esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Seasonality and Soil Physicochemical Changes in HTF  
The wet and dry seasons of the Brazilian tropical semiarid coast generated changes in most of the soil physicochemical properties (Figures 3 and 4), mainly for the bulk soils. In contrast, TOC, TN, and CCE were steadier when facing season changes, with no significant differences observed (Figure 3). The pH and exchangeable cations seasonal changes on bulk soils were noteworthy (Figure 3). The pH for the bulk soils was more alkaline in dry seasonal (BmB: 8.2 \(\pm\) 0.07; BpB: 8.4 \(\pm\) 0.1; SvB: 8.4 \(\pm\) 0.5) compared to the wet season (BmB: 8.09 \(\pm\) 0.1; BpB: 8.01 \(\pm\) 0.1; SvB: 7.9 \(\pm\) 0.1). The EC in bulk soils ranged from 17.6 to 29.9 mS cm-1 (Figure 3). During the dry season (lower soil moisture contents), higher EC values were observed (Bm: 22.1 \(\pm\) 5.7; Bp: 22.7 \(\pm\) 1.4; Sv: 28.9 \(\pm\) 1.5 mS cm\({}^{-1}\)) when compared to the wet season (Bm: 18.1 \(\pm\) 1.8; Bp: 17.1 \(\pm\) 1.4; Sv: 18.9 \(\pm\) 0.5 mS cm\({}^{-1}\); Figure 3).  
For the rhizosphere soils, some properties were influenced by seasons in a lower magnitude. A similar trend of bulk soils was registered, mainly related to Eb and EC values (Figure 3). Suboxic soil conditions prevailed both during the dry season (BmR: 111 \(\pm\) 9.8; BpR: 208 \(\pm\) 21.5; SvR: 263 \(\pm\) 26.8 mV) and the wet season (BmR: 296 \(\pm\) 28.5; BpR: 258 \(\pm\) 12.7; SvR: 249 \(\pm\) 13.4 mV). Additionally, seasonal changes also significantly affected (\(p<\) 0.05) the soluble contents (K\({}^{+}\), Na\({}^{+}\), Mg\({}^{2+}\)) in bulk soils (Figure 4), so that during the dry season the contents of soluble K\({}^{+}\), Na\({}^{+}\), and Mg\({}^{2+}\) for Bm decreased respectively from 370 \(\pm\) 78, 9783 \(\pm\) 860, and 1666 \(\pm\) 232 mg L\({}^{-1}\) to 297 \(\pm\) 12, 8545 \(\pm\) 449 and 1088 \(\pm\) 133 mg L\({}^{-1}\). On the other hand, the bulk soils of Bp and Sv presented the opposite trend, with higher soluble- K\({}^{+}\) (Bp: 377 \(\pm\) 20 mg L\({}^{-1}\); Sv: 556 \(\pm\) 51 mg L\({}^{-1}\)), soluble-Na\({}^{+}\) (Bp: 9783 \(\pm\) 860 mg L-1; Sv: 13,083 \(\pm\) 1342 mg L\({}^{-1}\)) and soluble-Mg\({}^{2+}\) (Bp: 1593 \(\pm\) 165 mg L\({}^{-1}\); Sv: 1741 \(\pm\) 388 mg L\({}^{-1}\)) were registered during the dry season (Figure 4).  
Moreover, plant species at the studied HTF seem to be associated with different soil properties within each season (Figure 6C). Samples collected over the dry period (BpD; SvD and BmD) were correlated to soluble cations and EC, and the Fe forms F1 (Exchangeable and soluble Fe), F2 (Fe associated with carbonates), and F4 (Fe in the low-crystalline form, Lepidocrocite). On the other hand, samples from the wet period (BpW, BmW, and SvW) were mostly associated with high Eb values, exchangeable cations, and more stable Fe forms, i.e., crystalline Fe-oxyhydroxides (F5). The soil properties that favored the clustering related to Bm species, in both seasons, were correlated to available P and organic carbon (Figure 6C). While Sv and Bp was correlated to soluble cations in the dry season, and to exchangeable cations in the wet season (Figure 6C).
# Fragile Watermarking for Image Authentication Using the Characteristic of SVD
## 4 Experiments and Performance Analysis  
Several experiments are conducted to evaluate the performance of the presented method. In these experiments, we adopt six test images with a size of 256 \(\times\) 256 as the host images, which are shown in Figure 5. In addition, two secret keys \(k_{1}\) and \(k_{2}\) are set as 75 and 32, respectively.
## 3 LLR Calculation under Phase Noncoherent Channel
Therefore, we can modify (13) as  
\[\zeta_{m}\approx\ln\frac{I_{0}\left(\max\limits_{S_{y}:c_{m}=0,l}\left[\frac{ \rho}{\sigma^{2}}\middle|\sum\limits_{k=1}^{N}w_{k,\hat{y}_{i}}\right|\right] \right)}{I_{0}\left(\max\limits_{S_{y}:c_{m}=1,l}\left[\frac{\rho}{\sigma^{2} }\middle|\sum\limits_{k=1}^{N}w_{k,\hat{y}_{i}}\right|\right]\right)} \tag{14}\]  
Under low SNR, taking the first two items of Taylor series expansion for \(I_{0}(\cdot)\)[31], (14) can be simplified as  
\[\zeta_{m}\approx\ln\frac{1+\frac{\rho^{2}}{4\sigma^{4}}\left[\max\limits_{S_{ y}:c_{m}=0,l}\left(\left|\sum\limits_{k=1}^{N}w_{k,\hat{y}_{i}}\right|\right) \right]^{2}}{1+\frac{\rho^{2}}{4\sigma^{4}}\left[\max\limits_{S_{y}:c_{m}=1,l} \left(\left|\sum\limits_{k=1}^{N}w_{k,\hat{y}_{i}}\right|\right)\right]^{2}} \tag{15}\]Note that when \(z\to 0\), \(\ln(1+z)\approx z\), and \(\ln\frac{(1+z_{1})}{(1+z_{2})}\approx z_{1}-z_{2}\). Then, (15) can be further simplified to  
\[\zeta_{m}\approx\frac{\rho^{2}}{4\sigma^{4}}\left\{\left[\max_{S_{y}\times m=0, \left(\left|\sum_{k=1}^{N}w_{k,\delta_{l}}\right|\right)\right]^{2}}-\max_{S_{y} \times m=1,l}\left(\left|\sum_{k=1}^{N}w_{k,\delta_{l}}\right|\right)\right]^{2}\right\} \tag{16}\]  
In (16), \(\frac{\rho^{2}}{4\sigma^{4}}\) is a constant term. If the min-sum (MS) algorithm for LDPC code or the soft output Viterbi algorithm (SOVA) algorithm for the convolutional code is considered, removing \(\frac{\rho^{2}}{4\sigma^{4}}\) will not affect the decoding result. Therefore, after eliminating \(\frac{\rho^{2}}{4\sigma^{4}}\) in (16), the LLR can be given as follows:  
\[\zeta_{m}\approx\left[\max_{S_{y}\times m=0,l}\left(\left|\sum_{k=1}^{N}w_{k, \delta_{l}}\right|\right)\right]^{2}-\left[\max_{S_{y}\times m=1,l}\left( \left|\sum_{k=1}^{N}w_{k,\delta_{l}}\right|\right)\right]^{2}, \tag{17}\]  
where \(l\in\{1,2,3\},m\in\{0,1,\cdots,4N-1\}\).
# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 2 KLQ-CFAR Detector  
In the KLQ-CFAR detector, the sample quartile estimator in the OS-CFAR detector is replaced by the Kaigh-Lachenbruch Quantile estimator. KLQ-CFAR has better detection performance than OS-CFAR in homogeneous and multiple-target environment.  
Set \(N\) independent and identically distributed samples \(X_{e}\) = \(X_{1}\), \(X_{2}\), \(\ldots\), \(X_{N}\), sort them in ascending order to obtain \(X_{i:N}\), \(i\) = 1, 2, \(\ldots\), \(N\). Randomly select \(k\) samples from the complete sample and get \(M\) = \(C_{N}^{k}\) subsamples. i.e., \(X_{(i)}\), \(i\) = 1, 2, \(\ldots\), \(M\). After sorting each subsample \(X_{(i)}\) in ascending order, select the \(r\)th sample \(X_{rk}^{(i)}\) as the estimation of \(X_{(i)}\). The Kaigh-Lachenbruch Quantile estimator can be obtained as shown in (1):  
\[\hat{Q}_{\text{KLQ}}(p)=\frac{1}{M}\sum_{i=1}^{M}X_{rk}^{(i)} \tag{1}\]  
where \(r=[(k+1)p]\), \(p\) is the quantile.  
According to Ref. [9], \(P_{\text{FA}}\) of KLQ-CFAR detector is shown in (2):  
\[P_{\text{FA}}=E[\exp[-\tau h(\mathbf{X_{e}})]]=M(\tau) \tag{2}\]  
where \(E[\ ]\) is the mathematical expectation, \(\tau\) is the threshold factor, \(h(\mathbf{X_{e}})\) is the estimation of clutter background, \(M(\ )\) is the moment-generating function (MGF) of the random variable \(h(\mathbf{X_{e}})\).
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 6 Discussion
### Sensitivity to Historical Data  
The decision to utilize the CatBoost algorithm was motivated by its strong ability to effectively handle categorical data and missing values. Nevertheless, it is imperative to acknowledge that alternative algorithms may exhibit different levels of sensitivity when it comes to older historical data. The analysis conducted indicates that the incorporation of these data has the potential to introduce additional interference into our model, which could potentially impede the accuracy of our predictions. The aforementioned discovery is consistent with observations made in the field of time-series forecasting, where the significance and practicality of past data play a crucial role in making precise predictions for the future [42].  
The phenomenon known as \"concept drift\" brings attention to the difficulties posed by dynamic and rapidly evolving systems, such as cloud computing. The phenomenon being discussed pertains to alterations in the processes that generate data over a period of time. This suggests that models that are trained on older data may exhibit suboptimal performance when attempting to predict more recent or future observations [43]. The utilization of up-to-date historical data that accurately represents the present condition of the system may result in advantages in dynamic systems such as cloud pricing [44].  
In domains such as cloud computing, which are marked by continuous technological progress, competitive dynamics, and evolving market demands, the relevance of older data may diminish over time. The cost structure for providing these services can be subject to change due to the continuous introduction of new instance types and the phase-out of older ones. These changes can be influenced by factors such as technological advancements, economies of scale, and fluctuations in energy costs.  
Therefore, our research proposes that the utilization of recent data could potentially offer a more accurate basis for making predictions about future prices. The utilization of outdated data has the potential to introduce extraneous information and diminish the precision of predictions, thereby reflecting circumstances that are no longer applicable.  
Further research could entail conducting comparative analyses using various machine-learning algorithms. The CatBoost algorithm was chosen based on its unique strengths. However, it is important to note that alternative models may exhibit different performance characteristics and varying sensitivity to the influence of older data.
## 3 Operating Modes of the Proposed Solar Tracking System
### Automatic Operation Mode  
Automatic operation mode allows the STS to automatically stay perpendicular to the Sun's rays during the day. Figure 7 shows the schematic control diagram of this mode, which employs an Arduino Mega as a control unit. Similar to the case of manual mode, this control system also employs a closed-loop on-off control; however, the control feedback is performed with the ASS-MEMS detailed in Section 2. Because of the feedback and the wide FOV of the solar sensor, the Sun can be easily located at sunrise; however, if the STS is started in the afternoon, the system may require a manual pre-location. In this operation mode, the ideal position of the STS is when the \(\alpha\) and \(\beta\) angles are 0. Therefore, the control error \(e\) is calculated by Equation (10), where the reference \(r\) is equal to 0\({}^{\circ}\), and \(f(\alpha,\beta)\) is the Sun's position measured by the solar sensor; see Figure 3. Once \(e\) is calculated, the on-off control determines the behavior of the solar tracker's slewing drive, considering the three possible states of the controller shown in Figure 6: with a clockwise turn, with a counterclockwise turn or with a motor off. The direction of rotation may vary according to the configuration of each STS:  
\[e=r-f(\alpha,\beta)=-f(\alpha,\beta). \tag{10}\]  
Figure 5: Schematic control diagram of the manual operation mode.  
Figure 6: On-off control states.  
In order to evaluate the solar tracking accuracy, the IEC 62817 standard considers two types of STEs: the instantaneous pointing error (ISTE) and reported solar tracking accuracy (RSTA) [1]. Both types, however, represent the angular difference between the beam solar radiation and the place the solar tracker is pointing to. It is worth noting that STE is the sum of all errors generated in the actuators, in the gearbox, in the control algorithm, in the controller, in the solar tracker installation, among others [1]. In order to directly measure the ISTE and the RSTA, two devices were used, and they are described below.  
1. An ISS-D5 digital solar sensor. This digital solar sensor, based on a microelectromechanical system (DSS-MEMS), measures the tracking error in two orthogonal axes at a FOV of \(\pm\)5\({}^{\circ}\) with an accuracy of \(<\)0.005\({}^{\circ}\) and a position error of \(<\)0.1\({}^{\circ}\). Furthermore, it does not require constant calibration [39]. This sensor is manufactured by Solar MEMS Technologies(r).
2. A pinhole projection system (PPS). This device is suggested in point 7.3.2 of the IEC 62817 standard. In addition, it has two flat plate systems with a pinhole to project the Sun's rays to measure the deviation error between the PPS surface normal and incident solar rays. One plate was marked with concentric circles of different millimeter diameters, and the other has a sun vector pinhole that allows for projecting the Sun's rays, as seen in Figure 8a,b. This device can measure pointing errors in the azimuth and zenith axes.
# An Emergency Alert Broadcast Based on the Convergence of 5G and ATSC 3.0
## 5 Conclusions
However, note that the current 5G and ATSC 3.0 systems have distinct limitations (which include the relay capabilities of mobile phones, the scheduling issues for interference suppression, and network overhead) in incorporating such a cooperation technique into emergency alert broadcast scenario. In order to enable such a cooperation approach, we are going to focus on the developments of techniques for efficient relay capabilities, efficient device-to-device communications including interference-awareness scheduling, and simple relay-node management in further study. All authors contributed equally to this paper. All authors have read and agreed to the published version of the manuscript. This research was supported by Disaster-Safety Platform Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF\(-\)2018M3D7A1084906). The authors declare no conflict of interest.
## 3 Results
### Method Validation
#### 3.2.4 Recovery and Matrix Effect  
Extraction efficiency or recovery and matrix effects of ERDA and IS from plasma samples were evaluated using QC plasma samples of ERDA at three different concentration levels: low (1.5 ng/mL), medium (150 ng/mL) and high (750 ng/mL). The recovery value ranged from 85.06% to 86.93% for ERDA and was 86.25% for IS, which indicates an excellent extraction recovery of the optimized method for ERDA from plasma samples. Matrix effects ranged from 89.89% to 91.60% for ERDA and were 89.49 for IS (Table 3). These results reflect that the matrix effect of the current method is negligible.
###### Abstract  
In the charge-coupled device (CCD) and micro-electro-mechanical system (MEMS) accelerometer based low-cost multi-loop optoelectronic control system (OCS), due to accelerometers' drift and noise in low frequency, the disturbance suppression (DS) is insufficient. Previously, based on the acceleration and position dual-loop control (ADLC), researchers combined a disturbance observer (DOB) with a virtual velocity loop to make some medium-frequency DS exchange for low-frequency performance. However, it is not optimal because the classic DOB based on accelerometers' inaccurate signals cannot observe accurate disturbance in low frequency and the velocity based on a CCD and accelerometer time-domain fusion carried the CCD's delay, resulting in the drop of medium-frequency DS. In this paper, considering the CCD's advantage in low frequency and the accelerometer's strength in high frequency, we propose to fuse their signals twice with a modified complementary filter method to respectively acquire an acceleration and velocity. The new acceleration with no drift and less noise but lower bandwidth creates a new acceleration model and is only used in fusion DOB (FDOB), while the velocity with little delay is to build an additional velocity loop. Compared with the traditional DOB enhanced by the time-domain fusion velocity loop, experiments verify that the proposed multiple fusion would apparently enhance the system's DS, especially in low and medium frequency.  
multiple fusion; the optoelectronic control system; disturbance suppression; low-cost; disturbance observer; virtual velocity loop +
Footnote †: journal: Sensors
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 3 Methodology
### Temporal Dependence Analysis  
LSTM networks have the ability to learn the long-term temporal dependence of marine temperature. However, the desire to simulate the periodicity and trend of temperature changes requires a very long input sequence. This will lead to a lengthy process throughout the training process.  
Figure 2: Structure of the long short-term memory (LSTM) cell.  
In order to ensure the accuracy of the prediction while reducing the amount of training, we will use the ocean temperature time closeness, trend and period to select primary data for modeling.  
Figure 3 shows temporal dependencies in different observations. Figure 3a,d show temperature profiles at two different depth in the ocean. The \(X\) axis represents the time interval (from January to December) and the \(Y\) axis represents the monthly average temperature of the corresponding month. The curves from different depths all show an empirical temporal correlation in time series, i.e., the temperature of recent time intervals are more relevant than ones of distant time intervals. This indicates that the ocean temperature has closeness in the time dimension. The five curves have different shapes, indicating that different closeness at different depths. At the same time, comparing Figure 3a,d from different latitude and longitude, it shows that different regions also have different characteristics of closeness. In Figure 3b,e, we can clearly observe that the annual period. The period distribution is very clear at 0 and 50 m, but not so obvious at 100, 200, and 500 m. Figure 3c,f show the trend of monthly mean temperature for July at different depths during the period of 2004-2016. It can be seen that the trend of temperature's increase or decrease is different at different depths. Temperature changes at different locations and depths also various. In general, their temperature changes are affected by their closeness, period and trend, but the degree of impact may be different.  
Trends are usually reflected by observations, where we use multiple periodic points to represent trends. Inspired by the above analysis, observations at different depths are affected by closeness, period, and trend. We propose a temporal dependence parametric matrix fusion method, as shown below.  
\[X_{TD}=W_{c}\circ X_{c}+W_{p}\circ X_{p} \tag{7}\]  
Among the quotations, \(\circ\) is Hadamard product, \(X_{c}\) and \(X_{p}\) represent closeness and period, respectively, \(W_{c}\) and \(W_{p}\) are the parameters used to weight the closeness and period. \(W_{c}\circ X_{c}\) is the length of closeness sequence and \(W_{p}\circ X_{p}\) make the lehgh of period sequence. \(W_{c}\circ X_{c}\) and \(W_{p}\circ X_{p}\)  
Figure 3: Temporal dependencies in different observations.  
together decide the length of trend. For a single observation, it will become a temporal sequdence after fusion [20].
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 3 Traditional Feature-Based Mosaic Process for SAR Images  
The traditional feature-based image mosaic process for SAR can be roughly divided into five parts: image preprocessing, keypoint detection and matching (registration), homography matrix calculation, image projection and image blending [27; 28]. The entire traditional feature-based image mosaic process is shown in Figure 2.
## 3 Results and Discussion
### Response Surface and Contour Analysis
#### 3.3.1 Effect of SP and PFA Interaction  
Keeping the SF at an intermediate level, the response surface and contour in SP-PFA are shown in Figure 6. As can be seen in Figure 6a, the entire response surface takes on an \"arch\" shape when the SF is 5%. This indicates that the compressive strength tends to increase and then decrease as the SP and PFA content increases, with a maximum value existing. As can be seen from the contour lines in Figure 6b, the contour lines in the upper right corner of the picture are more densely distributed, which indicates that when the SP content and the PFA content are high, the change in PFA content has a greater effect on the fluctuation of the compressive strength. It is worth noting that the higher and lower contents described here correspond to the ranges set in this study. In the case of PFA, for example, the content in this work is between 10% and 20% by mass of cement, so this is the higher PFA content described to indicate a content close to, but not exceeding, 20%. The same rule is followed in the subsequent discussion.  
Figure 5: Compressive strength experiments, predicted values, and relative errors.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **SP** & **PFA** & **SF** \\ \hline
1 & 7\% & 12\% & 2.5\% \\
2 & 10\% & 15\% & 6\% \\
3 & 12\% & 17\% & 2.5\% \\
4 & 15\% & 20\% & 7\% \\ \hline \end{tabular}
\end{table}
Table 8: The SP, PFA, and AF content of the validation experiments.
# An Analysis of MPTCP Congestion Control
## 3 Path Selection
### Path Diversity  
Internet paths are unpredictable and change dynamically. Both network and end-hosts play a part in changing a paths characteristics. On a single path, it is possible for packets to arrive out-of-sequence. This problem becomes more complex when multiple paths are used to transfer data as each path can have different characteristics. For example, a flow through a smartphone's cellular interface often experiences higher RTT as compared to a smart-phone's Wi-Fi interface [11]. Some common issues include head-of-line blocking and window limitation.
# IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies
## 3 Review of IoT-Enabled Smart City Components and Solutions
### Smart Energy  
Smart energy systems involve the intelligent integration of decentralized renewable and sustainable energy sources and their efficient distribution [91] and aim to optimize power consumption [92]. Smart grids take advantage of ICT and IoT technologies for the better management of power generation and distribution, exploiting, for instance, prediction models (developed from collected consumption data) and often ensuring the self-healing of the energy network supply [93]. Smart grids help energy load balancing on the basis of usage and availability. In this way, it is possible to switch automatically to alternative sources of energy, as well as predicting future energy demand and estimating the power availability and price [14].  
New generations of smart energy IoT devices have been designed for alternative energy harvesting, such as triboelectric nanogenerators (TENG) and electrostatic energy harvesters (EEH) [59]. A multiplicity of IoT sensors is involved in the smart energy context, such as light dependent resistors (LDRs), sensors for measuring light luminosity [94] and the consumption of solar radiation and electricity [95].  
Nice, France, has made efforts to improve smart energy management by scheduling electricity consumption in residential and business locations. The smart grid in Nice was created through a smart solar neighborhood in city areas by supplying and storing distributed electricity [58]. The city of Padova has performed several initiatives in intelligent energy management, such as a smart lighting system in which each smart light device is geolocated in the city and is powered by photometer sensors that monitor the intensity of the light emitted by the lamps and check that the correct operation of the bulbs is performed [96]. Atlanta has implemented a smart neighborhood project to reduce the Home Energy Rating System (HERS) score. Energy optimization platforms manage home appliances, switching to solar power and batteries if available [3]. In Helsinki, smart grids help to reduce energy usage by 15% [97]. The Masdar City project aimed to be one of the most efficient and environment-friendly systems in the UAE by exploiting renewable energy technologies. The city has planned a solar power array and rooftop solar panels that can provide more than 10 MW. This, combined with wind energy harvesting technologies, can supply energy for a target population of about 40,000 citizens [98].