esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data
## 3 Results and Discussion
### Validation with AERONET Measurements  
Images observed by the HJ-1 A/B satellite from April to October during 2012-2015 were used in this study. The Level 2.0 product of AERONET measurements within 1.5 h of satellite overpass time were employed for validation. The AOD retrieved from the HJ-1 A/B CCD data was compared with in situ-measured AOD data at the XiangHe, Beijing-RADI, Beijing-CAMS, and Beijing sites (Table 4). Figure 11a shows a scatter plot of AODs retrieved without any prior knowledge versus AERONET measurements (validation approach I). It can be seen that the overall correlation coefficient \(R\) was 0.866, the RMSE was 0.167, and the MAE was 0.131, and EE reached 53.9%. The correlation coefficients of the four sites were all above 0.8, with that of the XiangHe site reaching above 0.9. The inversion accuracy of the vegetated areas (XiangHe) was slightly higher than that of the urban areas (Beijing-RADI, Beijing-CAMS, and Beijing). Therefore, the \(R\) and EE of the XiangHe site were greater than those of the other three sites, whereas its RMSE and MAE were smaller than those of the other three sites. As can be seen from the scatter plot, the inversion results were distributed around the 1:1 diagonal, with a relatively large error when AOD was low. If the AOD measurements of an AERONET site were used as prior knowledge to get optimal offset step \(i\), and the measurements of other AERONET sites were used for validation (validation approach II), the AOD of the entire image could be much more accurately obtained by this method (\(R\) is 0.989, RMSE is 0.052, MAE is 0.042, and EE is 96.7%; Figure 11b).  
Figure 10: Distribution of interband regression coefficients \(a\) and \(b\) as well as \(R^{2}\) and RMSE over the semi-hemispherical space. (**a**) \(a\); (**b**) \(b\); (**c**) \(R^{2}\); (**d**) RMSE. The black circle denotes the solar zenith angle (30\({}^{\circ}\)), and the land cover type is broadleaf deciduous forest.
# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 3 Network Performance Analysis
### Network Performance
#### 3.2.3 Errors in Neighbor Discovery  
LSM-UVCN is highly dependent on the connection matrix. If there are bit errors during the transmission of neighbor discovery control frames in the process of neighbor discovery in the network, nodes will generate incorrect connection matrices as a result. This will prevent the node from establishing a correct transmission path until the next neighbor discovery.  
Given that the total frame length of the neighbor discovery control frame is \(B_{\text{ctrl}}\), a node can only establish a correct connection matrix if it receives all control frames sent by the \((n-1)\) other nodes without any bit errors. The probability \(P_{\text{single}}\) that a single node successfully establishes the connection matrix is \(P_{\text{single}}\).  
\[P_{\text{single}}=(1-BER)^{(n-1)B_{\text{ctrl}}}. \tag{7}\]\(N_{\text{occupied}}\) denotes the total number of nodes occupied by a single transmission path. The nodes occupied by a transmission path include not only those in the Work state but also all Silent nodes within one hop of any Work node. Given that the average number of neighbors within one hop for each node is \(\pi\rho A^{2}\). In a single transmission path, the probability that all nodes successfully establish a connection matrix is \(P_{\text{matrix}}\).  
\[\left\{\begin{array}{l}N_{\text{occupied}}=(m+1)+2(\pi\rho A^{2}-1)+(m-1)(\pi \rho A^{2}-2)\\ P_{\text{matrix}}=(1-BER)^{(n-1)N_{\text{occupied}}B_{\text{ctrl}}}\end{array} \right.. \tag{8}\]
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Built-Up RS-Based Proxies
#### 4.1.1 Buildings Category
Furthermore, geometric deformation of entire buildings, such as changes in building shape and size, is employed as a proxy to extract the partly damaged buildings using satellite images ([PERSON], 2012). Change in the symmetry of windows on the facade of a building serves as a proxy for damage assessment from oblique aerial imagery ([PERSON], 2012). To do so, local symmetry points are detected in a sliding window. Then, histogram bins of those points in the vertical and horizontal directions are generated to distinguish damaged and undamaged building facades using the Gini Index. Using changes to different components of the buildings, e.g., facade and wall, as a proxy to evaluate damages to the buildings in detail remains a challenge and continues to be actively studied (e.g., [PERSON], et al. (2018)). Further improvements are required for robust and accurate damage assessment per building. This can be done by extracting structural damage patterns from RS data, for example by making use of the characteristics of facade wall geometry, similar to using facades windows symmetry (Figure 3f) ([PERSON], 2012), wall slenderness, area of wall, or use of misalignment of wall openings ([PERSON], 2012) as RS-based proxies. Figure 3: Examples of remote sensing-based built-up proxies. (**A**) Roof with dislocated tiles, (**B**) cracks in concrete facade, (**C**) cracks and hole in brick facade ([PERSON], 2012), (**D**) inclined building ([PERSON], 2012; [PERSON], 2012), (**E**) debris, rubble piles, spalling ([PERSON], 2012), and (**F**) facade windows symmetry ([PERSON], 2012). Building morphology acts as a proxy to extract building-based land use [32; 48; 87; 89] and to measure living conditions [34; 42; 49]. It has been used for post-disaster recovery and vulnerability [89] evaluations, but hardly in other DRM situations. In a different approach, [PERSON], et al. [91] apply the position of buildings in relation to the street level as a proxy to determine the likelihood of constructions to suffer damage in the case of a flood event and assess the building vulnerability. Accordingly, buildings with lower elevations than the street are more vulnerable. Building-based functional assessment has not been sufficiently addressed through the developed proxies that only consider the land use information. For example, functionality of the buildings may change during the post-disaster recovery phase, which cannot be identified only by extracting the reconstructed buildings. Nevertheless, facades are good sources of information for building-based functional assessment, and new proxies could make better use for example of the presence of cars in a driveway, flower pots in front of windows of residential buildings, or the presence of panel and banners on top of shops for extracting the functional use of buildings.
## 3 Case Analysis
### Evaluation and Analysis of Model Prediction Results
#### 3.4.4 Model Input Variable Sensitivity Analysis  
To further quantify the contribution of each input feature to the cooling load prediction results, this study conducted a sensitivity analysis of the iTransformer-BiLSTM model based on the SHAP (SHapley Additive exPlanations) method. Figure 16 shows the SHAP beeswarm plot of the features, while Figure 17 presents the bar chart of feature rankings based on the mean absolute SHAP values.  
From the beeswarm plot (Figure 16), it can be observed that the SHAP values of the historical cooling load feature exhibit the widest distribution, with significant dispersion on both the positive and negative sides. This indicates that the historical cooling load exerts a bidirectional regulatory effect on the prediction output across different value ranges. Similarly, the temperature differences between supply and return air in Zones A, B, C, and D also display bidirectional dispersion, reflecting the dynamic response of  
Figure 15: Error Analysis of the Comparison between the Main Model and Other Combined Models (Taking the First Day Mentioned Above).  
indoor environmental parameters to cooling load fluctuations under different operating conditions. In contrast, the SHAP values of outdoor humidity are primarily concentrated on the positive side with a relatively narrow dispersion range, suggesting that it mainly provides a stable positive contribution. The SHAP values for outdoor temperature are relatively small and tightly clustered, indicating a minor influence on the prediction results.  
The bar chart (Figure 17) further quantifies the average contribution level of each feature. Results show that historical cooling load accounts for the highest overall contribution and plays a dominant role. This is followed by the temperature difference between supply and return air in Zone C and outdoor humidity, suggesting that variations in Zone C air temperature and ambient humidity conditions are also important drivers of cooling load prediction. This is likely because Zone C, located in the central area of the factory workshop, more comprehensively represents the overall indoor cooling load variations, while the significance of outdoor humidity mainly stems from its direct impact on the latent heat load of the air-conditioning system--higher humidity levels during fresh air handling lead to greater cooling load demand.  
Overall, the sensitivity analysis results indicate that the iTransformer-BiLSTM model can effectively identify the differences in feature contributions to cooling load prediction, demonstrating the model's reasonable responsiveness to key variables under complex operating conditions.
# Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging
## 2 Materials and Methods
### Preprocessing  
The hyperspectral datacube had a number of undesirable effects and noises, such as baseline shifts, light scattering, unrestrained external factor, and random noises that may influence the results. Prior to the development of multivariate models, spectral preprocessing was used to reduce unwanted effects and noises. An average spectrum was found by calculating the average spectrum from all pixels within the region of interest (ROI). This method was employed to smooth the impact of random noises across the region, and then it was used for further processing. The hyperspectral images were calibrated with a white and black reference image for removing the camera sensor's dark current influence [38]. By establishing a square region on ROI over the sample, the calibrated pictures were cropped. We conducted preprocessing based on methods previously mentioned in the literature [39; 40; 41; 42].
## 4 Experimental Results and Analysis
### Simulation Scenario
#### 4.1.1 The Satellites  
In this paper, the number of satellites is set to 10, remote satellites S1 to S10 are established in STK, and the distribution of the satellites in STK is shown in Figure 7.
# Enhanced Linearity in Intracranial Pressure Monitoring System Through Sample Isolation Bridge ROIC
## 3 ROIC Implementation Details
### Sample-Data Isolation Based SAR ADC
Since the circuit draws power solely during regeneration, its average power consumption remains minimal. The suggested ADC incorporates dynamic SAR logic, illustrated in Figure 10. Unlike the traditional method that employs flip-flops, this dynamic logic approach minimizes the number of transistors and nodes, resulting in improved speed and reduced power consumption. In this study, a basic regenerative feedback mechanism maintains the outputs \(P\) and \(N\), guaranteeing output reliability, illustrated in Figure 11.  
Figure 8: The schematic of proposed bootstrapped switch. During the sampling phase, \(M_{10}\) and \(M_{11}\) reset both outputs (\(P\) and \(N\)) to a low state. Subsequently, signal \(D\) is elevated to a high level while \(CLK\) is grounded. At the same time, signals \(P\) and \(N\) capture the outputs of the comparator, \(ON\) and \(OP\), due to the regenerative feedback provided by \(M_{12}\) and \(M_{13}\). As soon as the falling edge of \(VALID\) is reached, signal \(Q\) is charged to \(VDD\), signifying the completion of this operation, which transitions into signal \(D\) for the subsequent dynamic logic. To ensure that signals \(ON\) and \(OP\) remain latched, even if the comparator outputs alter later, \(M_{14}\) and \(M_{15}\) are deployed.
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 1 Introduction  
In the last few years, there has been exponential growth in mobile devices, with the commercialization of 5G significantly impacting traditional terrestrial and non-terrestrial networks [1; 2; 3]. The implementation of 5G has led to significantly higher data transfer rates compared to previous generations like 4G and has allowed terrestrial networks to handle more data traffic, enabling faster downloads, uploads, and streaming for users. The technology can now handle a larger number of users. In the development of 5G, 3 GPP has played a fundamental role by providing (a) standardization, such as technical specifications for mobile networks and standardized protocols; (b) technical development, such as NR technology, network slicing, air interface, and core network functions; and (c) continuous improvement by releasing new versions with additional features and improvements.  
As a dynamic collaboration among telecommunication standards organizations [4; 5], 3 GPP's core mission revolves around the continual evolution of mobile communication protocols. The expansive scope of 3 GPP's work includes the foundations of transformative technologies such as LTE and 5G [6]. From defining the intricacies of the air interface and RAN to shaping the architecture of the core network and services, 3 GPP exerts its influence across the entire spectrum of mobile communications. Today, 3 GPP standards serve as the cornerstone for cellular providers and equipment manufacturers globally. As the preeminent cellular standard organization, 3 GPP occupies a pivotal role in charting the course of mobile communication's future [7]. The role of 3 GPP is essential in the context of PLS in ISTNs:* Detailed channel models that accurately represent real-world propagation conditions are developed by 3 GPP. These models are crucial for designing effective PLS algorithms. It defines modulation schemes, coding techniques, and other physical layer parameters that directly influence PLS in ISTNs. In addition, 3 GPP addresses interference mitigation techniques, which are essential for PLS, as interference can degrade the performance of secure communication systems.
* Technical standards for mobile communication systems are set by 3 GPP. These standards directly influence the design and implementation of physical layer technologies. By understanding 3 GPP's role in standardization, we can better appreciate the constraints and opportunities for PLS development within the context of ISTNs.
* Furthermore, 3 GPP is at the forefront of technological advancements in mobile communications. Analyzing their role in technical development provides insights into emerging trends, challenges, and potential solutions that directly impact PLS research and development.
## 2 Motivation and Contribution  
With the increasing interest in zero-energy devices, it is time for the concept to be introduced in practical settings such as warehouses. The use of sensors and wireless communication in large-scale warehouses and related relays provide the opportunity to explore the use of various energy harvesting strategies. We propose a hybrid switch with decision-making between ABCS and SWIPT for wireless data transfer purposes. It determines the parameters of the received data signal alongside the energy available in the battery and switches between SWIPT and ABCS to maintain continued operation.  
In large-scale warehouses, the data collection sensor/robots move through the warehouse, and when the size of the warehouse increases, it is ideal to have a relay to support the transmission of the gathered data to the required destination. Our switch can be implemented as the relay, and since it operates purely through harvested energy, it can be placed per the physical requirements without constraints related to the power supply for the switch.  
Considering the advances that can be achieved through the combination of SWIPT and ABCS towards performance and efficiency improvement, the contributions of this paper can be summarized as follows:  
* Development of an adaptive switching algorithm that optimizes the trade-off between energy harvesting and data transmission in indoor IoT environments.
* Design of a hybrid relay system capable of seamlessly transitioning between ABCS and PS-SWIPT modes to maintain quality of service while maximizing energy efficiency.
* Comprehensive performance analysis of the proposed system in a simulated warehouse environment, demonstrating its superiority over single-mode ABCS or SWIPT systems.
* Investigation of the system's behavior under various channel conditions and distances between nodes, providing insights into its applicability in diverse industrial IoT scenarios.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 5 Results and Discussion
### Simulation
#### 5.1.2 Delay Performance Analysis  
The proposed QoS framework had the lowest recorded value in terms of network delay, and as expected, the malicious scenario produced the highest level of delay for any 16-node scenario as shown in Figure 11. The delay in the sinkhole scenario was caused by the time the attack exceeds the failure threshold value before forwarding the packets to the rest of the network. The framework significantly reversed this action by a significant margin meaning that the intrusion was identified, blacklisted, and alternative routes that guarantee an acceptable level of QoS were chosen. In the framework scenario, the first node had a delay of 12,369.96 microseconds. Node 3 has an elevated delay value of 13,739.96 microseconds.  
Figure 9: Network model of 16 nodes with two sinkhole nodes added.  
The delay slightly fluctuated between 13,663.037 and 13,739.96 microseconds for nodes 7 and 12. The rest of the nodes maintained a constant delay of 12,017.41 microseconds. The delay was relatively low as compared to that of the sinkhole scenario.  
Figure 11: Delay for 16 nodes.  
Figure 10: Throughput for sinkhole and framework scenarios.
## 3 Incremental Nonlinear Dynamic Inversion Control Law Design
### Reference Model with Pseudo-Control Hedging  
The concept of pseudo-control hedging (PCH) was initially proposed as a means to enhance the performance of adaptive control [33], specifically by modifying the reference model in situations where input position and rate saturation occur. In the presence of actuator saturation, it is possible for the control performance to degrade or even for the controller to become unstable [34]. To address this issue, PCH is introduced. In comparison with the reference governor (RG) [35], which is employed as a control reconfiguration strategy in fault-tolerant control, the PCH method offers the advantage of low computational cost, as it eliminates the need for computing the auxiliary reference (AR) sequence. In the ideal scenario, where the effect of actuator dynamics is not considered and the true values of angular acceleration and angular rate are used, the PCH signal \(v_{h}\) is obtained by subtracting the estimated saturated input \(v_{sat}\) from the virtual command output \(v_{cmd}\). The command signal \(v_{ff}\) is utilized as a feedforward term to improve the tracking performance, as depicted in Figure 6. In order to make the steady-state value of PCH equal to \(v_{h}\), a control gain is introduced, which can be formulated as  
\[v_{pch}=K_{h}v_{h} \tag{28}\]  
where \(v_{\text{pch}}\) is the PCH signal used to slow down the dynamics of the reference model when the saturation is triggered, and the gain \(K_{h}\) can be calculated as [36]  
\[K_{h}=K_{r}/(K_{e}-K_{r}) \tag{29}\]  
where \(K_{r}\) and \(K_{e}\) are gain of the reference model and the error controller, respectively. The inequality \(0<K_{r}<K_{e}\) holds, because in general control practice, the reference model is designed to have slower dynamics compared with the error controller. This is implemented to prevent the error controller from being overloaded and to ensure a stable closed-loop system.  
With regard to the roll mode dynamics described in Equation (25), the selected maximum absolute value for the aileron deflection angle is 5 degrees. The reference model and error controller gains are chosen as 8 and 10, respectively.  
Figure 7 compares the response of the system states and the virtual control command in the hedged and unhedged simulations. During the first 3 s of the simulation, the results are nearly identical, indicating that the PCH is not activated when the control output is not saturated. However, at 3.5 s, the control effectiveness of the roll mode drops by 95%, resulting in the roll angular rate being unable to maintain the commanded value, as shown in Figure 7a. At the activation of the PCH, depicted in Figure 7b, the virtual control requirements are reduced by slowing down the reference signal. At the 4 s mark, when the command shifts to the opposite direction, the virtual command of the PCH is increased to minimize the tracking error, as illustrated in Figure 7a. When the control output saturates, the PCH is capable of more rapid tracking of the command value.  
Figure 6: Reference model with PCH.
## 4 Analysis and Discussion
### Analysis of Cross-Word Embeddings  
#### 4.2.1 Word Translation: Cross-Lingual Alignment of Word Embeddings  
To examine whether word embeddings of two languages are located closely in a vector space when they have similar meanings, we perform the following word-translation experiments.  
We obtain bilingual dictionaries from Project MUSE [17]. They have 20,549 Korean-English translated word pairs, 22,357 English-Korean pairs, 25,969 Japanese-English pairs, and 35,353 English-Japanese pairs. For our experiments, we align those word pairs to make English-Korean-Japanese word triples, so we collect 10,295 triples.  
We translate a source word into a target word by choosing the \(K\)-nearest neighbors (\(K=1\)) to the source word in the vector space of cross-lingual word embeddings. The performances of word translation shown in Table 4 are measured by precisions, which are the ratio of correct translations out of the total words. The higher score indicates the better result.  
Shared vocabularies between two languages play an important role in grouping word embeddings according to their meanings, not their languages, in a vector space. Therefore, word embeddings from two different languages are not easy to map onto each other without shared vocabularies.  
However, the word-translation task shows an unexpected result. The word embeddings of the Korean-Japanese unsupervised NMT system are better clustered according to their meanings than those of the Korean-English unsupervised NMT system, although the former had few shared vocabularies, while the latter had many.  
As long as two languages have many common characteristics in morphology and syntax, it is possible to construct cross-lingual word embeddings, even if the two languages share little vocabulary with each other.  
One interesting result is that the precisions of supervised NMTs are lower than those of unsupervised NMTs in both Korean-Japanese and Korean-English. The reason is thought to be that supervised learning shifts the embeddings of a few words contained in a parallel corpus in a vector space, taking into account the contexts in which the words are used. However, a word-translation task does not consider the contexts and only considers a neutral meaning of the word.