esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Training Configuration  
The algorithm was implemented using TensorFlow [25] on an NVIDIA DGX-1 station. The training took 94 h (about 8 k iterations). The frequency of computing the actions on which the policy was operating was set to 5 Hz. Table 2 shows the hyperparameters used during training. The evolution of the reward over training is shown in Figure 4.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(q\) & Number of consecutive frames used in each observation & 5 \\ \(T^{th}\) & Data collection threshold & 1100 \\ \(\lambda\) & GAE decay factor & 0.92 \\ \(\gamma\) & Discount factor & 0.98 \\ \(T_{max}\) & Max. episode time & 120 s \\ \(E\) & Number of epochs & 10 \\ \(e\) & Used in the surrogate objective function & 0.2 \\ \(c_{1}\) & The value loss factor & 5.0 \\ \(c_{2}\) & The entropy loss factor & 0.01 \\ \(l_{r}\) & Learning rate & \(5\times 10^{-5}\) \\ \(E_{g}\) & Number of episodes to change the goal positions & 50 \\ \(T_{max}\) & Max. episode time & 120 s \\ \(r_{terminal}\) & Terminal reward & 15 \\ \(w_{g}\) & Goal’s distance factor & 200 \\ \(w_{oth1}\) & Occupied goal reward Factor 1 & 3.5 \\ \(w_{oth2}\) & Occupied goal reward Factor 2 & 3.409 \\ \(w_{t}\) & Time reward factor & \(-5\) \\ \(R\) & Robot radius & 0.2 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hyperparameters and constants used for training. GAE, generalized advantage function.  
Figure 3: The simulation environment. The size of the environment is 4 m \(\times\) 4 m, and it is shown holding two robots with two goal positions.
# Numerical Test of Several Controllers for Underactuated Underwater Vehicles
## 2 Underwater Vehicle Equations of Motion
### Model of the 3-DOF Vehicle  
The full model of the vehicles considered in this work can be found in [38] (for ROPOS) and in [14,39] (for Kambara). The reduced equations of motion (in the horizontal plane) for an underactuated vehicle used, e.g., in [6,11,36], can be written as follows:  
\[\dot{x} =u\cos\psi-v\sin\psi, \tag{1}\] \[\dot{y} =u\sin\psi+v\cos\psi,\] (2) \[\dot{\psi} =r,\] (3) \[\dot{u} =\frac{m_{2}}{m_{1}}vr-\frac{d_{11}}{m_{1}}u+\frac{\tau_{u}}{m_{1 }},\] (4) \[\dot{v} =-\frac{m_{1}}{m_{2}}ur-\frac{d_{22}}{m_{2}}v,\] (5) \[\dot{r} =\frac{m_{1}-m_{2}}{m_{3}}uv-\frac{d_{33}}{m_{3}}r+\frac{\tau_{r }}{m_{3}}, \tag{6}\]  
where \(\tau_{u}\), \(\tau_{r}\) are the surge force and the yaw torque, respectively. Moreover, \(u,v,r\) mean the vehicle velocities, and \(x,y,\phi\) the vehicle position in the inertial frame. The other symbols denote \(m_{1}=m-X_{u}\), \(m_{2}=m-Y_{\phi}\), and \(m_{3}=J-N_{r}\) (the mass and the appropriate added mass). In Equations (4)-(6), \(u,v\) are linear velocities, \(r\) is angular velocity, \(m\) is the mass, \(J\) is the inertia, \(d_{11}=X_{u}\), \(d_{22}=Y_{\psi}\), \(d_{33}=N_{r}\), are the drag coefficients (with sign \(+\)[11] or \(-\)[17,18]). The quadratic drags are omitted to enable the comparison of algorithms under the same conditions. The disturbances are not taken into account because of the assumption that it is an the preliminary investigation of algorithms. The equations of motion are assumed in the form which enables comparison of the selected controllers. However, in the second part of this work, the disturbances are taken into account but only for control schemes which gave acceptable results in the first part of the test.  
Figure 1: Underwater vehicle model sketch.
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## 2 Mathematical Model
### Constraints
#### 2.3.3. Uninterruptible Loads  
Uninterruptible loads (_UIL_) have very similar operational characteristic to interruptible loads and are also allowed to be shifted. However, uninterruptible loads, like a clothes dryer (CD) or a dishwasher (DW), cannot be interrupted once it starts working. Therefore, additional constraints should be proposed [20].  
\[x_{UIL,i}=\left\{\begin{array}{ll}0&\forall i\in[1,b_{UIL})\cup(e_{UIL},N],i \in N^{+}\\ 0\text{ or }1&\forall i\in[b_{UIL},e_{UIL}],i\in N^{+}\end{array}\right. \tag{14}\]  
\[\sum_{i=b_{UIL}}^{e_{UIL}}x_{UIL,i}=l_{UIL} \tag{15}\]  
\[\sum_{i=j}^{j+l_{UIL}-1}x_{UIL,i}=(x_{UIL,j}-x_{UIL,j-1})\cdot l_{UIL}\forall j \in(b_{UIL},e_{UIL}-l_{UIL}+1],j\in N^{+} \tag{16}\]  
\[P_{UIL,i}=x_{UIL,i}*p_{UIL}^{r} \tag{17}\]  
Similar to interruptible loads, \(x_{UIL,i}\) is the working status of uninterruptible loads (0--off, 1--on); \([b_{UIL},e_{UIL}]\) represents the allowable working period; \(l_{UIL}\) is the required working time; \(p_{UIL}^{r}\) represents the rated power. Among the constraints, Equation (16) is the additional one for describing the uninterruptible feature.
## 1 Introduction
Adjustments in these parameters allow optimization based on the desired output. The formation of biochar is often favored by lower heating rates, while gas or bio-oil yields are maximized by quick heating rates [21]. Although specific temperatures may fluctuate depending on the unique characteristics and organic content, gas release during pyrolysis typically starts at temperatures over 400-500 \({}^{\circ}\)C [22]. Research indicates that pyrolysis temperatures typically range from 400 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C, with heating rates spanning from a minimum of 3 \({}^{\circ}\)C/min to a maximum of 20 \({}^{\circ}\)C/min [23]. Nitrogen is commonly used to create a non-oxidative environment, with residence times varying from 30 min to 20 h. Raw material pretreatment often involves washing, drying, or pre-carbonization [24]. In many studies, bones undergo pre-carbonization and are generally dried at 80 \({}^{\circ}\)C for 24 h.  
Several studies have examined the thermochemical treatment of poultry waste in combination with plant materials like pine cones. This approach aims to enhance the process efficiency and to analyze kinetic parameters, thermodynamic properties, and potential synergistic effects [24]. In response to current trends in advancing regenerative agriculture technologies, a range of innovative concepts for compartmentalized furrows, specifically designed for use on degraded lands, has been further developed [25; 26; 27]. Transforming poultry waste into biochar fertilizers aids in waste management and carbon sequestration, providing an eco-friendly approach to boosting soil productivity. This method helps prevent nutrient leaching, enhances soil aeration, and supports higher crop yields, positioning it as a valuable resource for sustainable agriculture. The present research paper focuses on an integrated approach to managing non-recyclable waste from poultry slaughterhouses (specifically feathers and bones), and using them to improve soil characteristics on challenging terrains. The waste undergoes mechanical processing and thermochemical treatment and is then incorporated into soil micro-basins on challenging terrains. This approach aims to enhance water retention, improve soil loosening, and reduce the risk of landslides.
## 3 Data Analysis
### Effect of S1-SSM Temporal Resolution
For example, confusion will appear between high values due to extreme rainfall events occurring six days before the S1-SSM acquisition and high values due to light or moderate rain a few hours before the S1 acquisition date. Figure 6: S1-derived SSM maps overlaid with GPM precipitation data. (**a**) 16 October 2016, (**b**) 26 January 2017, and (**c**) 27 January 2018. Contour lines represent precipitation amounts in mm.
# Moon Sensor Station to Improve the Performance of Lunar Satellite Navigation Systems
## 2 Satellites-Only and Performance Evaluation  
Referring to Figure 1, having \(N\) different satellites in view from the lunar receiver, \(N\) pseudoranges can be measured. A common way to represent this situation is exploiting the following equation:  
Figure 1: Local sensor working principle scheme. \[\hat{\mathbf{\rho}}^{T}(\mathbf{\theta})=\left[\hat{\rho}_{1}(\mathbf{\theta})\quad\hat{\rho}_ {2}(\mathbf{\theta})\quad\ldots\quad\hat{\rho}_{N}(\mathbf{\theta})\right]+\mathbf{n}^{T }(\mathbf{\theta}) \tag{1}\]  
where the vector \(\hat{\mathbf{\rho}}\) is composed of the pseudorange measurements from each satellite, the state vector \(\mathbf{\theta}\) is composed of the problem unknowns (position and clock bias of the user), \(\mathbf{\theta}=(x_{u},y_{u},z_{u},dt_{u})\), and \(\mathbf{n}(\mathbf{\theta})\) is the measurement noise vector. Considering that the satellite \(i\) has a known position \(\mathbf{s}_{i}=(x_{i},y_{i},z_{i})\), the \(i\)_-th_ pseudorange has the following formulation:  
\[\rho_{i}(\mathbf{\theta})=\sqrt{(x_{u}-x_{i})^{2}+(y_{u}-y_{i})^{2}+(z_{u}-z_{i}) ^{2}}+ct_{u} \tag{2}\]  
and having \(N\geq 4\) measurements, it is possible to linearize Equation (2), obtaining [24]:  
\[\Delta\mathbf{\rho}=\mathbf{H}\Delta\mathbf{\theta}+\mathbf{n} \tag{3}\]  
where \(\Delta\mathbf{\rho}=\hat{\rho}(\mathbf{\theta})-\mathbf{\rho}(\mathbf{\theta})\) is the difference between the measured and expected pseudorages, \(\Delta\mathbf{\theta}=\hat{\mathbf{\theta}}-\mathbf{\theta}\) is the difference between the estimated and the real user state vector, and \(\mathbf{H}\) is the Jacobian matrix of the vector \(\mathbf{\rho}(\mathbf{\theta})\). It must be noted that this formulation is a common simplification of the localization problem in which the error contributions are all considered additive and independent, composing the vector \(\mathbf{n}\). Later, more details on the error model used in the paper will be given. Exploiting the Weighted Least Square (WLS) approach, it is possible to estimate the user parameters:  
\[\Delta\mathbf{\theta}=\left(\mathbf{H}^{T}\mathbf{N}^{-1}\mathbf{H}\right)^{-1} \mathbf{H}^{T}\mathbf{N}^{-1}\Delta\mathbf{\rho} \tag{4}\]  
where \(\mathbf{N}\) is the measurement error covariance matrix. Two methods can be followed to evaluate the performance of the localization techniques: Cramer-Rao Lower Bound computation and system simulation, using Monte Carlo runs.
# Microscopic Numerical Analysis of Sand Liquefaction Under Subway Train Load
## 3 Numerical Modeling and Microscopic Parameter Calibration
### Microscopic Parameter Calibration  
The values of the microscopic parameters of the particle model are crucial for the numerical simulation results. Before conducting the cyclic triaxial test simulation, the microscopic parameters should be calibrated first. Microscopic parameters are usually set based on macroscopic curves obtained from indoor experiments. By gaining a deep understanding of the effects of parameter changes on macroscopic mechanical responses and continuously adjusting them using trial and error methods, the macroscopic mechanical responses of numerical specimens are made to be close to the results of indoor unit tests.  
The parameter calibration in this study was conducted using laboratory consolidated undrained (CU) triaxial compression tests. The overall calibration workflow is as follows:  
(1) Sample Preparation and Preloading: As illustrated in Figure 6, the numerical specimen was generated and preloaded to stabilize initial conditions.  
(2) Adjustment of Contact Parameters: Microscopic parameters (e.g., friction coefficient, contact stiffness) were iteratively modified to optimize model performance.  
(3) Consolidation Phase: A servo-controlled system was employed to apply target confining pressure (\(\sigma_{3}\)) to the specimen boundaries, ensuring uniform stress distribution.  
(4) Monotonic Loading: Axial compression was applied under strain-controlled conditions at a rate of \(0.5\%/\min\), satisfying quasi-static loading criteria (Equation (7)). Undrained conditions were maintained by dynamically adjusting lateral wall velocities to prevent volume change. Loading ceased when axial strain reached 5%.  
\[I_{inertia}=\dot{\epsilon}\frac{d}{\sqrt{\sigma_{0}/\rho}}=\frac{0.5\times 10^{-2} }{60}\times\frac{2.33\times 10^{-3}}{\sqrt{100\times 10^{3}/2640}}=3.15\times 10^{- 8}<10^{-3} \tag{7}\]  
(5) Simulated data were compared with laboratory test data. If the curves were generally consistent, cyclic triaxial simulation could proceed. In case of significant discrepancies, a detailed analysis of the gaps between the two was conducted, followed by targeted adjustments to the friction coefficient and contact stiffness. After adjustments, the process returned to Step 2 for rerunning the simulation until reasonable agreement between the simulated and test data was achieved.  
After continuous parameter adjustment, the simulation results shown in Figure 7 were finally obtained. It can be observed from the figure that the simulation results are basically close to the indoor test results.  
Figure 6: Flowchart of numerical simulation calculation process.  
The root mean square error (RMSE) between the calibrated model and experimental data is 12.7 kPa in the axial strain range of 2-5% (Table 3).  
Therefore, it can be considered that this set of microscopic parameters has certain rationality and can be used for subsequent numerical analysis. The final determined microscopic parameters of the model are shown in Table 4.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Protocol Operation
#### 3.7.4 QoS-Based  
QoS-based routing protocols take into consideration the QoS requirements in addition to minimizing energy consumption in the network. In fact, QoS metrics such as reliability, delay and bandwidth must be satisfied when routing data packets to the destination. Multipath-based protocols MCMP and ECMP, presented previously, belong to this class. Among other QoS-based routing protocols, we find:  
* **QoS-based energy-efficient sensor routing (QuESt) protocol**  
QuESt protocol is designed as a QoS-based protocol. It uses the multi objective genetic algorithm (MOGA) to determine optimal routes by optimizing multiple QoS parameters. Although this protocol is able to recognize a group of QoS-based close ideal paths, congestion treatment is not mentioned in this protocol [107].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Protocol**} & \multicolumn{3}{c}{**Network Operation:**} & \multirow{3}{*}{**Path Disjointedness**} & \multirow{3}{*}{**Energy Efficiency**} & \multirow{3}{*}{**Reliability**} & \multirow{3}{*}{**Fault Tolerance**} & \multirow{3}{*}{**QoS**} \\ \cline{2-2} \cline{6-7}  & **Alternative Path Routing** & & & & & & \\ \cline{2-2} \cline{6-7}  & **Alternative Path Routing** & & & & & & \\ \cline{2-2} \cline{6-7}  & **A**T**-**A** & & & & & & \\ \cline{2-2} \cline{6-7}  & **A**T**-**A** & & & & & & \\ \cline{2-2} \cline{6-7}  & **A**T**-**A** & & & & & & \\ \hline
**B** & **✓** & & & & & & \\ \hline
**H**DMRP & **✓** & & & & & & \\ \hline
**M**-**MPR** & **✓** & & & & & & \\ \hline
**E**ECA & & & & & & & \\ \hline
**L**IEMRO & & & & & & & \\ \hline
**M**CMP & & & & & & & \\ \hline
**ECMP** & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of multipath-based routing protocols for WSNs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol** & **Description** \\ \hline
**SPIN-PP**[106] & Intended to p2p communication networks. \\ \hline
**SPIN-EC** & Same as SPIN-PP, but with limited energy. \\ \hline
**SPIN-BC** & Optimized for broadcast channels. \\ \hline
**SPIN-RL** & Specifically used in cases of channel loss in broadcast networks. \\ \hline
**M-SPIN**[107] & Transmit data only to sink node instead of the whole network to save energy. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Family routing protocols.
## 2 Materials and Methods
### Assessment of the Ability to Produce Metabolites Involved in Degradation of the Consolidants
#### 2.3.4 Assessment of the Decomposition of Consolidants by Colorimetry  
Colorimetric assessments are used to evaluate colour changes [51] and the efficiency of the removal of consolidants, such as Paraloid B72, in various cultural heritage items, including wall paintings [52; 53].  
_Colour changes_ were assessed using an X-rite portable colorimeter (Ci64 UV), recording data in the 400-700 nm wavelength range with a 10 nm increment. The equipment had a variable aperture of 4 or 8 mm, and the standard illuminant D65/10\({}^{\circ}\) was used. All inoculated areas were measured, with a total of 48 areas being analysed. For each analysed area, three measurements were performed, and the reported data represent the means of those values. Data were represented in the CIELab colour space. The CIELab colour space, defined in 1976 by the International Commission on Illumination (CIE 1978), is one of the most widely used systems for reporting colorimetric data. It is a three-dimensional space in which colours are represented based on their lightness (\(L^{*}\)), red-green value (\(a^{*}\)) and yellow-blue value (\(b^{*}\)). From the colorimetric data, the \(\Delta E\) value was calculated using Equation (1) in order to describe the difference in colour between samples prior to and after the removal process [54]. \(\Delta E\) can be mathematically explained as the geometric (Euclidian) distance between two points corresponding to the two samples in the colour space, where \(L^{*}_{1}\), \(a^{*}_{1}\) and \(b^{*}_{1}\) are the CIELab coordinates of the reference sample (the sample withoutsurface contaminant, prior to application of the consolidant) and \(L_{2}^{*}\), \(a_{2}^{*}\) and \(b_{2}^{*}\) are the coordinates corresponding to the same area after the cleaning process:  
\[\Delta E_{ab}^{*}=\sqrt{\left(L_{2}^{*}-L_{1}^{*}\right)^{2}+\left(a_{2}^{*}-a_{1 }^{*}\right)^{2}+\left(b_{2}^{*}-b_{1}^{*}\right)^{2}} \tag{1}\]  
Removal efficiency was assessed based on Equation (2):  
\[\text{Removal efficiency }(\%)=\frac{\Delta E_{0}^{*}-\Delta E_{n}^{*}}{ \Delta E_{0}^{*}}\times 100 \tag{2}\]  
where \(\Delta E_{0}^{*}\) represents the difference between the samples with and without the consolidant and \(\Delta E_{n}^{*}\) is the difference between the reference and treated areas.
# Blockchain for Future Wireless Networks: A Decade Survey
## 2 Background
### Security in Wireless Networks (From 4G to 6G)
There are various encryption methods available, such as public and private cryptosystems comprising advanced encryption standards (AES), Rivest-Shamir-Adleman (RSA), blowfish, triple data encryption standard (DES), and many more. Further, to augment the user's security and privacy, encryption algorithms can employ hashing algorithms, such as secure hash algorithm (SHA) and message digest, that strengths the WN security. * Characteristic of channel--The aforementioned design factors are for higher-layer WN applications, but with the current exploration of radio frequencies, the attackers dwell in the physical layer security, wherein they exploit radio waves to intercept the ongoing communication. Thus, it is indispensable to understand the wireless channel and secure it by analyzing characteristics, such as bandwidth, data rates, channel quality indicators, i.e., signal-to-noise ratio (SNR), the reference signal received power and received signal strength indicators of the channel. Furthermore, a message generatedat the sender machine has to pass through the dynamic wireless channel, which is time variant and has a lot of obstructions, such as interference, multipath propagation, delay, attenuation, path loss, and fading, which deteriorates the data rates of the WN. Therefore, an attacker tries to investigate such indicators to proliferate their physical attacks that dampen the performance of the wireless communication. Hence, it is essential to reinforce the wireless channel with effective channel coding, equalization techniques, and embracing physical layer security. * Secrecy capacity--The notion of security and privacy in WN is not limited to studying the application and middle layer security, but also needs to investigate different malicious intent propagated at the physical layer. One such mechanism is the secrecy capacity, which is intrinsically associated with the channel capacity, where the channel is the broadcasting or transmitting the message from the legitimate user. Here the intended receiver is treated as an illegitimate user or an eavesdropper who is trying to intercept and decode the message from the legitimate user. An eavesdropper can decode the message if the channel gain between the transmitter and an eavesdropper is higher than the channel gain between the transmitter and receiver. This also means that the eavesdropper has a higher channel capacity resulting in decoding the messages of the users in close proximity. Therefore, as a network analyst, it is imperative to analyze channel gains and data rates to eliminate the eavesdropper from future communication. Despite the aforementioned design factors of WN security that are precisely implied to secure the WN, attackers can still prolong their malicious intent to jeopardize the performance of different interfaces of WN, i.e., 4G, 5G, and 6G. Therefore, this subsection presents the security issues and their relevant solutions that are proposed by the scientific community globally.