esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
### Study Area and Data Analysis  
Two rivers, named Baranduzchai and Nazluchai for full streamflow data, and with two measurement stations, named Dizaj and Tapik, in the ULB in Iran, were considered. The Baranduzchai River has an area of 1203 km\({}^{2}\) and is located in the north-west of Iran between Urmia Lake, Iraq, and Turkey, at 44\({}^{\circ}\)45\({}^{\prime}\) to 45\({}^{\circ}\)14\({}^{\prime}\) latitude and 37\({}^{\circ}\)06\({}^{\prime}\) to 37\({}^{\circ}\)29\({}^{\prime}\) longitude. The stream is 75 km long, and the basin's maximum altitude is 1250 m. It has four hydrometric stations named Babarud, Dizaj, Gasemlu, and Bibakran. The Nazluchai River is almost 93 km long and has an area of 2030 km\({}^{2}\). Almost 90% of this stream is located in Iran, with the remaining 10% in Turkey. The stream's maximum altitude is 3600 m. It has four hydrometric stations named Abajalu, Tapik, Karim Abad, and Marz Sero. In this research, Dizaj and Tapik stations were selected, as shown in Figure 1.  
In the present study, the dataset contained 31 years of monthly streamflow data (372 months) from January 1986 to December 2016. These data were obtained from the Urmia Lake Research Institute (ULRI) in Urmia, Iran. In this study, 70% and 30% of data from the beginning (260 months and 112 months) were used for calibration and validation stages, respectively. Figure 2 shows the monthly streamflow data in both stages for this period.  
Figure 1: The studied rivers and selected stations.  
Figure 2: Monthly normalized streamflow data for Dizaj and Tapik stations during 1986–2016 in calibration and validation stages.  
The geographical and statistical properties of monthly discharge of both streams are shown in Tables 1 and 2, respectively. As can be seen from Table 2, standard deviation values are higher than skewness values in both river gauges. It is worth mentioning that these statistical properties have been calculated before normalization of data, and they belong to the only streamflow data before any changes.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 3 Bistatic Scattering RFI Impact on GEO SAR
### Impact of Bistatic Scattering RFI on GEO SAR
#### 3.1.1 Non-Specular Bistatic Scattering Case  
Generally, if the bistatic scattering signal does not form a specified angle to produce the specular scattering case, non-specular scattering occurs (out-of-plane case (\(\theta_{1}=\theta_{2}\), \(\psi_{s}\
eq 0\)) and in-plane case (\(\theta_{1}\
eq\theta_{2}\), \(\psi_{s}=0\))). For the out-of-plane case, \(\sigma_{B}^{0}\) turns minimum when \(\psi_{s}\) approaches \(90^{\circ}\) and \(\sigma_{B}^{0}\) is almost same with that in the in-plane case when \(\psi_{s}\) is near \(0^{\circ}\) or \(180^{\circ}\)[38]. Thus, if we consider the in-plane case for the worst condition, based on the constant-\(\gamma\) bistatic-scatter-region model [38], in-plane land clutter bistatic scattering coefficient can be approximately expressed as  
\[\sigma_{B}^{0}=\gamma(\sin\theta_{1}\sin\theta_{2})^{1/2}, \tag{11}\]  
where \(\gamma\) is the normalized reflectivity parameter, which can be estimated by  
\[\gamma=\frac{\sigma_{M}^{0}}{\sin\theta_{1}} \tag{12}\]  
where \(\sigma_{M}^{0}\) is the monostatic scattering coefficient.  
Regardless of the large-bistatic-angle case (e.g., forward-scatter region), we simply focus on the bistatic scattering case with the relative small bistatic angle for the above analysis. Such as for ALOS-2 PALSAR, the bistatic angle formed by the LEO SAR and the GEO SAR is general less than \(30^{\circ}\) when the LEO SAR signal is received by the GEO SAR within its antenna mainlobe. For the LEO SAR signal  
Figure 4: Sketch of the bistatic scattering RFI geometry.  
received outside the GEO SAR antenna mainlobe, as there will be a large attenuation for the RFI signal, we ignore the large-bistatic-angle case. Moreover, although the GNSS system and the GEO SAR can generate the bistatic system with large bistatic angle, because \(\sigma_{B}^{0}\) is generally smaller than \(\sigma_{M}^{0}\)[38], a small bistatic angle assumption is better for analysing the worst cases.  
In the non-specular case, the received RFI signal is weak as the gain in the scattering direction of the GEO SAR is low.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 4 Results
### Multiple Polarization Roughness Estimates
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(a\) & \(b\) & **R** & **RMSD** \\ \hline \(s_{H}/\sigma_{h_{H}}\) & 0.86 & 4.08 & 0.85 & 0.42 \\ \hline \(s_{V}/\sigma_{h_{V}}\) & 0.91 & 3.97 & 0.84 & 0.40 \\ \hline \(s_{R}/\sigma_{h_{R}}\) & 1.08 & 3.72 & 0.87 & 0.38 \\ \hline \(s_{L}/\sigma_{h_{R}}\) & 0.94 & 3.90 & 0.84 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Coefficients and correlation between the model described by Equation (26). As can be seen, the slope rms presents a slightly higher sensitivity to the DEM slope (DEMSLPSTD, \(\sigma_{SLP}\)) and rms height (DEMSTD, \(\sigma_{DEM}\)) variations. The correlation coefficient between \(\sigma_{DEM}\) and \(\sigma_{SLP}\) and \(\sigma_{h}\) are 0.39 and 0.38, while for s are 0.54 and 0.52, respectively. Different models are tested to find a relationship between both magnitudes and \(\sigma_{h}\) and \(s\). It is found that the following linear relationship has a large correlation between DEMSTD and DEMSLPSTD and s:  
\[s=3.83+0.0386\cdot\sigma_{DEM}-0.0757\cdot\sigma_{SLP}. \tag{27}\]  
This linear fit presents a correlation coefficient of 0.54, with an RMSD of 4.84. A similar fit can be implemented for the \(\sigma_{h}\) parameter, however, the correlation and RMSD are, R = 0.40, and RMSD of 0.80 cm for the equation:  
\[\sigma_{h}=4.96+0.0036\cdot\sigma_{DEM}+0.039\cdot\sigma_{SLP}. \tag{28}\]  
Note that, the resolution of our \(\sigma_{DEM}\) is meters, ranging from 0 to 500 m, with a resolution of 9 km. By scaling the magnitude by 0.0036 the model is telling us that \(\sigma_{h}\) scale is \(\sim\)277 times smaller than the 9 km resolution from the DEMSTD product used, which is \(\sim\)30 m, 10 times the size of the GNSS chip at L2C.
## 3 Results
### Comparison Structural and Functional-ROI  
#### 3.3.1 Frequency Domain Analysis  
Functional-ROIs analysis relies on assessing the temporal homogeneity of a voxel and its neighboring voxels independent of the hemodynamic model employed by the GLM. Consequently, the signal dynamics of these voxels were analyzed using both structural and functional-ROIs for all signals. Figure 4 shows a frequency analysis revealing a main peak at 0.03 Hz (corresponding to the task which lasted 16 s) and well represented by GLM analysis. However, the magnitude of task frequency (0.03) is also increased in the GLM analysis when the functional-ROI is applied (max 0.35) (Figure 4b) in contrast to the structural-ROI GLM analysis (max 0.2) (Figure 4a). A higher magnitude indicates stronger neural activity in the area being measured. A structural-ROI covers a broader anatomical region in the brain and does not distinguish between specific functional areas, encompassing all the voxels within that anatomical zone. In contrast, a functional-ROI is more targeted, containing only the voxels actively engaged in the task. The strong correlation between the functional-ROI and neural activity indicates its precise representation and capture of neural processes.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 5 Mror: Mobile Reliable Opportunistic Routing for CRSN
### Route Maintenance  
In order to detect the local minimum problem, node \(i\) compares the distance-to-sink of its immediate downstream node, node \(j\) with the POS of its LMR node. If the POS of node \(j\) is less than the POS of its LMR node, the packet is sent back to node \(j\) by piggybacking a local-minimum-alert message. Once the packet is successfully received by either node \(j\) or the LMR node, node \(i\) afterwards switches its status to a non-eligible VCG member node. The node that received the local minimum alert message will become responsible for seeking a different path for the packet via VCG-based data forwarding initiative determination, as discussed in Section 5.4, and receiver contention prioritization, discussed in Section 5.6.  
Another possibility that can occur is the failure of an LMR node. The failure of an LMR node can be detected when the VCG members did not receive a still-alive packet for \(z\) consecutive times. At this instance, the VCG member node that is closest to the previous LMR node becomes the next LMR node. This is achieved by making all of the VCG members initiate a random timer, NLMR, having a value between \(CW_{\min}\) and \(\left(CW_{\min}+\left(\frac{CW_{\min}}{2}\right)^{d}\right)\). The node whose timer expires first announces itself as the LMR node, thus reflecting its proximity to the position of the previous LMR node. It should be noted that the scheme is designed such that LMR nodes only take part in packet forwarding if they happen to be the best nodes at that instance fulfilling the forwarding criteria; as a result, failure of an LMR node is usually a rare occurrence.  
In addition, although for the worst case scenario, nodes have to switch between receiving and sending channels, it has been noted that not synchronizing this process results in more efficiency  
Figure 8: Receiver contention prioritization and backoff scheme.  
for the system. This is because the distributed nature of the scheme always ensures the availability of a potential forwarding node without jeopardizing the downstream flow in any way. Moreover, as discussed in Section 5.3, during the route selection phase, the scheme aims at minimizing the occurrence of multiple channel switching. Figure 9 shows the full MROR algorithm flow.
## 2 Motivation Example
### The Vulnerability and StackGuard Mechanism  
For ease of illustration, we compile the example program shown in Figure 0(a) by executing the command 'gcc demo.c -m32 -static -fstack-protector -o demo' under Linux. After compilation, we obtain a 32-bit executable image that is statically linked in ELF format, with StackGuard option turned on.  
As can be seen in the example, the protection mechanism provided by StackGuard is composed of 2 key components--one for canary storage and the other a sanity check. The canary storage phase is shown by 1 in Figure 0(b), consisting of two store instructions copying memory content located by logical address 'gs:0x14' (which is the defacto runtime canary initialized by the glibc runtime environment) to a memory cell in the stack frame. After the canary storage operation is completed, the layout of function func's stack frame becomes the one shown in Figure 0(c), from which we can see that the stored runtime canary resides at an address that is both lower than the memory cells that containing the caller's EBP and call site address and higher than the address of the variable 'buffer' on the stack.  
The sanity check phase is shown by 2 in Figure 0(b). It is located before the return point of function func. The sanity check firstly fetches the value stored at virtual address 'ebp-0 xc', then checks whether it equals the original canary through an exclusive OR operation. If the two values are the same, the control flow would return normally; otherwise, function_stack_chk_fail_local would be invoked (shown by 3 in Figure 1), immediately terminating the execution.  
We can see in line 3 in Figure 1a that call on read occurs. Due to this call, a stack overflow vulnerability exists as at most 0x100 bytes can be read from the external environment and filled into a 0x60-sized buffer. For programs not protected by StackGuard, a simple input containing 0x100 'A' would directly hijack the control flow, facilitating current AEG systems generating effective exploit. However, it is not the case for this example.  
As the canary to be checked lays in the stack region between the input buffer and saved return address, overwriting the return address through stack overflow on the local variable buffer would inevitably overwrite the stored canary. Unless the stored canary is overwritten with the exact value StackGuard adopts at runtime, which is generally impossible for current test case generation methods, the execution would generally directly terminate as the sanity check fails in this case.
# Data Mining Approaches for Landslide Susceptibility Mapping in Unyeonsan, Seoul, South Korea
###### Abstract  
The application of data mining models has become increasingly popular in recent years in assessments of a variety of natural hazards such as landslides and floods. Data mining techniques are useful for understanding the relationships between events and their influencing variables. Because landslides are influenced by a combination of factors including geomorphological and meteorological factors, data mining techniques are helpful in elucidating the mechanisms by which these complex factors affect landslide events. In this study, spatial data mining approaches based on data on landslide locations in the geographic information system environment were investigated. The topographical factors of slope, aspect, curvature, topographic wetness index, stream power index, slope length factor, standardized height, valley depth, and downslope distance gradient were determined using topographical maps. Additional soil and forest variables using information obtained from national soil and forest maps were also investigated. A total of 17 variables affecting the frequency of landslide occurrence were selected to construct a spatial database, and support vector machine (SVM) and artificial neural network (ANN) models were applied to predict landslide susceptibility from the selected factors. In the SVM model, linear, polynomial, radial base function, and sigmoid kernels were applied in sequence; the model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy, respectively. The ANN model yielded a validity accuracy of 78.41%. The results of this study are useful in guiding effective strategies for the prevention and management of landslides in urban areas.  
spatial data mining; SVM; ANN; validation; ROC Article
## 4 Analysis of Classification Results
### The Impact of Training Sample Size on the Classification Results  
Investigating how different proportions of training data affect classification results, the dataset was divided into the first 10%, 20%, 30%, 50%, 70%, and 90% as training datasets, with the remaining data used as validation datasets. The dataset partitioning results are shown in Table 2. Each scenario underwent 100 validations, acquiring 100 classification precision each time, which are displayed in box plots as shown in Figure 12.  
\begin{table}
\begin{tabular}{l c c c c c} \hline Training data & 30 & 60 & 90 & 150 & 210 & 270 \\ Verifying data & 270 & 240 & 210 & 150 & 90 & 30 \\ \hline \end{tabular}
\end{table}
Table 2: Dataset partitioning results.  
Figure 11: Distribution of classification accuracy based on DBN and WT-DBN: (**a**) Classification accuracy. (**b**) Density curve.  
Based on Figure 12, excluding outliers, the accuracy ranges when 30 groups of data were used as training data were [76.2963%, 77.7778%]; when 60 groups were used, the accuracy range was [89.5833%, 91.6667%]; with 90 groups, the accuracy range was [97.1429%, 97.6191%]; with 150 groups as the training dataset, the accuracy range was 99.3333%; and with 210 and 270 groups as the training dataset, the accuracy reached 100% without any outliers, indicating more stable results. The findings demonstrate that increasing the training data can lead to better classification outcomes for WT-DBN, but it also increases training time. The proposed WT-DBN achieved an accuracy of over 76% with only 30 groups of training data, which increased to over 89% and 97% with 60 and 90 groups, respectively, indicating accurate classification with fewer training data. Therefore, this method provides the opportunity to utilize varying quantities of training datasets for signal classification and recognition, allowing for the determination of the appropriate training dataset quantities based on specific scenarios to balance classification effectiveness and training time requirements.
# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 2 Related Works
### Business Intelligence on Semantic Web
The research also found a set of OLAP query functions and operators using the SPARQL language, and improved the QB vocabulary in order to enhance multidimensional statistical data representations [27]. Moreover, [PERSON] and [PERSON] developed Open Cube (OC) vocabulary using RDFS to publish multidimensional cubes on the SW. The OC was developed to support models with dimensions with multiple hierarchies, but OC had the disadvantage that it could not reuse QB vocabulary that had been used before [28]. [PERSON] et al. developed a framework called Exploratory OLAP over RDF sources. This research proposes a system that uses RDF vocabularies to define an OLAP cube with a multidimensional schema. The system intelligently executes the query to extract and summarize data and build a cube based on the vocabulary [19]. [PERSON] et al. developed an extension of QB that models multidimensional data; it is called QB4 OLAP. QB4 OLAP supports flat hierarchies, and is recursive, ragged, and uses many-to-many relationships in the multidimensional model [29]. Furthermore, [PERSON] et al. developed a new method to speed up query execution using a materialized view of the RDF graph. Their approach involves the physical storage of summarized triples in the system for faster processing of SPARQL queries (MARVEL). Many application tools were built using this approach and were used by governments and in industry, e.g., [[http://opengovintelligence.eu](http://opengovintelligence.eu)]([http://opengovintelligence.eu](http://opengovintelligence.eu)), accessed on 5 April 2021 [30]. On the other hand, [PERSON] et al. proposed a method for directly querying the cube data modeled by QB vocabulary with an interactive OLAP via MDX to SPARQL mapping without materialization. This research also constructed a formal algebra of OLAP operations on data cubes published as Linked Data [31]. The combination approach fully integrates SW in all layers of the BI. Several researchers proposed this combination approach: [PERSON] et al. proposed a bottom-up redesign of the core data analytics concepts and RDF data tools. This design is fitted with Linked Data with a rich semantic and heterogeneous format. This design uses a full RDF warehousing approach, where the data source and the DW are in RDF graph format. This research also defines analytical schema and queries and optimization techniques with schema materialization [32]. [PERSON] and [PERSON] created ETL tools to integrate data from various heterogeneous sources by utilizing semantic data to enrich the integration process [33]. [PERSON] et al. proposed a programmable framework called Semantic ET (SETL) that could support the development of a Semantic Data Warehouse. SETL used TBox and QB4 OLAP schema to create Multidimensional data for Semantic and Non-Semantic Data Sources [34]. Our study focuses on developing a vocabulary extension of QB4 OLAP vocabulary based on an analytical approach.
## 3 Proposed Crypto Processor Design  
Generally, a crypto processor design contains an arithmetic and logic unit (ALU), a control unit, and an instance of data memory [2]. The ALU executes required arithmetic operations such as addition, subtraction, multiplication, inversion, etc.; data memory keeps the initial, intermediate, and final results; the control unit generates the control signals for efficient control functionalities. Our proposed crypto processor design over \(GF(2^{233})\) for PM computation of ECC contains three identical blocks of ALU, control unit, and data memory, as shown in Figure 1. The control unit takes the inputs from outside and also produces outputs. The inputs are \(clk\), \(rst\), \(LD\), \(LK\), \(din\), \(start\), \(addr\), and \(data\); the outputs are \(dout\) and \(done\). As the names imply, the \(clk\) and \(rst\) signals determine the clock and reset behavior of the proposed design. The \(LD\) and \(LK\) signals specify load data and key, respectively. The \(din\) signal shows input data to the crypto processor. The \(start\) specifies the start signal to the processor. Read/write addresses to load/store data to/from memory are specified by the corresponding \(addr\) and \(data\) signals. The \(dout\) signal shows the processor output, and a \(done\) signal determines the completion of the PM computation of ECC. Despite these I/O signals, the red dotted lines in Figure 1 determine the control signals that the control unit generates. Additionally, a KeyReg in Figure 1 is a 233 bit buffer that keeps the secret key or the scalar multiplier, i.e., \(k\), to implement PM Algorithm 1. Below, we describe three blocks (data memory, ALU, and control unit) of our proposed crypto processor design over \(GF(2^{233})\) for PM computation of ECC.
## 1 Introduction
This study focuses on addressing the challenges of utilizing NLOS signals for precise positioning in urban environments. By leveraging the classical MUSIC algorithm, we propose an innovative approach to invert NLOS signals and generate virtual satellite configurations, enabling the effective use of otherwise obstructed satellite signals. Notably, our methodology requires only a single receiver and eliminates the need for additional sensors such as fisheye cameras or LiDAR. To address the high computational costs traditionally associated with MUSIC, we incorporate satellite ephemeris as a priori information to reduce the dimensionality of the spectral search, significantly improving the algorithm's efficiency while maintaining accuracy. This integrated methodology not only advances the application of NLOS signals in positioning systems but also provides a computationally efficient solution that minimizes hardware complexity and power consumption, making it highly suitable for resource-constrained environments. Comprehensive validation demonstrates the proposed method's potential to transform urban satellite positioning and signal processing practices (Table 1).
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
## 2 System of Remote-Controlled Robotic Arms
### Follower: QArm  
The QArm, acting as the follower in this robotic system, has a roll-tilt-roll configuration with four joints: base, shoulder, elbow, and wrist. Controlled by QUARC software within MATLAB/Simulink, it ensures precise synchronization with the leader robot. While the QArm follows the three active joints of the leader, the wrist joint remains inactive. This setup enables the QArm to reliably replicate the leader's actions, ensuring coordination in complex tasks.