esa-sceva/satcom-chunk-collection huggingface dataset:
# Wedge Design and Dynamic Characteristics Analysis of Unequal-Length Wedge Slant Support Clutch
## 4 Dynamic Characteristics Analysis of Critical Disengagement of Slant Support Clutch  
### Critical Disengagement State of Clutch  
As shown in Figure 2, the center of mass of the wedge is at the flange side of the connection line A and B, between the wedge and the inner and outer rings, i.e., opposite to the joint direction. Therefore, the torque generated by the centrifugal force of the wedge is opposite to the pre-joint torque generated by the ring spring. Considering that the closing moment of wedge to outer ring contact point A is zero, there is:  
\[M_{C}+M_{T}+M_{G}+M_{I}=0 \tag{1}\]  
\[\left\{\begin{array}{l}M_{C}=F_{C}L_{C}\\ M_{T}=F_{r}L_{r}\\ M_{G}=mgL_{g}\\ M_{I}=F_{l}L_{i}\end{array}\right. \tag{2}\]  
where \(L_{c}\), \(L_{r}\), \(L_{g}\), and \(L_{i}\) are the arms of \(F_{c}\), \(F_{r}\), \(mg\), and \(F_{l}\) with respect to the outer contact point A, respectively. As there is no large displacement of the wedge under the overrun state, the torque of the ring spring \(M_{c}\) and the heavy torque \(M_{g}\) can be regarded as fixed values. According to the above formula, with the increase in the centrifugal force \(F_{c}\), the contact force \(F_{i}\) between the wedge and the inner ring gradually decreases until \(F_{i}\) = 0, the outer ring reaches the critical clutch disengagement speed.  
The theoretical model in [21] was used to solve the critical disengagement speed of the slanted support clutch in the overrunning state; the wedge parameters were substituted to obtain the above formula. The calculation results are listed in Table 6. There was a significant positive correlation between the theoretical critical disengagement speed of the clutch and length of the middle section of the wedge; that is, the critical disengagement speed of the wedge increased with the length of the middle section of the wedge. Increasing the speed at which the wedge disengages can improve the differential engagement performance of the clutch. The gravity of the wedge had a significant influence on its critical detachment speed, with the critical detachment speed band accounting for approximately 5% of the total critical detachment speed value.
## 3 Results
### Focus of the Studies  
The focus of the study (Figure 12) has been split into five categories, namely the monitoring of crops, agroforestry, land use, and land use change (LULUC) analyses with specific focus on crops, climate impact, and the variables causing them and a category \"Other\" for the articles not classified in the abovementioned categories. Studies that analyze broad LULUC without focusing on specific crops/croplands or employ single NDVI image analyses have been excluded from this review due to their insufficient informational value. An increase in studies in \"Crop Monitoring\" and \"Agroforestry\" are the main drivers of an increased number of articles over time covering the topic of this review. \"Climate Impacts\", which covers drought analyses, soil moisture and evapotranspiration modeling as well as large-scale phenology of crops (greening/browning) follows. The chosen focus is thematic, emphasizing the possibilities and limitations of RS data across various fields of application. A concise overview is provided of each study's contributions and focus. Detailed information on specific methodologies is available in the referenced original studies. It is important to note that while vegetation indices are frequently used in methodologies, this review was not specifically designed to give a comprehensive overview of VIs and their relationship to crop monitoring. Instead, the aim was to showcase the broad potential of RS applications in small-scale agriculture in West Africa. For in-depth discussions on VIs for crop monitoring and discrimination, readers may refer to specialized reviews such as those by [PERSON] et al. [93] or [PERSON] and [PERSON] [94]. The following categories are strict thematic divisions, as each study addresses different aspects, while exploring various possibilities of RS and facing similar challenges. This overview (Table 2) aims to provide a comprehensive summary of the RS research conducted on small-scale agriculture in West Africa:  
Figure 12: Histogram showing the distribution of the main thematic focuses of the reviewed publications over time.
## 8 Experimental Scenarios and Results
### Peak Versus Nonpeak Auto Mode Detection with MAGIS  
During rush hour, (7-9 a.m. and 5-7 p.m.) with 10 pings with 10 min duration scenario, the A-A rate is found to be 68%, 73%, 81%, for DL, SP, and MAGIS approaches, respectively. During the non-rush hour period (10 a.m.-2 p.m.) the A-A rate is found to be 56%, 67%, 74%, for DL, SP, and MAGIS approaches, respectively. It is interesting to note that the mode detection rates are better during the peak period. It seems that due to congestion, the vehicles tend to experience more fluctuations in accelerations and decelerations unique to each mode and it actually helps the ANNs to detect the intrinsic patterns embedded in each mode.
## 1 Introduction  
In complex and dynamic environments, wheeled robots require real-time, efficient navigation to adapt to varying terrains and diverse obstacles, for which deep reinforcement learning methods offer new solutions to promote navigation efficiency [1]. Such methods continuously learn strategies to avoid obstacles through dynamic interactions between the robot and its environment. The Deep Deterministic Policy Gradient (DDPG) algorithm is a typical deep reinforcement learning algorithm that shows significant potential in small-scale path planning and dynamic obstacle avoidance in highly complex environments. However, the traditional DDPG shows two main limitations within the context of robot navigation: firstly, DDPG does not consider the kinematic characteristics of wheeled robots and might produce inefficient planning [2]; secondly, DDPG is prone to be trapped in local optima [3], due to the lack of global information in a complex environment.  
This paper proposes the D*-KDDPG algorithm, an improved version of DDPG. It introduces two key enhancements: first, an upgraded reward function that considers the kinematic analysis of wheeled robots, thereby improving the model's training efficiency and motion strategy optimization. Second, integrating the D* algorithm, which provides global map information as a guide, significantly enhances navigation efficiency and path quality by eliminating local optima in large-scale environments. Last, the paper thoroughly investigates the D*-KDDPG's effectiveness under various map setups and scenarios, providing solid evidence of its potential in real-world applications.  
The organization of this paper is as follows: Section 2 presents an overview of the research background and related work. Section 3 introduces the D*-KDDPG method, including the principle of DDPG, the robot kinematic analysis, the update of the reward function, and the integration of the D* algorithm. Section 4 validates D*-KDDPG's effectiveness in the Gazebo environment. Section 5 summarizes the key findings of this research and proposes future directions.
# The Ionospheric Scintillation Effects on the BeiDou Signal Receiver
###### Abstract  
Irregularities in the Earth's ionosphere can make the amplitude and phase of radio signals fluctuate rapidly, which is known as ionospheric scintillation. Severe ionospheric scintillation could affect the performance of the Global Navigation Satellite System (GNSS). Currently, the Multiple Phase Screen (MPS) technique is widely used in solving problems caused by weak and strong scintillations. Considering that Southern China is mainly located in the area where moderate and intense scintillation occur frequently, this paper built a model based on the MPS technique and discussed the scintillation impacts on China's BeiDou navigation system. By using the BeiDou B1I signal, this paper analyzed the scintillation effects on the receiver, which includes the acquisition and tracking process. For acquisition process, this paper focused on the correlation peak and acquisition probability. For the tracking process, this paper focused on the carrier tracking loop and the code tracking loop. Simulation results show that under high scintillation intensity, the phase fluctuation could be \(-1.13\pm 0.087\) rad to \(1.40\pm 0.087\) rad and the relative amplitude fluctuation could be \(-10\) dB to \(8\) dB. As the scintillation intensity increased, the average correlation peak would decrease more than \(8\%\), which could thus degrade acquisition performance. On the other hand, when the signal-to-noise ratio (SNR) is comparatively lower, the influence of strong scintillation on the phase locked loop (PLL) is much higher than that of weak scintillation. As the scintillation becomes more intense, PLL variance could consequently results in an error of more than \(2.02\) cm in carrier-phase based ranging. In addition, the delay locked loop (DLL) simulation results indicated that the pseudo-range error caused by strong scintillation could be more than \(4\) m and the consequent impact on positioning accuracy could be more than \(6\) m.  
ionospheric scintillation; acquisition process; tracking process; BeiDou system; multiple-phase screen theory +
Footnote †: journal: Sensors
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 4 Dissection of CTP in EH-WSN
### Experiment T-III  
Experiments T-I and T-II examined the case in which a node entered the recharging status and then left the network. Furthermore, we conducted experiment T-III which involved a node that went back to the network after completing the energy replenishment. Figure 5 shows partial results of T-III.  
Before the time point of T-III: 260 s, as shown in Figure 5a, the network keeps connected and runs normally, even though node \(C\) changes its parent from \(A\) to \(B\) when the experiment goes to T-III: 16.691 s. After node \(X\) is shut down at T-III: 260 s, a loop is formed, going through \(A\), \(B\), and \(C\), and has always been kept there until node \(Y\) appears in the network. By the analyses of experiment T-II, we have understood why turning off \(X\) can cause a loop through \(A\), \(B\), and \(C\). After \(Y\) finishes its energy recharging, it goes back to the network and immediately starts probing, aimed to find a valid route the sink. From Figure 5b, we can see that \(S\) receives the probes sent by \(Y\), right after T-III: 404 s and interacts with \(Y\), thereby establishing a valid one-hop path from \(Y\) to \(S\). Meanwhile \(A\) still does its utmost to broadcast the messages of loop detection. Finally, \(A\) rendezvouses its potential forwarder, \(Y\), which can provide a loop-free path for \(A\). At T-III: 405.42 s, the loop is removed and the network keeps loop-free until the end of T-III. The relief of loop is basically thanks to the re-connection between \(Y\) and \(S\). This re-connection sharply contrasts with the case in T-II, where \(Y\) and \(S\) cannot connect with each other to build a valid link due to the severe lack of handshaking opportunities.  
**Remark 3**: _In CTP, a n-node loop (\(n\geq 3\)) will be relieved if any node in this loop can find a forwarder that has a valid path to the sink, because the time-intensive loop-detecting beacons broadcasted by that in-loop node will be responded in short term by some valid forwarder with a high probability; in other words, proactive and timely beaconing in the vicinity of loops would be helpful to alleviate or even remove loops._
## 2 Methods
### Dual-View Feature Encoding Model  
In addition to time series features, we utilize medical-related information extracted from clinical notes to enhance the performance of clinical outcome prediction. We design a DVFE model to extract the global semantic information and local structural information of clinical notes from the sentence level and entity level perspectives, respectively. Specifically, we use sentence-aware attention model (SAA) to obtain the potential features and contextual associations of clinical notes from a global perspective. Clinical text consists of sentences, represented as \(N_{s}=\{s_{1},s_{2},\ldots,s_{N_{s}}\},s_{i}\) denotes sentences. Here, we use the model pre-trained on clinical corpus ClinicalBERT [35] to extract the embedding of the [CLS] tag of each sentence as the global semantic information, as shown in Equation (2).  
\[E_{s}=\text{ClinicalBERT}(s_{i})_{[CLS]} \tag{2}\]  
Furthermore, we use the self-attention mechanism to learn the association between sentences and use weighted aggregation to obtain context-enhanced sentence representation. Finally, the global representation of clinical text processed by maximum pooling is shown in Equation (3).  
\[\widetilde{C}_{s}=\text{Max-pooling}(\text{TransfE}(E_{s_{1}},E_{s_{2}}, \ldots,E_{s_{N_{s}}})) \tag{3}\]  
Here, \(\text{TransfE}(*)\) represents the encoder model of Transformer. Sentence-level modeling based on attention mechanisms can effectively identify cross-sentence semantic associations in clinical texts and improve the representation ability of clinical texts through the importance weighting method.  
From the perspective of entity-level granularity, we use the Med7 [36] as a medical entity extractor to obtain medical-related keywords containing structured information from clinical notes. Med7 is a clinical named entity recognition model pre-trained on annotated EHR data. Here, we use Med7 to extract a series of medical entities from clinical notes and then use the Bio + ClinicalBERT (BCB) [37] to embed these entities. The principle is shown in Equation (4).  
\[\begin{split}\widetilde{C}_{e}=\text{BCB}(\text{Med7}([c_{1},c_{2 },\ldots,c_{N_{e}}]))\\ \widetilde{C}_{e}=\{e_{1},e_{2},\ldots,e_{N_{e}}\}\end{split} \tag{4}\]  
where \(e_{1}\) represents the medical entity and \(N_{e}\) represents the number of entities.
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
## 3 Results  
The statistical characterization of supraharmonics in low-voltage networks is performed considering actual data available from the PQube Live World Map of Power Quality [22]. The overall supraharmonic emission (through the TSHD) and individual supraharmonic components are characterized at several local locations across the world and through several weeks. Many case studies and scenarios have been developed; several of them are presented in this section.
# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## 3 Indirect Type Electrochemical HbA1c Sensors
### MIC Type
For the FAO- and FPOX-based sensors, graphene, AuNPs, core-shell magnetic nanoparticles, metal oxide nanolayers, and nanotubes are the common materials used as electrodes. Using novel nanomaterials as sensing and conduction materials, electrochemical sensors were developed and are expected to provide more convenient, accurate and reliable platforms for the diagnosis of diabetes in the future [88; 89; 90; 114; 115; 116]. In the MIC type indirect sensors based on imprinting technology, the specific binding sites of the target molecules can be easily customized in the polymer network. At present, these sensors can cover the scope of clinical detection, but most of them are highly cross-linked rigid polymers with low flexibility, so their activity is far lower than that of natural enzymes. Molecularly imprinted microgels and other flexible sensors combined with artificial enzymes may promote the application of imprinted sensors in the clinic. In addition, electrochemical measurements can be combined with the FIA system, which has broad prospects in the field of HbA1c detection. The interface materials and detection results of direct type sensors are compiled in Table 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Electrode Type/Interface** & **Detection** & **Detection** & **Potential** & **Sample** & **References** \\  & **Material** & **Range (FV)** & **Limit** & & & \\ \hline \multirow{8}{*}{FAO} & ITO electrode/AuNP-PTA-TiO\({}_{2}\) & \multirow{8}{*}{0.3–2000 μM} & \multirow{8}{*}{0.2 μM} & \multirow{8}{*}{0.2 μM} & \multirow{8}{*}{\(\sim\)0.06 V} & Human whole & \multirow{8}{*}{[92]} \\  & nanocomposites & & & & blood & & \\ \cline{1-1} \cline{4-6}  & FTO glass & & & & & Human whole & \\ \cline{1-1} \cline{4-6}  & electrode/nitrogen-doped & & & & & blood & \\ \cline{1-1} \cline{4-6}  & graphene, AuNPs & & & & & \\ \cline{1-1} \cline{4-6}  & Au electrode/PtNPs-RGO- & & & & & \\ \cline{1-1} \cline{4-6}  & NWCNT & & & & & \\ \cline{1-1} \cline{4-6}  & Au electrode/AuNPs-PtNPs, & & & & & \\ \cline{1-1} \cline{4-6}  & poly-indole-5-carboxylic acid & & & & & \\ \cline{1-1} \cline{4-6}  & Au & & & & & \\ \cline{1-1} \cline{4-6}  & electrode/ZnONPs-polypyrrole & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Indirect type electrochemical HbA1c sensors.
## 2 Materials and Methods
### Experimental Procedure
#### 2.1.4 Torrefraction Experiments  
Figure 2 shows the proposed experimental torrefraction methodology, which is based on three phases: (a) raw material, (b) torrefraction process, and (c) numerical modeling. First, the raw material used is wheat straw biomass, which has been ground into a fine powder to be sieved, weighed, and standardized into 50 g samples (%wt\({}_{i}\)) for each test. Then, the conditions of the experiment are established, such as the duration of the test or residence time, the heating rate, and the maximum isothermal temperatures. To eliminate any trace of air (oxidizing agent) from entering the reactor, nitrogen (N\({}_{2}\)) is injected at a flow rate of 50 mL/min before starting the experiment for 5 min. Once the residence time is completed, after the experiment is carried out, the sample is removed from the reactor and weighed to obtain the final percentage of weight loss of the sample (%wt\({}_{j}\)). Four isothermal torrefraction temperatures were established for the design of the experiments proposed in this work, ranging from 250 to 275, 300, and 325 \({}^{\circ}\)C, at a heating rate of 20 \({}^{\circ}\)C/min. A residence time of 100 min was established. For the torrefraction process, a scale reactor was used, which has a K thermocouple that is used as a sensor to control and monitor the temperature. An Arduino UNO board (manufactured by Smart Projects, Scarmagno, Italy), a MAX6675 module (manufactured by Maxim Integrated Products, Sunnyvale, CA, USA) as an instrumentation stage between Arduino and the sensor, a 3500 W 220 VAC clamp resistor, an Autonics SPC1-50 power controller (manufactured by Autonics, Seoul, Republic of Korea), and a first-order Smith predictive control system with a delay (at 225 s)  
\begin{table}
\begin{tabular}{c c c} \hline
**Biomass** & **Wheat Straw** & **Analytic Method** \\ \hline \multicolumn{3}{c}{Proximate analysis (\%wt)} \\ \hline Humidity & 8.590 \(\pm\) 0.568 & ASTM E871-82 \\ Volatile matter (VM) & 69.930 \(\pm\) 0.256 & ASTM E872-82 \\ Fixed carbon (FC) & 18.250 \(\pm\) 1.670 & \\ Ash & 3.220 \(\pm\) 0.351 & ASTM E1775 \\ \hline \multicolumn{3}{c}{Elemental composition (\%wt)} \\ \hline Carbon & 46.205 & \\ Hydrogen & 6.275 & Thermo Scientific \\ Oxygen (by difference) & 43.908 & iCAP 7400 ICP-OES analyzer \\ Nitrogen & 3.612 & \\ \hline HHV (MJ\(\cdot\)kg\({}^{-1}\), *) & 18.535 \(\pm\) 0.436 & ASTM D5865 \\ \hline \end{tabular}
\end{table}
Table 2: Properties of raw wheat straw (dry basis *).  
implemented in Matlab(r) software were used version 2021a. The experimental results were stored in a database for further numerical modeling, which will be discussed in the next section.
## 4 Comparison of Different Wheel-Rail Contact Models
### Comparison under Seismic Conditions
#### 4.2.2 Running Safety Assessment  
In order to clarify the impact of different models on the running safety of trains during earthquakes, the calculation results of the running safety indices and running safety assessment results based on different models were compared. Three indices were adopted in this study: (i) derailment coefficient (Q/P), (ii) wheel unloading rate (\(\Delta\)P/P), and (iii) wheelset lateral force (\(\bigcirc\)Q). According to the Technical Code for Dynamic Acceptance of High-Speed Railway Engineering (TB10761-2013), the limit values of each index are listed in Table 2.  
Table 3 shows the maximum values of the three running safety indices calculated based on different models. The results demonstrate that the selection of algorithms in wheel-rail contact modelling has a significant effect on the safety assessment of trains during earthquakes. All the indices calculated based on Models 1 and 2 exceed their limits, indicating that the train has entered a dangerous state of operation. In comparison, all  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Running Safety Indices** & **Q/P** & \(\Delta\)P/P & \(\bigcirc\)Q (kN)** \\ \hline Limit & 0.8 & 0.8 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Limit values of train running safety indices.  
Figure 18: Maximum wheel-rail forces calculated based on different models: (**a**) Left vertical wheel-rail force; (**b**) Left lateral wheel-rail force; (**c**) Right vertical wheel-rail force; (**d**) Right lateral wheel-rail force.  
the indices calculated based on Models 3 and 4 are within the safe range, indicating that the train is in a safe state of operation. Comparing the results of Models 1 and 2, the relative calculation errors of Q/P, \(\Delta\)P/P, and \(\sum\)Q reach to \(-4.60\%\), \(-5.0\%\), and \(-8.84\%\), respectively. This finding indicates that the running safety of trains during earthquakes is underestimated when considering only the single-point wheel-rail contact. Comparing the results of Models 2 and 3, the relative calculation error of Q/P, \(\Delta\)P/P, and \(\sum\)Q reach to 11.54%, 20.99%, and 432.09%, respectively. This finding indicates that calculating the wheel-rail normal compression based on vertical penetration will significantly overestimate the running safety indices under earthquakes, leading to misjudment of the running safety of trains. Comparing the results of Models 3 and 4, there is no significant difference in the indices, indicating that although using an empirical formula for wheel-rail normal contact stiffness will have a certain impact on the calculation accuracy of wheel-rail contact force, the influence on the calculation results of the running safety assessment of trains under earthquakes is small.
## 1 Introduction
The main purpose is to model the radio wave propagation adequately in order to optimize the distance between devices in an actual network deployment, which is based on user location, by means of an in-house generated application which has been tested under real conditions. This paper is divided into the following sections: Section 2 describes the simulation procedure for the channel characterization of the considered scenario. Section 3 shows simulation results such as bi-dimensional planes of received power, power delay profiles and delay spread, among others. Section 4 describes the radioplanning analysis for different technologies (ZigBee and Bluetooth). Section 5 presents the experimental-setup in the vegetation environment and the comparison between simulation and measurements. Section 6 describes the proposed application within the considered scenario, followed by the last section of concluding remarks.