esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 2 Materials and Methods
### In-SAR Techniques
#### 2.3.2 SBAS-InSAR  
The SBAS technique retrieves displacement rate maps and subsidence time series of coherent targets by efficiently implementing an appropriate combination of numerous interferograms with the relevant small baselines [12; 14; 31]. The resulted small baseline interferograms are fulfilled in a linear model. The combination of small baselines is determined by the vector including values of the unwrapped differential interferometric phase. The sampling rate will be increased by the solution of the linear model. Meanwhile, the backscatter is preserved and phase noise is reduced by setting a coherence threshold [31]. This method, devised especially to settle the unwrapping issue in fields of large and comparatively stable subsidence, is described by [33] in detail.  
There are four main steps for SBAS-InSAR processing:  
(1) Generation of multiple differential interferograms  
Similar to PS-InSAR, the super master image was selected automatically considering the influence of the decorrelation effect of the temporal-spatial baseline on the interferogram (maximum temporal baseline: 750 d and maximum normal baseline: 50%) and each slave image was co-registered to the super master image. In this study, interferometric pairs with poor unwrapping and low coherence were eliminated. At last, a combination with 173 differential interferograms was produced (Figure 3b).  
(2) Phase unwrapping for the wrapped phase signal  
The minimum cost flow (MCF) network [34; 35] was used to unwrap the phase with an unwrapping coherence threshold of 0.25. A few interferogram pairs with unwrapped phase error and low coherence were not taken into account in the next step.  
(3) Refinement and re-flattening  
By choosing and refining stable ground control points (GCPs), the unwrapped phase was corrected using the method of the calculation for residual phase content and phase slope. The criteria for selecting GCPs is described by [PERSON] et al. [36] and 25 GCPs were selected in the study area. Meanwhile, the topographic phase was removed based on the SRTM DEM and the nonlinear subsidence part was moved apart by means of spatial and temporal bandpass filtering [13]. Next, time series deformation in the study area was retrieved by subtracting the orbital ramps and atmospheric artifacts.  
(4) Geocoding and raster to shape conversion  
Geocoding was done in the LOS direction using SRTM DEM. The parameter of velocity precision threshold was set to 1 mm/year. Finally, in order to compare with PS results, the time series displacement of SBAS was converted to a shape file with the WGS-84 coordinate system.
# Design and Implementation of Simulation System for Multi-UAV Mission
## 4 Path Planning Simulation Test for Two UAVs
### Mission Scenario Description  
The scenario is as follows: after a geological disaster in a city where a large area of buildings has collapsed and communications have been disrupted, two UAVs need to be dispatched to search and identify the area where the casualties may exist. At this point, the general location of the target area is known, and the mission environment is divided into a 10 \(\times\) 10 grid on the map. The specific reconnaissance areas of the two UAVs are designated by the superior, and the routes are crossed, so the planning module needs to make further judgment, as shown in Figure 6. Under the condition of setting velocity, if two UAVs arrive at the intersection point of the planned routes at different times, it means that there will be no collision, and the planning result is valid. Otherwise, re-planning is required.  
Figure 5: Default IRIS in Gazebo environment.
## 5 Data Analysis and Hypothesis Testing
### Perceived Information, Perceived Value, and Purchase Intention  
As H5 states, consumers' perceived information influences a product's perceived value, thereby affecting purchase intention, i.e., perceived value plays a mediatory role. Similar to the test presented in Section 5.5, the selected sample quantity was 5000. At a 95% confidence interval, the data showed that perceived value mediated perceived information and purchase intention. The confidence interval of the Bootstrap test was [0.4563, 0.7598]. This excludes 0, indicating the existence of a mediating effect with a value of 0.5906. The data show that consumers' perceived information increases a product's perceived value, thereby positively affect purchasing intention.
## 3 System Implementation Based on LPA Synchronization Algorithm  
The overall implementation of the multi-\(h\) CPM system is shown in Figure 3, where the notations are listed in Table 3. Vectors are shown in bold. The subscripts (R) and (I) represent the real and imaginary parts of the outputs, respectively. Using TPT, FPT with \(L^{\prime}=2\), and SSP with 4-value phase state partition, the number of the phase state is reduced from 512 for the optimal detection to 16. The LPA-based synchronization removes the usage of EL MF banks to lower the complexity further. With the above reduced-complexity methods, the transmitter and receiver are implemented on Xilinx Kintex-7 FPGA and run at a global clock of 200 MHz and a bit rate of 50 Mbps. The carrier frequency is set to 70 MHz, and the baseband signal is sampled with 8 samples per symbol for the transmitter and receiver. Next, we introduce the implementation details for the main parts of the overall system.  
Figure 2: Data-aided (analytic) and decision-directed (simulation) S-curves based on LPA estimator of Equation (32) and SLPA estimator of Equation (33): (**a**) \(M=4\), 3 RC, \(h=\{\frac{4}{16},\frac{5}{16}\}\). (**b**) \(M=4\), 3 RC, \(h=\{\frac{5}{16},\frac{6}{16}\}\). (**c**) \(M=4\), 3 RC, \(h=\{\frac{9}{16},\frac{10}{16}\}\).
## 4 Implementation of the System  
### DApp  
The main functionality of the DApp described in Section 3.2 was implemented as follows:  
* Authentication. The application first asks the user to verify his/her identity through his/her Metamask wallet (illustrated in Figure 6). Then, users can sign the messages or the transactions that will be submitted to the network (an example is shown in Figure 7).
* AR access/first issue. After a successful PK/SK verification, the application will look for the student's PK in the off-chain storage. Note that, as it was previously mentioned, the student's data must be already present in the database (i.e., the AR database is indexed by the PKs, as they are unique values). Once found, the application checks the transaction hash of the student's AR. If the transaction hash field is not empty, then the AR is already recorded in the blockchain, so the application can display the AR to the student directly (an example of a retrieved AR record is shown in Figure 8). On the contrary, if the transaction hash field is empty, that means that the AR is stored in the database, but it is not recorded in the blockchain. Therefore, the administration would perform a transaction to trace it in the blockchain for the first time.
* AR update. Any slight modification on the AR file will completely change its hash. Therefore, a new hash represents an AR update (or a new AR) that is not traced in the blockchain. When this happens, the application adds the AR (and all the updated ARs) to a 'pending AR list'. This list of ARs is what the administrator will see on his/her interface (an example is shown in Figure 9), waiting to be confirmed after the validation process.
* AR verification. As it was previously mentioned, a student can download his/her AR to share it with third parties, typically, an entity that wants to verify a CV. Such a downloading can be performed through a link that is enabled once the student has gained access to his/her information (such a link can be observed at the top of Figure 10). Then, the mentioned third-party would validate the CV merits by introducing the AR file into the application through the menu shown in Figure 11. The verification process is very fast (just a few seconds), as the application only needs to encrypt the AR (using the keccak256 function) and then to check whether its hash matches one of the hashes stored in the blockchain.  
Figure 6: Metamask signature request in the DApp.  
Figure 8: Example of a retrieved AR shown by the DApp, where several subjects are not yet evaluated.  
Figure 7: Message signed successfully with the developed DApp.  
Figure 9: Pending AR list shown to the administrator.
# Impact of Sentence Representation Matching in Neural Machine Translation
## 3 Sentence Representation Matching  
### Motivation  
Beam search is a promising method for NMTs by overcoming the problem of greedy searches in sequential target word generation. On the other hand, the impact of beam search inversely implies that the sequential decisions by the model are likely to be incorrect to select the best sentences in many cases. There are many possible causes for the inaccurate prediction of the composition of target words, such as inaccurate model representation, complex parameter landscapes, and noisy data.  
A possible cause is the simple representation of the correctness of target words. In current NMTs, cross-entropy is the most popular cost function and is composed of probabilities of selecting each correct word of a target sequence. Therefore, only one variable is responsible for representing whether the selected target word is correct. Using only one variable may be risky because the second probable word and its highly probable following sequences may give higher cross-entropy than any sequences derived from the correct word selection. This case is a deceptive example of restricting accurate word composition in decoders.  
Another cause is the slow parameter update in NMT structures. In LSTMs, the gradient vanishing [26] which occurs over time steps and over stacks in the vertical direction of the structures is resolved, respectively, by using memory cells and input feeding or multidimensional memories [15; 27]. They are applied to the encoder and decoder, but the interface part is often a feedforward layer suffering from gradient vanishing. This vanishing limits the achievable translation quality in general and may restrict the learning of the correct composition.  
Using a different type of cost function is an effective method to solve this problem. In our preliminary work [4], we evaluated the performance of applying a matching method for an English to French translation task with 1.5 million sentences and confirmed its improvement. We extended the work for more rigorous analysis to learn about how to control the hyper-parameters with respect to their impact on training.
## 5 Discussion
### Land subsidence in Guangzhou Due to Over-Exploitation of Groundwater  
Guangrao County, indicated by the green rectangle in Figure 2, is the exclusive inland subsidence in the Shandong peninsula and has been suffering subsidence for decades [10; 11; 14]. Figure 8a,b show the average deformation rates derived from RADARSAT-2 datasets from 2012 to 2016 and Sentinel-1A datasets from 2016 to 2018, from which the land subsidence evolution during two periods can be clearly seen. All the deformation measurements have been projected in the vertical direction. The land subsidence cones remained the same, while the land subsidence area and intensity had changed significantly. Quantitatively, the maximum deformation rate increased from 80 mm/a to 130 mm/a in the Gruangrao subsidence center, and the deformed areas with deformation rates greater than 10 mm/a increased from about 240 km\({}^{2}\) to about 460 km\({}^{2}\) (Figure 8).  
To better analyze the temporal characteristics of the observed deformation, deformation time series at three feature points (Figure 8a) are extracted during two observation periods shown in Figure 9, where quasi-linear deformation during two periods are revealed. The annual subsidence rates during these two observation periods varied from 88 mm/a to 126 mm/a at Guangzhou cones, from 88 mm/a to 126 mm/a at Dawang subsidence cones and from 43 mm/a to 87 mm/a at Xingfu subsidence cones. Cumulative deformation time series at points a, b and c reached 708, 620 and 398 mm, respectively.  
The primary source of the land subsidence in this area is mainly attributed to the long-term overexploitation of deep confined aquifer groundwater, which was mainly used for farmland irrigation, industrial and commercial water supply [10; 11; 14; 25; 26].  
Figure 8: Average vertical deformation maps in Guangzhou city derived from (**a**) RADARSAT-2 (2012–2016) datasets and (**b**) Sentinel-1A (2016–2019) datasets.
## 1 Introduction  
During video transmission, the channel introduces noise and degrades the transmitted information. To minimize the negative impacts experienced while transmitting data, various measures are implemented by employing tactics belonging to a specialized category of channel coding techniques known as forward error correction (FEC). These highly efficient strategies entail the incorporation of different codes with error-resolving capabilities, including Reed-Solomon, convolutional, Bose-Chaudhuri-Hocquenghem (BCH), and low-density parity-checking (LDPC) codes [1]. This approach has demonstrated significant success in reducing transmission errors.  
The main advantage of channel coding lies in enhancing the system's performance over an uncoded transmission. Certain digital systems utilize a specific class of codes known as concatenated code pairs, which employ two levels of coding, namely, internal and external codes. Several systems use pairs of concatenated codes, exemplified by the digital video broadcasting terrestrial (DVB-T) system. With the escalating demand for broadcasting services, there has emerged a requisite for developing a novel, more efficient standard [2]. This necessity led to the inception of DVB-T2 (digital video broadcasting--second-generation terrestrial), which offers advanced technology and augmented capacity across diverse terrestrial domains. Several notable enhancements have been introduced, with the evolution in FEC codes standing out as one of the most prominent. Assessing this transition is imperative to gauge the efficacy of the implemented improvements and comprehend their ramifications.  
Although the DVB-T and DVB-T2 systems prove effective in error correction, it is essential to acknowledge the gap in current research. Many studies comparing or evaluating their performances primarily concentrate on technical aspects such as bit error rate and error correction capability, often neglecting user experience quality, particularly video quality [3]. Given the importance of video degradation in Quality of Experience (QoE), a methodology has been devised to evaluate video quality temporally.
## 2 System Modeling
### Hydraulic System  
Due to the development of hydraulic seals, it is assumed that the external leakage is negligible and can be ignored, and the load pressure dynamic can be described as [36]  
\[\frac{V_{t}}{4\beta_{e}}\dot{P}_{L}=-D_{m}\dot{\theta}-q_{L}+Q_{L}+q(t) \tag{6}\]  
where \(V_{t}\) represents the whole control volume of the actuator, \(\beta_{e}\) is the bulk modulus of the hydraulic oil, \(q_{L}\) denotes the internal leakage caused by the pressure discrepancy between the chambers of the actuator, \(Q_{L}\) signifies the load flow, and \(q(t)\) is the modeling uncertainties.  
The internal leakage model is given by [37]  
\[q_{L}=c_{1}P_{L}^{2}+c_{2}P_{L}+c_{3} \tag{7}\]  
where \(c_{1}\), \(c_{2}\), and \(c_{3}\) are constants.  
The load flow is computed by [16]  
\[Q_{L}=C_{d}wx_{y}\sqrt{\frac{1}{\rho}(P_{s}-\text{sgn}(x_{v})P_{L})} \tag{8}\]  
where \(C_{d}\) denotes the discharge coefficient, \(w\) is the spool valve area gradient, \(\rho\) is the oil density, and \(P_{s}\) represents the supply pressure.  
Since the tracking performance is only slightly improved and the system order increases significantly if the full dynamics of the servo valve are considered [38], to reduce the complexity of control design, a simplified model in which valve spool displacement is directly proportional to the control input can be used as  
\[x_{v}=k_{v}u \tag{9}\]  
where \(x_{v}\) signifies the spool displacement, \(k_{v}\) represents the servo valve coefficient, which is determined based on its datasheet provided by the manufacturer, and \(u\) is the control input voltage.  
Based on (8) and (9), one obtains  
\[Q_{L}=k_{t}u\sqrt{(P_{s}-\tanh(c_{v}u)P_{L})}+\Delta Q_{L} \tag{10}\]  
where \(k_{t}=C_{d}wk_{v}\sqrt{1/\rho}\) denotes the total load flow coefficient, \(c_{v}\) is a positive constant, and \(\Delta Q_{L}\) represents the approximation error when smoothing the load flow Equation (8).
## 5 Considerations on the Proposed Scheme
For each block of the cover image, the secret key \(K\) produces the right singular vectors and the specific pair of their components. Note that more than one couple of vectors can be chosen to embed the watermark, with the purpose to improve the overall robustness of the watermarking process. Thus, the secret key \(K\) can be represented mathematically as a structure with the following components:  
\[K=\left\{\kappa^{seed},\kappa^{c}1,\kappa^{c}{}_{2},\ldots,\kappa^{c}{}_{n^{ 2}}\right\} \tag{15}\]  
where \(\kappa^{seed}\) is a random number used as the seed of the watermark sequence \(W\); \(\kappa^{c}{}_{b}\) is a vector with the following structure \[\kappa^{c_{b}}=\big{(}i,j,k,l\big{)},\ \ \ b\in[1..n^{2}],\ \ \ i,j\in[3..M]\ \ \ \ k,l\in[1..M],i\
eq j\ \text{and}\ k\
eq l \tag{16}\]  
where, for every block, \(i\) and \(j\) identify the singular vectors in the matrix \(V\left(v_{i}\text{ and}\ v_{j}\right)\) and \(k\) and \(l\) define the components of \(v_{i}\) and \(v_{j}\) used to compute the secret hyper-plane P(\(k\), \(l\)). Note that the first two singular vectors are excluded from rotation since the corresponding singular values, \(s_{l}\) and \(s_{2}\), concentrate a large amount of energy of the image. Their modification is therefore extremely difficult without excessively affecting the quality of the image. By using such a key structure, the overall security of the scheme is considerably improved, because it is computational infeasible, for an attacker, to find the embedding space of the watermark, i.e., the right singular vectors and their corresponding components. For this reason, attacks such as singular vector substitution or re-watermarking are unlikely to succeed without severely damaging the attacked image. It was recently discovered that singular vectors are affected by a form of ambiguity called _sign ambiguity_[56]. More precisely, the SVD arbitrarily assigns the sign of each singular vector. This has significant consequences in many applications which use SVD to process and analyze data. In our scheme, the sign ambiguity of the singular vectors changes the sign of their components which are used in the detection process to recover the embedded watermark angle; therefore it can modify the extracted feature set \(F\)' to such an extent that it becomes impossible to correctly detect the presence of the watermark. To solve this ambiguity we apply the algorithm proposed by [PERSON] et al. [56], which suggests to determine the sign of a singular vector by computing the sign of the inner product of this vector and individual data vectors taken from the data set, that is, in our case, the original image. With the proposed solution, good results are obtained when the inner products are not close to zero. Instead, to avoid an arbitrary sign assignment when the inner products are close to zero, the algorithm proposed in [56] considers the combined magnitudes of both left and right singular vectors. As far as we know, our method is the first SVD based watermarking scheme which takes into account the sign ambiguity of the singular vectors and attempts to solve it.
## 3 Results and Discussion
### Method Knowledge of Space
#### 3.2.2 GC-MS Conditions  
In the GC methodology, some parameters, including the column, splitless mode, flow rate of the carrier gas, and temperature gradient, must be taken into consideration [37; 38]. There are several columns for GC analysis and all of them can be suited for numerous analyses; however, it is necessary to evaluate the operational range of the parameters that originate the best possible results. There are few studies on the optimization of GC-MS conditions for volatile amines. The obtained results are presented in Supplementary Material Table S2. [PERSON] (2006) [12] presented a more complete study but with a high emphasis on the SPME fiber selection using a unidimensional approach. On the other hand,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**SPME Fibers**} & \multicolumn{3}{c}{**Amines**} \\ \cline{2-5}  & **DVB/CAR/PDMS** & **CAR/PDMS** & **PDMS/DVB** & **TMA** & **DMA** \\ \hline
**pKa** & - & - & - & 9.80 & 10.37 \\
**Molecular Weight** & 40–275 & 30–225 & 50–300 & 59.11 & 45.08 \\
**Polarity** & Bipolar & Bipolar & Bipolar & Polar & Polar \\
**Thickness (\(\mu\)m)** & 30–55 & 75 & 65 & - & - \\
**Core type** & Stabflex & Fused silica & Fused silica & - & - \\
**Operating temperature** & 230–270 & 250–320 & 200–270 & - & - \\
**Extraction mechanism** & Adsorbent & Adsorbent & Adsorbent & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some characteristics of SPME fibers and targeted volatile amines.  
[PERSON] (2012) [23] studied the impact of some GC-MS conditions on TMA and DMA analysis, such as the injector temperature, oven temperature, and split ratio. In the present study, and according to the obtained results, it was decided to proceed the investigation with a BP-20 (polar) capillary column, an oven temperature ranging between 35 and 85 \({}^{\circ}\)C, ramp speed 40-60 \({}^{\circ}\)C/min, and a flowrate between 0.8 and 1.3 L/min. The fixed parameters are described in Section 2.4, where the GC-MS conditions are detailed.