esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Electronic Structure  
The calculated band structures for the five pyrophosphate crystals are shown in Figure 2. They are all insulators with large direct band gaps at \(\Gamma\) ranging from 3.70 eV to 5.22 eV and listed in Table 3. However, on a closer inspection, we find that in C3 and C4 the states near the VB top are actually metallic bands with both occupied and unoccupied band below and above the Fermi level. This is illustrated in Figure 2c',d' next to Figure 2c,d.  
The top of valence band (VB) in all five crystals are flat and the bottom of conduction band (CB) have curvatures. The calculated CB effective masses listed in Table 1 are comparable with each other  
Figure 2: Calculated band structures of five pyrophosphate crystals. (**a**) C1, (**b**) C2, (**c**) C3, (**d**) C4, (**e**) C5, and (**c**’), (**d**’) magnified y-axis from -1.5 eV to 0.05 eV for C3 and C4, respectively, with dashed line showing Fermi level.  
around 0.143 \(m_{e}\) to 0.149 \(m_{e}\). They can be compared to other wide-gap semiconductors such as AIP (0.13 \(m_{e}\)) [47], GaN (0.19 \(m_{e}\)) [48], ZnSe (0.17 \(m_{e}\)) [49], ZnTe (0.16 \(m_{e}\)) [50], CdS (0.20 \(m_{e}\)) [51], and CdSe (0.13 \(m_{e}\)) [52]. The small effective mass implies these crystals could have large electron mobility in the CB. Therefore, pyrophosphate crystals with wide band gap and high electron mobility could be used in radiation detector [53].  
The total density of states (TDOS) for all five crystals with energy range from \(-\)25 to 25 eV are shown in Figure 3. The TDOS are further resolved into partial density of states (PDOS) for the four structural units. These give more detailed information on the interaction between different groups and the difference among five crystals. For example, in crystals containing K, a sharp peak at around-10 eV is due to semi-core nature of K-3p orbital. We see a slight double peak for K due to two different types of K based on sites. (NH\({}_{4}\))\({}_{2}\)Zn(H\({}_{2}\)P\({}_{2}\)O\(\gamma\))\({}_{2}\)\(\cdot\)2H\({}_{2}\)O is the only crystal containing NH\({}_{4}\) ligand instead of K. The sharp peak at around \(-\)16.38 eV is due to both N and H atoms, and a smaller peak at \(-\)6.49 eV is due to N-2p orbital. For C3 and C4, the gap state arises purely from Cu 3d electrons with possible interaction with water and the pyrophosphate group (H\({}_{2}\)P\({}_{2}\)O\(\gamma\))\({}_{2}\). In Figure 3f, we specifically show the PDOS of Cu in C3 and C4 in the energy range from \(-\)8 eV to 8 eV with the zero of energy located at the Fermi level. It should be mentioned that the current calculation is not spin-polarized based on the assumption that the pyrophosphates studied in this paper are all paramagnetic. To get much deeper insight on Cu-containing pyrophosphates, spin-polarized calculation, or DFT +U approach should be attempted.  
Figure 3: Calculated total density of states (DOS) and partial density of states (PDOS) from four structural unit. (**a**) C1, (**b**) C2, (**c**) C3, (**d**) C4, (**e**) C5, and (**f**) shows PDOS of Cu from C3 and C4.
# Pressure Drop and Particle Settlement of Gas-Solid Two-Phase Flow in a Pipe
## 5 Conclusions  
In order to clarify the effect of inlet velocity, particle volume concentration, particle mass flow, and particle diameter on the sedimentation degree of particle and pressure drop in gas-solid two-phase flow in a pipe with a circular cross-section, the continuity equation, momentum equation, and state equation in the range of mixture inlet velocities ranging from 1 m/s to 30 m/s, particle volume concentrations ranging from 1% to 20%, particle mass flows ranging from 5 t/h to 25 t/h, and particle diameters ranging from 50 \(\upmu\)m to 1000 \(\upmu\)m were solved numerically based on a two-fluid model. Some results werevalidated by comparing the experimental results. The main conclusions can be summarized as follows:  
(1) The gas and particle velocity distributions are asymmetrical around the center of the pipe and as the maximum velocity point moves up. The distance between the radial position of the maximum velocity and the center line for the gas is larger than that for the particles. The particle motions lags behind that of the gas flow.  
(2) As the flow develops downstream, the particle volume concentration increases and decreases gradually at the bottom and the upper part of the pipe, respectively, showing obvious particle sedimentation. The particle settlement phenomenon is more serious, and the particle distribution on the cross-section is more inhomogeneous with the decrease in the mixture inlet velocity, particle volume concentration, and particle mass flow as well as with the increase in the particle diameter.  
(3) The pressure at the inlet is the maximum, and it then decreases gradually to the atmospheric pressure at the outlet. It can be divided into three areas according to the pressure changes, i.e., inlet area, transition area, and fully developed area. The distinction between the three areas is more obvious as the particle volume concentration increases. The pressure drop per unit length increases as the mixture inlet velocity, particle volume concentration, and particle mass flow increase and as the particle diameter decreases.  
(4) Finally, the expressions of settlement index and pressure drop per unit length functions of the mixture inlet velocity, particle volume concentration, particle mass flow, and particle diameter are derived based on numerical data in order for the settlement index and pressure drop to be calculated conveniently.  
Conceptualization, Y.W. and W.L.; methodology, W.L. and L.L.; software, W.L. and L.L.; validation, L.L. and W.L.; writing, W.L. and L.L.; resources, W.L. and Y.W.; review, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant no. 12132015).  
There are no conflicts of interest regarding the publication of this paper.
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 2 Contention Window Adaptation Mechanism
### The Proposed Mechanism
#### 2.2.1 Modeling the Gain
The numerator of \(\Delta G\) is a weighted average of the last terms of \(P(d)\). However, as it is explained in Appendix A, the random variable \(d\) concentrates its probabilities in the first values of \(d\). On the other hand, the numerator is always greater than 1. Thus, \(\Delta G\) is a value very close to 0 and can be disregarded in Equation (4), and consequently,  
\[P(win)=P((U_{1}<U_{2})\wedge(U_{1}<U_{3})\wedge...\wedge(U_{1}<U_{n}))\]7  
It can be seen that the expression of \(G\) does not depend on the random variable \(d\), which means that the gain of the reference station does not depend on the rest of stations. As said before, a similar analysis can be carried out in the case that the \(default\_CW_{min}\) value is increased in \(k\) units, obtaining the following expression:  
\[P(win)=P((U_{1}<U_{2})\wedge(U_{1}<U_{3})\wedge...\wedge(U_{1}<U_{n}))\]8  
Again, \(G\) does not depend of the random variable \(d\). Due to the fact that \(G\) does not depend of the number of competing stations, \(G\) can be used to propose new solutions that manage QoS requirements in wireless networks. The gain gives us: (1) a measure of the cost/benefit of modifying the default \(CW_{min}\), (2) a simple way to measure the competitive advantage gained by a station in front of others, and (3) a scalable strategy to fairly compensate the positive and negative gains around the network. Figure 2 shows the evolution of the gain \(G\) when the \(CW_{min}\) value is decremented in \(k\) units (it is represented as \(G^{+}\)) and when it is incremented in \(k\) units (it is represented as \(G^{-}\)). As it can be clearly seen, \(G^{+}\) has a quasi-exponential behavior, while \(G^{-}\) decreases slowly in a liner way. The different behavior of \(G^{+}\) and \(G^{-}\) is going to determine the number of terminals that are required to manage a certain bandwidth redistribution between _requesting terminals_ and _giving terminals_. To compensate the gain obtained by a terminal that reduces its contention window size, it will be necessary to increase drastically the contention window size of a _giving_ terminal (if it is possible), or even better, to select a group of _giving_ terminals for distributing the total increase among them. Each scenario of application of the proposed DEDCA mechanism will determine design constraints that, known the behavior of \(G^{+}\) and \(G^{-}\), will allow to obtain the number of involved terminals and the adequate \(k\) values. Finally, it is important to point out that the only requirement of the model is the fact that each contending station selects uniformly the duration of its backoff period. No other assumptions are needed. The simplicity of a mathematical model is often the guarantee that it will be valuable in real situations, especially in VANET, WSN and IoT networks.
# Measurement of Head-Related Transfer Functions: A Review
## 4 Multi-Loudspeaker-Based Fast HRTF Measurement Methods
### Continuous Measurements  
Some methods have been developed for continuously measuring HRTFs assuming that HRTF is a continuous function of spatial directions [35]. This mechanism requires the subject to rotate continuously while the loudspeakers play back excitation signals. [PERSON] et al. [159] proposed a theoretical method for capturing HRTFs with a rotating subject (moving microphones) and a fixed sound source. The HRTF for any azimuth angle can be reconstructed in the spatio-temporal frequency domain by applying the projection-slice theorem within only 0.66 s. However, it is only a theoretical measurement setup, practical measurements have not been performed and verified. [PERSON] et al. [160] continuously measured HRTFs on the horizontal plane with a rotating subject and a fixed loudspeaker by applying the MLS method. The HRIRs for different azimuth angles were calculated by the cross-correlation method within one MLS period. [PERSON] et al. [140] measured HRTFs with a continuously moving loudspeaker. The loudspeaker moved slowly (\(2^{\circ}\)/s) around the subject with a fixed elevation angle and repeatedly emitted exponential sweep signals. The HRIRs were calculated by applying the linear deconvolution method within one period of the sweep signal. The measurement methods in [140,160] are actually based on the deconvolution techniques commonly used to measure static HRTFs (see Section 2). For these two measurement systems, the length of the excitation signal and the rotational speed of the subject/loudspeaker should carefully be chosen to ensure the relative changes in azimuth between the subject and the loudspeaker can be neglected within one period of the signal.  
[PERSON] et al. [161] proposed a fast measurement method with a rotating subject while the loudspeakers play back exponential sweeps under the MESM mechanism. This method extended the step-wise MESM approach and substantially reduced the measurement time. In addition to the continuous MESM approach, adaptive filtering algorithms are suitable for continuously recording multi-directional HRIRs based on the method described in Section 2.2.3[80; 162]. In the following, these two main continuous measurement approaches are briefly described, namely the continuous MESM and time-varying adaptive filtering method.
## 4 An Exploration of the Advantages of Genetic Algorithms
### The Multi-Objective Optimization Method
#### 4.2.1 Weighted Method  
The weighted method is a simpler approach, which forms a single parameterized objective by linearly combining multi-objective functions and setting weight vectors based on the importance of each objective. The order of the candidate vectors may vary from objective to objective because there may be a difference in priority or focus between the objectives. Moreover, the effectiveness of the solution is significantly affected by the underlying weight vectors. The weighted method can be expressed by Equation (2) [180]:  
\[\left\{\begin{aligned} min\ J=\omega_{1}f_{1}(x_{1},x_{2}, \cdots,x_{n})+\omega_{2}f_{2}(x_{1},x_{2},\cdots,x_{n})+\cdots+\omega_{n}f_{n }(x_{1},x_{2},\cdots,x_{n})\\ s\cdot t\cdot g(x)_{i}\leq 0,i=1,2,\cdots,m\end{aligned}\right. \tag{2}\]  
In the realm of optimization, (\(f_{1}\), \(f_{2}\), \(\cdots f_{n}\)) represent the various objectives. The constraints associated with these objectives are denoted by (\(g(x)_{i}\)). Additionally, (\(\omega_{1}\), \(\omega_{2}\), \(\cdots\)\(\omega_{n}\)) signify the corresponding weights, with a crucial stipulation that the aggregate of these weights must equal 1. The weighted method has significant advantages in that its simplicity and intuitive nature allow researchers to flexibly control the balance between different objectives by adjusting the weights conveniently. However, the weighted methods also face the challenge that their weight allocation often relies on the experience and subjective judgment of researchers, which may lead to uncertainty and inconsistency in the results, thus affecting their validity and reliability in multi-objective optimization to a certain extent.
## 3 Bioprocess and Bioreactor Dynamic Model
### The Structured Hybrid Kinetic Model
The adopted model for the ATP-ADP-AMP dynamics (V1-V6, and equilibrium relationships) in (Figure 2--the pink square, and Tables 2 and 3) proposed by [PERSON] [11; 45; 54], and by [PERSON] et al. [43] was proved to fairly represent the dynamics and the thermodynamics of the cell modules [a,b].\"  
Module [c] TRP synthesis: Modelling the TRP synthesis on a deterministic (mechanism-based) approach is difficult because this cellular process is known as being, under certain conditions, a QSS, or an oscillatory one [44; 45; 79; 80; 81; 90]. However, to avoid extended models, which pose difficulties in their estimation and use, most of the reduced dynamic models from the literature do not distinguish the 'process' components from'regulatory' components, and lumped reactions/species are considered instead, with the regulatory performance being included via adjustable model parameters and terms. Kinetic models trying to reproduce the TRP-operon expression self-regulation [80; 81] are too extended for our engineering evaluations purposes. Dueto the process complexity, some modelling approaches [61] focus more on determining correlations between flux distribution, flux control, and the optimized enzyme activity distribution. However, by employing a kinetic model that is too reduced, such models are not able to be linked to the dynamics of the CCM modules. For these reasons, in the present analysis, simulations of the TRP synthesis (Figure 2) were performed by using the reduced kinetic model constructed by [PERSON] et al. [11; 44]. The mass balances of the key species of the TRP-operon expression are given in (Table 4). This dynamic model is derived from those of [PERSON] et al. [79], but it explicitly connects the TRP module to the glycolysis module [a] through the PEP precursor-sharing node (in Figure 2). Consequently, PEP is included as a substrate in the TRP module [c] mass balance
# Formation Mechanism of Crystal Spots in Jian Kiln Oil-Spot Glaze Revealed by Simulation Experiments
## 3 Results and Discussion  
### Appearance Analysis  
Figure 2 shows optical magnification images of the glaze surfaces of samples RC-1 to RC-9, removed from the kiln at different temperature points and subjected to air quenching. From Figures 1 and 2, it can be observed that the glaze surface of sample RC-1 began to melt at 1190 \({}^{\circ}\)C, with the color gradually changing from reddish-brown to black. At 1220 \({}^{\circ}\)C, Fe\({}_{2}\)O\({}_{3}\) in sample RC-2 decomposed unstably, generating numerous bubbles. However, due to the high viscosity of the glaze melt, numerous pores appeared on the glaze surface. Simultaneously, Fe\({}_{2}\)O\({}_{3}\) accumulated on the glaze surface along with the bubbles, precipitating silver crystalline spots with diameters ranging from 1 to 2 mm. As the temperature further increased to 1315 \({}^{\circ}\)C, sample RC-3 exhibited more complete melting, with the glaze surface appearing black and smooth without silver spots, indicating that the previously formed spots had reintegrated into the glass phase. After maintaining the maximum temperature for 60 min, the RC-4 glaze surface remained dark black, with improved melting characteristics and reduced high-temperature viscosity, resulting in glaze flow. Bubbles generated by iron oxide decomposition rapidly escaped. After the reduction phase, the glaze surface of sample RC-5 formed oil-drop spots 2-3 mm in diameter, with larger spots composed of multiple smaller oval spots. The spots were primarily light blue, consistent with the characteristics of oil-spot glaze from the Song Dynasty Jian kiln [13], suggesting that the reducing atmosphere during the high-temperature stage played a key role in oil-spot formation. As the kiln naturally cooled, the reducing gases (H\({}_{2}\) and CO) were gradually depleted, and the atmosphere shifted to oxidation. At 1180 \({}^{\circ}\)C, the glaze spots on sample RC-6 exhibited yellow-brown crystalline blooms, with yellow-brown streaks forming around the spots. At 1150 \({}^{\circ}\)C, lamellar crystals within the spots of sample RC-7 grew to several hundred micrometers, with the largest reaching approximately 500 \(\upmu\)m. The yellow-brown streaks outside the spots darkened, accentuating the black appearance of the spots. At 900 \({}^{\circ}\)C, the crystals within the glaze spots of sample RC-8 continued to grow, with the spots turning yellow-brown and exhibiting reddish-brown edges, while the streaks outside the spots gradually faded. As the kiln cooled naturally to room temperature, the glaze spots on sample RC-9 exhibited a deepened reddish-brown color, with the streaks diminishing and eventually disappearing, while the spot appearance turned black. Observations of the air-quenched samples from the firing process revealed two distinct precipitations of spots, with significant differences in appearance, as well as in the temperature and atmospheric conditions under which they formed.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 5 Evaluation Result
### Effect of Topology
#### 5.3.1 Position of Reference Node in FTSP  
FTSP in a multi-hop network has been reported to have poor performance. To understand the effect of hop distance from the reference node, the maximum hop distance between the reference node and the farthest node(s) is changed, as shown in Figure 9. The results of this simulation are shown in Figure 10. It is clear that the number of rounds required to synchronize the entire network in FTSP depends on the hop distance to the farthest node(s) as shown in Figure 10a. Because GTSP does not require a reference node, it is not affected by this. To reduce the waiting time in the \"flooding\" message, the NUMENTRIES_LIMIT value in FTSP setting can be reduced (e.g., two entries). This configuration allows the nodes to start forwarding the timing message after receiving a sufficient number of messages. The position of the reference node affects the network synchronization error as shown in Figure 10b. Thus, the choice of reference node is an important factor in achieving better performance with FTSP. In a static topology, it is easy to choose a reference node that allows the hop distance values to be minimized. However, in a dynamic network, the position of the reference node can be changed regularly. Additionally, FTSP does not provide any mechanism to ensure that a center node will become a leader in the election process. Hence, this is an open issue with FTSP.  
Figure 8: Network error and maximum neighbor error of FTSP, GTSP and E-FTSP with different synchronization periods.  
Figure 9: Hop distance between reference node (root) and farthest node in grid topology.
# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 5 Explainability in Federated Intrusion Detection  
### Motivation and Context  
As FL matures as a key technology in machine learning to preserve privacy, its integration into cybersecurity systems has raised critical concerns about transparency and interpretability. FL-dependent models, especially in intrusion detection systems (IDS), are often viewed as black boxes, making predictions without an understandable rationale [37; 39]. In critical domains such as healthcare, industrial control, and transportation, this lack of explanation undermines user confidence, prevents auditing, and complicates incident response [41]. In addition, regulations such as the General Data Protection Regulation (GDPR) [13] and upcoming AI governance frameworks increasingly require decisions to be explained, especially when model predictions affect user safety or access to services [42]. Improving interpretability is therefore not only a feature of usability, but also a legal and ethical imperative. The goal of Explaining AI (XAI) is to address this challenge by providing  
Figure 9: Federated intrusion detection in a smart healthcare IoT network.  
information about how and why machine learning models make decisions [39; 40]. However, the integration of XAI into the FLS ID presents unique constraints: the explanation must be generated locally to preserve privacy, avoid detection of sensitive training data, and respect the decentralized nature of federated systems [37; 43].
## 3 Experiments
### Recognition Results for the UMB-DB  
We perform the same experiments on the UMB-DB database to demonstrate the effectiveness of the proposed method. Similarly, we divide the unobstructed face data in this database into a training set, a validation set, and a test set. The validation set and the test set are not involved in the training; the former is used to select the best model and the latter is used to determine the performance of the model. The experimental results are shown in Table 5. The proposed method has a 100% recognition rate for both Top-1 and Top-5 categories for unobstructed pose faces and expression faces. The Top-1 and Top-5 recognition rates for obscured faces are 93.41% and 96.60%, respectively. The recognition performance metrics are significantly better than other existing state-of-the-art deep network methods. We can observe that, compared to the experimental results for the Bosphorus dataset, the recognition rates of the proposed method and other deep networks on UMB-BD are reduced to different degrees. This is because the occlusion scenario in the UMB-BD dataset is more complex and diverse, which affects all of the methods to different degrees. Additionally, the proposed method still maintains a Top-1 recognition rate of over 93%. Moreover, some methods perform better for unobscured faces but perform poorly on obscured faces. In contrast, the proposed method achieves the best recognition for both unobscured and obscured faces, showing the robustness of the proposed method.
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
## 2 Related Work
However, the papers listed above provide a good starting point for researchers who are interested in this topic. These papers demonstrate the growing interest in developing new and improved polling schemes for wireless industrial communication networks. However, there is still room for further research in this area, and the literature on the applications of polling specifically in LoRaWANs is not as extensive as other wireless solutions that have been around for longer. One promising area of research is the development of polling schemes that are adaptive to the dynamic conditions of wireless networks. This would allow polling schemes to optimize their performance for different traffic loads, channel conditions, and device types.