esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dissipation of Pore Water Pressure
In the HS_80% model, the pore pressure values at point H (soil surface) were zero throughout the application of all loads, implying that, at least from this degree of saturation, the behavior begins to resemble that of a saturated condition. Conversely, in the SS_60% model, there were varying values at this point depending on the applied load, consistently bearing the least load in all analyzed cases [48]. Similarly, at t = 0, the pore pressure value increasingly resembles the applied load as stress increases due to the rise in the degree of saturation. In contrast, in the SS_60% model, pore pressure values do increase with increasing stress but never approach the expected limit completely. Thus, when applying the 3200 kPa stress, the SS_60% model shows a 22% difference from the expected value at t = 0, whereas the HS_80% model shows only a 3% variation compared to the same value. This implies that the variation in pore pressure at t = 0 will only increase from these percentages for smaller stresses. The dissipation of pore pressures was faster in the Hardening Soil models for higher loads. In saturated soils, the complete dissipation of pore pressures for lower stresses (10-400 kPa) occurred within 100 to 1000 min, while higher stresses (800-3200 kPa) occurred within 10 to 100 min. In unsaturated soils, the interaction between air and water in the pores led to more complex behavior and less uniform dissipation.
## 3 Results
### Time and Frequency Spectrum Distribution of Radiation  
After analyzing the motion trajectory and radiation power, this paper also examines the spatial distribution of radiation, as well as its temporal and spectral characteristics, to investigate whether the radiation distribution in other dimensions is affected by the motion  
Figure 6: Changes in the shape of the radiation spatial distribution within the \(tspan=40\)\(\sim\)60 range.  
trajectory during the interaction between the electron and the laser pulse. The radiation power is primarily distributed at \(\omega_{0}<6000\), with the overall radiation power decreasing as the frequency increases. The temporal and spectral distributions of the radiation are shown in Phase 4 of Figure 7, which also represents the time spectrum and frequency spectrum of the entire process.  
At the same time, we independently calculate and plot the time-frequency spectra for the four stages and study the detailed analysis of the radiation power. The time spectrum and frequency spectrum of the radiation for the four stages are shown in Figure 7.  
Figure 7: Time spectrum and frequency spectrum under four phases.  
In Figure 7, Phase 1 generates very little energy and exists only in the extremely low-frequency range at \(\omega<1000\). In Phase 2, the energy begins to increase, with the peak power reaching approximately 5000, and higher-frequency radiation starts to appear in the range of \(\omega<6000\). Phase 3 is the main stage of radiation generation, during which the radiation energy across all frequency bands increases significantly. Under the conditions described in this paper, the peak power can reach approximately 14,000. The radiation frequency is distributed within \(\omega<\) 10,000, but it is still primarily concentrated in the relatively low-frequency region. The temporal and spectral distributions in Phase 4 are similar to those in Phase 3, with an increase in the power components of certain frequencies based on Phase 3, but without significant changes.
# Multifunctional Integrated Underwater Sound Absorption Materials: A Review
## 2 Underwater Acoustic Absorption Mechanisms and Research Advancements in This Area
### Research Advancements in Underwater Sound Absorption Materials
#### 2.2.3 Ceramic-Based Foams  
To enhance the corrosion resistance of materials, researchers have turned to ceramic foams. These materials have high chemical stability, corrosion resistance, stiffness, and good mechanical properties, making them ideal for sound reduction in critical environments. However, there are few studies on the underwater sound absorption properties of ceramics, particularly in high-pressure environments. [PERSON], [PERSON] et al., developed porous silicon nitride ceramic foams to reduce underwater noise and found that porosity, pore size, and thickness were contributing factors to better sound absorption across almost all frequencies, achieving a sound absorption coefficient higher than 0.99 by inserting an air gap [47; 48; 49]. [PERSON], [PERSON] et al., prepared the open-celled SiC foams and its photograph of a section of SiC foam sample and SEM photographs of the cell structure are shown in Figure 6a,b shows the underwater acoustic absorption coefficient varies with the frequency. The result shows that the underwater acoustic absorption coefficient was mainly observed over the low- and middle-frequency range (200-4000 Hz) [50].  
One limitation of these materials is their brittleness, which restricts their applications. However, recent research by [PERSON], [PERSON] et al., utilized silicon nitride with plastic deformabil  
Figure 5: **(a)** Typical microstructure of semi-open aluminum foam; and **(b)** effect of air cavity between two 10 mm thick aluminum foam. The error of measurement is on the order of 5–10% [45].  
Figure 6: **(a)** Photograph of a section of SiC foam sample and scanning electron microscope photographs of the cell structure; and **(b)** underwater acoustic absorption coefficient of the sample with thickness of 90 mm and pore size of 2 mm over a wide frequency band [50].  
ity, covalently bonded via bond switching at coherent interfaces [51]; such research shows great potential for addressing current limitations.
Maximizing the Downlink Data Rates in Massive Multiple Input Multiple Output with Frequency Division Duplex Transmission Mode Using Power Allocation Optimization Method with Limited Coherence Time  
[PERSON]  
1 Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, Iraq; marwah@doc.  
and the overhead in the systems rely on the duplexing mode of operation. For example, there are the time division duplex mode (TDD) and the frequency division duplex (FDD) operation modes. In the TDD mode, the uplink CSI estimate is used in the downlink precoder design at the base station (BS) without the need for downlink CSI estimation. This is due to the uplink/downlink channel reciprocity that is obtained in TDD operation systems [8]. In this case, there is no overhead from CSI estimation, since it is proportional to the number of users \((K)\) and not to BS antennas \(N\)[9, 10, 11, 12]. Despite the favorable outcomes of the TDD mode with downlink CSI estimation, most of the existing mobile networks operate in the FDD operation mode. For instance, the current wireless networks, which are advanced long-term evolution (A-LTE) networks, mostly operate with the FDD mode [13]. In addition, calibration errors in the uplink/downlink radio frequency (RF) chains and transceiver hardware limits are usually the source of the limitations of TDD operation systems [14, 15, 16, 17]. Hence, this research concentrates on FDD transmission, where the downlink CSI estimation is determined using a dedicated downlink training sequence during the CSI estimation phase. Specifically, to acquire CSI estimation in the FDD mode, the BS needs to design a specific training sequence and direct this sequence to the users [18, 19]. However, the BS with a MMIMO system operates with a large number of antenna elements, which poses a significant challenge in channel estimation, especially in FDD operation with limited coherence time (LCT).  
To this end, several research works have investigated the downlink CSI estimation in FDD MMIMO systems, thereby taking into account the spatial correlation at the BS; see, e.g, [20, 21, 22, 23, 24, 25, 26, 27, 28]. Another area of study has been on CSI estimation utilizing methods based on compressed sensing (CS) [29, 30, 31, 32, 33, 34]. In addition, a further advancement in CSI estimation is the application of hybrid two-stage precoding techniques in FDD MMIMO systems [35, 36, 37, 38]. However, in the aforementioned research works, a uniform power allocation has been assumed, and the DASR has not been optimized with respect to power allocation. To the best of our knowledge, maximizing the DASR with respect to the optimized energy allocation has not been investigated in the literature.
# Efficient Runtime Firmware Update Mechanism for LoRaWAN Class A Devices
## 3 Developed System
### Implementation of the Proposed System  
To validate the proposed firmware update mechanism, a system comprising three components was implemented: an RFID-based access control electronic device equipped with LoRaWAN communication, The Things Network server, and a proprietary application server for information management.  
The electronic device includes a LoRa radio transceiver and a LoRaWAN stack for communication with the network server. The LoRaWAN network server acts as an interface between the remote end devices and the application server. Once registered and configured on the network server, remote end devices gain network access to forward information to the application server. The application server consists of middleware and a database, with the middleware acting as an intermediary between the network server and the database.  
This system implementation aims to evaluate the performance of the proposed firmware update process, demonstrating a reduction in the amount of data required for updates. The proposed solution also eliminates the need for image reconstruction on IoT devices, removes bootloaders, utilizes available resources efficiently, significantly reduces downtime, and creates a less complex system accessible to all.  
Figure 7: Graphical representation of the middleware function.
## 3 Information-Theoretic Results
### The Rate-Distortion-Perception Frontier
Therefore, the rate region \(\mathcal{R}_{D,0}^{\infty,\infty}\) is a subset of the RD region \(\mathcal{R}_{D}\), i.e., \(\mathcal{R}_{D,0}^{\infty,\infty}\subseteq\mathcal{R}_{D}\). In light of these results, we draw the following conclusions regarding the introduction of the realism constraint:  
* Common randomness and private randomness are helpful in achieving (near) perfect realism. * The introduction of the perceptual constraint results in a higher rate requirement, even when common randomness and decoder randomness are unlimited. As illustrated in Figure 0(a), where a Gaussian source is considered, the perceptual constraint modestly increases the bitrate required in theory, suggesting an increase in traffic in communication networks. The red arrows indicate the additional rates needed under conditions of no common randomness and unlimited common randomness, respectively. In practice, decoder randomness can be implemented using a random number generator, while common randomness can be achieved through a shared random seed in end-to-end training, where neural network models are deployed at both the encoder and decoder. The information-theoretic results presented reveal that incorporating perceptual constraints necessitates a fundamental rethinking of RD analysis. Key findings include the establishment of the RDP function as a lower bound on achievable rates, the demonstration that common and private randomness can improve performance under perceptual constraints, and the characterization of how these constraints affect system design in practical communication scenarios.
# SoftRec: Multi-Relationship Fused Software Developer Recommendation
## 4 Experiment and Discussion
### Experimental Results and Discussions
#### 4.2.2 Performance in Tackling Data Sparsity (RQ2)  
We test SoftRec in scenarios 1) with different sparsity of developer-task interactions (described in Section 4.1.5 Scenario Description). Figure 3a,b show the compared results of precision, recall and ndcg for the GitHub and GitLab datasets, where \(\rho_{1}\) and \(\rho_{2}\) denote the initial data density of interaction matrices in GitHub and GitLab datasets, respectively. We have the following observations.  
When the data density decreases from \(\rho_{1}\) and \(\rho_{2}\) to 0, the precision, recall and ndcg values of all approaches decrease quickly. However, SoftRec significantly and consistently outperforms than other approaches. For example, for GitHub dataset, when the data density is \(0.6*\rho_{1}\), the values of precision, recall and ndcg of SoftRec are 43.55%, 47.17% and 28.71% higher than other compared approaches on average. For GitLab dataset, when the data sparsity is \(0.6*\rho_{2}\), SoftRec's precision, recall and ndcg are 25.29%, 22.66%, 24.78% higher than other compared approaches on average. Besides, as the data density gradually decreases, the performance of SoftRec decreases more slowly than other compared methods. This phenomenon indicates that SoftRec performs better when tackling data sparsity. Another discussion is that why SoftRec performs better than other compared approaches in GitHub dataset than in GitLib dataset. The reason might be that the GitHub dataset is more sparse than the GitLab dataset (e.g., in GitHub dataset \(\rho_{1}=3.75\%\), in GitLab dataset \(\rho_{2}=6.01\%\)), and the compared approaches do not fully mine various implicit relationships, making its performance worse when the interactions becomes more and more sparse. However, SoftRec takes advantage of various implicit relationships, and this effectively alleviates the sparsity of interaction and improves the recommendation accuracy.
## 1 Introduction  
Lithium-ion batteries (LIBs) has been used actively due to their advantages such as a high energy density and low self-discharge compared to nickel-cadmium and lead-acid batteries. LIBs are also growing in popularity for aerospace, electric vehicle, and military applications. Thus, much research focusing on performance improvement has been actively carried out [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. However, few studies have overcome the disadvantages of the conventional LIB manufacturing processes. Among the many manufacturing technologies, laser cutting is actively and successfully replacing the conventional cutting processes due to its advantages [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. [PERSON] et al. [16; 17; 18] applied the mathematical model to understand multi-physical phenomena in the laser cutting process for lithium-ion batteries. Furthermore, experimental studies were conducted to investigate the physical phenomena and interaction characteristics during laser cutting of electrode for lithium-ion batteries [11; 13; 14; 15; 18]. [PERSON] et al. [30; 39] used both a continuous (CW) and pulsed laser to cut electrodes for lithium iron phosphate battery electrodes. The process efficiency and quality were characterized and found that the cutting efficiency increases with shorter pulses, higher velocity, and shorter wavelength [30; 39]. [PERSON] et al. [40] compared a CW and pulsed laser to evaluate the cut quality by defining frazzling and clearance widths. The influences of pulse overlap on cut quality for the laser cutting of electrodes for lithium-ion batteries were studied by [PERSON] et al. [41] They evaluated the heat affected zone and delamination width. [PERSON] et al. [42] applied laser cutting on pure lithium metal foils since the lithium metal anodes are hardly separated by die cutting for mass production. Thus, they presented the remote laser cutting to show the potential to separate anodes with high quality. [PERSON] et al. [34] compared the cut quality of laser cutting of lithium-ion electrodes with green and infrared pulsed lasers and found that the green laser provided a clearance width below 20 \(\upmu\)m and the infrared laser provided the high-productivity criterion.  
The electrodes' chemical, physical, and geometrical characteristics are important and most critical parameters during laser cutting. However, no such factors have been considered. Therefore, this study proposes the effect of compression of cathode on laser cutting for lithium-ion batteries. First, materials, experiments, and measurement methods are described. Second, the laser cutting characteristics using compressed and uncompressed cathodes are analyzed and compared. Finally, the conclusions are presented.
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 4 Proposed Method
### Feature-Space Metric Learning  
The second step involves employing episodic training to train the metric embedding module. Here, we define the parameters of the metric module as \(\theta_{M}\). Initially, a set of few-shot tasks \(T^{S}\) is randomly selected from the source domain. These tasks have the same structure as the \(C^{T}\)-way, \(K\)-shot, and \(M\)-test fault-diagnosis tasks in the target domain. Specifically, we define \(S^{S}=\big{(}X_{Sa}^{S},y_{Sa}^{S}\big{)}_{a=1}^{N^{S}_{S}}\) and \(Q^{S}=\big{(}x_{Qa^{\prime}}^{S}y_{Qa}^{S}\big{)}_{a=1}^{N^{S}_{Q}}\), representing the support set and query set in \(T^{S}\), respectively. Here, the size of \(N^{S}_{S}\) is \(C^{T}*K\), and the size of \(N^{S}_{Q}\) is \(C^{T}*M\). Next, the pre-trained feature extractor is employed to derive the basic feature \(b\) from all the original fault data. The metric embedding module can further processes these features into an embedding vector. By comparing the embedding vector of the query samples with that of the support samples, the query samples are categorized. The predicted label for the \(n\)-th query sample from a task in \(T^{S}\) is determined through a weighted average of all the support set labels:  
\[\hat{y}_{Qn}=\sum_{a=1}^{N_{S}}w\big{[}f_{M}(b_{Qn}),f_{M}(b_{Sa})\big{]} \cdot y_{Sa}. \tag{4}\]The weight \(w\) is calculated by the SoftMax standardization of the distance between the embedding vector of the query sample and the support sample:  
\[w\big{[}f_{M}(b_{Q_{n}}),f_{M}(b_{Sa})\big{]}=\frac{\exp\bigl{(}-d\big{[}f_{M} \big{(}b_{Q_{m}}\big{)},f_{M}(b_{Sa})\big{]}\bigr{)}}{\sum_{j=1}^{N_{S}}\exp \bigl{(}-d\big{[}f_{M}\big{(}b_{Q_{n}}\big{)},f_{M}\big{(}b_{S_{j}}\big{]} \big{]}\bigr{)}}, \tag{5}\]  
where \(d\) is the distance calculation function, and the cosine distance is used here:  
\[d\big{[}x_{i},x_{j}\big{]}=\frac{x_{i}\cdot x_{j}}{|x_{i}|\cdot|x_{j}|}. \tag{6}\]  
The cross-entropy loss on all few-shot tasks in the source domain serves as the optimization function for metric embedding module training:  
\[\begin{split}\text{L}_{M}(\theta_{M})&=\sum_{ \text{T}^{S}}\text{L}_{M}\Big{(}\text{T}^{S};\theta_{M}\Big{)}\\ &=\sum_{\text{T}^{S}}\Biggl{[}-\frac{1}{\text{N}^{S}_{Q}}\sum_{n =1}^{N^{S}_{Q}}\sum_{i=1}^{S^{S}}\mathbb{I}\big{[}\text{y}_{\text{Q}n}=i\big{]} \cdot\log\bigl{(}\hat{y}_{\text{Q}n}(i)\bigr{)}\Biggr{]}.\end{split} \tag{7}\]  
The Adam optimizer is adopted as the gradient descent algorithm. In particular, the parameters are updated as follows:  
\[\theta_{M}=\theta_{M}-\alpha_{M}\
abla_{\theta}\text{L}_{M}\Big{(}\text{T}^{S }\Big{)}. \tag{8}\]  
During the federated process, the centralized cloud server aggregates parameters in the following manner:  
\[\theta_{M}=\frac{1}{M}\sum_{i=1}^{M}\theta_{M}^{s,i}. \tag{9}\]
## 3 Integrated Navigation
### Aiding Methods
#### 3.2.2 Non-Holonomic Constraints and Pseudo-Measurements of Height  
In vehicular navigation one possible approach to mitigate INS error accumulation is to apply constraints based on the motion of the vehicle. In other words, it is possible to generate constraint equations reflecting the behavior of the vehicle during navigation [15; 16; 17].  
In the context of this paper, it is assumed that the vehicle does not slip sideways or jump/bounce such that the motion is essentially in the vehicle's longitudinal direction. The above assumptions constitute the so called non-holonomic constraints and are described mathematically as:\[v_{x}^{b} = \eta_{x} \tag{10}\] \[v_{z}^{b} = \eta_{z}\]  
where \(v_{x}^{b}\) and \(v_{z}^{b}\) are the velocity components in body's (vehicle's) lateral and upward directions respectively, and \(\eta_{x}\) and \(\eta_{z}\) are the (fictitious) measurement noises denoting the possible discrepancies in the above stated assumptions. The latter are needed since there may be some small motions in the lateral and vertical directions.  
Equation (10) can be used during GNSS outages to aid the INS navigation using the following equation:  
\[\underline{\partial V}^{b}=R_{n}^{b}\underline{\partial V}^{n}+R_{n}^{b} \underline{V}^{n}\times\underline{\varepsilon}^{n} \tag{11}\]  
which expresses how velocity aiding in the body frame, \(\underline{\partial V}^{b}\), can improve velocity and attitude estimation.  
A second constraint equation can be generated considering the fact that the height does not change significantly in land navigation over short time periods. Hence during GNSS outages the height can be considered constant and equal to a reference value \(h_{\textit{ref}}\), computed just before the outage in good visibility condition [19]. Mathematically, this is written as:  
\[h\approx h_{\textit{ref}} \tag{12}\]  
Although this may seem equivalent to the second equation in Equation (10), there is a subtle difference. In particular, Equation (10) refers to the body frame, whereas the height pseudo-observation applies in the local level frame. Since it is possible that the body frame is inclined relative to the true vertical (e.g., if the car is tilted due to uneven weight distribution), these two constraints are not, strictly speaking, equivalent.  
Both the non-holonomic constraints and the pseudo-measurements of height can be used as measurement models of a complementary Kalman filter during GNSS outages as shown in Figure 3. As such, the non-holonomic and height constraints are herein respectively named velocity and height pseudo-measurements. Finally, although not shown in Figure 3, these can also be applied in the presence of GNSS updates.
# Molecular and Hormonal Mechanisms Regulating Fleshy Fruit Ripening
## 6 The Identification and Function of Major Regulator TFs  
Developments in map-base cloning helped focus attention on four spontaneous ripening mutations: _rin_, _nor_, _cur_ and _Nr_, identified by tomato breeders, that produced pleiotropic non-ripening mutant phenotypes. Genetic analysis suggested that WT versions of these genes were absolutely required in order for ripening to occur, and they were considered as candidates for'master regulators'. The _Nr_ mutation was shown to encode an altered ethylene receptor that functioned as a negative regulator that was unable to bind ethylene [257]. Map-based cloning showed that _rin_ encoded a MADS TF, MADS-RIN, _nor_ encoded a NAC TF, NAC-NOR, and _cur_ encoded a SQUAMOSA promoter-binding protein-like TF, SPL-CNR. In addition to these spontaneous mutants, other TFs have also been identified such as TOMATO AGAMOUS-LIKE1 (TAGL1), APETALA2a (AP2a) and FRUITFULL (FUL1 and FUL2) [4]. TAGL1 (also mentioned in Section 4.1) is highly expressed during fruit ripening, interacts with MADS-RIN [258] and is a candidate for controlling ripening processes. Silencing the encoding _FUL_ genes separately resulted in very mild alterations in tomato fruit pigmentation, indicating that FUL1 and FUL2 probably have redundant functions and FUL1/2 and TAGL1 may regulate different subsets of the known MADS-RIN targets. Further, _MADS-RIN_ and _TAGL1_ were found to be upregulated in the pericarp of _FUL1_/2 RNAi fruits, pointing to a negative feedback loop from FUL1/2 to these genes [4].  
Recent experiments using CRISPR/Cas9 technology [259] to generate mutants deficient in MADS-RIN, NAC-NOR and SPL-CNR produced a much less severe ripening phenotype than that shown by the original spontaneous mutants _rin_, _nor_ and _Cur_, and this has led to a complete reappraisal of their role in ripening [14; 15; 16; 17; 18; 19; 260].