esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experimental Results Analysis
#### 4.3.2 Ablation Experiments  
Before conducting the ablation experiment, this paper will compare the average PCK values of different numbers of attention heads on both FSD and MPII datasets, as shown in Table 7. It can be seen that when the number of attention heads is six, the average PCK values are the largest in both datasets, which are 92.1% and 92.0%, respectively.  
In order to evaluate the experimental effectiveness of the proposed method more accurately, the average PCK values of the proposed method on both FSD and MPII datasets with different experimental settings are shown in Table 8.  
\(I_{original}\) denotes the original ST-GCN network model, \(I_{original}+I_{DETR}\) denotes the network model after adding the improved DETR structure, \(I_{original}+I_{MATT}(h=6)\) denotes the network model after adding the MATT module, \(I_{original}+I_{Res}\) denotes the network model after adding the residual network, and \(I_{original}+I_{Res}\) denotes the network model after  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**h (Multi-Head)** & **Mean PCK of FSD** & **Mean PCK of MPII** \\ \hline
2 & 90.6\% & 91.2\% \\ \hline
4 & 91.7\% & 91.8\% \\ \hline
6 & 92.1\% & 92.0\% \\ \hline
8 & 91.0\% & 90.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mean PCK of different attention head counts in FSD and MPII datasets.  
Figure 15: Confusion matrix results on MPII of our proposed method.  
adding the DETR, MATT (\(h=6\)), and residual modules, i.e., the method proposed in this paper. It can be seen that although adding \(I_{original}\), \(I_{MATT}(h=6)\), or \(I_{Res}\) can all improve the average PCK values of the corresponding network models on both FSD and MPII datasets, \(I_{DETR}\) is more effective compared to \(I_{MATT}\) and \(I_{Res}\), while the corresponding average PCK values are the largest when the methods mentioned in this paper are used simultaneously.
## 4 Results and Discussion
### Flow around Two Stationary Cylinders
#### 4.1.1 Lift and Drag Coefficient  
Figure 5 shows the variation of the mean drag coefficient and the root mean square of the lift coefficient with the \(x\)-direction spacing ratio. According to Figure 5a, the lifting force of the upstream and downstream cylinders was more consistent under different Reynolds numbers. In the case with \(Re=2000\), the lift of the upstream and downstream cylinders suddenly increased to the maximum value when \(d=3D\) and then decreased slowly with the increase of the spacing ratio. The lift of the upstream cylinder was always smaller than that of the downstream cylinder. This is because the boundary layer separation of the upstream cylinder strengthened the vortex shedding strength of the downstream cylinder. When \(Re=6000\)-14,000, the lift force of the upstream and downstream cylinders was almost not affected by Reynolds number. The lift of upstream and downstream cylinders increased suddenly at \(d=2.5D\), and then the lift of the upstream cylinders tended to stabilise with the increase of the spacing ratio, while the lift of the downstream cylinder first dropped sharply and then decreased slowly with the increase of the spacing ratio until the lift of the upstream cylinder was less than that of the upstream cylinder at \(d>4.5D\). As the spacing ratio increased, the enhancement effect decreased continuously. From Figure 5b, the drag of the upstream cylinder was greater than that of the downstream cylinder and was always positive. For \(Re=2000\), the drag of upstream cylinder increased suddenly when \(d=3D\) and then became stable with the increase of the spacing ratio. However, the drag of the downstream cylinder was negative when \(d=2D\) and suddenly increased to a positive value when \(d=3D\). Then, with the increase of the gap ratio, the drag increased slowly. As such, there was no obvious jump phenomenon. When \(Re=6000\)-14,000, the drag curves of upstream cylinder almost coincided, and all increased sharply when \(d=2.5D\) and then became stable with the increase of the spacing ratio. The drag curve of the downstream cylinder appeared to have obvious jump phenomenon at \(d=2.5D\), and the drag value decreased first and then increased slowly with the increase  
Figure 4: The amplitude and frequency responses of two tandem cylinders with roughness strips.  
of the spacing ratio. In conclusion, for the two stationary cylinders in tandem, there was a special spacing ratio, \(d_{c}\), where the jump of force coefficient could be observed--when \(Re=2000\), the \(d_{c}=3D\) and \(Re\geq 6000\), \(d_{c}=2.5D\).
# Feedback Stabilization of First Order Neutral Delay Systems Using the Lambert W Function
## 3 Main Results
### Stabilization of the NDDS  
In this section, we consider a state feedback controller of the form:  
\[u(t)=k_{d}\dot{x}(t-\tau_{1})+k_{p}x(t-\tau_{2}), \tag{28}\]  
where, \(k_{p}\) and \(k_{d}\) are the proportional and derivative gains, respectively. Our objective was to analytically determine the set of controller parameters that makes the NDDS (2) exponentially stable. Including the state feedback controller (28), the NDDS (2) can be written as:  
\[\dot{x}(t)+(a-k_{d})\dot{x}(t-\tau_{1})+(b-k_{p})x(t-\tau_{2})=0, \tag{29}\]  
Therefore, the characteristic equation of the NDDS (29) is expressed as follows:  
\[q(\lambda)=\lambda+a_{f}\lambda e^{-\tau_{1}\lambda}+b_{f}e^{-\tau_{2}\lambda}, \tag{30}\]  
where, \(a_{f}=a-k_{d}\) and \(b_{f}=b-k_{p}\). Using Theorem 4, we first determine \(k_{d}\) such that \(-1<a_{f}<0\) and subsequently, we find \(k_{p}\) from Equations (8) and (13). The main results of this section are summarized in the following theorem.  
**Theorem 6**.: _If \(a<k_{d}<a+1\) and_  
\[k_{p}=b+\frac{1}{\tau_{1}}(1-W(-ea_{f}^{-1}))\bigg{(}1+a_{f}e^{W(-ea_{f}^{-1})-1 }\bigg{)}e^{\tau_{2}\lambda_{m}}, \tag{31}\]  
_where, \(\lambda_{m}=\frac{1}{\tau_{1}}\bigg{(}1-W(-ea_{f}^{-1})\bigg{)}\), then the state feedback controller (28) makes the NDDS (2) exponentially stable._
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 7 Performance Evaluation
### Communication Overhead  
The communication overhead increases in the proposed PPFL algorithm because the servers and clients communicate with each other using the encrypted model vectors to protect the model parameters. Table 1 shows the communication overhead with respect to the key size when the number of parameters is 105,506, and the data length for one parameter is 16 bits. The communication overhead remains almost constant regardless of the key size as shown in Table 1. If the key size increases, the ciphertext length may become longer, but since the number of parameters included in the ciphertext increases, the key size has little effect on the communication overhead of the proposed algorithm. In addition, as the number of clients increases, the communication overhead linearly increases because the amount of data exchanged between CS and CP increases.
# Occupancy Reward-Driven Exploration with Deep Reinforcement Learning for Mobile Robot System
## 4 The Proposed Occupancy-Reward-Driven Exploration
### MDP Model for the Robotic Exploration  
In this paper, the MDP model for robot exploration is formalized as follows. At each iteration \(t\), the laser sensor emits and inserts the rays on the occupancy map. If the rays return any numeric data, it means that they hit an obstacle that is located nearby. Otherwise, data of NaN format (Not a Number) denote the absence of obstacles and the presence of free space. Both these types of data are the observations, \(O_{t}\), that are sent to the DDPG agent from the RL environment. For the sake of clarity, the occupancy map is a form of visualization in the RL environment. Furthermore, the function approximator inside of the agent generates and passes actions \(a_{t}\), which are the robot velocities. The RL environment receives the actions, upgrades the occupancy map, and computes a scalar reward \(r_{t}\) according to the changes. Figure 5 illustrates the MDP model based on the robot exploration process.  
The reward is a key parameter that motivates the system in making the appropriate decisions. This means that the reward has a strong effect on the motion of the robot. In this paper, the reward is computed according to the explored segments of the occupancy map in each time step \(t\). Since the reward is calculated on the RL environment side, the RL environment for the mobile-robot exploration is presented first below. Then, the reward function is introduced in detail in Section 4.4.
## 4 Experimental Results and Discussions
### Generalizability of the Proposed Method  
To evaluate the generalization performance of the proposed method, the model trained on Dataset 1 was tested on four additional heterogeneous datasets. These datasets were captured using various scanners, including Leica, Faro, and Z + F, and feature a range of point densities, as detailed in Table 8. The method demonstrated strong and consistent performance on Datasets 3, 4, and 5, all of which represent shield tunnels. The F1-score for these datasets were 84.46%, 84.66%, and 96.06%, respectively. This consistent performance across diverse shield tunnel data confirms the robust generalization capability of the proposed method.  
In contrast, a notable performance difference was observed for Dataset 6. While the model maintained excellent recall (99.88%), its precision was significantly lower at 73.20%. This performance drop is attributable to the fundamental design of the proposed method, which is specifically optimized for circular shield tunnels. The approach enhances detection accuracy by identifying the distinct geometric characteristics of ring se  
Figure 9: Comparative results of different methods for Dataset 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data** & **Structure** & **Source** & **Scanner** & **Point Cloud** & **Precision** & **Recall** & **F1-score** \\  & & & **Type** & **Density** & & & \\ \hline Dataset 3 & Shield & Nanjing, line 3 & Z + F & 1685 pts/m\({}^{2}\) & 81.64 & 87.49 & 84.46 \\ Dataset 4 & Shield & Wuxi, line 2 & Faro & 3094 pts/m\({}^{2}\) & 83.65 & 85.71 & 84.66 \\ Dataset 5 & Shield & Hangzhou, line 2 & Leica & 1677 pts/m\({}^{2}\) & 94.28 & 97.90 & 96.06 \\ Dataset 6 & Horseshoe & Nanjing & Leica & 2086 pts/m\({}^{2}\) & 73.20 & 99.88 & 84.48 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The recall, precision, and F1-score using heterogeneous datasets (%).  
the \"Distance to ellipse\" feature. The horseshoe-shaped cross-section of Dataset 6, however, violates these core geometric assumptions. This structural difference not only renders the \"Distance to ellipse\" feature inapplicable, but also means the tunnel lacks the regular, pronounced ring seams that the classifier uses as crucial contextual cues. The resulting drop in precision is therefore a logical consequence of applying a specialized model to a fundamentally different tunnel structure.
# A Decentralized Receiver in Gaussian Interference
## 3 Bounds on Communications Rates
### Achievable Rate through Distributed Compression
}R_{G}(\mathbf{\rho})-\lambda \tag{36}\]  
_where_  
\[\mathcal{R}_{DC}^{\lambda}(\mathbf{r})\triangleq\left\{\mathbf{\rho}:\forall S \subseteq[1:N],I\left(\mathbf{Y}_{S};\mathbf{Z}_{S}(\mathbf{\rho})|\mathbf{Z}_{S^{C}}(\mathbf{ \rho}),Y_{0}\right)<\sum_{n\in S}r_{n}+\lambda\right\}. \tag{37}\]  
_The rate requires the following facilities:_  
* _The transmitter and base coordinate a codebook of appropriate rate and distribution._
* _Each_ \(n\)_-th helper is informed of a quantization rate_ \(\rho_{n}\)_, binning rate_ \(r_{n}\) _and hull parameter_ \(\lambda\) _(optimal choice derived from_ \(\mathbf{h},\mathbf{\Sigma},L\)_)._
* _Each helper coordinates a random dither with the base._
* _The base is informed of_ \(\mathbf{\rho},\mathbf{r},\lambda,\mathbf{h},\mathbf{\Sigma},L\)_._  
All the details of Theorems 3.1 and 3.2 are shown in Appendix B. Remark 6: _The capacity of the system is \(R_{\overline{DC}}(L)\) under the following restrictions:_  
* \(\mathbf{\Sigma}\) _is diagonal (no interference)._
* _The base does not have its own full-precision observation of the broadcast (_\(h_{0}=0\)_)._
* _The broadcaster must transmit a Gaussian signal._
* _Construction of helper messages is independent of the transmitter's codebook_ \(\mathcal{X}\)_._  
This is demonstrated in Appendix C. The last three assumptions are necessary:  
* Since the base has codebook knowledge, it is possible for the transmitter to send a direct message to the base, which is not accounted for in the compress-and-forward strategy used for \(R_{\overline{DC}}(L)\). * The Gaussian broadcast assumption is needed because of a counterexample given in Reference [1]. * Codebook independence is necessary because \(R_{\overline{DC}}(L)\) is strictly less than the upper bound in Equation (16), but, by Remark 3, this upper bound is achieved in some regimes. Theorem 7 and Remark 7 strengthen Corollary 1 and Theorem 5 from Reference [1] since we show that the same rate can be achieved with less cooperation between transmitter and receivers. [PERSON] et al. [1] used a \"nomadicity\" assumption which asserts that the mapping from transmitter messages to codewords is not present at the helpers, whereas Theorem 7 shows (the same argument applying to the general discrete case) that indeed the same rate is achievable when helpers have _no knowledge at all_ of the codewords the transmitter is using. Similar to the Gaussian distortion achievable rate, the distributed compression technique does not require cooperation between the transmitter and helpers. It does, however, require a priori sharing of codes from the helpers to the base so that the helpers can perform distributed compression.
## 3 Remote Terminal Unit (RTU)
### Hardware Description of the RTU
#### 3.1.2 Power Source Module  
The power source is constituted of a single 20-W solar panel, an intelligent battery charger and a 12 V, 7 A lead-acid battery. The solar panel is the photovoltaic power source for the battery charger. Its characteristics assume 1 kW m\({}^{-2}\) illumination and 25 \(\,\)C solar panel temperature. It provides 20 W maximum peak power and 1.19 A of peak current, or enough to supply the acquisition and communications modules. The intelligent battery charger is based on the UC3906 integrated circuit. It monitors and controls both the output voltage and current of the charger through three separate charge states; a high current bulk-charge state, a controlled over-charge and a precision float-charge or standby state. The charging process, in other words, begins when the output current of the charger is limited to a low-level until the battery reaches a specified voltage (preventing a high current charging if a battery cell is shorted) then, the charger enters in a high rate bulk-charge state. In this state, the charger supplies a peak current until the battery voltage reaches 95% of its maximum capacity. Following, the charger changes to the over-charge state in order to charge completely the battery, in this state the charging current will be reduced to the minimum as the voltage approaches to its maximum capacity, and then, the charger will change to a floating or standby state. As the load (constituted of the acquisition and communication modules) is connected, if the battery drops 10% below the float level, the charger will reset itself and begin again the charging process.
## 2 Tracking Algorithms Overview
### Triangulation Problem  
This section introduces a suitable tracking algorithm and the corresponding equations required to estimate the position of the RSO, and the errors associated with the measurements. Attention is then turned towards the uncertainty quantification of navigation and tracking errors in the form of a covariance matrix to generate an overall uncertainty volume. Figures 3 and 4 illustrate the tracking problem in both ground-based and space-based scenarios respectively.
## 2 Basic Theory
### Framework of Auto-Encoder  
Auto-encoder (AE) is a kind of neural network to acquire feature information with the unsupervised learning process. Compared with classical neural networks, AE can extract feature information without teacher data effectively and eliminate dependence on human experience or expertise at facilities diagnosis.  
As shown in Figure 2, the output layer vectors have the same dimension with the input layer while minimizing reconstruction error between the input data and the output data. And the input data can be compressed in a hidden layer in which the number of neurons is smaller than that of the input layer.  
\[H^{(1)}=\mathbf{f}_{h}\Big{(}\mathbf{w}^{(1
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 5 Experimental Results and Discussion
### Comparative Analysis
#### 5.2.2 End-To-End Delay  
It is a time-based \(QoS\) metric that computes packet transmission between the source and the end server. It computes in all stages after the packet is sensed at the source level. Three kinds of delays are computed: propagation delay, processing delay, transmission delay and queuing delay. The mathematical formulation for the delay is computed as follows,  
\[End\text{-to-}End\ Delay=\sum_{P_{i}\in P}T_{R(P)}-\sum_{P_{j}\in P}T_{S(P)} \tag{17}\]  
where \(T_{R(P)}\) is the sum of time taken for packets reception and \(T_{S(P)}\) is the sum of time taken for packets transmission.  
Figure 10 represents the efficiency of delay for emergency packets. It is analyzed for the number of simulation rounds. Delay increases gradually according to the simulation rounds since the number of rounds depends upon the number of communications to the destination. In Figure 11, the delay in B-DEAH is much smaller than that in previous works. A conflict between emergency and nonemergency packet transmission is avoided in this work since emergency packets are transmitted to the emergency sink and then to the monitoring server. Hence, the waiting time of each packet is reduced by 50% compared to previous works. Furthermore, emergency data are transmitted to separate channels and nonemergency, i.e.,  
Figure 9: Network throughput vs. simulation rounds (periodic packets).  
periodical data packets forwarded using the best set of predicted channels. Thus, the delay in channel selection is minimized in the proposed B-DEAH method.  
Important (critical) messages must be transmitted more quickly than nonimportant messages. In previous works, delay increased due to the interference between channels. The delay difference between previous works is 15% less than PCA, 27% less than E-HARP and 50% less than CF-EHARP. The delay in the E-HARP is reduced due to the error correction mechanisms. After error correction, original packets are transmitted between the source and the destination. Similarly, PCA does not invoke any attackers in between the route so that the overhead induced by the attackers is reduced. In the following, the performance delay is computed for periodic packets.
## 5 Conclusions  
In conclusion, the effect of CaS nanostructures--prepared from the microwave-mediated decomposition of dimethyl sulfoxide (DMSO) in the presence of calcium acetate Ca(CH\({}_{3}\)CO\({}_{2}\))\({}_{2}\)--in the proliferation, cell cycle, and death mechanism of adenocarcinoma cells was investigated. Light scattering measurements revealed that dispersions with CaS nanostructures in the size range of a few A to about 1 nanometer were formed when DMSO was decomposed in the presence of Ca(CH\({}_{3}\)CO\({}_{2}\))\({}_{2}\). Theoretical calculations at the DFT/B3 LYP/DGDZVP level of theory were consistent with (CaS)\({}_{n}\) clusters (\(n=1\), \(2\), \(3\)and 4) in DMSO in the size range of 0.4 to 1.1 nm. The absorption spectra of the CaS nanostructures were dominated by strong bands in the UV as well as weaker absorption bands in the visible. We found that a single dose of CaS nanoclusters smaller than 0.8 nm in diameter did not affect the survival and growth of normal fibroblasts, but it inhibited the proliferation of carcinoma cells in vitro. Larger CaS nanoclusters, about (1.1 + 0.2) nm in diameter, have a similar effect on carcinoma cell proliferation and survival rate. Human carcinoma cells treated with CaS nanocluster dispersion exhibited a decreased ability to properly enter the cell cycle marked by a decrease in cell concentration in G0/G1 phase at 24 h post-treatment and then an increase in cells held in the SubG1 and G0/G1 phases up to 72 h post-treatment. Apoptosis and necrotic channels were found to play significant roles in the death of human carcinoma cells exposed to the CaS nanoclusters. In contrast, any effect on normal fibroblasts appeared to be short-lived and non-detrimental. The interaction between CaS and several functional groups was further investigated by theoretical calculations to learn about the possible interactions that could result in the release of Ca2+ or S\({}^{-2}\) ions. CaS is predicted by theoretical calculations at the DFT/B3 LYP/6-311G level of theory to interact with the thiol (R-SH), hydroxide (R-OH), amino (R-NH\({}_{2}\)), carboxylic acid (R-COOH), ammonium (R-NH\({}_{3}\)+), and carboxylate (R-COO\({}^{-}\)) functional groups. None of these interactions are predicted to result in the dissociation of CaS. Thermodynamic considerations, on the other hand, are consistent with the dissociation of CaS into Ca2+ ions and H\({}_{2}\)S in acidic media, both of which are known to cause apoptosis or programmed cell death. Passive uptake and the extracellular pH of carcinoma cells are proposed to result in the selectivity of CaS to inhibit human carcinoma cell proliferation with no effect on normal fibroblast cells. The results encourage further research with other cell lines in vitro as well as in vivo to translate this nanotechnology into clinical use.
## 4 Results and Discussion  
As explained at the beginning of the paper, this work is focused on the physical aspects of the problem, which implies a deep insight into the flow characterization, tank and fluid kinematics (initial jet velocities, final standing wave...) and the sloshing force interchanged between the fluid and the tank.  
The sloshing force is measured experimentally according to Expression (4) and computed numerically according to Expression (20). Both forces have been accurately compared as part of the code validation in [22; 26]. The advantage of the computational methodology is among others that the pressure and viscous contributions can be obtained separately. To understand the contributions of these two sloshing force sources, two cases corresponding to two different fluids, water and glycerine, have been studied with the same initial acceleration \(a_{0}=10g\), same scale \(\lambda=1/5\) and same mass ratio \(r=7.4\%\). As these two fluids have different densities, the initial filling level is different for these two cases, (\(f=50\%\) for water) and (\(f=39\%\) for glycerine), in order to keep the mass ratio constant. These two fluids, water and glycerine, have the properties shown in Table 1. As Table 1 shows, the flows studied have very different Reynolds numbers, which implies a laminar regime in the case of glycerine and a fully turbulent flow for the water. This difference in terms of Reynolds numbers is mainly caused by the large difference in terms of viscosity, and consequently significant differences in the flow behavior and force decomposition between the pressure and viscous parts are observed.
## 4 Matrix Converters in the Connection of Renewable Energies to the Grid
### Matrix Converter Applications for Integration of Photovoltaic (PV) Systems  
This section investigates the application of matrix converters for the integration of PV arrays in SGs, and the possibilities for improving the PQ in the connection to the SG. In particular, the connection of a PV array to the SG with a unidirectional 3 \(\times\) 2 matrix converter topology (shunt connection) or using an IMC (series and shunt connections) is considered.