esa-sceva/satcom-chunk-collection huggingface dataset:
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 3 Channel Model  
For the channel model, we consider a flat fading Rayleigh channel where both the BS and the RIS are affected by spatial correlation. Note that mirrors can be optimally selected for a particular user in order to minimize the spatial correlation in the RIS. However, this approach is beyond the scope of this work. The proper operation of the system requires that each user could be illuminated by a subgroup of mirrors of at least \(N_{s}^{\prime}=N_{r}\). If \(N_{s}^{\prime}>N_{r}\), the receptor can take advantage of diversity gains. In order to evaluate the effects of the spatially correlated fading channel, the following Kronecker model is considered [30]:  
\[\mathbf{G}=\mathbf{R}_{\mathbf{r}}^{1/2}\mathbf{H}^{w}\mathbf{R}_{\mathbf{t}}^ {1/2}, \tag{11}\]  
where the elements of the matrix \(\mathbf{H}^{w}\) are assumed to be independent and i.i.d. complex Gaussian random variables with mean zero and variance one, \(\mathcal{CN}(0,1)\). The matrices \(\mathbf{R}_{r}\) and \(\mathbf{R}_{t}\) are the receive and transmit correlation matrices, respectively. The correlation matrices are defined using the exponential model as  
\[\mathbf{R}_{t}=\begin{bmatrix}1&\rho_{t}^{*}&\rho_{t}^{2*}&\cdots&\rho_{t}^{( N_{t}-1)*}\\ \rho_{t}&1&\rho_{t}^{*}&\rho_{t}^{(N_{t}-2)*}\\ \rho_{t}^{2}&\rho_{t}&1&&\vdots\\ \vdots&&&\ddots&\\ \rho_{t}^{(N_{t}-1)}&\rho_{t}^{(N_{t}-2)}&&&1\end{bmatrix}, \tag{12}\]  
\[\mathbf{R}_{r}=\begin{bmatrix}1&\rho_{r}^{*}&\rho_{r}^{2*}&\cdots&\rho_{r}^{( N_{r}-1)*}\\ \rho_{r}&1&\rho_{r}^{*}&&\rho_{r}^{(N_{r}-2)*}\\ \rho_{r}^{2}&\rho_{r}&1&&\vdots\\ \vdots&&&\ddots&\\ \rho_{r}^{(N_{r}-1)}&\rho_{r}^{(N_{r}-2)}&&&1\end{bmatrix}, \tag{13}\]  
where \(\rho_{t}\) and \(\rho_{r}\) are the correlation coefficients between adjacent antennas at the transmitter and the receiver sides, respectively. In this work, we consider a correlated channel in the transmission and the reception side of the first hop for all evaluated systems.
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 7 Related Work
[41] proposed another model called Stochastic Time Petri Net (STPN), whose expressive power is between that of an SMC and GSPN, and demonstrated that the verification of STPN can also resort to (a variant of) the Volterra equation system. Despite an extension to the previous work, no efficiency benefit is gained with their technique. [PERSON] et al. [42] verified stochastic automata with underlying SMCs against \"non-nested until\" formulas in CSL by unfolding the model into a region tree, where regions are defined by relations between randomised clocks. And a direct computation of probabilities to reach individual regions includes the number of integration operations, which grows exponentially as the number of regions increases. Compared with the above model-based and quantitative analysis literature, our work adopts a very different approach. We exploited the history-independence property of SMCs (that is, the moment property of convolution) and developed a sequence of linear systems to compute the moments of the FPT, which contains the moments of state holding times as the coefficients. In addition to the computational efficiency, an advantage of working with moments is the easy estimation of moments from empirical data, especially for probability distributions whose forms are unknown. It is also noteworthy that, beyond the context of model-based and quantitative analysis, our approach is related to the literature that utilises mergeable summaries of moments to describe data streams and that inverts the quantiles of the streams from the moments [25].
## 3 Proposed SPMIPv6 Protocol
### SPMIPv6 Protocol Architecture
#### 3.1.3 Ip-Sensor Node  
SPMIPv6 domain consists of numerous sensor nodes based on IPv6 addresses. We consider the domain as a federated IP sensor domain. There are two types of sensor node. One type contains the tiny TCP/IP communication protocol stack with adaptation layer and IEEE 802.15.4 interface. This type can forward information to other node of similar type as well as information sensing from the environment. Actually this type of sensor node acts as a mini sensor router. The other type of sensor node has the protocol stack and environment sensing capability but can forward the sensed information to nearby mini sensor router node. However, both of these types are able to perform end to end communication.
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 5 Experimental Results
### Capacity Evaluation  
As there is a reverse operation between the payload capacity and visual image quality, increasing capacity degrades image quality (PSNR), so we considered this in our proposed method. The payload capacity of the proposed method was analyzed by the number of embedded bits per pixel (bpp). The maximum payload capacity for a chosen color host image of size 256 \(\times\) 256 and watermark image of size 32 \(\times\) 32 computed as (32 \(\times\) 32)/(256 \(\times\) 256 \(\times\) 3) = 0.00520833 (bpp). It was obvious that our method scored satisfying results among the compared method as shown in Table 6, and at the same time preserved image quality as much as possible.
# A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning
###### Abstract  
This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning.  
depth sensing; memoryless planning; safe flight corridor; drones; UAV +
Footnote †: journal: Sensors
## 3 Results and Discussion  
In this section, we present experimental results for filters constructed for \(f_{0}=13.56\) MHz to remove residual carrier from tag responses in HF RFID. A tuneable design was developed to allow the demonstration of filter operation and extension to complex filters.
# A Review of Thermal Error Modeling Methods for Machine Tools
## 2 Thermal Error Modeling Method for Machine Tools
### Hybrid Model  
Due to the complex structure and varying operating conditions of the machine tool, its thermal characteristics are nonlinear and change in real time. Therefore, it is difficult to establish a thermal error prediction model with good versatility, high accuracy, and strong robustness based on a single data fitting method. In order to solve this problem, some scholars have tried to establish a hybrid prediction model or a comprehensive prediction model of thermal error, which could combine the advantages of the separated two modeling methods [84].  
In 2010, [PERSON] and [PERSON] [85] proposed a support vector machine-neural network hybrid model. In order to verify the superiority of the hybrid model over the models based on the other two single method (SVM and NN), all three models were compared. The results are shown in Figure 4.  
When the spindle speed was 2000 rpm, the errors based on these three models were all within 0.5 \(\upmu\)m. However, as seen in Figure 4, the absolute error percentage of the hybrid model was only 1.95%, while the absolute error percentages of the other two single models were respectively 2.74% and 2.63%. This indicated that its forecast performance was better than any a single forecasting method. In 2012, [PERSON] et al. [62] proposed a gray neural network model that combined the gray system with the neural network model. When applied the model on the five-axis machining center spindle, it showed good fitting accuracy and prediction accuracy. In 2015, by combining the GM(1,n) model with the LSSVM model, [PERSON] et al. [86] established a hybrid prediction model, in which the prediction validity algorithm was used to adjust the model weighting coefficient. The optimal effective degree composite forecasting model (OE-CM) was established to obtain the best forecasting effect. Similarly, in the research of [PERSON] et al. [87], a hybrid model of spindle thermal error based on time series and neural network was mentioned. The model was applied to compensate the thermal of the VM850 vertical machining center. By using this hybrid model, the number of temperature sensors was reduced and the error prediction of the machine tool was more comprehensive and stable. It is worth noting that the model still had high prediction accuracy in a complex processing environment. In 2018, [PERSON] et al. [88] applied a combined algorithm (BP neural network based on the BAT algorithm) to solve the thermal error modeling problem. The experimental results showed that the RMSE of the prediction error and the measurement data was 3.80011 \(\upmu\)m. In addition, in [PERSON]'s [89] paper published in 2020, based on a hybrid model of COMSOL and PLS, a thermal error modeling method for electric spindles was applied to avoid the shortcomings of poor generalization of a single model. The overall model prediction performance of the hybrid model was better, and it could predict the thermal deformation of the electric spindle well under different working conditions.
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
###### Abstract  
Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The Food and Agriculture Organization of the United Nations (UN) has projected that by 2030, developing countries will expand their irrigated areas by 34%, while water consumption will only be up 14%. This discrepancy highlights the importance of accurately monitoring water flow and volume rather than people's rough estimations. The smart irrigation systems, a key subsystem of smart agriculture known as the cyber-physical system (CPS) in the agriculture domain, automate the administration of water flow, volume, and timing via using cutting-edge technologies, especially the Internet of Things (IoT) technology, to solve the challenges. This study explores a comprehensive three-dimensional problem space to thoroughly analyze the IoT's applications in irrigation systems. Our framework encompasses several critical domains in smart irrigation systems. These domains include soil science, sensor technology, communication protocols, data analysis techniques, and the practical implementations of automated irrigation systems, such as remote monitoring autonomous operation, and intelligent decision-making processes. Finally, we discuss a few challenges and outline future research directions in this promising field.  
Internet of Things; smart agriculture; smart irrigation system; sensors
## 3 Experimental Setup
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Size** & **Dimensions** & **Clusters** & **Description** \\ \hline \(S\) & 5000 & 2 & 15 & Varying overlap \\ _G2_ & 2048 & 1–1024 & 2 & Varying overlap and dimensionality \\ _DIM_ & 1024 & 3–1024 & 16 & Varying dimensionality \\ \(A\) & 3000–7500 & 2 & 20–50 & Varying number of clusters \\ _Unbalance_ & 6500 & 2 & 8 & Both dense and sparse clusters \\ Birch & 100,000 & 2 & 1–100 & Varying structure \\ _Sim5_ & 2970 & 2–10 & 5 & Small subclusters close to bigger ones \\ \hline
**Data** & **Size** & **Dimensions** & **Classes** & **Description** \\ \hline Iris & 150 & 4 & 3 & Three species of iris \\ _Arrhythmia_ & 452 & 279 & 13 & Different types of cardiac arrhythmia \\ Steel Plates & 1941 & 27 & 7 & Steel plates faults \\ Ionosphere & 351 & 34 & 2 & Radar returns from the ionosphere \\ _USPS_ & 9298 & 256 & 10 & Numeric data from scanned handwritten digits \\ _Satinage (Train)_ & 6435 & 36 & 6 & Satellite images \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of datasets. Figure 1: Scatter plots of _Sim5_ datasets. with 2-20 clusters with their centroids on a sine curve; Unbalance, with eight clusters in two separate groups, the one having three dense and small clusters and the other five more sparse and bigger clusters; and a subset of G2, with 20 datasets--the lowest and highest overlap in ten different dimensions from 1 to 1024. Finally, together with the six real datasets, we had altogether 62 datasets in our tests.
## 4 Discussion
On the one hand, this study has a retrospective and bicentric design involving a relatively small number of patients, which may introduce a demographic (selection) bias, implying that these finding may not be broadly applicable to other population settings. Additionally, the lack of access to clinically relevant information about this sample further hinders our understanding of how this model might impact different patient groups. Subsequent prospective studies with a larger patient population and better control of clinical variables are needed in order to corroborate our results for future application. On the other, this algorithm was constructed using a relatively low number of CE still-frames, and it does not necessarily guarantee that the model will exhibit the same diagnostic performance when applied to full-length CE videos. We were also unable to conduct a subanalysis by device or lesion type due to the limited sample size, which lacked sufficient statistical power. This also represents a limitation of the study, as it prevents us from evaluating how device-specific image characteristics may influence the model performance, as well as determining whether the accuracy is consistent across different lesion types. The paucity of esophageal mucosa frames is a topic worth mentioning, as it turns out to be both a limitation and strength of this work. Until now, there were no published esophageal-specific trained DL models for lesion detection. This may be explained due to the limited number of esophageal frames provided by each exam, impeding the development of dedicated databases. Despite being at a lower technological readiness level due to the limited data and methodological challenges, this achievement marks a significant achievement. It stands as the first published DL algorithm in this esophageal location, representing as well the accumulated experience of the scientific group in the field of AI-enhanced CE, although we also recognize that future studies are needed to explore its broader applicability. The use of AI algorithms that further improve the diagnostic performance of CE can potentially shift the imbalance that currently favors EGD and may render CE as a cost-effective and patient-friendly option for the evaluation of esophageal pathology. In the case of esophageal varices, systematic reviews with meta-analyses have been published, demonstrating that, with the correct protocol, the pooled-accuracy for the detection of such lesions can reach up to 90% of cases [10]. Similarly, when it comes to Barrett's metaplasia in patients with gastro-esophageal reflux disease, systematic reviews with meta-analyses indicate that CE's diagnostic performance metrics can be comparable to those of EGD (with a pooled sensitivity of 78% and specificity 86% in CE, versus 78% and 90%, respectively, in EGD) [9]. Considering that it is possible for CE to detect esophageal lesions at a similar level as the gold standard, EGD, one may hypothesize that the addition of AI reading-assisted systems could potentially result in a further improvement in its diagnostic yield.
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 3 Methodology  
The sugarcane harvest monitoring includes two independent algorithms of harvest date determining based on the decision trees generated from time series of optical and SAR data respectively. Time series of the average Sentinel-2 _NDVI_ values and average Sentinel-1 SAR coherence in VV polarization at the level of a grid cell were calculated for test sugarcane fields. The _NDVI_ data are also used to check and clarify the SAR algorithm results.
## 5 Analysis
### Security
#### 5.1.2 Self-Controllable Trajectory Sharing  
In our proposed system, even though vehicle's shared trajectory data are maintained and transferred by SM, it is the owner vehicle of the trajectory data that can decide what RSUs can access its trajectory data on the cloud. As mentioned before, distribution of the trajectory data of \(V_{d}\) is performed by SM according to socialspot list \(SR_{d}\) and re-encryption keys \(RK_{d}=\{rk_{V_{d}\to sRSU_{j}}|sRSU_{j}\in SR_{d}\}\) generated by \(V_{d}\) in the enrolment phase. If \(V_{d}\) does not want an \(sRSU_{j}\) existing in \(SR_{d}\) to access its trajectory data any more, \(V_{d}\) can revoke \(sRSU_{j}\)'s decryption right by generating new re-encryption keys for sRSUs in \(SR_{d}\) except \(sRSU_{j}\). Once \(V_{d}\) uploads updated \(SR^{\prime}_{d}\setminus\{sRSU_{j}\}\) and \(RK^{\prime}_{d}\) for each sRSU in \(SR^{\prime}_{d}\), SM will exclude \(sRSU_{j}\) and not provide \(sRSU_{j}\) with the re-encrypted trajectory data of \(V_{d}\). In our revocation procedure of Section 4.5, \(V_{d}\) can generate re-encryption keys without involvement of SM and sRSUs, and it does not require renewing public keys of sRSUs due to the functionality of certificateless proxy re-encryption scheme. Therefore, our proposed system can provide a flexible and self-controllable trajectory data sharing mechanism.
## 3 Experimental Research
### Step 4: GR Calculations
#### 3.4.4 Earth Resistance of a Triangular Mesh  
The GR of the triangular mesh (see 3 RT configuration of Figure 1) is calculated using EQUATION (2), where \(R_{1}\), \(R_{2}\) and \(R_{m}\) are defined with Equations (11), (12) and (13), respectively.  
Figure 3: Coefficient \(k_{2}\).  
\[R_{1}=\frac{\rho}{3\pi d}\bigg{[}\ln\left(\frac{6d}{a^{\prime}}\right)+\frac{3k_{1} \times d}{\sqrt{A}}-k_{2}\bigg{]} \tag{11}\]  
\[R_{2}=\frac{\rho}{6\pi L_{r}}\bigg{[}\ln\left(\frac{4L_{r}}{b}\right)-1+\frac{2k _{1}\times L_{r}}{\sqrt{A}}\big{(}\sqrt{3}-1\big{)}^{2}\bigg{]} \tag{12}\]  
\[R_{m}=R_{1}-\frac{\rho}{3\pi d}\bigg{[}\ln\left(\frac{L_{r}}{\sqrt{h\times 2a}} \right)-1\bigg{]} \tag{13}\]  
where:  
\(\rho\): ground resistivity, [\(\Omega\cdot\)m]  
\(L_{r}\): rod length, [m]  
\(b\): rod radius, [m]  
\(R_{1}\): resistance to earth of the conductor, [\(\Omega\)]  
\(R_{2}\): earth resistance of the rods, [\(\Omega\)]  
\(R_{m}\): mutual resistance between mesh and rods, [\(\Omega\)]  
\(d\): separation distance between two rods, \(d>L_{r}\), [m]  
\(h\): conductor burial depth, [m]  
\(a\): conductor radius, [m]  
\(a^{\prime}\): is \(a\) for the conductor on the surface of the earth, [m] or  
\(a^{\prime}\): is \(\sqrt{a\cdot 2h}\) for conductors buried at a depth \(h\), [m]  
\(k_{1}\) and \(k_{2}\): coefficients [see Figures 2 and 3, respectively]  
\(A\): is the area covered by the conductors, [m\({}^{2}\)]
## 3 Self-Built Signature Dataset  
To bridge the gap in the field of multilingual signature verification, we established a multilingual offline handwritten signature dataset consisting of Chinese and ethnic people. This dataset includes signatures from ethnic people such as the Uyghur, Kazakh, and Kirgiz people, covering diverse cultural backgrounds in Central Asia. With this dataset, we are able to provide more comprehensive data support for multilingual OSV, as well as new resources for language learning and cross-cultural research. Figure 1 shows examples of the multilingual signature dataset.  
Figure 1: Some samples of our self-built signature dataset. Each row represents a language, and from top to bottom, there are samples of signatures in Chinese, Uyghur, Kazakh, and Kirgiz. The two samples on the left are genuine signatures, while the two samples on the right are forged signatures.  
To build a multilingual offline handwritten signature dataset, we followed a three-step process: collection, scanning, and segmentation. During the collection phase, volunteers were asked to write their names in their native language in 24 boxes on a sheet of A4 paper. Each individual user was given 24 corresponding forged signatures, which were imitated by volunteers of the same ethnicity according to their own writing style and habits. In the scanning stage, we used a Lenovo M7400 scanner to scan the collected signature images at 300 dpi and used Adobe Photoshop CC2022 software to crop and number sequentially and save them as BMP images. Finally, we used these images to create a multilingual dataset of offline handwritten signatures by removing signatures unsuitable for human experimentation, which we use for research alongside publicly available datasets.  
To establish a comprehensive basis for research such as multilingual signature verification in Central Asia, we present a dataset that possesses several unique characteristics and challenges. First, the dataset is a large-scale multilingual signature dataset that includes four languages: Chinese, Uyghur, Kazakh, and Kirgiz. These languages represent multiple ethnicities and cover people from various regions and cultural backgrounds, thus exhibiting a high degree of diversity. Second, the handwritten styles and features of these signatures are also distinct, rendering the dataset more challenging. Third, the dataset includes a significant number of real, personal handwritten signatures that were collected manually at different times and in natural environments.
## 3 Results and Discussion  
### Characterization of MGONCs  
#### 3.1.1 XRD  
The polycrystalline structure of the MNs and MGONCs were determined using X-ray diffraction (XRD). Figure 3 displays the X-ray diffraction pattern of the MNs and MGONCs. The diffraction peaks of MNs were observed at 2\(\theta\) values of 30.1\({}^{\circ}\), 35.4\({}^{\circ}\), 43.0\({}^{\circ}\), 53.4\({}^{\circ}\), 57.9\({}^{\circ}\), and 62.5\({}^{\circ}\), corresponding to electron diffraction peaks at positions (220), (311), (400), (422), (511), and (440), respectively. The results were consistent with Fe\({}_{3}\)O\({}_{4}\) nanoparticles and well-matched with JCPDS card No. 22-1086 [37]. For MGONCs, in addition to the diffraction peaks observed above, there was also a diffraction peak at 2\(\theta\) values of 10.8\({}^{\circ}\), which could be attributed to the diffraction peak of the graphene oxide crystal plane. Based on these findings, the successful syntheses of Fe\({}_{3}\)O\({}_{4}\) nanoparticles and MGONCs were confirmed.  
Figure 3: X-ray diffraction pattern of Fe\({}_{3}\)O\({}_{4}\) nanoparticles.