esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## 2 Literature Review of PLS Techniques for IoT
### Pros-and-Cons Analysis of the Existing PLS Techniques  
Each of the aforementioned PLS techniques has its advantages and disadvantages. AN injection is easy to be implemented because the artificial noises can be produced using a pseudo-random number generator, for which many existing algorithms can be directly utilized. However, the secrecy gain offered by the AN injection approach is at the cost of additional energy consumption, which is used to send the artificial noise signal. Compared with the AN injection technique, the CS-based secure transmission method does not rely on the expenditure of additional power and is thus more energy efficient. However, a measurement matrix has to be shared between the legitimate transceivers while being kept secret from the eavesdroppers, which requires agreed knowledge about the CSI at different parties. This causes non-negligible overheads in the protocol design. The bit flipping technique can significantly reduce the implementation complexity and overcome the shortcomings of the CS-based approach. However, in the bit flipping method, the sensors within the weak group have to transmit the false data to confuse the eavesdropper, yielding a waste of power and bandwidth. Cooperative secrecy might be the most widely adopted PLS approach. The introduction of the cooperation mechanism offers the low-power devices the capability of combating the powerful eavesdroppers with less resource consumption. Nevertheless, the major disadvantage of the cooperative secrecy technique is that additional signaling is needed to coordinate different devices within the network, which complicates the protocol design as well. The physical layer encryption is essentially a cross-layer approach, which combines the secret key generation at the physical layer and the encryption at the application layer. The greatest asset of this approach is that it can be easily incorporated with the existing network security protocols, which is based on cryptographic techniques at the application layer. On the other hand, however, the effectiveness of the physical layer encryption heavily depends upon the fact that the communicating parties can reach an agreement on the generated keys, which is rather challenging in wireless environments.
## 3 Results
### Description of Results and Meta-Analysis
#### 3.4.2 Patient-Related Outcomes  
Three cross-sectional studies reported on pain. [PERSON] et al. [25] reported the presence of soreness/discomfort dichotomously as present in 0 patients in the KM group and 5 patients in the no KM group; [PERSON] and [PERSON] [23] reported V0 AS discomfort (VAS = 0 extreme discomfort; VAS = 100 maximum comfort) in the KM \(\geq\) 2 group as 97.0 \(\pm\) 8.5 while in the KM \(<\) 2 mm group it was 53.8 \(\pm\) 30. [PERSON] et al. [34] reported awareness of food impaction and pain/discomfort. They reported that implants with inadequate KM had greater food impaction (53% versus 40%, \(p\) = 0.193) and pain/discomfort during oral hygiene practices (28% versus 10%, \(p\) = 0.027) than those with adequate KM.  
[PERSON] et al. [32] reported at 18 months of discomfort during brushing measured on a VAS (VAS = 0 no discomfort, VAS = 100 extreme discomfort) in the KM \(<\) 2 group as 12.28 \(\pm\) 17.59 while that in the KM \(\geq\) 2 group was 4.25 \(\pm\) 8.39. Although a meta-analysis could not be performed, a consistent trend toward the worst pain/discomfort in the KM \(<\) 2 mm group was observed.  
None of the included studies reported on patient satisfaction and quality of life.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 5 Discussion
### Band Ratios  
Si-O stretching vibrations cause quartz to display a distinctive spectral emissivity pattern in the TIR range. Because of the spectral characteristics of quartz in ASTER bands 10, 11, and 12, the QI formula distinguishes the fine details in the QI image (Figure 10). The QI image produced by this formula using ASTER emissivity data identifies quartz-rich zones. The quartz index image is of higher quality when band 13 is used in the formula. QI image shows that most of the Landi Kotal slate (LK) and Shagai formation shale (SS) and part of the Lowara Mena complex (LMC) are dominated by the presence of quartz, and this can also be verified in the petrographic study results (Figure 15). The carbonate band ratio image (Figure 11) shows that most of the Khyber limestone (K) and part of the Ghundai Sar formation (GS) are dominated by the presence of carbonate, and this can also be verified in the petrographic study results (Figure 15).
## 3 Results and Discussion
### Optical Modulation Index  
As mentioned above, a DDMZM was used to generate the multi-twin-SSB signal together with the optical carrier required for a KK-receiver. However, the optical modulation index (OMI) which was related to the carrier to signal power ratio (CSPR) should be optimized carefully. The OMI was defined as \(V_{r}{}^{rms}/V_{\pi}\)[14], where \(V_{r}{}^{rms}\) was the root-mean-square of the electrical input signal to the DDMZM. Figure 3 shows the SNR performance of ONU1, ONU2, ONU3 and ONU4 as a function of OMI after 50-km transmission with a fiber launch power of 0 dBm.  
A larger OMI is typically preferred for higher modulation efficiency. However, the MZM is known to have a nonlinear transfer function and a large OMI will introduce extra higher order nonlinear terms [9]. The nonlinear terms from the modulator deteriorated the SNR performance when OMI was higher than 0.08 as shown in Figure 3. On the other hand, a smaller OMI kept the modulator working under a linear range and a small SSB signal was generated with limited SNR. In this case, the system's SNR was determined by the channel noise and a sharp decreasing in SNR was found as OMI decreased from 0.08. About 0.9 dB and 2.3 dB improvements were observed with the KK-receiver for the ONU1 and ONU2, respectively, from Figure 3. The performances of ONU4 and ONU3 were almost the same as ONU1 and ONU2 because they were symmetric around the optical carrier. An optimum OMI value of 0.08 was obtained from Figure 3, which would be used in the following simulations.
## 5 Experimental Results and Discussion  
This section presents the performance of the proposed CTU-level decoding-complexity and rate control algorithm. In this case, the rate and complexity-controlling capabilities of the proposed algorithm are first compared with two state-of-the-art decoding-complexity-aware encoding algorithms in the literature. Thereafter, experimental results for the power consumption characteristics of the decoder during a video streaming session are compared for two different CPU frequency governing methods. Finally, the experimental results and observations are discussed in detail for different use cases.
# Reliability Study on FRP Composites Exposed to Wet-Dry Cycles
## 4 Discussion
### The Effects of Standard Deviations of the Load  
In this study, the mean of the load for CFRP was taken as the design value (1600 MPa) of TR-55, and five representative mean values, 0.5\(\sigma_{\text{ec}}\), 0.85\(\sigma_{\text{ec}}\), 1.0\(\sigma_{\text{ec}}\), 2\(\sigma_{\text{ec}}\) and 3\(\sigma_{\text{ec}}\), were chosen where \(\sigma_{\text{ec}}\) is the test standard deviation of CFRP (346.88 MPa) without environmental exposure. The mean of the load for GFRP was taken as the design value (700 MPa) of GB 50367-2013, and five representative mean values, 0.5\(\sigma_{\text{eg}}\), 0.85\(\sigma_{\text{eg}}\), 1.0\(\sigma_{\text{eg}}\), 2\(\sigma_{\text{eg}}\) and 3\(\sigma_{\text{eg}}\), were chosen where \(\sigma_{\text{eg}}\) is the test standard deviation of GFRP (134.48 MPa) without environmental exposure. The values of resistance were the same as those in the previous section; the reliability index can be calculated by the previous method as shown in Figures 9 and 10.  
Figure 8: The effects of mean of the load on the GFRP specimens: (**a**) Change of \(\beta\) with wet-dry cycles; and (**b**) change of \(P\) with wet-dry cycles.  
As shown in the Figures, the reliability of the FRP composite will visually decrease as the standard deviation of the load increases. When the standard deviation is lower than \(\sigma_{\rm eg}\), the difference in \(\beta\) with different standard deviations is slight and can always stay above 3.2. When the standard deviation is \(2\sigma_{\rm eg}\) or more, the \(\beta\) is less than 3.0 after 4000 times of wet-dry cycle exposure, and the failure probability increases very obviously. Therefore, the design value of tensile strength needs to be further conservative when the standard deviation of the load is relatively large.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 4 Research Challenges
### Massive Connections  
Next-generation smart cities will consist of a massive number of connected devices. That includes pedestrians, vehicle-to-everything communication, drones, IoT, and high-speed public transit systems. This may lead to a broad range of mobility scenarios that may happen. Therefore, smooth HO between these diverse mobility scenarios is a crucial challenge [56].
## 1 Introduction
Low-fidelity data, often generated through numerical simulations, are relatively easy to obtain but contain systematic errors due to the simplifications and assumptions inherent in the numerical models [33]. These errors reduce the accuracy of the trained models when they are applied to real-world engineering. In contrast, high-fidelity data, typically collected from field measurements, are more precise and reliable but are scarce due to the high cost, time, and complexity involved in data acquisition [34]. Both data types offer distinct advantages--low-fidelity data provides quantity and accessibility, while high-fidelity data ensures higher accuracy [35]. However, effectively combining both data types in machine learning remains a significant challenge. Models trained exclusively on low-fidelity data often inherit systematic biases, resulting in poor performance. Conversely, models trained only on high-fidelity data struggle to generalise due to the small data quantity, leading to issues like underfitting. The fundamental difficulty lies in the discrepancy between the two types of data--they differ not only in accuracy but also in statistical distribution, variability, and scale [36]. Naively combining them can cause overfitting or unstable predictions [37]. Additionally, conventional machine learning methods often fail to transfer knowledge between datasets with different levels of fidelity, which limits the ability to leverage their complementary strengths. There have been some research articles focusing on multi-fidelity machine learning for other multi-physics problems, which have shown excellent generalisability. Multi-fidelity machine learning was thoroughly studied with respect to challenging lid-driven cavitation [38], quantum control [39], and electrical capacitance tomography [40], and has been proven to be effective. Although machine learning has been a hot spot, such a multi-fidelity paradigm has not been introduced to LNG industries, to the authors' best knowledge. To that end, this study focuses on developing a multi-fidelity machine learning framework to address these challenges. By utilising both low- and high-fidelity data, the proposed framework aims to provide a reliable solution for the real-time monitoring of perlite settlement. The data experiments were performed based on a real-world LNG storage tank for the validation of the proposed data-driven approach and the comparison of machine learning algorithms. Rigorous ablation experiments were designed to provide insights into the performance of the model. The proposed data-driven approach was also seamlessly integrated into the digital twin of the LNG storage tank for real-time management.
## 4 Localized RCC
### Treatment  
The development of miRNA-based therapeutic strategies is still in its early phases. Signaling pathways involving several miRNAs have been identified as potential targets for the development of new drugs. One example is constituted by miR-1260b, consistently overexpressed in ccRCC, that has been proven to be inhibited by genistein, a flavonoid found in soy [36]. One major limitation to this new therapeutic approach is the vast number of miRNAs that can regulate a single gene, as proven by [PERSON] et al. [37]. Several phytocompounds are involved in miRNAs regulation and may affect cancer biology [38], resveratrol binds miRNA-33a and miRNA-122 and increases their levels; resveratrol also regulates the expression of miRNA-17 and suppresses their level through c-Myc; (\(-\))-epigallocatechin-3-gallate decreases levels of miRNA-33a and miRNA-122; curcumin downregulates miRNA-21, which regulates the pathway of PTEN [39]. Quercetin downregulates miRNA-103a-3p, miRNA-125b, and miRNA-1202 [40]. Sulforarophan increases the expression of miR-124, which regulates the IL-6 receptor [41]. Considering RCC biologic link to hypoxia and angiogenesis, several pathways have been investigated. MiR-210, miR-29b, miR-142-43p and miR-424-5p, which are pro-angiogenic miRNAs overexpressed under hypoxic conditions, could be targeted by antiangiogenic treatment [42]. Finally, miR-26, was confirmed to inhibit the coronin-3 pathway, involved in the migration and invasion of RCC, and therefore could serve as a potential therapeutic target for miRNA-based therapy [43].
## 3 Deep Learning Models and Methods
### Model Architecture of Next-Word Prediction
#### 3.2.2 Architecture  
The Block diagram shown in Figure 1 consists of Baidu's Deep Speech architecture [32], which converts raw Arabic audio signals into text data.  
The text output is applied in the text prediction structure shown in Figure 2. The model architecture is the design flow of next-word prediction.  
Figure 1: Baidu’s Deep Speech Arabic representation.  
Figure 2: Block diagram representation.  
The prediction model consists of three different layers, such as the embedding layer, the recurrent layer, and the dense layer. The key element of the next-word prediction model is an embedding layer that turns words into a numerical representation that includes syntactic and semantic information. The recurrent layer processes the data from the embedding layer and converts it into sequential data. The output data from the recurrent layer is processed into a dense layer to find the probability of each word in the vocabulary. The foundation of language understanding in next-word prediction is formed by these layers. The relationship between the words is maintained, which makes it possible for the archetype to envisage the most probable next word. To handle the vanishing gradient problem, capture word dependencies, and process sequential data, we use Long Short-Term Memory (LSTM).
# Flexible Vibrotactile Actuator Based on Dielectric Elastomer for Smart Handheld Devices
## 3 Experiments and Results
### Haptic Performance of the Proposed Actuator
If the response time is too slow, a realistic haptic sensation cannot be provided to a user in real time. Therefore, we measured the response time of the proposed actuator based on \(\text{dielect}_{1.5}\). The experimental setup was the same as that used in the first and second experiments. The amplitude of the applied voltage was fixed at 2 kV\({}_{\text{PP}}\), and the frequency was fixed at 150 Hz (resonant frequency). Figure 9g shows the measured response time of the proposed vibrotactile actuator. The response time can be defined as the time difference between t\({}_{1}\) and t\({}_{2}\). We defined t\({}_{1}\) as the moment the input voltage is at maximum and t\({}_{2}\) as the moment the actuator generates the first maximum amplitude. However, the actuator may not create a large enough vibration to stimulate human mechanoreceptors at t\({}_{2}\). Therefore, we conservatively computed the response time by subtracting t\({}_{3}\) (when the vibrational output reached 90% of its maximum amplitude) from t\({}_{1}\). The response time of the proposed actuator was observed as approximately 18 ms. The measured response time of the proposed actuator was faster than that of a commercially available linear resonant haptic actuator (<30 ms), which is the most widely used in handheld devices [22]. In addition, we measured the displacement of the proposed vibrotactile actuator. The experimental setup is almost the same as the experimental environment for observing vibrational acceleration. The displacement of the proposed actuator was measured by a laser scanning vibrometer (PSV-500, Polytec GmbH, Waldbronn, Germany) as shown in Figure 10a. Based on this experimental environment, we measured the displacement of the actuator as a function of frequency (1-300 Hz) at a fixed amplitude (2 kV\({}_{\text{PP}}\)) (Figure 10b). Furthermore, the displacement was investigated as a function of input voltage (0 to 2.0 kV) at a fixed frequency (150 Hz) (Figure 10c). In Figure 10c, we drew the only maximum displacement of measured vibration. So, we also plotted vibrational displacement when an input voltage is 0.25 kV\({}_{\text{PP}}\), 1.0 kV\({}_{\text{PP}}\), and 2.0 kV\({}_{\text{PP}}\), respectively. The largest displacement (3.02 \(\upmu\)m) was observed at the resonance frequency (150 Hz). The result also shows that displacement was controlled by the input voltage. Figure 10: (**a**) Experimental environment for investigating the displacement of the proposed actuator; (**b**) the displacement of the proposed actuator according to the input frequency; and (**c**) displacement as a function of input voltage at the resonant frequency.
# Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry
###### Abstract  
The significant wave height (SWH) of the sea is an important parameter and plays an important role in the prediction of waves and ocean dynamics. However, traditional methods, e.g., buoys or traditional remote sensing techniques such as X-band radar image have small measurement range and high cost. Recently, Global Navigation Satellite System-Reflectometry (GNSS-R) has provided a new opportunity to estimate the SWH, especially the space-borne Cyclone-GNSS (CYGNSS) launched on December 15, 2016. The GNSS-R uses the GNSS-reflected signal received by the receiver to invert ground physical parameters with all-weather, global fast coverage, high resolution, high precision, high long-term stability, rich signal sources, passive detection, and strong concealment. In this paper, the global ocean significant wave height is estimated using space-borne CYGNSS GNSS-R data for the first time though the relationship between the square root of the signal-to-noise ratio (SNR) data of CYGNSS delayed Doppler map (DDM) and the SWH. Then, the estimated significant wave height is compared with the satellite altimeter and buoy data. Compared with the AVISO SWH observation, the standard deviation value reaches 0.3080 m and the correlation coefficient reaches 0.9473 m. The correlation coefficient with the buoy SWH observation is 0.9539 m and the standard deviation is 0.2761 m. The SWH estimations from CYGNSS can provide important support in ocean shipping development, marine environmental protection, marine disaster warning and forecasting.  
s 2019 11 584 10.3390/rs11050584 11 March 2019
# Electrical Monitoring under Transient Conditions:
## 2 Foundations of the Transient Analysis of Electrical Quantities
### Transient Analysis of Motor Currents
However, some works have proven that they are also valid under other types of starting methods [21]: startup with auto-transformer, startup with stator resistors, star-delta startup, or even soft-started motors. In this latter case, a higher harmonic content is detected in the signals, but the fault-related patterns still appear [22]. A different question arises when the motor is started via Variable Speed Drive (VSD). In that case, since the supply frequency usually changes, the patterns linked with the fault components are different, but once they are characterized, the methodology is perfectly applicable, as reported in previous works that considered motors driven by a frequency converter [23; 24].
# State Residualisation and Kron Reduction for Model Order Reduction of Energy Systems
## 4 Application and Results
### Application of Kron Reduction Technique  
After applying state residualisaton, the classic Kron reduction technique [34] can be implemented to further reduce network complexity, and, subsequently, the simulation time. For illustration, a schematic diagram of the reduced model for Cases 1 and 2 for a fault applied at bus 19 is shown in Figure 4b. It is seen that the number of bus nodes is reduced from 39 to 13. Moreover, the voltage and current relationships of the Kron reduced network are represented by an algebraic matrix, which simplifies subsequent implementation.  
Figure 10: (a) Poles of linearised full EMT, phasor, and proposed reduced model, for tripping line B26\(-\)29 at 1 s under Case 3, (b) and (c) zoomed figure.  
Table 5 compares the simulation time of the reduced models by using the proposed and phasor approximation technique under Cases 1-3. It is seen that both the reduced models, based on phasor approximation and the proposed combination of state residualisation and Kron reduction, require less execution time than the full EMT models (i.e., 20-80%, depending on the test scenario), with the latter being faster than the former (15-20% reduction achieved). It is also seen that the reduction in simulation time, relative to the full EMT model, is different under the three cases, which is, as expected, given the nature of the disturbance event and its proximity to nearby generators.
# Multi-View Visual Relationship Detection with Estimated Depth Map
## 2 Related Work
### Multi-View Framework  
In practical visual scenes, information from one specific view is limited. Therefore, multi-view information can supply more abundant visual features of different perspectives of objects. Multi-view information has been applied in numerous visual tasks, such as object detection [9,11,12,13] and text detection [16,17]. In earlier work, [PERSON] et al. [9] proposed an object detection model which could detect object instances from arbitrary viewpoints. With the appearance of CNN, further multi-view deep models for object detection were constructed. [PERSON] et al. [13] proposed a framework which was composed of a 3D object proposal generation module and a multi-view features fusion module. [PERSON] et al. [11] utilized only 2D object detections from multi-view images to recover 3D object positions in generic visual scenes. [PERSON] et al. [12] proposed a multi-view detection approach to improve the ability to extract small objects. To solve the problems of occlusion and perspective distortion, [PERSON] et al. [16] constructed a novel text detection model based on multi-view images to identify co-occurring texts.  
In our proposed multi-view visual relationship detection framework, three image views are generated based on original RGB images and estimated depth maps. These multi-view images are mapped into appearance features and spatial features before they are input to a visual relationship classifier which can process different visual components in multi-view images simultaneously.