esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Optimized Kinematic Modeling Method
### Optimized Kinematic Modeling of the Mobile Manipulator Robot
#### 3.3.2 Optimized Kinematic Modeling of the Mobile Platform  
We assume that the mobile manipulator robot is mainly composed of rigid components to guarantee the stability and precision of the manipulator robot's movement. Meanwhile, the mobile platform mainly moves on the road with high friction and no slide or slip during movement. Therefore, the mobile platform is directly driven by the angular velocity of the motors mounted on the left and right wheels. With in-depth research on mobile manipulator robots, the mobile platform mainly realizes the movement of the mobile manipulator, which can be regarded as the transformation of the based-fixed frame. Therefore, we can determine the position and the posture of the mobile platform by the homogeneous transformation matrix from the world frame to the midpoint of the driving wheels.  
We directly established a coordinate system at the midpoint of the two driving wheels to replace the three coordinate systems previously established at the center of gravity of the mobile platform, which can be seen in Figure 3b. This method can reduce the difficulty of establishing the coordinate system of the mobile platform and increase the speed of the kinematic modeling. The homogeneous transformation matrix from the world frame to the midpoint of the two driving wheels can denote the position and posture of the mobile platform, which can be expressed as follows:  
\[{}^{O}T_{d}=\left[\begin{array}{cccc}C(q_{zcs})&-S(q_{zcs})&0&p_{xd}\\ S(q_{zcs})&C(q_{zcs})&0&p_{yd}\\ 0&0&1&p_{zd}\\ 0&0&0&1\end{array}\right] \tag{7}\]  
where \({}^{O}T_{d}\) indicates the position and posture of the midpoint of the driving axis in the world frame; \(C()\) and \(S()\) indicate \(cos()\) and \(sin()\), \(p_{xd}\), \(p_{yd}\), \(p_{zd}\) indicate the position of the midpoint of the drive axis in the world frame; and \(q_{zcs}\) indicates the rotation angle around the \(z\)-axis of the body-fixed frame.
## Data and Methods
### Earth Observation Data  
The moderate-resolution imaging spectroradiometer (MODIS) sensor on board NASA's Terra and Aqua satellites has a viewing swath width of 2330 km and images the entire surface of the Earth every one to two days. The MCD43A4 nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product, which is produced every 8 days within 16 days of acquisition, is preferable over the other surface reflectance products (MOD09A1/MYD09A1) because it is adjusted using a bidirectional reflectance distribution function to model at-nadir values ([PERSON], 2017). The Collection 5.1 MCD43A4 NBAR Level 3 and MOD17 8-day GPP product (MOD17A2) products were downloaded for each site from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC, daac.ornl.gov/modisglobal). Band 7 of the MCD43A4 NBAR product (henceforth, MODIS NBAR Band 7 or simply Band 7) covers the shortwave infrared wavelength range of 2105-2155 nm that is crucial for capturing sensitivity to variations in soil moisture ([PERSON] and [PERSON], 2017). These data are provided as ASCII files containing observations within a specified radius around a particular point of interest. We selected a 3 \(\times\) 3 km area around each flux tower site based on footprint analysis conducted in Demokeya by [PERSON] et al. (2017) and in Dahra by [PERSON] et al. (2017). Land surface temperature (LST) has been found to strongly influence ecosystem respiration ([PERSON] and [PERSON], 2017; [PERSON] and [PERSON], 2017) and has been used estimate vapor pressure deficit ([PERSON] and [PERSON], 2017). [PERSON] et al. (2017) used LST to characterize temperature and drought stress in a modeling framework that used MODIS-enhanced vegetation index (EVI) as a predictor of GPP. In order to test the applicability of LST for improving GPP modeling, we downloaded the 8-day MOD11A2/MYD11A2 Level 3 LST product (Collection 5.1) from the ORNL-DAAC database (daac.ornl.gov/modisglobal).
## 5 Open Research Issues, Challenges and Opportunities
### Coexistence and Interoperability Challenges  
Neighbouring WBANs operating in the same frequency bands are likely to interfere with each other. Coexistence and interoperability between co-located WBANs are thus a key requirement to enable the emergence of practical and effective Wearable WBAN systems. Indeed, Inter-WBAN interference is expected to seriously limit the performances of WBAN communications.  
A WBAN coexistence algorithm should ensure a proper functionality of multiple co-located WBANs (_i.e.,_ on the same body, or in the same environment) and carry out, either independently or cooperatively, their communications without severe performance degradation. Several WBAN coexistence schemes have been proposed in the literature (e.g., [137]), as well as by the IEEE 802.15.6 standard [9], as already discussed in Section 3.5, including the beacon shifting, channel hopping and interleaving mechanisms. However, it should be noted that most of these coexistence schemes are _non-collaborative_, where Inter-WBAN interference is mitigated as much as possible to maximize the throughput of a single WBAN. In other words, the IEEE 802.15.6 was mainly designed to enable efficient Intra-WBAN and Beyond-WBAN communications (_cf._ Figure 1), whereas Inter-WBAN communications are not supported efficiently.  
The design of efficient Wearable WBAN systems for the context of life and safety critical applications (_cf._ Table 14), will require the support of _cooperative_ and _collaborative_ coexistence mechanisms between co-located WBANs, where coordinators and/or on-body sensors of different WBANs can communicate with each other. Existing collaborative and non-collaborative coexistence approaches, which were initially presented in [138] for PANs, could be enhanced and improved to meet the specific requirements of WBANs, especially to enable efficient Inter-WBAN communications.
## 4 Performance Evaluation  
We conducted experimental simulations to evaluate the performance of SOCA using the MATLAB R2023b simulator. The simulation results were compared with the slot allocation scheme of the existing VDES to demonstrate the superiority of SOCA. In the following subsections, we provide a detailed description of the simulation configuration and the simulation results obtained.
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 1 Introduction
### Wireless Technologies
In the remainder of this paper, on-chip mm wave RF approach is utilised in our simulations, mainly because they support cm-long transfer ranges. For a review on reactive and radiative communications the reader should refer to [PERSON] et al. [1] and [PERSON] et al. [48]. In our chosen model, we assume a range of data transfer between 100 Kb/s and 1 Tb/s and a power cost below 1 pJ/bit. In current technologies, such a power consumption is realistic.
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
### AI-Based UAV Networks
#### 1.1.2 UAV Network Design Issues  
As UAV networks possess unique characteristics, therefore, the solutions developed to address various needs of other wireless networks such as MANETs and VANETs cannot be used for these networks. Therefore, researchers address the network issues faced by UAV networks a bit differently.  
To address the topology control for a swamp of UAVs, an autonomous flock control technique is proposed in [7]. The proposed solution supports the swamp of UAVs during their flight by maintaining their topology in an energy-efficient manner. The authors have used the principles of the Reynolds' Boid model [8], i.e., alignment, separation, and cohesion. The effectiveness of the proposed solution is evaluated by conducting a simulation using the OMNET++ Modeler. In [4], the authors discuss various wireless issues UAV networks are facing and also propose some AI-based solutions for the identified issues. The highlighted issues include high reliability, low latency, efficient handover, and efficient path planning. Various AI techniques are proposed to achieve better network conditions for multiple UAVs.
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 3 Communication
### Underwater Acoustic Communication
#### 3.2.2 Clock Synchronisation  
One of the drawbacks of using TDMA is that great importance must be placed on clock synchronisation. Due to clocks drifting over time, synchronisation needs to be re-executed periodically. Clock synchronisation between aPads is a trivial problem, since they are all on the surface and can easily use their GPS sensor to provide sufficient time precision at any time during mission execution. While the aMussels also have a GPS sensor, they do not have access to GPS signal for most of their deployment as they are underwater.  
The RTC module on the aMussel has a time accuracy of \(\pm\)4 ppm, meaning that the clock drift is within \(\pm\)0.345 s per day. Due to increased energy consumption, it is not a viable solution for aMussels to periodically surface to synchronise their clocks. Initial synchronisation at the time of deployment may be performed via GPS signal, but each subsequent synchronisation is executed using acoustic signals from aPads.  
To achieve clock synchronisation via acoustic signal from surface agents, one of the aPads transmits the clock synchronisation sequence containing current epoch time over the acoustic channel. Each of the aMussels on the receiving end stores the time into its RTC after adding known message transmission time \(T_{m}\). If the area of deployment is constricted to be within the range of a single aPad's  
Figure 13: Time scheduling modes of communication.  
acoustic signal (\(\approx\)500 m), the worst clock offset of an aMussel \(T_{c}\) is equal to maximal signal propagation time \(T_{p}=500\) m/1500 \(\frac{\text{m}}{\text{s}}=0.33\) s.
## 1 Introduction  
Atypical body temperature value can be an indication of an abnormal physiological process or health issue. Infrared thermal (IRT) imaging, also known as thermography, is an imaging modality capable of capturing the natural thermal radiation emitted by an object through the use of an IR camera [1]. In the case of the human body, this heat emission is mainly dependent on underlying skin structures pertaining to the vascular and nervous system [2]. This technique can be implemented in a static manner, by performing image capture without stressing the skin area under assessment, or dynamically, through the application in advance of a chemical, thermal or mechanical stimulus [3]. In either case, the international accepted guidelines should be followed to assure proper measurements and reproducibility of temperature readings [4, 5]. Its innocuousness, combined with the ability to perform a fast analysis at a low price, have made it an interesting method to assist physicians in diagnosis and/or treatment monitoring of several pathologies, such as diabetic foot [6], rheumatoid arthritis [7], breast tumors [8] and skin cancer [9].  
The acquired temperature matrixes, i.e., thermograms, require image processing strategies to retrieve temperature data that is meaningful for the condition under analysis. Still, the understanding of this information is a challenging and time-consuming task that can be eased through the implementation of artificial intelligence (AI) computational methods based on machine learning (ML) algorithms [10]. Over the last years, thermal information has been applied by researchers as input features for AI classifiers to give physicians a second opinion [11]. Support Vector Machines (SVM), Artificial Neural Networks (ANN), Decision Trees (DT), AdaBoost, k-Nearest Neighbour (k-NN), Naive Bayes (NB), Fuzzy methods and Random Forest (RF) are some of the examples of this type of learning. The success of its implementation is commonly based on the metrics of accuracy (ACC), sensitivity (SN) and specificity (SP), with higher rates being an indication of a good ML algorithm. It is, however, of great importance to examine performance metrics as a whole and not individually in order to avoid drawing poor conclusions [12].  
The aim of this work was to assess the biomedical applications of thermal image data with ML strategies. A systematic review is performed along with a meta-analysis based on reported classification metrics.
## 3 Fluctuation Correlations and Thermodynamics of Binary Symmetric Electrolyte Solutions
### Thermodynamic Relationships  
#### 3.3.1 Free Energy and Chemical Potential  
The focus of this section is on the effect of electrolyte solution non-ideality, which is represented by the correlation energy (50), on its thermodynamic properties. We start with the general thermodynamic relationship between the internal energy \(E\) and the Helmholtz free energy \(A\)[12,14]  
\[\frac{E}{T^{2}}=-\bigg{[}\frac{\partial}{\partial T}\bigg{(}\frac{A}{T}\bigg{)} \bigg{]}_{V} \tag{51}\]  
Substituting the correlation internal energy (50) into the above equation and integrating over the temperature yields  
\[A-A_{id}=-\frac{(ze)^{3}V}{12\pi\sqrt{k_{B}T}}\bigg{(}\frac{2c}{\epsilon \epsilon_{0}}\bigg{)}^{3/2},\ c=\frac{N_{e}}{V} \tag{52}\]  
Let \(N_{e}\) be the number of pairs of positive and negative ions, and \(N_{w}\) is the total number of water molecules. Since we are considering a dilute electrolyte solution, it is reasonable to assume that the total volume is \(V\approx N_{w}v_{w}\), where \(v_{w}\) is the molecular volume of water. Then, Equation (52) becomes  
\[A-A_{id}=-\frac{(ze)^{3}(2N_{e})^{3/2}}{3\sqrt{2}\pi\sqrt{k_{B}T}(N_{w}v_{w})^ {1/2}(\epsilon\epsilon_{0})^{3/2}}. \tag{53}\]  
Differentiating Equation (53) with respect to \(N_{w}\) allows obtaining the chemical potential of the solvent (i.e., the water)  
\[\mu_{w}=\bigg{(}\frac{\partial A}{\partial N_{w}}\bigg{)}_{N_{e}}-\bigg{(} \frac{\partial A_{id}}{\partial N_{w}}\bigg{)}_{N_{e}}=-\frac{\partial}{ \partial N_{w}}\bigg{[}\frac{(ze)^{3}(2N_{e})^{3/2}}{3\sqrt{2}\pi\sqrt{k_{B}T }(N_{w}v_{w})^{1/2}(\epsilon\epsilon_{0})^{3/2}}\bigg{]}. \tag{54}\]  
After some rearrangements, we finally obtain  
\[\mu_{w}-\mu_{w}^{id}=\frac{k_{B}Tv_{w}\kappa^{3}}{24\pi} \tag{55}\]  
where \(\mu_{w}\) is the total chemical potential and \(\mu_{w}^{id}\) is the ideal part defined by [12,14]  
\[\mu_{w}^{id}=\mu_{w}^{0}+pv_{w}+k_{B}T\ln\bigg{(}\frac{N_{w}}{N_{w}+2N_{e}} \bigg{)}. \tag{56}\]  
\(\mu_{w}^{0}\) is the standard chemical potential.
## 4 Results and Discussion
### Measurement of Additional Linewidth  
Linewidth broadening in fiber amplifiers has been controversial for decades [7; 22; 23; 24]. To precisely measure the additional linewidth of the low-noise Yb-doped fiber amplifier, we use a balanced Mach-Zehnder heterodyne interferometer scheme shown in Figure 5, with a delayed line fiber approximately 10 m long and a 100 MHz acousto-optic modulator (AOM). The two arms are combined by a 3-dB coupler and detected by a low-noise photodetector. The power spectrum of the photodetector output is displayed on a spectrum analyzer (R&S, FSU, Munich, Germany). The fiber amplifier is placed in one of the arms, and the two arms are tuned to have roughly the same path length to eliminate the linewidth error caused by the otherwise imbalance of the interferometer. The technique effectively deconvolves the seed laser spectrum from the amplifier spectral broadening and provides an output that consists solely of the amplifier spectral broadening response to a spectrum passing through the amplifier [24].  
The measured power spectral density of the amplifier spectral broadening is shown in Figure 6. The span and resolution of the spectrum analyzer (R&S, FSU, Munich, Germany) are set as 40 Hz and 1 Hz, respectively. The additional linewidth of the amplifier was measured to be less than 2 Hz @ 20 dB, and the 3-dB additional linewidth can be calculated to be 0.1 Hz [25]. The result indicates that the additional linewidth induced by the fiber amplifier could be negligible compared with the linewidth of the seed laser.  
Figure 4: Optical spectrum of the fiber amplifier at an output power of 2.5 W. Inset: Optical spectra of the seed and amplifier.  
Figure 5: Experimental setup for the additional linewidth measurement.
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 3 Data Acquisition and Preprocessing  
To achieve accurate inversion of grassland above-ground biomass in Xiwuzhumuqin Banner, this study integrates multiple types of data, including spectral reflectance data, terrain data, soil nutrient data, ground-truth biomass data, and meteorological data, based on the geographical, climatic, and ecological characteristics of the study area. These data are sourced from various platforms with different temporal and spatial resolutions, and through comprehensive processing, they can reflect the spatial distribution characteristics of grassland biomass in the study area.
## 3 Performance Analysis
### Spectral Efficiency Computation
#### 3.2.3 Spectral Efficiency of D2D Link \(\text{D}_{1}\to\text{D}_{2}\)  
Recall that \(\text{D}_{1}\) stays silent during \(P_{1}\) but forwards the decoded symbol of FU during \(P_{2}\). Irrespective of \(S_{1}\) and \(S_{2}\), the desired information of D2D receiver \(\text{D}_{2}\) is the antenna index of \(\text{D}_{1}\). Therefore, the overall SE of the D2D link \(\text{D}_{1}\to\text{D}_{2}\) is expressed as  
\[\begin{array}{rl}R_{\text{SE,D}_{2}}=&\frac{1}{2}N_{2}\bigg{\{}1+P_{e, \text{D}_{2}}^{A,P_{2}}\log_{2}(P_{e,\text{D}_{2}}^{A,P_{2}})\\ &+(1-P_{e,\text{D}_{2}}^{A,P_{2}})\log_{2}(1-P_{e,\text{D}_{2}}^{A,P_{2}})) \bigg{\}}.\end{array} \tag{11}\]  
In (11), the scaling factor \(\frac{1}{2}\) is used to denote that \(\text{D}_{2}\) is unable to receive data from \(\text{D}_{1}\) in \(P_{1}\).
## 3 Results
### Interactions between C and N Availability and N Fluxes  
To test the effect of C availability and SOM turnover on N fluxes and cumulative emissions, simple linear regression models were tested (Table 3).  
The relationship between hourly NO+N\({}_{2}\)O and CO\({}_{2}\) fluxes was very weak during the oxic incubation phase (adj. R\({}^{2}\) = 0.08, \(p\) < 0.001, Table 3). In contrast, NO+N\({}_{2}\)O+N\({}_{2}\) fluxes were highly positively correlated with CO\({}_{2}\) fluxes during the anoxic incubation period (adj. R\({}^{2}\) = 0.86, \(p\) < 0.001, Table 3). Similarly, fbD and rN\({}_{2}\)O were positively correlated with total CO\({}_{2}\) flux from soil, but the relationship was weak (adj. R\({}^{2}\) = 0.23 and 0.31, respectively, \(p\) < 0.001, Table 3).  
Cumulative anoxic N emissions (NO+N\({}_{2}\)O+N\({}_{2}\)) were significantly positively correlated with total litter C input (adj. R\({}^{2}\) = 0.51, \(p\) < 0.001, Table 3). Furthermore, we found a significant negative correlation between the ratio of cumulative N\({}_{2}\)O/(N\({}_{2}\)O+N\({}_{2}\)) emissions during anoxic incubation and the ratio of water-soluble C:N of maize litter types (adj.  
Figure 5: (**a**) Fraction of N\({}_{2}\)O originating from heterotrophic bacterial denitrification/nitrifier denitrification (fbD) and (**b**) fraction of residual unreduced N\({}_{2}\)O (rN\({}_{2}\)O). Values were calculated based on the isotopocule mapping approach by [PERSON] et al. (2017) [22] and represent results for scenario 1 (reduction-mixing) of bacterial denitrification with fungal denitrification (mean and standard deviation for n = 5, n = 4 for Control, data points missing for samples with an isotopic signature outside the reduction-mixing area, no N\({}_{2}\)O emitted from Control on 39 DAO).  
R\({}^{2}\) = 0.51, \(p\) < 0.001, Table 3) and also with the soil NO\({}_{3}\)\({}^{-}\):WEOC ratio at the end of the experiment (adj. R\({}^{2}\) = 0.41, \(p\) < 0.01, Table 3). When standardized against C input from litter, total cumulative N emissions did not differ between litter treatments (Supplementary Table S2).
# Investigation on Key Parameters in the Fabrication of Stamps for Transfer Printing of Micro Devices
## 1 Introduction
The adhesion force between the stamp and devices, originating from van der Waals force, can overcome the fact that, between devices and their carrier substrate, devices can then be successfully picked up by the stamp. Therefore, for the purpose of achieving the required adhesion force and picking up devices, it is necessary to press the stamp onto devices to squeeze out the air between the surface of the stamp and devices to ensure the effective contact between them. In cases of small elastic modulus of the stamp, the stamp will be severely deformed during picking up, deteriorating the yield of transfer printing. Therefore, it is of great significance to investigate the elastic modulus of fabricated stamps [33]. Some researchers have pioneered their work by employing casting and curing method to fabricate PDMS stamps. [PERSON] et al. [18] report a unique adhesive surface for deterministic assembly of solid micro/nanoscale parts into two- and three-dimensional configurations. [PERSON] et al. [34] demonstrate a method for integrating 2D/3D metamaterials/metadevices via the 3D modular assembly of each distinct meta-atom building block using an advanced mode of transfer printing. [PERSON] et al. [35; 36] present a novel composite stamp design for transfer printing and demonstrate the ability to print with cantilevered stamps. [PERSON] et al. [37] demonstrate that adhesion switchability associated with angled features of relief in elastomer stamps for transfer printing offers new modes of high efficiency operation compared to previously reported cases. Differently here, we place the emphasis on analyzing parameters that affect the size of the stamp, the mold, and the elastic modulus of the stamp. Furthermore, we describe fabrication processes in detail and challenge difficulties of the problem. We study the average deviation between the width of the post and the design width and state reasons for the deviation. Different types of morphologies of the photoresist mold during the development and reasons for the formation of different morphologies are summarized. We also study the law of development and establish mathematical models which provide criteria for determining fabricating parametric values based on target stamp specifications. The consequence of analyses greatly helps researchers reduce the exploration time required for fabricating stamps.