esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Question 10 Distribution of Respondents  
Question 10 looks at whether a common form of quality control, the selection of an instance of a process at random for validation of correctness and accuracy, would increase confidence in autonomous software data analysis. In response to this question, 71% of respondents indicated some form of agreement (between agree-no preference and strongly agree), 18% indicated no preference and 9% indicated some form of disagreement (between disagree-no preference and strongly disagree). Note that the percentages do not add up to 100% due to rounding, five surveys where this question was not responded to and one survey where an invalid (an indication of a response between two categories) response was provided. This data is presented in Table 9 and Figure 10.
# A Review of Oil Spill Remote Sensing
## 5 Detection of Oil on the Sea Bottom
### Ultrasonics  
Oil has a different acoustic profile than soft sea bottoms or hard sea bottoms. This small difference enables limited detection of oil on the sea floor using ultrasonics [64]. In each locale, ground-truthing is required to ascertain the characteristics of the sea floor versus that of the oil.
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System  
### Components of a Ship's Electrical System  
The ship power system, as shown in Figure 2, refers to the collection of various electrical devices used for supplying power to and controlling the ship [15]. It mainly consists of four components: the power supply part, distribution devices, the power feeder network, and ship loads. Additionally, some electric drive equipment, including control appliances and electrical measuring instruments, also belong to the ship power system.  
Classification of Ship Load Types  
1. Critical load  
Critical loads have the highest priority, and the quality of their power supply can directly affect the ship's vitality. These loads must not experience power loss or undervoltage, and they generally include operational loads that directly affect the safety of the ship's navigation; ventilation systems in enclosed compartments with personnel, such as those for defensive weapons [16]; emergency lighting and ship signal indicator lights; and support for the emergency generator system. In addition, there are also specialized systems with extremely high operational priority.  
2. Important loads  
Most ship loads are classified as important loads, which are crucial for the normal navigation of the vessel. A power loss or disruption in their supply can affect the operational efficiency of the ship, but they can be unloaded if necessary at critical moments.  
Figure 1: Network structure of a destroyer’s power system.  
General load  
General loads belong to non-essential loads; the quality of their power supply does not directly affect the safe navigation of the ship. They also have a lower operational priority [17]. During the ship's navigation, general loads can be unloaded at any time without adversely affecting the safe operation of the ship.  
Critical loads and important loads have two independent power supply escorts for redundancy [18]; general loads are supplied by only one generator through a single power line. The purpose of classifying ship loads into the three aforementioned categories is to enable different corresponding actions based on the importance of the loads in the event of failure. In accordance with the load priority principle, power supply restoration for the most critical loads will be prioritized during grid reconfiguration, contingent upon the established conditions and constraints. Conversely, general loads may not be considered for power supply restoration during grid reconfiguration due to the absence of alternate power supply pathways.
## 3 Results and Discussion  
Using spectral bands and indices of the preprocessed pre-fire and post-fire \(\mathrm{ZY1E/F}\) AHSI images, a comprehensive evaluation of fire severity estimation capabilities was carried out step by step. First, spectral values at the same locations of pre- and post-fire samples of different fire severities were extracted. The mean spectral values for each band were then calculated and plotted as spectral curves. A visual qualitative comparison was made between the spectral values and their variations in the post-fire image and the pre- and post-fire image combination. Next, corresponding index values at the same locations were extracted. The separability was calculated using the sample means and standard deviations of both spectra and indices, and quantitative comparisons of the fire severity classification capabilities were conducted. Finally, the fixed-step threshold search method was applied to segment spectral bands and indices of the pre- and post-fire images. Quantitative analysis of the classification ability for different fire severities was performed based on classification accuracy metrics.  
Figure 5: Workflow for determining fire severity classification thresholds. (**a**) Dual-directional search for intensity thresholds in positive order; (**b**) Dual-directional search for intensity thresholds in inverse order.
# Respiratory Sound Classification by Applying Deep Neural Network with a Blocking Variable
## 3 The Architecture of the Network
### Two Stage Training with the Blocking Variable  
A two-stage training process with the blocking variable was proposed to improve the performance of the model. Firstly, the network was trained with only the spectrogram and scalogram as the input. However, the distributions of respiratory sound data collected from different chest locations are highly skewed. For example, there is a strong presence of heart-beat sounds in the data collected from the anterior left (AI) and the trachea (Tc) region. Thus, we decided to add the location information at the second stage of training. We turned the blocking variable into one-hot encoding, and each encoding represented the chest location \"Tc\", \"Al\", \"Ar\", \"Pl\", \"Pt\", \"L\", and \"Lr\", respectively. The chest location of the respiratory sound, where it was collected from, would be used as the blocking variable and sent to the classifier together with features learned by the encoder. The details are shown in Figure 4. In the second stage, we retrained the encoder and the classifier, but the encoder was initialized from the first stage of training. We retrained the encoder in the second stage because we wanted our encoder to be slightly modified to fit the new classifier. The benefits of adding the blocking variable to the deep neural network are the same as the benefits of the blocking variable used in the statistical theory of the design of the experiments. Although the blocking variable itself has no effect in classifying the adventitious sound, it reduces the variance of the model error. This is according to the effectiveness of the blocking variable in the experiment design. The blocking variable is effective because we usually assume that all the data are independent and identically distributed. However, the sound collected in different chest positions did not have the same distribution, which would violate the model assumption. Adding the blocking variable lets the network know that data collected from the same chest location belong to a specific cluster, helping back-propagation to extract more robust and efficient features, and produce better prediction results.  
Figure 4: The architecture of the Blnet in the second stage training with the blocking variable. The parameters in the dotted line are initialized from the pre-trained network in the first stage.
## 6 Tracking and Tracing
### Classification of Technologies by Parameter  
Market Maturity/TRL  
Bluetooth has been found in a variety of consumer devices for decades. Not only does Bluetooth find a place in communication between machines and collision detection, but this application of Bluetooth in machine communication has even found research work in mining environments [72; 82]. ZigBee is a well-known technology that can be found in many smart home products. Thus, a clear TRL of 9 can be seen in the public market; however, with the special focus on the application in the mining environment, it looks different. The technology and concept of ZigBee have research potential. They have inherent features such as the recursive update possibility [41], which can be analysed in terms of mining applications. However, these considerations have not yet made it to product-based implementations for mining.  
Approval  
Due to the commercial spread of consumer products and the microwave frequencies used in the technologies, their use is fundamentally justifiable and not subject to any special requirements.  
Required Infrastructure and Intrusiveness  
The low power consumption of the technologies makes it possible to model off-grid node distribution. Of note is the emergence of sleep mode protocols in Bluetooth enabling the possibility of battery use [70].  
A big advantage of Bluetooth and Zigbee is that the hardware is already completely on the market in consumer electronics. Using Bluetooth that is already in devices that workers have available reduces intrusiveness and introduction of new devices [79]. For the network formed of non-worker-related units, the intrusiveness is minimal to non-existent. Nevertheless (local) setups for gateways connected to a wider network or full installations of a local sensor network have effects on maintenance and working procedures.  
Coverage and Robustness  
Near LOS communication could be achieved with the Bluetooth [72; 82] tests underground. ZigBee has experiments with a focus on underground deployment described alongside others in [83; 84; 85]. The penetration of rock or even rock masses has not yet been covered by research.  
Scalability and Number of Users  
All technologies can usually function with dozens of users [41]. Scalability with mesh functionality inherent in most ZigBee products is important in dynamic mining environments. Bluetooth does not inherently use such an approach.  
Data Rate/Network performance Bluetooth: 50 Mbits/s [70]  
ZigBee: 250 Kbits/s [86]  
The table below summarizes the technologies and their classification in each evaluation criterion introduced in Section 4. The scales for each criterion consist of three ordered labels, starting with the \"worst\" and ending with the \"best\" label. For better recognition, the labels are marked with colours in the Table 2, ranging from red (worst) to green (best) for each category.
## 3 Results
### Frequency Scanning  
The laser light leaking through one of the cavity's HR mirrors was coupled into an optical fiber for analysis by a wavemeter (HighFinesse WS6-600). The etalon was locked and a slow triangle voltage ramp was applied to the Piezo-mounted mirrors of the cavity. The wavelength change over time was recorded in both CW and CW+pulsed mode, shown in Figure 9. No significant change of behavior between the two operating modes could be observed within the resolution of the wavemeter and no mode-hops occurred. The tuning range of nearly 12 GHz shown here was slightly smaller than the present limit of 17 GHz set by the piezo actuator range. For tuning ranges of >12 GHz sometimes mode-hops occurred, which may be avoided by adding a thin etalon to the cavity, or synchronizing the birefringent filter rotation with the etalon scan. The wavelength change was not perfectly linear due to the piezo hysteresis and had some slight perturbations. As the cavity had no enclosure, these were likely caused by airflow or pressure changes.  
Figure 8: Transmission fringes of the FPI. Due to the pulsed operation a slow scan was performed, so that each transmission peak contained several pulses, allowing a fit of the peak envelope as shown in the inset.
## 5 Numerical Results
The DQN-based and the DDPG-based offloading paradigms achieve better performance than other offloading methods under different intelligent terminal energy constraints, which indicates the effectiveness of the proposed computational offloading algorithms. Moreover, the performance of the DDPG-based offloading paradigm is slightly better than the DQN-based offloading paradigm.
## 3 Results
Figure 6 illustrates results from additional measurements under identical conditions (V\({}_{\text{ds}}\) = 1 V, V\({}_{\text{read}}\) = 0.5 V), showing synaptic weights expressed as changes in Vth and I\({}_{\text{ds}}\). The analysis of Vth reveals a change of 1.721 V, ranging from 0.186 V to 1.907 V. For changes in I\({}_{\text{ds}}\), the synaptic weights can be precisely adjusted by more than 10\({}^{3}\) times, ranging from 10\({}^{-9}\) A/\(\upmu\)m to 10\({}^{-6}\) A/\(\upmu\)m, without compromising connection strength. This demonstrates the device's strong potential for application in future array designs. The retention characteristics of the device were analyzed under these operating conditions [41]. The device's maximum synaptic potentiation and depression values were evaluated by transfer curves, and changes in the memory window were monitored over a period of 2880 min for each operation. Figure 7a shows the changes in Vth over 2880 min. During synaptic potentiation, Vth increased from -0.2 to 0.13 V, while during synaptic depression it decreased from 2.2 to 1.93 V, resulting in a memory window ranging from approximately 2.4 V to 1.8 V. Figure 7b shows the corresponding I\({}_{\text{ds}}\) changes over the same period, indicating that a stable current is maintained around 10\({}^{-10}\) to 10\({}^{-6}\) A/\(\upmu\)m. The retention characteristics presented in this study were measured over a period of two days (2880 min) for both fully programmed and erased states. However, this does not imply that the device operates only for two days. The measurement duration was determined by time constraints associated with the use of the B1500A measurement system in the university cleanroom. As shown in Figure 6, after an initial fluctuation within approximately  
Figure 6: (**a**) Synaptic potentiation and (**b**) synaptic depression at a drain voltage of 1 V. Measurement of synaptic weights by (**c**) threshold voltage and (**d**) drain current (V\({}_{\text{ds}}\) = 1 V, V\({}_{\text{read}}\) = 0.5 V). the first 180 min, both Vth and I\({}_{\text{ds}}\) remained stable. This trend aligns with the typical retention behavior of non-volatile memory devices. In future work, long-term retention characteristics over several weeks to months will be investigated. Based on the retention results, gradual changes in Vth and I\({}_{\text{ds}}\) over time suggest that increasing the thickness of the control oxide film and implementing additional passivation layers would be necessary to prevent electron loss from the floating gate [42; 43]. These modifications could further improve device stability and reproducibility.
## 3 Soil Moisture Sensors for Wireless Network Applications  
Due to the large number of soil moisture measurements within a WSN, the reading of the sensor signal should be simple and unambiguous [14]. In addition, soil moisture sensors should be as affordable as possible to ensure that the number of sensor nodes is as high as possible [65]. Since capacitance and time-domain transmission (TDT) sensors meet  
Figure 6: European countries where NB-IoT services are offered (**upper left**), NB-IoT coverage for Germany provided by Deutsche Telekom (**right**), and a large-scale detailed map indicating local NB-IoT gaps in deep valleys in a rural Eifel region in Germany (**lower left**). The NB-IoT coverage maps are based on available interactive maps [55].  
these criteria best, these sensor technologies have been used most often in the development of WSNs for soil moisture (Table 2).  
Low-cost soil moisture sensors are often supplied with a factory calibration that does not achieve the maximum possible measurement accuracy of the sensor. For example, [PERSON] et al. [67] showed a significant overestimation of soil moisture for a capacitive sensor when the factory calibration was used. In addition, low-cost sensors typically show considerable sensor-to-sensor variability [67], which may also decrease the measurement accuracy of soil moisture sensor networks. One possible solution would be a direct calibration between sensor response and soil moisture for every sensor [79]. However, in the case of sensor network applications several hundreds of sensors are often used [16; 74; 80], which makes direct sensor calibration impracticable. Alternatively, a two-step calibration procedure can be applied [62]. In a first step, the relationship between sensor response and permittivity for each sensor of a wireless network is determined [81]. For this, media with well-known dielectric properties, such as air, glass beads, or dielectric liquids, such as 2-isopropylovanol [82] or 1,4-dioxane [83], are often used. The use of liquids is desirable to avoid air gaps and density variations and to enable a quick calibration of multiple sensors. In the second step, the dielectric permittivity is related to soil water content using empirical or semi-empirical models [25; 84]. For more accurate soil moisture measurements, a site-specific calibration accounting for soil textural variation can also be performed on a limited number of samples using TDR measurements [16; 66]. A more detailed discussion on the two-step calibration procedure is given in [PERSON] et al. [67].  
To enable efficient and economical use of limited water resources in crop irrigation, so-called soil moisture profile sensors (SMPS) for soil moisture measurement in the entire root zone are becoming increasingly popular [85; 86]. SMPS are also based on the electromagnetic method and, thus, have great potential for WSN-based climate-smart agriculture due to their ease of use and simultaneous measurement at different depths [87; 88].
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 3 The Proposed Safety Message Dissemination Scheme
### V2V Communication  
The proposed message dissemination scheme operates similarly to PBE, a cluster-based V2V communication scheme [13]. Figure 4 shows the operation structure of V2V communication. The proposed V2V communication scheme is divided into intercluster and intracluster communication. Intracluster communication refers to the communication between the CH and CMs within the same cluster. Intercluster communication refers to the communication between CHs in different clusters. CHs perform both intracluster and intercluster communication, whereas CMs perform only intracluster communication.
# Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses
## 4 Experiments
### Swarm GPS Phase Residuals  
Figure 7 summarizes the RMS of GPS phase residuals for Swarm satellites (Swarm-A, Swarm-B and Swarm-C), DOY 274-298, 2014. Table 2 shows the mean RMS of GPS phase residuals for Swarm-A, Swarm-B and Swarm-C, DOY 274-298, 2014. Figure 7 and Table 2 indicate that RMS of GPS phase residuals for Swarm-A is nearly equal to Swarm-C. The reasons may be that two Swarm satellites have the same orbit height and space environment. RMS of GPS phase residuals for Swarm-B is smaller  
Figure 6: F10.7 values during 1–25 October 2014.  
than those of Swarm-A and Swarm-C. The reason may be that Swarm-B orbit height is higher than those of Swarm-A and Swarm-C, and force model errors (e.g., atmospheric drag and solar radiation pressure) are increased with the reduction of the orbit height. Moreover, the mean RMS of GPS phase residuals for Swarm-A, Swarm-B and Swarm-C range from 9-11 mm, which has a good consistency for each day during high ionospheric activity.  
Therefore, the RMS of GPS phase residuals for Swarm-A, Swarm-B and Swarm-C are reasonable and stable by using the Swarm orbit strategy under high ionospheric activity.
# Dynamic Response Analysis of Soil around Curve Section Tunnel under Train Vibration Load
## 4 Model Establishment
### Establishment of Three-Dimensional Finite Element Model for Subway
#### 4.1.2 Selection of Material Parameters  
A three-dimensional numerical model of the coupled roadbed-tunnel-soil was developed based on the finite element software MIDAS. The simulated soil parameters are given in the geological survey report, and the soil layers in the study area are simplified into eight layers by weighted average of thickness, which are used as the soil layer parameters for the numerical model. Detailed soil parameters are shown in Table 2 below. The lining and the ballast bed were simulated using an elastic model, and the other soil layers were simulated using the Mohr-Coulomb constitutive model in GTS NX.
## 7 Simulation Results
### Traction and Braking Management  
The goal of the third simulation scenario was to show how the VCU manages both the acceleration and deceleration phases (Figure 14). In Figure 14a-c the reference and actual speeds are depicted in blue with dashed and continuous lines, respectively, while the internal force references generated by the speed controller are represented in red. In Figure 14b-d, the motor torques are depicted in blue (continuous and dashed lines for drive one and drive two, respectively) and the brake pressure is represented in red. Figure 14a,b show the front-to-rear distribution of the torques in the acceleration phase according to the strategy described in Section 3. Figure 14c,d report the results for a simulated braking maneuver which shows the VCU braking management strategy. The speed decreases with an opposite slope with respect to the acceleration phase and, looking at Figure 14d, the three braking phases described in Section 3 can be distinguished.
## 2 Approach
### Averaging and Moving Differential
Since the bandwidth of the self-injection locked laser was considerably higher than that of the commercial one used, the bandwidth of the acoustic event recorded with it has consequently broader spectrum in contrast to that of the event recorded using commercial laser under the same conditions. The presented algorithm, together with the modification of the equipment, made it possible to cut the DAS systems' cost, since it became possible to use a telecommunication semiconductor laser with distributed feedback in them. This laser had the bandwidth of 1 MHz. The hardware modification consisted in creating a mode of its operation with the external resonator frequency self-injection locking. This reduced the bandwidth to 6 kHz. The traces obtained using such a laser source and their array processing with the presented technique allowed one to use the system as a DAS with the quality of the results practically the same as that provided by the use of a commercial fiber laser with a bandwidth of 100 Hz as a laser source. The role of the acquired data quality criterion was performed by SNR. The difference in SNR estimated from data obtained using a commercial fiber laser and a modified semiconductor laser with additional processing of the results was about 0.4 dB. In the entire frequency range of the applied acoustic impacts, the SNR was at least 8 dB. As the main advantage of this algorithm, one can note efficiency with general simplicity. As a disadvantage, there is a decrease in spatial resolution, which is very important parameter for distributed sensing [49; 50; 51; 52; 53].