esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 3 Analyses of Possible Solutions and Their Adaptation to mURLLC
### Precoder Selection
They also proposed a heuristic to reduce the search space, which is called an RC-C2 (Reduced Complexity Combine-2) algorithm. In [PERSON] and [PERSON] [31], the authors consider adaptations of well-known unicast beamforming algorithms to the multicast case, e.g., multicast versions of Maximum Ratio Transmission (mMRT) [19; 32], Zero Forcing (mZF), and Minimum Mean Square Error (mMMSE) precoders. Since these algorithms require relatively simple linear algebra operations, they have very low computational complexity; however, they have very poor performance as we show in Section 4. The trade-off between computational complexity and performance can be reached by iterative algorithms that use relatively simple (compared to SCA-ADMM) linear algebra operations on each iteration. Examples are Iterative Update (IU) [33] and Multiplicative Update (MU) [20] algorithms that on each iteration increase the objective function value for Problem (2) (in case of IU algorithm) or, in case of the MU algorithm, for the following proportional fair problem:  
\[ \begin{split}&\max_{W_{j}}\left[\frac{1}{2}\sum_{i\in U}\log \bigl{(}SINR_{i,j}(W_{j})+\zeta\bigr{)}\right],\\ &\text{s.t.:}\ \|W_{j}\|_{F}^{2}\leq P_{TX},\end{split}  \tag{5}\]  
where \(\zeta\) is a small constant that is necessary for the numerical stability of the MU algorithm. Finally, such algorithms as SBFC (Successive Beamforming-Filter Computation) [33] or QR decomposition-based algorithm, called, hereafter, the QR [34] construct precoder, as a linear combination of orthogonal vectors that form a linear subspace of UE channels. Each vector and corresponding coefficient in a linear combination are selected to increase the SINR of each particular UE, thus satisfying their constraint in optimization problem, Problem (3). We study the above-described precoder construction algorithms in Section 4.2 and select those that best provide low complexity and high performance with respect to the SDR-based upper bound. Note that the channel matrix \(H_{i,j}\) may significantly change with time, while the precoder is constructed based on its periodic SRS measurements. Thus, we also study the influence of the SRS period on the performance of the selected precoders. Let us now consider how the gNB selects the precoder for different transmission attempts. For the first TX, the gNB constructs a precoder for the set \(U\) that contains all recipients of the multicast stream. The target set for the second TX depends on the used feedback scheme. In the case of NACK-only feedback, the gNB does not know which UEs have failed to decode a packet. Thus, the gNB uses the same set \(U\) as for the first TX. In the case of ACK/NACK feedback, the gNB knows the exact set \(U_{f}\) of UEs that have failed to decode the first TX. Having the lower number of UEs in the set \(U_{f}\), the gNB can change the precoder and increase SINR for these UEs in the second TX.
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 2 Establishment of Battery Model and Parameter Identification
### Identification and Verification of Battery Model Parameters
#### 2.2.1 Obtaining the OCV-SOC Relationship Curve  
The OCV-SOC curve was obtained from a 18,650 cylindrical lithium-ion battery manufactured by the Sanyo Corporation of Japan with a rated capacity of 2.6 Ah and a rated voltage of 3.7 V, as before. Charge and discharge experiments were carried out on the new battery at a constant temperature. The battery test platform is shown in Figure 4.  
The OCV-SOC curves are calibrated under constant current and constant capacity intermittent discharge conditions of 0.2 C, 0.3 C, 0.4 C, 0.5 C, 0.6 C, 0.75 C and 1 C. The specific calibration process of each group is as follows [3]:  
1. First, the battery is charged with a constant current (0.2 C), and then the battery is charged with a constant voltage (cut-off voltage of 4.25 V). After charging is completed, the battery is left for one hour to eliminate the polarization effect;
2. The battery is discharged with a constant current and a constant capacity (1/10 of the total capacity, 260 mAh);
3. After the discharge is complete, the battery is left for one hour, and then the open circuit voltage is measured;
4. Repeat steps 2 and 3 until the battery is completely discharged.  
The calibration experiment results are shown in Figure 5. When the SOC is greater than 10%, the curves almost coincide. This shows that the OCV-SOC curves corresponding to different discharge rates are approximate under the same temperature (25 \({}^{\circ}\)C) and health conditions. In this paper, the OCV-SOC curve under the condition of 0.2 C constant current intermittent discharge is selected as the reference curve.  
Figure 4: Battery test platform.  
Using MATLAB to fit the polynomial coefficients, the approximate linear relationship between the OCV and the SOC can be obtained, as shown in Equation (6):  
\[Voc=a1\times SOC^{6}+a2\times SOC^{5}+a3\times SOC^{4}+a4\times SOC^{3}+a5\times SOC ^{2}+a6\times SOC+a7 \tag{6}\]  
where \(a_{1}=-34.72\), \(a_{2}=120.7\), \(a_{3}=-165.9\), \(a_{4}=114.5\), \(a_{5}=-40.9\), \(a_{6}=7.31\), and \(a_{7}=3.231\).
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 1 Introduction
In this work we have performed an extensive characterization of WBAN links using real hardware nodes with commercial wireless transceivers in the following ISM bands: 433 MHz, 868 MHz and 2.4 GHz. Our experiments have systematically and comprehensively measured the RSSI and PRR metrics in several scenarios, both static and dynamic, with different node placements along the body. These metrics are obtained from regular data packets exchanged by the network nodes, without the need for any additional hardware or extra resources. The paper focuses on the impact of body movements and posture changes on the performance of particular channels formed between two nodes of the WBAN, specifically in terms of reliability. The main contribution of this work is an extensive dataset of real measurements of wireless link conditions obtained by different test subjects, spanning more than 6 h of recorded time. This dataset is accessible in the Supplementary Materials section at the end of this article. The study allows us to establish a correspondence between these wireless link variations and the particular motions and postures that caused them. In particular, our experiments are able to detect the attenuation caused by body parts interrupting the signal path and increasing the NLOS portion of wireless links, empirically confirming the theoretical assumption derived from path loss equations. Additionally, this work provides researchers with an empirical performance benchmark of WBAN channels so that they can adapt their strategies and mechanisms to their characteristics. For instance, TPC protocols and MAC scheduling schemes can greatly benefit from a more detailed knowledge of the time-variance of WBAN channels in real scenarios and activities. The rest of the article is structured as follows. Section 2 explains the experiment conditions in detail, including a description of the hardware used and the different test scenarios. Section 3 presents and discusses the results of these experiments. A broader more general discussion is presented in Section 4. Finally, the conclusions drawn from this work are included in Section 5.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 2 Related Work
For example, GCS-routing can be used with systems that require large network bandwidth, such as drone systems that incorporate continuous video transmission [21; 22; 23], and algorithms that optimize bandwidth usage by considering the physical characteristics of various nodes [24; 25; 26; 27; 28]. In addition, with an analytical model of communication recovery probability in emergency situations [29], GCS-routing can be utilized for drone assisted wireless communications more effectively.
## 1 Introduction
The POSS molecule is cube shaped, with functional groups located at each vertex, giving eight sites where a dendrimer can  
Figure 1: An exemplary synthesis of a hyperbranched siloxane using the Piers–Rubinsztajn reaction and hydrosilylation process. develop. These sites can be synthetically modified to contain groups capable of a variety of reactions. Most dendrimers are based on tetravinylsilane, tetraullysilane, pentaerythnitol or propyl amino compounds [22; 37; 38; 39; 40; 41; 42]. One of the silicon-based chemical compounds, which is widely utilized in chemical reactions leading to siloxane polymers or silane modified (macro) molecules or particles, is 3-aminopropyl trialkoxysilane (APTES). A total of 15,745 scientific articles and patents concerning the use of APTES in the synthesis of compounds and preparation of materials have been published since 1968, while from 2020, only journal publications in English amount to 1287 items [43]. The main areas of application of APTES include synthesis and modification of (nano) particles and surfaces with amine functionality on the outsphere, allowing further reactions, such as conjugation with polymers, drugs, radiotherapeutics, imaging agents [44], catalysts, anisotropic carbon nanomaterials, metal ion scavengers, antimicrobial agents and many others [45; 46; 47]. APTES is also applied for various polymers' modification, e.g., silanization of urethane prepolymers or epoxy resins [48]. The aim of this work was the synthesis of (hyper) branched oligomers based on trialkoxysilane in various conditions and further application of them in order to modify the urethane prepolymers. Hydroxyl-terminated trialkoxysilane was used as a monomer for homo-condensation. It was obtained by reaction of APTES with ethylene carbonate (EC). The reaction is known from the literature [22; 49; 50], and it is based on the attack of amine at the carbonyl carbon atom, followed by ring opening of the carbonate to give a urethane (carbamate) product (Figure 2). The next step was the condensation via substitution of ethoxy groups on silicon atom, with the terminal hydroxyalkyl groups present in the primary product with the evolution of ethanol (Figure 2). Accordingly, the impact of temperature and type of catalyst on process efficiency was investigated. Finally, two different alkoxysilane oligomers were applied for the modification of urethane prepolymer (PUR) forming silanized one (SPUR). The influence of the silanizing agent on the properties (mechanical, thermal) of the obtained products is shown.
###### Abstract  
Palmer Drought Severity Index (PDSI) is the most effective and well-acknowledged drought severity index that particularly determines the long-term drought conditions over the forest and other terrestrial ecosystems. However, the sensitivity of PDSI has not been explored yet based on productivity (i.e., Gross Primary Productivity (GPP)), biophysical parameters (i.e., bio-mass--Leaf Area Index (LAI) and Enhanced Vegetation Index (EVI) and greenness content--Normalized Difference Vegetation Index (NDVI)), and absorbed solar radiation by plants (i.e., fraction of Absorbed Solar Radiation (APAR)) over a humid-subotropical forest ecosystem. In this study, the sensitivity of the PDSI was analyzed through uncertainty and error estimation modeling from long-term (2015-2019) MODIS GPP and reflectance data using Google Earth Engine (GEE) over a humid-subtropical forest region of Arunachal Pradesh, India. It was experimentally observed that EVI was the most sensitive parameter to the PDSI in long-run observation based on a low uncertainty (2.39-3.01%) and error (0.07-0.12) compared to the other parameters. Besides, EVI had a strong agreement with PDSI compared to GPP, NDVI, LAI, and FAPAR, where the Pearson's r ranged from -0.87 to -0.63, except 2015. Hence, it is stated that EVI is the simple, effective, and most complementary indicator for assessing the PDSI over the forest regions of a tropical ecosystem. This study showed that EVI might be a promising tool for effectively evaluating long-term drought impacts on the forest ecosystem that indicates the actual water deficit-induced stress conditions.  
Palmer Drought Severity Index; Enhanced Vegetation Index; forest; tropical ecosystem; Google Earth Engine; India +
Footnote †: _Environment_ Sci. Proc. **2021**, 3, 57. [[https://doi.org/10.3390/IECF2020-07956](https://doi.org/10.3390/IECF2020-07956)]([https://doi.org/10.3390/IECF2020-07956](https://doi.org/10.3390/IECF2020-07956))
## 4 Model Optimization Based on Stepwise Regression
### Data Preparation  
In order to assess the fitting effect of the error model and the temporal validity of the parameters, the 18 sets of data collected from the first observation were used as training samples and the data gathered from the remaining 3 observations were used as test samples in this work. The distribution of the theoretical positions of the satellites in geographic coordinates during the observation period is shown in Figure 9, where the test samples cover an area partially beyond that of the training samples.  
Figure 10 depicts the attitude of the moving platform during the observations; aside from the first 18 pointing tests, the rest only contain 12 sets of data per test. It is obvious that the attitude of the platform in the last test is different from the previous ones. This difference is logical given that the first three observations begin at 20:00, 22:13, and 22:19 of the same day, while the last observation starts at 19:23 of the next day.  
The original pointing errors for the three subsequent tests of MPEOT can be acquired based on the theoretical positions of the target and the measured values of the encoder, as shown in Figure 11, with the errors in azimuth and elevation expressed by RMS, being  
Figure 10: Attitude of moving-platform (**a**) in Yaw (**b**) in Pitch (**c**) in Roll.  
Figure 9: Star map distribution of the experiment.  
153.09\({}^{\prime\prime}\), 163.61\({}^{\prime\prime}\), 136.38\({}^{\prime\prime}\), and 813.10\({}^{\prime\prime}\), 781.03\({}^{\prime\prime}\), 789.28\({}^{\prime\prime}\), respectively. These errors are much more than 60 arc seconds while presenting certain rules.
## 3 Methodology
### Compositing Method  
#### 3.2.1 Tiling  
Considering the orbit deviation and perturbation factors, the tile with fixed dimension (number of rows and columns) was generated for the compositing. On the one hand, the ground trace of the HJ-1A/B normally deviates toward the west within the nadir trace revisit period [28]. The deviation makes the selection of time series images that totally cover the same area difficult. On the other hand, due to the effects of atmospheric drag perturbation, non-spherical gravitational perturbation of earth and lunar-solar perturbations, the satellite orbit will deviate from the design orbit over a period of time [40]. The perturbations make the structure of the constellation unbalanced and affects the phase difference between satellites, which further made the ground trace overlapping irregular [41].  
Data volume of a single file was considered to determine the tile dimension. The tile was finally determined as 5000 pixels \(\times\) 5000 pixels to ensure the manageable file sizes. Figure 3 illustrated the generated tile grid system for China (left) and the standard tile quoted in this paper (right). The tile for China was generated in this paper for the purpose of selecting standard tiles of the case area. It was generated from the upper left coordinate of China in 150 km \(\times\) 150 km and was defined in the Albers Equal Area projection. The color gradient at the right side represented the observation frequency of all images used in this study. The majority of the study area had 180 or more acquisitions over one year.
###### Abstract  
In this paper, the vibration sensitivity and zero-expansion temperature of a 100 mm cubic Fabry-Perot (FP) cavity with reinforced-support are analyzed. Initially, the reinforced-support FP cavity was simulated and analyzed. Simulation results indicate that vibration sensitivity of the FP cavity is less than 1.5 \(\times\) 10\({}^{-10}\)/g, and zero-expansion temperature of the FP cavity is influenced by the support structure by approximately 1 \({}^{\circ}\)C. Subsequently, experiments were carried out. The experimental results demonstrate that vibration sensitivities of the reinforced-support FP cavity in the Z, X, and Y directions are 1.73 \(\times\) 10\({}^{-10}\)/g, 2.09 \(\times\) 10\({}^{-11}\)/g, and 2.13 \(\times\) 10\({}^{-11}\)/g, respectively. The zero-expansion temperature is about 29.5 \({}^{\circ}\)C. The vibration sensitivity of the reinforced-support FP cavity is comparable to that of the four-point support FP cavity, and zero-expansion temperature of the FP cavity is affected limitedly by the support structure. The same vibration sensitivity as the four-point support cavity and robust vibration resistance may expand the application of the transportable or spaceborne ultra-stable laser with cubic FP cavity.  
vibration sensitivity; zero-expansion temperature; FP cavity; ultra-stable lasers Article
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## Bus Depot Alsterdorf
### Charging and Preconditioning  
The available charging power for the buses is 150 kW. This is also the only available power level at the charging station. It is not possible to charge buses at any other level between 0 and 150 kW.  
An additional power consumption occurs during the preconditioning time. This is the electrical heating of the bus before it leaves the depot. It is assumed that the bus needs to heat for one hour prior to its departure if it was parked at the depot for longer than two hours.  
The amount of power consumed during the preconditioning phase depends on the ambient temperature. As the ambient temperature is set to \(-15\)\({}^{\circ}\)C, the heating power corresponds to 65 kW. This value was assumed according to the experiences gathered in Hochbahn.  
Preconditioning reduces the charging power available for the battery charging to 85 kW.
## 1 Introduction
Compared with the dissociation that occurs on the surface of solid-phase salt, studies have shown that the diffusion of the salt component in the liquid phase after dissociation under static conditions is the process control step [13; 22]. Research on the diffusion and mass transfer kinetics of salt solutes in the liquid phase has mostly focused on the steady-state process of single-component salts or simple salt-water systems, and the obtained diffusion constant is usually a fixed average value [23]. In contrast, in these studies, the actual hydrostatic extraction of soluble salt is not a steady mass transfer process. Furthermore, the local mass transfer rate, local concentration, and mass transfer driving force at different locations all change in real time, and the actual dissolution mining system is a mixed-salt system. In a multicomponent liquid system, the dissolution, diffusion, mass transfer [24], and crystallization [25] of different components are mutually restricted. Some multicomponent mineral leaching experiments [26] can indirectly prove the complexity of similar solute diffusion processes. Non-steady diffusion of solutes in a single-salt solution or in a quaternary interaction system that contains two anions and two cations was investigated [9], but a simple ternary system that contains the same cations or anions has not been involved. It is inferred that without the interactive influence of the oppositely charged ions, the ternary simple system may not be able to distinguish between the diffusion of different ions as clearly as the quaternary interactive system can. On the whole, the diffusion behavior of the ternary simple system is still worthy of further investigation. Bloedite is an example of such a simple ternary system. It is clear that its dissolution mining process is unstable, but the law of instability and quantitative prediction methods are still unclear. In response to this problem, this work attempts to establish a kinetic model that has a clear physical meaning for studying the unsteady mass transfer kinetics of multiple solutes in the water phase of bloedite under static dissolution and under diffusion conditions; this work also attempts to explain how the chemical composition in aqueous solution changes over time and how to use this rule in practice.
## 3 Methodology
### Experimental Test Bench  
According to the diagram in Figure 4, the experimental system consists of a (1) power supply, (2) amplifier, (3) signal generator (Agilent 33210A), (4) shaker (ModalShop 2025E), (5) cantilever structure, (6) position sensors (LVDTs), (7) digital oscilloscope (Agilent DSO3062A), (8) signal acquisition system (QuantumX MX840B), (9) microcomputer, (10) spectrum analyzer, and (11) digital multimeter (Hikari, HM-2082). The chosen electromechanical system involves the shaker fixed on an inertial table, along with other equipment [21].  
The transducers used consist of two active layers (bimorph) made of PZT ceramic material, operating electrically in parallel and designed to be sensitive to flexural mechanical forces. The parameters of the PZT-5H and P5-13B model transducers are listed in Table 2, and the details of the cantilever structures mounted with their respective models are illustrated in Figure 2.  
To facilitate installation in oscillatory mechanical systems and ensure rigidity, the transducers are secured using PVC bars. The piezoelectric constants d31 and d32, related to mode 31, are crucial for the efficiency of the piezoelectric material and are tabulated. The stiffness depends on the physical dimensions of the material and is related to Young's modulus.  
Figure 3: Typical scheme of an energy harvester’s system.  
Piezoelectric materials, known for their high impedance resulting in high voltages and low currents, are influenced by the dielectric constant (\(\varepsilon\)) in their energy efficiency. The dielectric constant, an intrinsic property, plays a crucial role in the generator's performance, with higher values being preferable. Additionally, the sensor's geometry can impact the effective dielectric constant, influencing the distribution of voltages and electric fields [22, 23]. Ceramic materials like PZT are often chosen due to their favorable electrical and mechanical properties, typically exhibiting satisfactory values for the dielectric constant \(\varepsilon\).  
It is important to highlight that a structure that undergoes deformation when subjected to a load is considered flexible. The system used models of the piezoelectric sensor as a cantilever beam, with controlled frequency and amplitude excitation.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Gaps in Underwater SLAM Research
#### 9.1.2 Absence of Evaluation Metrics Tailored for Underwater Environments  
Standard SLAM metrics, such as ATE or RPE, are widely used in terrestrial settings but do not always capture the unique challenges faced underwater. For instance, low visibility, sensor drift, and fluctuating lighting conditions can lead to highly non-Gaussian noise distributions, which are not adequately characterized by traditional metrics alone. Developing novel evaluation protocols and error metrics capable of assessing robustness under these distinct conditions is critical to advancing underwater SLAM. Incorporating environment-specific factors, such as turbidity levels or salinity-induced sensor bias, into metric design could yield more meaningful performance assessments.