}{ll}P2:\min_{O(t),t\tau}\mathbb{E}\Big{[}\delta+\sum_{k=1}^{N}Q_ {k}(t)*(D_{k}(t)-C_{k}(t)*\tau)+V\sum_{k=1}^{N}(T_{k}(t)+\beta*E_{k}(t))\Big{]}.\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad s.t.(1)(3)(4) \#\end{array}  \tag{21}\]Once \(P2\) is determined for all time slots, the solution for \(P1\) can be obtained. The optimal offloading strategy for each time slot can be computed based solely on the offloading decisions made during that time slot, without the need to consider offloading decisions made in other time slots. This greatly simplifies the optimization problem \(P1\). The drift-plus-penalty algorithm is able to guarantee that the long-term constraint of a stable queue holds constantly, and similar proofs have been provided and will not be repeated in this paper [22; 23]. Since the task queue of the LEO satellite satisfies boundedness assumptions, the proposed algorithm can achieve solutions that are arbitrarily close to optimal by setting \(V\) to a sufficiently large value while maintaining mean rate stable with \(O(V)\) for the virtual task queue [19].
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 4 Human Activity Signatures by Using a Distributed MIMO FMCW Radar System
### Scenarios of Human Activities  
To overcome the limitations of the SISO radar system in terms of activity directions, we illuminate the environment from different perspectives via the proposed distributed \(2\times 2\) MIMO radar framework as shown in Figure 7. Recall that Radar\({}_{i}\) comprises a transmitter antenna \(A_{i}^{T_{x}}\) and a receiver antenna \(A_{i}^{R_{x}}\) (\(i=1,2\)) as illustrated in Figure 7. The two radar subsystems have a common illumination region as depicted by a \(3\times 3\) grid in Figure 7, which depends on the FOV and the maximum unambiguous range of the radar. This common area essentially limits the region of operation, within which all activities have to be performed. A moving human body under observation is illuminated from two different angles, as shown in Figure 7. This multi-perspective view helps us to overcome the limitations of the SISO radar system. For instance, if the subject moves in the direction parallel to the boresight of Radar\({}_{2}\), then the Doppler frequencies measured with Radar\({}_{2}\) change considerably more over a larger range than the Doppler frequencies measured with Radar\({}_{1}\). In this case, Radar\({}_{2}\) will detect the motion of the subject more  
Figure 6: The distributed \(2\times 2\) MIMO radar setup in the presence of a moving person walking in an indoor environment.  
effectively than Radar\({}_{1}\). However, if the subject moves parallel to the Radar\({}_{1}\) boresight, then Radar\({}_{1}\) will obtain a much better micro-Doppler signature. The two radar subsystems in Figure 7 complement each other in the way that when the direction of motion changes from the \(x\)-axis to \(y\)-axis, the movement signature of the subject gradually disappears from the Radar\({}_{1}\) radial velocity distribution \(p_{11}(v,t)\) and appears in the Radar\({}_{2}\) radial velocity distribution \(p_{22}(v,t)\).  
To show the effectiveness of the proposed \(2\times 2\) MIMO scheme, we recorded the human motion in three different directions. In order to illustrate the direction of human motion in a particular experiment, we refer throughout this section to the \(3\times 3\) grid shown in Figure 7, in which three different scenarios of human movement are represented by different markings. The radial velocity distributions \(p_{11}(v,t)\) and \(p_{22}(v,t)\) of the two radar subsystems have been computed, and the results are delineated in the next subsection for a walking and falling activity of a person.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 6 Gasification of Plastic Waste: An Overview
### Co-Gasification
[156] in 2020 studied co-gasification of soda lignin produced from black liquor and various plastics (PE, PC, PP, and ABS) in SCW. Once again, synergistic effects were observed between the feedstock. Plastic decomposition generated many alkane gases (CH\({}_{4}\) and C\({}_{2}\)H\({}_{6}\)). The alkaline salts present in the biomass favored their reforming reaction, which led to the production of H\({}_{2}\). The gasification efficiencies of the plastics examined in the study were classified as follows: PE \(>\) PC \(>\) PP \(>\) ABS. [PERSON] et al. [90] in 2020 developed a new Ni-based catalyst (Ni-CaO-C) and examined its performance in pyrolysis/gasification of plastics (LDPE) and biomass (pine sawdust). They used a two-stage fixed bed reactor with quartz wool as a bed material. The experimental results indicate that the new catalyst leads to a syngas rich in H\({}_{2}\) and poor in CO\({}_{2}\). This result is related to the synergy between Ni (active core), CaO (CO\({}_{2}\) adsorption), and activated carbon (active in reforming reactions, massive pore structure, and good reduction ability). A summary view of all works is reported in Table 7. In co-gasification, both fluidized and fixed bed reactors are used. Quartz sand is often used as a bed material. The gasifiers agents commonly used are air (in some cases enriched with oxygen) and steam. The temperature range depends on the type of feedstock and is generally between 700 and 1000 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{Thermogravinetric analyzer} & \multicolumn{2}{c}{eucalyptus wood or rice straw + PE} & \multirow{2}{*}{-} & \multirow{2}{*}{1000 °C} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{[PERSON] et al. [155]} & 2019 \\  & & & & & & & & \\ \hline \multirow{3}{*}{autoclave} & \multicolumn{2}{c}{socal lignin} & \multirow{3}{*}{-} & 750 °C, & & & & & \\  & (from black, & 23–26 & - & & & & \\  & liquid + PE, NC, & & MPa & & & & \\  & PT and ABS & & & & & & \\ \hline \multirow{3}{*}{twostage fixed bed} & LDPF+ pine & quartz wool + & 800 °C & - & steam & - & [PERSON] et al. [90] & 2020 \\  & sawdust & N-CO-C & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 4 Results and Discussion
### Performance under Various Conditions
#### 4.4.1 Scenario 1  
The first scenario aimed at demonstrating the performance of the proposed DQN approach without the operation of the diesel generator. Figure 10 indicates the available power from the PV (green) and WT (blue) systems. The load demand is depicted by the red line. The simulation result is displayed in Figure 11. The three subfigures on the left apply for the DQN-based (red) EMS method, while on the right, apply for the CD-based (blue) EMS method. The first row displays the SOC of the battery. The second row displays the level of hydrogen in the tank. The third row displays the fuel consumption of the diesel generator.  
Figure 11 shows that the diesel generators remained shut down under both methods. The SOCs on the left and right are almost identical. Between steps 0 and 5, the battery was charged by the power production of the WT system. Between steps 5 and 11, the battery switched to discharging due to no power from PV and WT. Between steps 11 and 24, more renewable power was available, so the battery was charged, and the excess power was used to run the electrolyzer. The amount of hydrogen increased between 17 and 20 h and between 38 and 43 h. Under the DQN method, the battery itself handled the problem of insufficient renewable input. Since the fuel cell remained shut down, there was  
Figure 11: Comparison between the DQN and CD methods in Scenario 1.  
Figure 10: Load demand, PV, and WT power in Scenario 1.  
no reduction of the hydrogen level in the tank over the simulation time. Under the CD approach, the fuel cell operated during steps 21-25 and 45-46, reducing the hydrogen level.
# Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning
## 3 Preliminaries
### Offline Reinforcement Learning  
Markov Process: A Markov decision process (MDP) is a mathematical framework used to model decision making in scenarios where outcomes are partly random and partly under the control of the decision maker. In the context of traffic signal control, an MDP can be defined as a tuple \(\langle\mathbf{\mathcal{S}},\mathbf{\mathcal{A}},\mathbf{r},\mathbf{\gamma},\mathbf{\mathcal{P}}\rangle\), where \(\mathbf{\mathcal{S}}\) and \(\mathbf{\mathcal{A}}\) represent the state and action spaces; \(\mathbf{r}:\mathbf{\mathcal{S}}\times\mathbf{\mathcal{A}}\rightarrow\mathbb{R}\) is the reward function; \(\mathbf{\gamma}\in(\mathbf{0},\mathbf{1})\) is the discount factor; and \(\mathbf{\mathcal{P}}:\mathbf{\mathcal{S}}\times\mathbf{\mathcal{A}}\rightarrow\mathbf{ \mathcal{S}}\) represents the state transition. The MDP models the traffic signal control problem as a sequential decision-making process where the goal is to learn optimal policy \(\mathbf{\pi}^{*}\) that maximizes the expected reward over historical trajectories, expressed as follows:  
\[\underset{\mathbf{\pi}^{*}}{max}\mathbb{E}(\mathbf{s}\sim\mathbf{D},\mathbf{a}=\mathbf{\pi}^{*}( \mathbf{s}))[\mathbf{R}(\mathbf{s},\mathbf{a})] \tag{1}\]  
Offline Dataset: An offline dataset in offline reinforcement learning consists of historical data collected from past interactions with the environment. This dataset contains tuples of states, actions, next states, and rewards \(\big{(}\mathbf{s_{i}},\mathbf{a_{i}},\mathbf{s^{\prime}_{i}},\mathbf{r_{i}}\big{)}\), serving as the sole source of information for learning the optimal policy without a real-time interaction. The quality and diversity of the offline dataset are critical for the effectiveness of model training, as they directly impact the model's generalization ability and performance in real-world scenarios.  
Basic Framework of Offline RL: The offline reinforcement learning framework leverages a pre-collected offline dataset to learn the optimal policy without requiring an additional online exploration [16]. This framework is particularly suitable for scenarios where online experimentation is costly or poses safety risks, such as in traffic signal control. The three steps of the offline reinforcement learning process--data collection, model training, and deployment--are succinctly described in Figure 2.  
Figure 2: Framework of Offline RL. In the data collection phase, behavior policy \(\pi_{\beta}\) interacts with the environment to gather and construct an offline dataset, including state, action, next state, and reward information. In the training model phase, the offline dataset is used to learn optimal policy \(\pi\). In the deployment phase, the learned policy is deployed in a real-world or simulated environment to control traffic signals effectively.