esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Formulation
### Inner Loop of Joint Optimal Problem Formulation  
#### 4.2.1 Power Consumption  
To begin, by utilizing the defined system and channel models, the power consumption model for the \(i\)-th device-to-device (D2D) pair can be expressed as  
\[P_{i}(t)=P_{trans,i}(t)+P_{RIS,i}(t)+P_{D_{i}^{*}}+P_{D_{i}^{*}} \tag{13}\]  
The power consumption model for the \(i\)-th device-to-device (D2D) pair is given by  
\[P_{trans,i}(t)=\mu\mathbf{W}_{i}^{H}(t)\mathbf{W}_{i}(t) \tag{14}\]  
where \(P_{trans,i}(t)\) represents the transmission power of the \(i\)-th pair's transmitter (\(Tx\)), where \(\mu\) denotes the efficiency of the transmit power amplifier. Additionally, \(P_{D_{i}^{\prime}}\) and \(P_{D_{i}^{\prime}}\) denote the circuit power of the \(i\)-th pair's transmitter and receiver, respectively. \(P_{RIS,i}\) represents the power consumption of the selected RIS for the \(i\)-th pair.
## 3 Results and Discussion
### Model Validation  
Figure 8 shows the experimental and calculated values of COP for two-cycle heat pumps under the influence of evaporation temperature. R744 and R744/R1234 yf (90/10) are used as refrigerants in the transcritical cycle heat pump, and R515B is used as refrigerant in the subcritical cycle heat pump. From Figure 8, it can be observed that in the transcritical cycle heat pump, the overall error between the simulated and experimental values is less than 10%, and all simulated values are lower than the experimental values. Similarly, in the subcritical cycle heat pump, the accuracy of simulated values is relatively good, with an average error of 3.07%. This indicates that the model has high accuracy and reliability.
## 6 Experiments  
We tested our algorithms on randomly generated sets of SM and SMTI instances. For SM problems, we generated stable marriage problems of size \(n\) using the impartial culture model (IC) [43], which assigns to each man and to each woman a preference list uniformly chosen from the \(n!\) possible total orders of \(n\) persons. This means that the probability of any particular ordering is \(1/n!\).  
For SMTI problems, we generated problem instances using the same method as in [6]. More precisely, the generator takes three parameters: the problem's size, \(n\), the probability of incompleteness, \(p_{1}\), and the probability of ties, \(p_{2}\). Given a triple, \((n,p_{1},p_{2})\), a SMTI problem instance with \(n\) men and \(n\) women is generated as follows:  
1. For each man and woman, we generate a random preference list of size \(n\), i.e., a permutation of \(n\) persons;
2. We iterate over each man's preference list: for a man, \(m_{i}\), and for each women, \(w_{j}\), in his preference list, with probability \(p_{1}\), we delete \(w_{j}\) from \(m_{i}\)'s preference list and \(m_{i}\) from \(w_{j}\)'s preference list. In this way, we get a possibly incomplete preference list.
3. If any man or woman has an empty preference list, we discard the problem and go to step 1.
4. We iterate over each person's (men and women's) preference list as follows: for a man, \(m_{i}\), and for each woman in his preference list, in position \(j\geq 2\), with probability \(p_{2}\), we set the preference for that woman as the preference for the woman in position \(j-1\) (thus, putting the two women in a tie).  
Note that this method generates SMTI problem instances in which the acceptance is symmetric. If a man, \(m\), does not accept a woman, \(w\), \(m\) is removed from \(w\)'s preference list, as well. This does not introduce any loss of generality, because \(m\) and \(w\) cannot be matched together in any stable marriage.
## 5 Results and Discussion  
### Optimization Results  
After optimization, the distribution of the blade angle of the impeller and diffuser is shown in Figure 10. Structure is illustrated in Figure 11. The radial diffuser has changed from a plain configuration to a blade configuration. Main performance indexes are listed in Table 4.  
Table 4 suggests that the aerodynamic performance at the design point is relatively enhanced when all constraints are satisfied. The normalized efficiency at maximum state and cruise state increased 3.6% and 4.0%, respectively. Baseline and optimum characteristics are shown in Figure 12. The performance of the design point and non-design point is improved. Additionally, the processing costs reduced by about 30% owing to the number of blades reduced.
# The Influence of Heat Input on the Surface Quality of Wire and Arc Additive Manufacturing
## 3 Results and Discussion
### Results of Measured Temperature  
In the WAAM process, monitoring and controlling its heat is of great significance to improve the surface quality. Figure 5 shows the temperature change trend at different positions during the WAAM process. Because thermocouple 1 is close to the arc starting position, heat transfer is faster, so the initial temperature of position 1 is the highest, and the initial temperature of position 2 and position 3 remain at the same level. When the arc approaches the thermocouple, the thermocouple monitors the high temperature of the arc, so it will experience a sharp rise in temperature. As the arc moves on the substrate from thermocouple 1 to thermocouple 3, the temperature at thermocouple 3 rises sharply. Both thermocouple 1 and thermocouple 3 experience a process of sharp rise and fall in temperature. The change trend curve is roughly the same, but there are differences in time. The temperature profiles of thermocouple 2 are relatively flat, and the temperature of the three eventually stays at the same level. The temperature changes detected by thermocouple 1 and thermocouple 3 are more drastic because there will be a close moment between the position of the arc and the two monitoring positions, respectively, and aluminum alloy has good thermal conductivity, resulting in a sharp rise in the temperature at position 1 and position 3. The distance between position 2 and the arc position remains in a stable range, so the temperature at position 2 is a process of uniform rate rise.  
Figure 5 also shows the temperature change trend monitored by thermocouple 2 under different heat inputs. With the decrease of current intensity, the intensity of temperature change gradually decreases. Because the monitoring point is in the middle of the specimen and not close to the arc itself, the temperature changes relatively smoothly. As shown in Figure 5, the temperature of the monitoring point has a more obvious difference. It can be clearly seen that the temperature is proportional to the size of the current. That is to say, the change of the process parameters causes the differences in the heat input of each specimen. When different samples were manufactured by an additive, the length of  
Figure 4: Average width and height of specimens by wire and arc additive manufacturing with different heat input.  
Figure 5: The temperature profiles of different locations and the middle part of the specimen.  
the samples was the same, and the higher the walking speed, the earlier the maximum temperature monitored by thermocouple 2 should appear. However, a slight difference appeared in Figure 5, indicating that the influence of current and walking speed on heat input had a joint effect.
## 4 Results and Analysis
### Evaluation of Hybrid Encryption Algorithm Strength
#### 4.1.5 Linear Cryptanalysis
This type of behavior is visibly illustrated in Figure 15. This shows that the near concentration of correlation coefficients around the zero mark reflects a good unpredictability of outputs duly supported by high central density; such high central density  
Figure 13: Scatter plot of bit correlations for testing the possibilities of linear relationship among the 31 different messages after encryption. reflects the absence of clear patterns in the encrypted data, a condition for information confidentiality and integrity. To conclude, analyses of the hybrid encrypting algorithm through different methods show that the encryption algorithm gives out random-like outputs with most of the expected factors near a normal distribution of central density, symmetric at about zero. But with outliers, it shows areas requiring further improvement in security. Amid the usual audits, it may need other evolutions in its diffusion and non-linear transformations. This way, the masks stay robust against cryptographic attacks targeting their data.
## 2 Partial Discharge Detection Using the AE Method and Signal Processing Analysis
### Frequency-Domain Analysis  
Fast Fourier transform (FFT) is the first algorithm used to analyze signals in the frequency domain. Using FFT, vibration signals in the time domain are transmitted to the frequency domain. In this way, the dominant vibrational frequencies of the acoustic signals are identified. The FFT of acoustic signals is shown for the PZT and MFC sensors in Figures 8 and 9, respectively. In Figures 8 and 9, the x-axis reports the dominant frequencies, and the y-axis reports the values of the spectrum for the PD's acoustic signals. Any frequency whose spectrum values have a peak is called a dominant frequency. For the PZT sensor, the dominant frequency was around 1 MHz and for the MFC sensor, the dominant frequency was around 2 MHz, as shown in Figures 8 and 9, respectively. The center point of the two copper electrodes (PD source) was placed in a way that its distance to the sensors was almost equal. With the path of the signal to the sensor being the same, the sensors captured the same signal that was the PD signal generated by the PD source (acting as a fast edge signal generator). It can be concluded that the differences in the amplitude of FFTs were due to the sensitivity and frequency responses of  
The secondary processing of the FFT signals was performed by power spectral density (PSD) analysis. PSD can be calculated by dividing the square of the spectrum values by the frequency of that spectrum. With this method, high frequencies have a lower PSD value. The spectrum of acoustic signals generated by PD could extend up to a few MHz, so it is reliable to eliminate the higher frequencies to reject noise. This method could be useful when the signal has a low SNR and it is difficult to find dominant frequencies using the FFT method. The PSD of the signals is shown in Figure 10. For the PZT sensor, the peak wave frequency was 0.9766 MHz, and for the MFC sensor, the frequency of the peak wave was 2.075 MHz.  
In the monitoring system of transformers, transient noise (rain, wind, etc.), Barkhausen noise, and electromagnetic interference can affect the frequency analysis, so if the signals are only analyzed in the frequency-domain mode, this can lead to wrong conclusions. The Fourier transform and power spectral density do not provide any information about when the dominant frequencies occur. Therefore, for signal analysis, a method should be used that provides frequency information in addition to time information of the PD phenomenon.
# Reinforcement Learning Based Resource Management for Network Slicing
## 6 Performance Evaluation
### Competitiveness  
To verify the competitiveness of our algorithm, we next evaluated the performance of our algorithm by comparing it with the following strategies:  
* _Fixed-resource strategy_ allocates enough resources in an exclusive and excessive manner. This strategy is the most straightforward algorithm.
* _Half-of-demand strategy_ allocates resources equal to a half of the entire resource demand of the previous episode.
* _Previous-slot-demand strategy_ dynamically allocates resources corresponding to the resource demand of the previous time slot.  
Among these strategies, _fixed resource_ and _half of demand_ are static resource allocation algorithms, whereas _previous demand_ and our Q-learning-based algorithm are dynamic trading strategies. Figure 4 shows the purchase and sale amounts obtained by each of the strategies. The same simulation parameters were used in all four simulations as mentioned above, where the flow ratio was chosen randomly. In the static resource allocations, available resources are distributed among tenants based on a predefined algorithm regardless of current states of slices. Note that we set the fixed purchase amount of _fixed-resource_ method so that the QoS satisfaction rate is less than 0.01 (i.e., \(R_{n,t}^{buy}=800\)). As expected, the _fixed-resource_ method, which is the basic strategy of a legacy network, does not violate the QoS but results in a very low resource utilization. Although the _half-of-demand_ strategy tries to overcome the low resource utilization problem of the _fixed-resource_ strategy, the QoS can be violated to a greater extent when increasing the proportion of inelastic flows in the slice. In the dynamic resource adjustment algorithms, the slices' states are used to effectively allocate existing resources. The two dynamic strategies adopted a trading interval of 5. We can observe in Figure 4a that our algorithm performed very close to the optimal policy (i.e., the sale amount equals the purchase amount at every time slot). In particular, unlike the _previous-slot-demand_ strategy, our algorithm pre-purchases resources to avoid the QoS violation when resources are abruptly needed.  
Figure 4: Change of the trading resource amount with respect to the time slot.
## 3 Design and Implementation of the Multi-Attribute PLA Scheme for LoRaWAN
### LoRaWAN Architecture and Deployment  
The LoRaWAN architecture used in this study follows a hierarchical structure involving end devices (EDs), gateways (GWs), Join Server (JS), Network Server (NS), and Application Server (AS). Figure 2 illustrates the LoRaWAN architecture for the proposed forest fire detection use case.  
The sensors deployed across a monitored area form the ED to GW communication. The monitored sensors collect environmental data and transmit them to gateways using LoRaWAN's long-range, low-power communication protocol. These static devices represent forest fire detection sensors in this scenario.  
Then, in the GW to NS section of the architecture, the GWs forward data to the JS during the Over-The-Air Activation (OTAA) process for device authentication. The NS ensures routing and data integrity while managing communication between GWs and the AS. Then, in the next section where the AS is located, the process of authentication happens, where a decision is made based on the received data and an actionable insight is generated, such as fire alerts.This deployment mimics real-world LoRaWAN use cases, addressing vulnerabilities in the OTAA process, where unencrypted join-request messages make the network susceptible to spoofing. The proposed PLA scheme targets these vulnerabilities by enhancing physical-layer security.
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 4 Discussion
### Influence of Alternating Temperature on the Shear Wave Velocity  
The highest shear wave velocity increase and final shear wave velocity values were found for T2, followed by T1, and finally, T3. The literature confirmed that with the increase in the calcium carbonate content in the sand matrix, the shear wave velocity of the cemented specimen increased [48; 49]. This was also confirmed in this experiment, where the trend of shear wave velocity in Figure 8 is consistent with the trend of calcium carbonate content in Figure 5. The shear wave velocities of all three experimental groups showed a steady increase until 60 h, and did not increase after 60 h. Combined with the analysis of calcium carbonate content, it is clear that the reaction process of EICP is gradually completed, and the calcium carbonate content progressively stabilized after 60 h. This indicates that the shear wave velocity is strongly influenced by the calcium carbonate content of the cemented specimen after 60 h, regardless of the temperature alteration process. This also suggests that the shear wave velocity can be used as a non-destructive evaluation indicator of calcium carbonate precipitation [39].  
At 96 h, the shear wave velocities of T1 and T3 were close, but T2 was significantly higher than those of T1 and T3, exceeding 100 m\(\cdot\)s\({}^{-1}\). Since the absolute values of shear wave velocity increase in all three experimental groups are within 200 m\(\cdot\)s\({}^{-1}\), it can be seen that the shear wave velocity increase in T2 is more advantageous.
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## Materials
### Sentinel-1 Synthetic Aperture Radar (SAR) Time Series  
In this study, a total of 348 C-band (5.405 GHz) S1 SAR images acquired by S1A and S1B in both ascending (afternoon at 18:00 UT) and descending modes (morning at 06:00 UT) were used. Over the Montpellier and Tarbes sites, 92 images (46 ascending and 46 descending) images were obtained for each of the two sites for the period between March 2017 and November 2017. This period corresponds to the irrigation period over these two sites. However, for the Catalonia site, 162 images (82 ascending and 82 descending) were used covering a period between September 2017 and December 2018 that correspond to the irrigation information obtained by SIGPAC for 2018. All the images were acquired in the interferometric-wide (IW) swath with VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations. In this study, only VV polarization was considered since it is more sensitive to the soil water content than the VH polarization [32]. The 348 images are derived from the Ground Range-Detected (GRD) product with pixel spacing of 10 m \(\times\) 10 m. The images were downloaded via the European Space Agency (ESA) website ([[https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)]([https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home))). The S1 toolbox developed by ESA was used to calibrate the 348 S1 images. The calibration (radiometric and geometric calibrations) converts the digital number into backscattering coefficients in linear units (radiometric calibration) and ortho-rectifies the images (geometric calibration) using a 30-m digital elevation model of the Shuttle Radar Topography Mission (SRTM).  
Figure 2 shows the repetitiveness of the S1 data in ascending \"A\" and descending \"D\" acquisition modes over the three study sites for August 2017 (Montpellier and Tarbes) and August 2018 (Catalonia). For each month, 10 SAR images (5 ascending and 5 descending images) are acquired over each study site. For the Montpellier site (Figure 2a), the descending SAR image (morning) is acquired 36 h before the ascending evening image with an incidence angle of 38.1\({}^{\circ}\) and 39.3\({}^{\circ}\), respectively. For Tarbes (Figure 2b), the morning acquisition is also 36 h prior to the evening acquisition with an incidence angle of 36.2\({}^{\circ}\) and 40.3\({}^{\circ}\), respectively. Over Catalonia (Figure 2c), the morning acquisition is only 12 h prior to the evening acquisition (both images acquired on the same date) with an incidence angle of 39.5\({}^{\circ}\) and 43.2\({}^{\circ}\), respectively.
## 3 Steel Truss Experiment  
### Experiment Introduction  
The experimental model is a hexagonal steel truss structure with bolted connections located on an outdoor floor, as shown in Figure 3. The model has a side length of 1.5 m and a height of 6 m, divided into four layers. The distance from the ground to the bottom layer is 0.5 m, while the height of the other layers is 1.8 m. The model columns are made of Q235 seamless steel pipes with a diameter of 16 cm. All horizontal and diagonal members are made of equal angle steel L40 \(\times\) 4. Each end of the members is connected to the column node plate using two M20 bolts. Each column is connected to the ground using four anchor bolts. There are six nodes on each level of the model, with a total of twenty-four connection nodes across four levels. For the convenience of subsequent analysis, all nodes are uniformly numbered, as shown in Figure 3. For example, \"J4\" represents the fourth node on the ith level.  
The experimental data acquisition system consists of sound pickup devices, data acquisition equipment, and a computer. The sound pickup device consists of an eight-element circular microphone array placed at the central position of the bottom of the model. The array has a diameter and height of 20 cm, with MPA416 omnidirectional microphones used. The data acquisition module utilizes a PXI dynamic signal analyzer, as shown in Figure 3,c.  
Experimental steps: (1) Simulation of damage condition and noise condition: Loose bolts at different positions and degrees are simulated to mimic the condition of loose bolt damage, as shown in Table 1. A Bluetooth speaker controlled by a smartphone placed at node J12 at the bottom layer of the model simulates seven common environmental sounds: the cicada sound, the construction noise, the wind sound, the thunder sound, birdsong, the rain sound, and speech, as shown in Figure 3. (2) Model excitation: A small eccentric vibrator was used to excite the model to simulate environmental vibration. By adjusting the motor frequency, significant vibration of the model was achieved. When testing the  
Figure 3: Experimental model and test system composition: (**a**) node and sound source, (**b**) 8-element ring microphone array, (**c**) PXI dynamic signal test analyzer, (**d**) small eccentric vibrator, (**e**) experimental model, and (**f**) dimensions of experimental model.  
nodes of the first and second layers, the vibrator was fixed at the crossbar position between nodes J\({}_{12}\) and J\({}_{13}\). When testing the nodes of the third and fourth layers, the vibrator was fixed at the crossbar position between nodes J\({}_{32}\) and J\({}_{33}\), as shown in Figure 3d-f. (3) Signal acquisition: We used a PXI dynamic signal analyzer to collect sound signals from both the bolt loosening damage condition and seven different ambient noise conditions, with a signal sampling rate of 20 kHz.