esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 3 Results
### Comparison and Discussion of Compaction Quality Prediction Models
In contrast, the accuracy of the IFA-RF model decreases gradually with noise increases, without any sharp drop, indicating that the proposed model has strong noise resistance. Therefore, in terms of robustness, the IFA-RF model is well suited for establishing a dynamic evaluation model for the compaction quality of earth-rock dams. However, since IFA-RF is also a black box fitting model, the predicted values inherently exhibit a degree of uncertainty. The authors intend to investigate this further in future research. Furthermore, we plan to proactively collect additional independent datasets in future studies to conduct comprehensive external validation of the IFA-RF model, thereby rigorously assessing its generalizability and strengthening the reliability of its application in diverse real-world scenarios.
# Optimal Collision-Free Grip Planning for Biped Climbing Robots in Complex Truss Environment
## 2 Problem Statement
### The Problem of Optimal Collision-Free Grip Planning  
From Figure 2, we can see that BiCRs may collide with the truss members during climbing, no matter when it moves forward on a member or transits between two adjacent members. Without loss of generality, the collision-free grip planning problem in a truss environment can be simplified as the model shown in Figure 3. For ease of understanding, we define three categories of operational regions. They are (1) the operational region \(\mathfrak{R}_{init}\) representing the starting point \(t_{init}\) and the destination \(t_{goal}\), which are given beforehand, (2) the operational region \(\mathfrak{R}_{t}\) for performing transitions between adjacent members, which is output from the global path planner, and (3) the operational region \(\mathfrak{R}_{o}\) for negotiating obstacles when moving on a member. The operational regions for transitions are highlighted with green, while that for negotiating obstacles are with red in Figure 3. Accordingly, the optimal collision-free grip planning problem is to compute \(\mathfrak{R}_{o}\), then optimally determine grips within \(\mathfrak{R}_{t}\) and \(\mathfrak{R}_{o}\), and finally arrange grips between \(\mathfrak{R}_{t}\) and \(\mathfrak{R}_{o}\) so that adjacent grips satisfy the robot kinematics.  
Figure 4 shows us the flowchart of our proposed optimal collision-free grip planning algorithm. Each feasible route \(\boldsymbol{\Gamma}_{i}=\{\mathfrak{M},{}^{W}\boldsymbol{R},\mathfrak{R}_{t}\}\) is extracted for grip planning successively. For each member \(\mathfrak{M}_{j}\) of \(\boldsymbol{\Gamma}_{i}\), the operational region \({}^{j}\mathfrak{R}_{o}\) for negotiating obstacle members will be computed with a mathematical model (which will be presented in Section 3). After getting the operational regions for negotiating obstacles on all via members, an optimization model is used to determine the grips \(\mathbb{S}_{or}\left(G_{or},C_{or}\right)\) within the operational regions. The objective of this optimization model is to achieve the minimum number of climbing steps and the good manipulability of the transition grips. The optimization model also takes the relationship between step length and climbing gaits into account. Finally, a gait interpreter is designed to arrange grips outside of the operational regions, to obtain the entire grip sequence \(\mathbb{S}\left(G,C\right)\). Guiding by the output of the grip optimization model, the gait interpreter can ensure a shifting configuration exists for each pair of adjacent grips. This shifting configuration will be the input of single-step motion planning. During the grip planning procedure, the route will be determined to be blocked if solving of the operational region \({}^{j}\mathfrak{R}_{o}\) for negotiating obstacles fails or no solution for the grip optimization model.  
Figure 3: A general model of collision-free grip planning for BiCRs.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ARQ & Automatic Repeat Request \\ CDF & Cumulative Distribution Function \\ CTMC & Continuous-Time Markov Chain \\ FEC & Forward Error Correction \\ FSO & Free-Space Optical Communication \\ FSO/RF & Hybrid Free Space Optics and Radio Frequency Systems \\ IoT & Internet of Things \\ LTE & Long-Term Evolution \\ LoS & Line-of-Sight \\ MAC & Medium Access Control \\ ML & Machine Learning \\ MTTF & Mean Time to Failure \\ RF & Radio Frequency \\ SNR & Signal-to-Noise Ratio \\ WiMAX & Worldwide Interoperability for Microwave Access \\ \end{tabular}
## 2 Modeling  
### Introduction of the PCTCS and Assumptions  
Figure 1 illustrates the schematic diagram of the PCTCS in our previous study, which consists of components such as a refrigerant pump, mini-channel evaporators, a plate-type condenser, a reservoir, and connecting pipes, along with additional elements such as joints, sensors, and valves. The PCTCS was used to cool 40 CPUs, evenly distributed across 20 servers within a 42U rack. For mathematical modeling convenience, we present a streamlined version of the PCTCS as depicted in Figure 2. This version, while maintaining the system's fundamental features, is based on certain assumptions and omits some components, such as valves, joints, bends, and sensors, as shown in Figure 1. The influence of these excluded components on the PCTCS is expressed as local pressure drops. These effects are simulated by adjusting the diameter and length of the interconnecting pipes to reflect the resulting alterations in pressure. Figure 3 presents the pressure-enthalpy (P-h) diagram of the system. The modeled PCTCS includes 20 pairs of mini-channel evaporators arranged in series. However, for the sake of simplicity, only one pair is simulated. Figures 2 and 3 utilize numbers 1-8 to identify various positions within the PCTCS system. To provide clarity, their meanings are as follows:  
Number 1: Reservoir and the pump inlet;  
Number 2: Pump outlet;  
Number 3: Inlet of the first evaporator;  
Number 4: Outlet of the first evaporator;  
Number 5: Inlet of the second evaporator;  
Number 6: Outlet of the second evaporator;  
Number 7: Inlet of the condenser;  
Number 8: Outlet of the condenser.  
According to Figure 2, we constructed mathematical models, including a refrigeration pump model (1-2), mini-channel evaporator model (3-4, 5-6), plate condenser model (7-8), and connecting pipe model (2-3, 4-5, 6-7, 8-1). Based on the analysis conducted above, we developed a steady-state distributed-parameter model for the PCTCS, incorporating the following assumptions and explanations:  
Figure 1: Schematic diagram of the PCTCS in our previous study [27].  
The system's fluid flow and heat transfer are presumed to be steady-state;
* The reservoir's volume is sizable and not fully liquid-filled. Due to its proximity to the pump, variations in the refrigerant state between the reservoir inlet and pump inlet are negligible;
* All components of the PCTCS, excluding the evaporator and condenser, are assumed to be adiabatic;
* Power disparities among servers, along with any variations in the refrigerant flow and state upon server entry, are ignored. Consequently, the heat transfer and pressure drop across each server are identical, and the refrigerant state on exiting each server is assumed to be uniform;
* The fluid is assumed to be in thermodynamic equilibrium;
* Parameters are averaged across sections, and a one-dimensional model is applied.
## 2 PTOS Control Scheme
### TP-Transition Process
The optimal control \(u\left(t\right)\) when taking the minimum value of \(H\) should be taken  
\[u^{*}\left(t\right)=-a_{\max}\cdot\mathrm{sign}\left[\lambda_{2}\left(t\right) \right]. \tag{20}\]  
It can be obtained from Equation (20), the function of \(u\left(t\right)\) is a switching function, and the time of switching depends on the sign of \(\lambda_{2}\left(t\right)\). When \(0\leq t\leq t_{f}\), \(\lambda_{2}\left(t\right)\) should have both positive and negative value. Moreover, \(u\left(t\right)\) should be \(a_{\max}\) before the first switch. Therefore, the coefficient of \(\lambda_{2}\left(t\right)\) should satisfy the condition \(c_{1}<0,c_{2}<0\). Now, suppose \(\lambda_{2}\left(t_{1}\right)=0\), that is, \(0<t_{1}<t_{f}\), then  
\[u^{*}\left(t\right)=\left\{ \begin{array}{cc}a_{\max},&0\leq t\leq t_{1}\\ -a_{\max},&t_{1}\leq t\leq t_{f}\end{array} \right.. \tag{21}\]  
From the equation of \(z_{2}\left(t\right)=u\left(t\right)\), combined with Equation (14), the state \({z_{1}}^{*}\) is  
\[{z_{1}}^{*}\left(t\right)= \begin{cases}\dfrac{a_{\max}}{2}t^{2},&0\leq t\leq t _{1}\\ -\dfrac{a_{\max}}{2}\left(t-t_{f}\right)^{2}+r_{A},&t_{1}<t\leq t_{f}\end{cases}  \tag{22}\]  
with  
\[t_{1}=\sqrt{\dfrac{r_{A}}{a_{\max}}},t_{f}=\sqrt{\dfrac{4r_{A}}{a_{\max}}}.\]  
Now, we have obtained the \(r_{tp}\) signal by minimum principle as shown in Equation (23), and the signal diagram is shown in Figure 4, which completes the implementation of the jumping signal to the slowly changing signal. The performance index of the derivation process is time optimum, which is to minimize the response time of the system in Equation (12). Furthermore, \(r_{tp}\) always makes the system tracking error small, which greatly reduces the overshoot, avoids the windup phenomenon, and achieves fast and smooth tracking performance. Note that the design of \(r_{tp}\) is universal; that is, for a target position signal of any amplitude, the TP signal can be obtained on the premise that the ultimate acceleration ability of the control system actuator is known. \[r_{tp}\left(t\right)= \begin{cases}\frac{a}{2}t^{2},&0\leqslant t\leqslant t_{1}\\ -\frac{a}{2}\left(t-t_{f}\right)^{2}+r_{A},&t_{1}<t\leqslant t_{f}\\ r_{A},&t_{f}<t\end{cases}  \tag{23}\]  
with  
\[a=a_{\max},t_{1}=\sqrt{\frac{r_{A}}{a_{\max}}},t_{f}=\sqrt{\frac{4r_{A}}{a_{ \max}}},\]  
where \(r_{A}\) is the absolute amplitude of input signal.
## 4 Measurement Methods and Data Analysis  
### Measurement Methods  
The flow chart of the measurement method is shown in Figure 2. First, we took the elevation of the circular trajectory generator as the y-plane and the center of the circular trajectory generator as the origin of the coordinates. Second, we adjusted the level of the laser tracker and took the horizontal plane of the laser tracker as the z-plane to establish a coordinate system. Meanwhile, four transfer stations were arranged near the circular trajectory generator to unify the coordinate system after adjusting the laser tracker to the specified position.  
To start, we set the distance between the laser tracker and the circular trajectory generator (e.g., the measurement distance is 1 m), set the data collection method (e.g., equal spacing of 5 mm) for the laser tracker, and then performed data collection. After the microsphere of the laser tracker received light, we continuously increased the speed of the circular trajectory generator to R\({}_{\mathrm{m}}\), at which the laser tracker missed the target, and calculated the dynamic limit speed (R\({}_{\mathrm{L}}\)) of the laser tracker according to R\({}_{\mathrm{m}}\). We reduced the speed of the circular trajectory generator to 0.7 R\({}_{\mathrm{L}}\) and ran it again. After the speed of the circular trajectory generator reached a stable level, we used the laser tracker to collect data. The dynamic indication error and dynamic indication variable can be obtained by calculating the operation results. Under the same measurement spacing, we changed the data collection method and repeated the above steps to complete the measurement with the six measurement methods in sequence.
## 1 Introduction
### Technological Background  
ML technology has gained a lot of attention for enabling intelligent solutions in wireless networks including mobile communication networks [9], wireless sensor networks [10], transportation systems [6], non-terrestrial networks [11]. Complex wireless communication problems such as resource management [12], data offloading toward edge networks [13], spectrum management [14], routing [15], user-server allocation [7], etc., can effectively solve through different ML techniques. Among others, FL is widely considered a distributed learning approach to provide efficient learning solutions. In one paper [16], the authors proposed energy-efficient FL solutions in wireless communication environments. Work in [17] discusses the applicability of FL solutions in smart city environments. FL is also widely used to solve vehicular network problems, especially in edge computing environments [6]. FL solutions are also considered over different satellite networks [18]. However, these works have analyzed the performance of FL solutions in different wireless environments, without considering the practical implementations.  
Recently, several authors considered a learning testbed implemented through different sensory nodes to measure the ML solution's performances. Raspberry Pi devices are commonly considered to analyze the performance of ML solutions proposed to solve different wireless networking problems. In [19], the authors proposed Reinforcement Learning (RL)-based solutions for efficient routing processes in software-defined wireless sensor networks. The testbed includes Raspberry Pi devices as sensor nodes to analyze the performance of RL solutions. In one paper [20], the proposed deep learning-based solutions for detecting a speed bump in an intelligent vehicular system to assist drivers are tested with the help of Raspberry Pi devices and associated camera modules. In one paper [21], an approach called Distributed Incremental Learning (DIL) was introduced to mitigate \"Catastrophic Forgetting\" in healthcare monitoring. However, the large model size (49 KB) poses challenges for Raspberry devices, and there is a lack of data batch quality details affecting convergence. In one paper [22], the proposed communication-efficient FL solutions are tested with the help of Raspberry Pi devices in edge computing environments. However, to the best of our knowledge, the most recent literature lacks the study associated with the analysis of FL performance in the presence of heterogeneous clients. Given the importance of different IoT subsystems with heterogeneous nodes, such studies can be extremely useful from the 6G network's perspectives. This is one of the main motivations behind this experimental analysis of the FL process in the presence of diverse sets of clients.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 3 Proposed Methodology: DCBA Fault Diagnosis
### Integrated Diagnosis Model
#### 3.2.1 Sliding Window Processing  
In the sliding window processing layer, due to the fact that the original one-dimensional samples only contain feature information and lack temporal information to assist in fault diagnosis tasks, we comprehensively considered both feature and temporal information of the target. This is achieved by processing the original data X, resulting in two-dimensional samples that integrate feature and temporal information. The processed two-dimensional samples are utilized in the proposed fusion diagnostic model, enabling the model to simultaneously learn feature and temporal information, thereby enhancing the performance of fault diagnosis. Compared to models that only consider feature information, models that incorporate both feature and temporal information can better capture the dynamic characteristics of the target system, thereby improving the accuracy and robustness of fault diagnosis. The sliding window processing procedure is illustrated in Figure 4.
## 2 Experiments and Methods
### Overburden Saturation Monitoring Method  
In order to study the porosity saturation of overburden during the grouting process, sensors (Figure 2) were placed at different locations along the strike direction in the central position of the internal inclination of similar materials to realize the dynamic monitoring of the strike direction of the underlying overburden and the moisture content in the vertical direction. The relationships between the actual volumetric water content of similar materials and that of the sensor were established through experiments to ensure the accuracy of the sensors. Through sensor calibration [30], the relationship between the actual volumetric water content and the volumetric water content measured by the sensors could be obtained. According to the relationship between mass moisture content and volume moisture content, the relationship between the actual mass moisture content of similar materials and volumetric water content measured by the sensors could be obtained. The output data of the sensor is the volumetric moisture content. The relationship between saturation and volumetric water content measured by the sensors of similar materials is  
\[S=0.7353\ln(\theta\prime)-0.9967 \tag{1}\]  
where \(\theta^{\prime}\) is the volumetric water content measured by the sensors (%), and \(S\) is the saturation (%).
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 3 The Software-Defined Media Ontology
### Description of Class Definitions
#### 3.3.2 **Recorder**, **Processor**, **Player** Class  
The **Recorder**, **Processor**, and **Player** classes represent the equipment and functions used for recording, processing/editing, and playback. Each class has a **Composite** subclass and subclasses corresponding to the type of digital media handled. Figure 4 shows the structure of each class. The **CompositeRecorder**, **CompositeProcessor**, and **CompositePlayer** subclasses describe the composite objects described in Section 3.1. The **Composite** subclasses are defined as subclasses of each class, and the **hasComponent** and **isComponentOf** properties express inclusion relationships. As a result, the objects of each class can be grouped freely and described hierarchically. The **CompositeRecorder** class is used as an example. The **CompositeRecorder** class is a subclass of the **Recorder** class that represents a composite of **Recorder** objects as a single **Recorder** object. **CompositeRecorder** represents the **Recorder** objects in the inclusion relationship using the **hasComponent** property. By linking multiple **Recorder** objects with the **hasComponent** property, **Recorder** objects can be grouped and represented as a single **Recorder** object. The **isComponentOf** property expresses an opposite relationship. For example, when one wants to represent four sets of monaural **AudioRecorder**'s together as a four-channel **AudioRecorder**, one can create a **CompositeRecorder** object using the **isComponentOf** property to link the four monaural **AudioRecorder** objects. From the linked monaural **AudioRecorder** object, the **CompositeRecorder** object can be connected with the **isComponentOf** property to describe the object inclusion relationship in both directions. By defining **CompositeRecorder** recursively, it is possible, for example, to represent multiple audio and video recording devices that were used to record a concert as a single recording device. Similar expressions are possible using the **CompositeProcessor** and **CompositePlayer** subclasses. The **CompositeMedia** subclass, a subclass of the **Media** class, can also be used for similar representations but has a more extended structure, which is explained in detail in the Section 3.4 clause.
# Compact Midwave Imaging System: Results from an Airborne Demonstration
###### Abstract
These detectors are able to deliver similar performance at operating temperatures near 150 K as those made from the incumbent InSb and HgCdTe technology. The significant increase in operating temperature allows for smaller, lighter, and lower-power Stirling cycle coolers which are consistent with the resources available from a CubeSat. This paper presents the initial results of an airborne flight test carried out in early 2021, as well as provides a brief description of the CMIS design. The paper also describes how CMS could provide a low-cost option for a future space mission.
## 3 Results and Discussion
### The result of the Structure Tuning
The absorption of the third absorption peak increased from 75% to 99% due to the coupling between the innermost ring and the middle ring. Furthermore, using this effect, we  
Figure 2: The simulated absorption spectrum of the absorber which the Graphene at the different Fermi levels (blue line is \(E_{f}\) = 0.7 eV, orange line is \(E_{f}\) = 0.8 eV, gray line is \(E_{f}\) = 0.9 eV, yellow line is \(E_{f}\) = 1.0 eV). expected to achieve the conversion from three frequencies to dual frequencies. In a way, we achieved the switch between triple-band to dual-band by fluctuating the SGR. We can therefore develop a true multi-function by using the Fermi level adjustment of the different sizes of the graphene ring. Of course, in the tunable conductance model, we focus more on the influence of the regulation of the Fermi level on the response frequency. Before the Fermi level adjustment, we hope to design an absorber with a good absorption in the work frequency. Therefore, we started a discussion on size adjustment and found out the relationship of the size and absorption spectrum. We keep the Fermi level and other structural parameters unchanged, and the size of the outer ring is from 0.9 \(\upmu\)m to 1.5 \(\upmu\)m, with 0.2 \(\upmu\)m as an interval. We also obtained a lot of interesting results, which are shown in Figure 4.  
We can see that there is a transformation from 0.9 \(\upmu\)m to 1.5 \(\upmu\)m, which is the frequency of the perfect absorption changes (the variation of the absorption peak). We achieve a high coherent  
Figure 4: The simulated absorption spectrum of the absorber which the two rings’ sizes are constant (0.3 \(\upmu\)m and 0.5 \(\upmu\)m) and the biggest SGR’s size is varying from 0.9 \(\upmu\)m to 1.5 \(\upmu\)m. (The blue line is 0.9 \(\upmu\)m, red line is 1.1\(\upmu\)m, gray line is 1.3 \(\upmu\)m and orange line is 1.5 \(\upmu\)m). Figure 3: The simulated absorption spectrum of the absorber which the two rings’ Fermi level is constant (1.0 eV) and the smallest SGR’s Fermi level is varying from 0.7 eV to 1.0 eV. (blue line is 0.7 eV, red line is 0.8 eV, gray line is 0.9 eV, orange line is 1 eV). absorption which can maintain an absorption rate above 70% from 0.8 THz to 1.7 THz. Also, the proposed MPAs can realize perfect absorption at the 0.89 THz and at the 1.07 THz rates. The frequency of this two absorption peak is hardly affected. Moreover, we also can provide a conclusion that a single ring can have an entirely different effect on different rings in the proposed absorber. For the middle ring, the coupling can make the absorption enhance. For the inner ring, the coupling make the absorption reduce. The effect of the absorption can be explained by the hybridization model.
## 3 Solution  
### Sensor Selection  
To enable effective monitoring of energy and air consumption from the Wiedemann TPP, KEYENCE MP-F series sensors (Figure 4) were selected based on their functionality, ease of integration, and cost-effectiveness [18]. These sensors measure critical air parameters, including pressure, temperature, and humidity, and can also track electricity consumption when used in conjunction with the KEYENCE MP-FEA1 Energy Monitor [19]. Additionally, the built-in shut-off valve helps minimize air loss by automatically restricting airflow during equipment downtime, contributing to energy conservation.  
For data transmission and integration into the existing industrial network, these sensors support multiple communication protocols. They can interface with the KEYENCE MP-FEN1 Ethernet Module [20] and NQ Series network communication modules [21] (Figure 4), facilitating seamless connectivity with industrial monitoring and control systems. This setup ensures compatibility with various communication standards, enabling efficient data collection and real-time analysis.