esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Particularities, Prediction, and Synthesis Methods in High-Entropy Alloys
## 2 Structural Particularities of HEAs, Prediction and Estimation Concepts
### Microstructural Stability
#### 2.2.2 Severe Lattice Distortion  
The effect of severe lattice deformation is usually compared to single-dominant alloys, where the lattice nodes are mainly occupied by the dominant constituent. For multicomponent alloys, each element has the same possibility of occupying the network node, if the chemical ordering is not taken into account [13]. Significant lattice defects occur due to the varying atomic sizes within the densely packed complex phases. The dislocation at any given point in the atomic lattice is influenced by the specific atom at that location and the types of neighboring atoms. Such distortions that occur in the crystal lattice of a high-entropy alloy are more severe than when they occur in conventional alloys. The uncertainty that arises in the case of occupying the atom positions from these deformations contributes to the excess configurational entropy and contributes to the decrease in the intensity of the X-ray diffraction maxima [8; 14]. The severe deformation of the crystal lattice is used to explain the increase in hardness, the reduction of electrical and thermal conductivity, and the reduction of the temperature dependence of these properties. Although this information appears to have a rational basis, research is still being conducted to quantify these effects and to be able to differentiate them from the effects of other processes. For instance, differences in elastic modulus between the constituent atoms can contribute to alloy hardening. Additionally, electrical and thermal conductivity may be affected by the electronic structures resulting from variations in the bonding states between atoms [13].
# Advances in Organic and Organic-Inorganic Hybrid Polymeric Supports for Catalytic Applications
## 3 Organocatalyst Asymmetric Reactions
The same reactions were also carried out in wet DMSO, obtaining an improvement of ee values but a decrease of yield in some cases. In presence of catalyst **97**, the self-aldol reaction of propanal, butanal and 3-phenylpropanol in water as the only solvent gave moderate to good yields and high enantioselectivities. Recyclability of catalyst **97** was studied in the self-aldol reaction of butanal. It was recovered by simple filtration and recycled 10 times showing a moderate decrease of catalytic activity at the end of the recycling test. **Scheme 33. Synthesis of polystyrene-immobilized triazolylproline organocatalyst **97** and its application in the asymmetric cross and self-aldol reactions of aldehydes. A new polystyrene-supported squaramide for the enantioselective flow synthesis of pyranonaphthoquinones was reported [60]. In detail, PS-squaramide **100** was prepared following an synthetic strategy that consisted in immobilization of intermediate **98** onto the Wang resin, containing a bis-phenylmethylene ether handle, and in successive nucleophilic displacement with the enantiopure (1\(R\),2\(R\))-2-(piperidin-1-yl)cyclohexanamine (Scheme 34). After having studied the behavior of resin in batch by the addition of 2-hydroxy-1,4-naphthoquinone to (_E_)-2-nitro-3-phenyl-allyl acetate, the continuous flow experiment was addressed. A new device, in which a packed bed reactor loaded with PS-squaramide **100** was coupled with the T-junction connecting the saturated solution of sodium bicarbonate and the coil reactor, was assembled. A library of enantioenriched pyranonaphthoquinones was prepared by sequential pumping of different solutions containing hydroxynaphthoquinone and a representative set of Morita-Baylis-Hillman acetates. The total residence time of the each process including two sequential reactions--the organocatalyzed Michael addition and the base promoted cyclizations--and in-line work-up was only 30 min. All the pyranonaphthoquinones prepared with this methodology showed excellent stereoselectivities and good yields.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 4 Experiments
### Ablation Studies
#### 4.4.3 Visualization Analysis  
To qualitatively assess the effectiveness of the proposed node-partition bias within the attention mechanism, we visualize the learned attention weights between joints. As shown in Figure 4, DGAN assigns significantly higher attention weights between nodes representing the left hand and head during the action \"drinking,\" while assigning lower attention scores to joints in the lower body. This behavior allows the model to concentrate effectively on coordinated movements between the hand and head, capturing crucial discriminative features of the drinking action. Similar patterns are observed for other actions, demonstrating that the dynamic attention mechanism of DGAN selectively captures essential global dependencies while maintaining local structural integrity. This selective attention pattern provides intuitive interpretability for model decisions, confirming that DGAN indeed learns to emphasize critical body parts and interactions crucial for accurate action classification.  
\begin{table}
\begin{tabular}{l c} \hline
**Model** & **Accuracy (\%)** \\ \hline DGAN+DGAN & 88.26 \\ DGAN+MSTC+MLP & 89.27 \\ MSTC+DGAN & 88.63 \\ DGAN+MSTC & 90.90 \\ \hline \end{tabular}
\end{table}
Table 4: Optimization exploration based on DGAN.  
\begin{table}
\begin{tabular}{l c} \hline
**Partition Strategy** & **Accuracy (\%)** \\ \hline None & 90.32 \\ Learned Partition & 90.36 \\ Empirical Partition & 90.68 \\ Proposed Partition & 90.90 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of different partition strategies.  
As shown in Figure 5, the model achieves high recognition accuracy across most action categories. For the few cases where errors occur, such as in the reading or writing actions, occlusion of key joints or significant noise in the skeleton data may lead the dynamic attention mechanism in DGAN to mistakenly focus on less informative joints, resulting in misclassification. For instance, inaccurate localization of limb keypoints in some samples may cause the model to introduce spurious connections due to noise. Nevertheless, the overall impact of such errors is limited, owing to the presence of the masking mechanism which effectively suppresses unreliable connections.  
Short-term actions are typically characterized by rapid, instantaneous completion (e.g., drop), whereas long-term actions often exhibit longer temporal duration (e.g., drink). From the accuracy distribution across different action types in Figure 5, it can be observed that there is no significant performance gap between the recognition of short-term and long-term actions. This indicates that the model maintains consistent recognition performance for both high-dynamic, short-duration movements and stable, temporally extended actions. Therefore, action duration is not a dominant factor affecting the model's recognition performance. Instead, the model appears to rely more heavily on spatial structural features and discriminative pose patterns, rather than the temporal extent of the actions.
# Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route
## 3 Preliminaries
### Road Travel Time Estimation  
Our approach to compute the best matching results for ride-sharing service consists of shortest road traveling time computation. Traditional methods compute it directly in large-scale road networks, which will cause an expensive computation cost and cannot be applied with cryptographic primitives. To solve this, a zone-based method could be used to estimate the road traveling time [19]  
First, the road network is partitioned into \(m\times n\) zones \(\{z(i,j)\}_{(i,j)=(i,1)}^{(m,n)}\). For each grid, we set an anchor point for travel time estimation between zones. All anchor points are selected considering the number of its adjacent edges (namely the degrees) and the geographical distance to the center of its located zone. We use \(\{AP(i,j)\}_{(i,j)=(i,1)}^{(m,n)}\) to represent the anchor point of each zone.  
Once the process of anchor setting is finished, the travel time between zones can be estimated and precomputed with the distance between anchor points as follows:  
\[t_{z(i,j)\to z(x,y)}=t_{AP(i,j)\to AP(x,y)}\ (1\leqslant i,x\leqslant m,1 \leqslant j,y\leqslant n) \tag{7}\]  
For any two points in the road network, we denote as \(s\) and \(d\), respectively. The travel time between \(s\) and \(d\) can be estimated with the following expression:  
\[t_{s\to d}\approx t_{s\to AP(s)}+t_{AP(s)\to AP(d)}+t_{AP(d)\to d} \tag{8}\]  
where \(AP(x)\) denotes the anchor point of the zone in which \(x\) is located. The information of road network and anchor points are public.  
In our proposal scheme, for a ride request with start point \(s\) and destination \(d\), the ride requester can easily compute \(t_{s\to AP(s)}\) and \(t_{AP(d)\to d}\) at the local mobile device. Then the rider can submit them after encryption to hide the precise locations \(s\) and \(d\) in two located zones.
## 4 Experimental Results and Discussion
### Comparative Analysis of Model Performance with Different Recursive Layers
Many UG models, regardless of their specific design variations, use comparable sensors for measuring parameters like pitch, yaw, roll, and depth, as well as environmental variables such as temperature and salinity. These shared sensor and data configurations suggest that DFFormer's core predictive algorithms can be feasibly adapted to other UG models, allowing it to generalize effectively across various UG types with minimal modification. In terms of generalizability to different marine environments, while oceanographic conditions such as temperature, salinity, and current patterns do vary across seas, DFFormer's design leverages a multi-scale feature extraction method that is particularly robust in capturing and adjusting for local variability. The integration of discrete wavelet transform (DWT) and fast Fourier transform-based attention (FFT-Attention) allows DFFormer to effectively model both high- and low-frequency characteristics in UG data. This dual approach enables DFFormer to capture complex, location-specific patterns, thus potentially enhancing its adaptability across different oceanic conditions.
# A Review of Sharp-Switching Band-Modulation Devices
## 1 Introduction
### Electrostatic Doping for Reconfigurable Devices  
Band-modulation devices are virtual PNPN structures with electrostatic doping induced by gate action (Figure 2). Electrostatic doping is a unique asset of modern devices with an ultrathin body layer [8; 9; 10; 11]. A gate voltage attracts free carriers that spread in the entire body, rather than being confined at the semiconductor-dielectric interface, as in bulk-silicon systems. This effect, known as 'volume inversion' or 'volume accumulation' [3], gives rise to populations of electrons (for V\({}_{\text{G}}\) > 0) or holes (for V\({}_{\text{G}}\) < 0) that behave, in many respects, as those created by conventional physical doping. The tremendous advantage is that the polarity and concentration of such electrostatic doping is adjustable on demand by the gate voltage. The opportunity to emulate P-N junctions in ultrathin fully-depleted semiconductor layers represents a paradigm shift in the design of reconfigurable and multifunctional circuits [11; 12].  
Consider the FD-SOI device in Figure 2a that combines electrostatic and physical doping. The end contacts, \(N^{+}\) cathode and \(P^{+}\) anode, are formed by ion implantation or in situ doping. The undoped body is controlled by a partial front gate (V\({}_{\text{FG}}\)) and by the ground-plane acting as a back gate (V\({}_{\text{BG}}\)). Each gate can induce \(P^{*}\)-type or \(N^{*}\)-type electrostatic doping or just leave the gated and ungated regions fully depleted (\(I\)). The concentration of electrostatically-induced carriers declines from the voltage-controlled interface to the opposite interface. The thinner the body, the more homogeneous the vertical profile of the effective doping.  
According to the polarities of the two regions, there are nine possible combinations of the electrostatically-doped body segments, corresponding to P-N diodes, PIN diodes, IMOS, TFET, and PNPN band-modulation transistors. This single structure, which is able to impersonate nine different devices, has received the name 'hocus-pocus diode' [12], in order to emphasize the magic of electrostatic doping.  
Unlike FD-SOI devices, FinFETs, nanowires, and nanosheets do not enjoy the advantages of a built-in back gate. Nonetheless, the hocus-pocus diode can still be implemented using two adjacent front gates (or surrounding gates), as shown in Figure 2b. In this review, we will mostly focus on the Z\({}^{2}\)-FET variant of Figure 2a, which offers a perfectly sharp junction at the gate edge [13; 14].
## 4 Our DRL-JCBRTP Framework
### Design Principle of Reward Functions
\] (5)
* **MTE cost \(mteCost\)**: The MTE cost refers to the maximum TE of a tracked neighbor vehicle \(v\in\mathcal{V}_{G}\), where it is always less than tracking threshold \(E_{T}\), as follows: \[mteCost=\omega_{TE}\cdot\underset{\forall v}{\max}(TE(v)),\quad TE(v)\leq E_{T}.\] (6) Although it does not incur a risk of collision, the MTE cost can be interpreted as the upper bound of the tracking accuracy of a neighbor vehicle. * **Beacon interval cost _biCost_**: The beacon interval cost informs the agent about the elapsed time since its last beacon broadcast, which is crucial for ensuring the freshness of kinematic information updates. It is proportional to the ABI to quantify the beacon aging: \[biCost=\omega_{BI}\cdot ABI.\] (7)
* **Congestion cost _cgCost_**: The congestion cost encourages the agent to make judicious decisions regarding beacon transmission in relation to both current CBR and TP level, as follows: \[cgCost=\omega_{CG}\cdot P_{TX}(p),\] (8) where congestion weight \(\omega_{CG}\) plays a pivotal role when the agent opts for the appropriate action \(a=(1,p)\) by discriminating the weights by comparing the current CBR with the CBR threshold \(\theta_{G}\), as follows: \[\omega_{CG}= \begin{cases}0,&\text{if }b=0;\\ CBR,&\text{if }b=1\text{ and }\text{CBR}\leq\theta_{G};\\ \delta,&\text{if }b=1\text{ and }\text{CBR}>\theta_{G},\end{cases} \] (9) where parameter \(\delta\) acts as a penalty constant that significantly raises the congestion weight \(\omega_{CG}\) when the CBR exceeds the threshold \(\theta_{G}\). This penalty increases the congestion cost, signaling to the agent that the selected TP is causing the CBR to exceed the desirable range of CBR. In addition, TP cost \(P_{TX}(p)\) is proportionally scaled to the TP \(p\), as follows: \[P_{TX}(p)=C_{1}+\frac{p-p_{0}}{\Delta p}C_{2},\] (10) where \(C_{1}\) is the default cost coefficient for beacon transmission, \(C_{2}\) the incremental cost coefficient of TP, \(p_{0}\) the smallest TP in dBm, and \(\Delta p\) the TP gap. We notice that the first three awareness components, i.e., mDFD, mTFD, and MTE, are not locally available to the agent. During the training phase of the SAC network, these additional terms are used to calculate the target Q-value, which is then approximated by the critic network. However, these terms are no longer needed in the test phase of the SAC network, as the Q-value predicted by the trained critic network will be used for action-value function approximation.
## 2 System Model and Problem Formulation
### Signal Model  
We can express the transmitted signal at the BS as  
\[\mathbf{x}=\sum_{k=1}^{K}\mathbf{w}_{k}s_{k} \tag{1}\]  
where \(\mathbf{w}_{k}\in\mathbb{C}^{M\times 1}\) is used to denote corresponding transmit precoding vector, while \(s_{k}\) denotes the transmit message to user \(k\). It is assumed that \(s_{k}\), \(k=1,\dots,K\), are independent random variables with zero mean and unit variance. Then, we use \(y_{k}\) to denote the signal received by user \(k\). The expression of \(y_{k}\) is given below. Note that the difference between the two paths is usually negligible as the RIS is usually placed close to the transmitter or receiver to get minimal path loss. Otherwise, it needs to conduct an estimation of the delay at the BS [5; 24] and phase compensation at the receiver when the difference between the two propagation delays gets large [25].  
\[\begin{split} y_{k}&=\underbrace{\mathbf{h}_{d,k}^{ H}\mathbf{x}}_{\text{Direct link}}+\underbrace{\mathbf{h}_{r,k}^{H}\mathbf{\Theta}^{H}\mathbf{G}^{H} \mathbf{x}}_{\text{RIS-aided link}}+u_{k}\\ &=(\mathbf{h}_{d,k}^{H}+\mathbf{h}_{r,k}^{H}\mathbf{\Theta}^{H} \mathbf{G}^{H})\sum_{k=1}^{K}\mathbf{w}_{k}s_{k}+u_{k}\end{split} \tag{2}\]  
where \(u_{k}\sim\mathcal{CN}(0,\sigma_{0}^{2})\) refers to the additive white Gaussian noise (AWGN) in the channel [26].
## 3 Numerical Simulation
### The Effect of the Preload \(N_{0}\) for Vibration Reduction  
In order to predict the effect of the preload \(N_{0}\) on the blade tip vibration reduction, a normalized energy density (\(E_{\rho}\)) is defined below as a measure of vibration response so that the vibration reduction effect of the damper in various conditions can be assessed. The smallest \(E_{\rho}\) is considered to indicate the best vibration reduction. \(E_{\rho}=\left(\int_{t_{1}}^{t_{2}}x_{\text{tip}}(t)^{2}\text{d}t\right)/\left( t_{2}-t_{1}\right)\) (\(t_{1}=19\) s; \(t_{2}=19.5\) s).  
As illustrated in Figure 20, with the increase of the pre-load (\(N_{0}\)), the normalised energy density (\(E_{\rho}\)) decreases continuously at first, then increases and finally stays the same. It is found that there is an optimal pre-load which can make the biggest vibration reduction. Hence, the damper mass can be optimized in order to reduce the vibration and thus dynamic stresses of a blade to the maximum extent. This could provide a useful guideline for the design of the damper. Other parameters can also affect vibration reduction but are not reported here.
## 4 Performance Analysis
### Throughput Efficiency  
The throughput efficiency of an error control scheme is defined as the ratio of the transmission time of the data packet to the total time taken in successfully delivering the packet. The throughput efficiency of our system is thus expressed as:  
\[\varepsilon_{{}_{opt}}=\frac{T_{{}_{t}}\cdot I_{{}_{DATA}}}{T_{{}_{t}}\cdot I_ {{}_{Pac-0}}+2T_{{}_{p}}+T_{{}_{\infty-op}}} \tag{1}\]  
where \(T_{{}_{t}}\) is the transmission time of a single bit, \(T_{{}_{p}}\) is the source-to-destination propagation delay and \(I_{{}_{DATA}}\) is the data payload length of _Pac_-0. Time duration \(2T_{{}_{p}}\) accounts for the round trip delay caused by the very first original transmission of the data packet and the final reception of the _ACK_ at source. In between these two events, if the cooperation process comes into play the extra delay incurred is accounted by _T_\({}_{CO-OP}\).
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 3 WSN Architecture
### Routing Algorithms and Applications  
Forests vary greatly in density and topography, ranging from dense, tall-tree areas to open, sparsely vegetated spaces, with terrain that can be flat or mountainous. This diversity requires a highly adaptable and flexible WSN for effective environmental monitoring. To address these challenges, a hybrid network system combining multiple structures will provide comprehensive coverage and efficient data transmission across different forest conditions. The hybrid WSN architecture ensures adaptability in both dense and open terrains, whether flat or rugged. This flexibility allows for the optimization of sensor numbers, efficient deployment, and the selection of appropriate routing algorithms. Different routing algorithms can be employed in the proposed network configurations. Mixed forests where some areas are treeless and others are dense or sparse can also be encountered. In these forest types, the WSN design excludes the treeless regions, focusingon the other region to deploy the sensing nodes. Figure 3 illustrates some examples of truncated flats and mountainous forests and the corresponding node deployments.  
The proposed WSN architecture supports the integration of adaptive, semi-adaptive, and deterministic routing algorithms, tailored for wildfire detection. Adaptive routing dynamically adjusts paths to network changes, ensuring rapid rerouting during failures--ideal for dense forests requiring emergency responsiveness. Semi-adaptive routing balances flexibility and cost, using fixed paths but adapting selectively, reducing overhead for non-critical tasks. Deterministic routing is limited to redundant setups. For non-dense forests, a hybrid adaptive-semi-adaptive approach optimizes adaptability and efficiency. A dynamic arbitration scheme resolves conflicts by prioritizing critical data (e.g., fire alerts), queuing lower-priority transmissions to prevent deadlocks and ensure timely delivery to the central host. Dynamic arbitration is used to ensure equitable data processing, preventing any node from being indefinitely delayed. This hierarchy ensures robustness across varying forest topologies and operational demands.
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 4 Multi-Queue Model and Priority-Based Transmission
### Mathematical Analysis
#### 4.2.2 Average Queue Waiting Time in \(\text{Q}_{2}\)  
The average queue waiting time \(\overline{W_{\text{Q}_{2}}}\) of a tagged \(\text{T}_{2}\) packet depends not only on the packets found in \(\text{Q}_{1}\) upon arrival, but also the subsequent arrivals of \(\text{T}_{1}\) packets. According to [PERSON]'s formula [39], there are on average \(\overline{\alpha_{1}W_{\text{Q}_{1}}}\) packets found in \(\text{Q}_{1}\); in addition, a number of \(\text{T}_{1}\) packets may arrive while the tagged packet waits in \(\text{Q}_{2}\), which is on average \(\overline{\alpha_{1}W_{\text{Q}_{2}}}\) packets. Since \(\text{Q}_{2}\) follows the EDFapproach to schedule T\({}_{2}\) packets, it follows the same scenario illustrated in Figure 6. Hence, \(\overline{W_{Q_{2}}}\) can be expressed as follows:  
\[\overline{W_{Q_{2}}}=T_{c}\left(\frac{3}{2}\left(\overline{\alpha_{1}\overline{W _{Q_{1}}}+\overline{\alpha_{1}}\overline{W_{Q_{2}}}+\overline{N_{2}^{B}}+ \overline{N_{2}^{A}}}\right)+\frac{1}{2}\right). \tag{23}\]  
Following the same procedures of Equations (13)-(21), \(\overline{W_{Q_{2}}}\) is given as:  
\[\overline{W_{Q_{2}}} =\frac{T_{c}}{2}\left(3\left(\left(\alpha_{1}\frac{1-\left(\alpha_ {1}T_{c}\right)^{K}}{1-\left(\alpha_{1}T_{c}\right)^{(K+1)}}\right)\left( \overline{W_{Q_{1}}}+\overline{W_{Q_{2}}}\right)\right.\right. \tag{24}\] \[+\left.\left.\left(\alpha_{2}\frac{1-\left(\alpha_{2}T_{c}\right) ^{K}}{1-\left(\alpha_{2}T_{c}\right)^{(K+1)}}\right)\left(\max\left\{0,\left( \overline{W_{Q_{2}}}-D_{Q_{2}}\right)\right\}+\min\left\{\overline{W_{Q_{2}} },D_{Q_{2}}\right\}\right)\right)+1\right).\]