esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
This study employed an integrated remote-sensing identification approach to systematically detect landslide hazards in the central region of Tianshui City. By collaboratively interpreting multi-source remote-sensing data, a total of 127 potential landslide hazard points were identified. Given that the study area is located in the Loess Plateau with highly variable terrain that lacks ground-based monitoring stations, the accuracy of the InSAR detection results may have been affected. To address this limitation, the study conducted cross-validation using ascending and descending SAR datasets, and further incorporated high-resolution optical imagery from Google Earth for auxiliary verification, ultimately ensuring the effective identification of landslide hazard points.  
The results indicate that the integrated remote-sensing method not only improves the efficiency of landslide hazard identification but also significantly reduces interpretation errors. From the perspective of landslide-formation mechanisms, optical remote sensing relies primarily on geomorphological features and slope-failure indicators, typically identifying landslide deposits or previously occurred landslides that tend to be relatively stable or low in activity and thus more concealed. In contrast, InSAR technology detects subtle ground deformations over specific time intervals, allowing for the identification of slowly deforming potential landslide bodies and compensating for the limitations of optical imagery in early-stage detection. During the initial stage of landslide evolution, when surface disturbances are not yet clearly visible, optical imagery may fail to capture distinct features, and therefore cannot be solely relied upon to validate InSAR results. The two methods are complementary in their strengths. Therefore, in engineering construction and geohazard mitigation efforts in Tianshui, the combined application of InSAR and optical remote sensing is essential for improving both the comprehensiveness and accuracy of landslide hazard identification, providing a solid scientific foundation for informed decision-making and risk management.
# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 2 Literature Review Methodology and State-of-the-Art
### State-of-the-Art: A Technology Review from 2005 to 2024
#### 2.3.3 Infrared Detectors
Although it is only a screening test, it allows the identification of people with a respiratory infection. Observing the deformation of the platform on which the patient walks is used for the classification of ambulatory patterns [107]. Such investigations also increase the comfort of the patient. Gathering data such as temperature from a patient's foot allows ANNs to detect places of higher blood pressure. This information could be used to prepare shoes that are better fitted and more comfortable for these people [108]. Doctors may find useful a mechanism for tracking the position of ultrasound head during an abdominal diagnostic. This improves the quality of classification and diagnosis of difficult-to-identify cases [91]. Position detection is also important when navigating special robots that are used to inspect ventilation pipes. Using infrared detectors together with recorded microphone data allows them to detect damage in this installation [83]. Robots that use peristalsis to move in pipes and quickly overcome elbows are also being designed. By analysing data from various sources, including infrared detectors, such robots can locate damage, attempt to repair it, or bypass it [84]. It is worth mentioning two more areas that were relatively little analysed using infrared detectors. In [93], they were used together with touch sensors and others to improve robot grip. In this way, the accidental crushing and slipping of objects was reduced and grip stability was increased. In [100]. the focus was on the use of infrared to assess the temperature of the monitored ground. In this way, the degree of its moisture was estimated. This information can be particularly useful in agriculture.
## 3 Results and Discussion
### Synthesis and Spectroscopic Characterization
In compounds **1**, **2**, **5**, and **7**, characteristic wide bands at about 3400 cm\({}^{-1}\) corresponding to the N-H bond in unsubstituted indole rings were found. The C=O bonds from the caffeine ring (**3**, **4**, **8**-**10**) were observed as two bands in the 1663-1706 cm\({}^{-1}\) range. Bands originating from the C-N bonds in benzimidazole-indole and caffeine rings were found in the 1280-1380 cm\({}^{-1}\) range. The most characteristic feature of the FT-IR spectra present in the structure of the steroid part is two strong representative bands at 17535-1704 cm\({}^{-1}\) and 1297-1157 cm\({}^{-1}\) region, appropriately assigned, respectively, to the symmetric groups v(C=O) and v(C-O). In addition, characteristic stretching vibrations of C-H bonds were present in the 2950-2865 cm\({}^{-1}\). In the ESI MS spectra of newly obtained bioconjugates, the positive ion mode showed signals from the \(m/z\) value corresponding to [M+H]\({}^{+}\) (for **1**), [M+Na]\({}^{+}\) (for **2**-**4**, **5**-**7**, **10**) and [M+K]\({}^{+}\) (for **5**, **6**). The NMR (\({}^{1}\)H and \({}^{13}\)C), ESI-MS, and FT-IR spectra of the investigated compounds are provided in the Supplementary Materials (Figures S1-S30).
## 2 Materials and Methods
### Transmission Structure Addressing the Current Challenge  
To compensate for the effective characteristic impedance mismatches, an interconnect featuring a nonuniform-width inner conductor is proposed, as illustrated in Figure 4. The nonuniform-width inner conductor incorporates a periodic structure that aligns with the periodic cylindrical air cavities. The widest sections of the inner conductor occur at cross-sections where the air cavities are most pronounced, such as at X3, with the maximum width denoted as \(b_{max}\). The narrowest sections, located where no cavities are present, such as at X1, match the width of a traditional SICL, denoted as \(b\). The inner conductor width is adjusted to maintain an effective characteristic impedance of 50 \(\Omega\) at each cross-section, following the steps outlined below:  
Step 1: Determine the width \(b\) of the inner conductor at cross-section X1, ensuring an effective characteristic impedance of 50 \(\Omega\).  
Step 2: Calculate the maximum width \(b_{max}\) of the inner conductor at cross-section x3, targeting an effective characteristic impedance of 50 \(\Omega\).  
Step 3: Calculate the corresponding inner conductor widths for intermediate cross-sections between X1 and X3, maintaining an effective characteristic impedance of 50 \(\Omega\).  
Step 4: Connect the calculated widths smoothly using a continuous curve to form a nonuniform-width unit.  
Figure 4: Profile of an SICL with two rows of periodic cylindrical air cavities and a nonuniform-width inner conductor with periodic variations, viewed from the longitudinal sectionY0: (**a**) overall view; (**b**) vertical view.
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 2 Related Work
### Classical Computer Vision-Based Approaches  
In the field of lung nodule analysis, many computer vision methods based on hand-crafted features have been used, such as region growing [14], active contours [15], level sets [16], graph cuts [17], adaptive thresholding [18], Gaussian mixture models (GMM) with fuzzy C-means [19], and region-based fast marching [20]. However, it is difficult to generalize a nodule segmentation model based on hand-crafted features that can be useful for CT images. All the aforementioned traditional approaches are semi-automated or depend on several image pre-processing and post-processing techniques. For instance, a contrast-based region growing method and fuzzy connectivity map of the object of interest (i.e., nodule) were used in [14] to segment various types of pulmonary nodules. This method did not perform adequately with irregular nodules due to merging different criteria in the region growing technique that needed a fine-tuning for parameters of the setting. Geometric active contours with a marker-controlled watershed as well as Markov random field (MRF) was used in [15] to segment the lung nodule. In turn, ref. [16] used a shape prior hypothesis along with level sets that iteratively minimized the energy needed to segment juxtapleural pulmonary nodules. However, these two methods depend on manually selected seeds in the nodule region, and thus the precision of the segmentation process depends on the proper selection of seeds. A graph cut algorithm with an expectation-maximization (EM) algorithm was proposed in [17] for lung segmentation on chest CT images. This algorithm yields acceptable segmentation results; however, it has a high computational cost because it focuses on training a GMM and searching on the corresponding graph using a heuristic searching algorithm. Ref. [18] used an adaptive thresholding technique along with a watershed transform to detect lung nodules. However, this approach mainly relies on different pre-processing and post-processing procedures. Ref. [19] combined GMM knowledge within the conventional fuzzy C-means method to improve the robustness of pulmonary nodule segmentation. The major disadvantage of fuzzy C-means algorithms is that they are sensitive to noise, outliers, and primary cluster selection. A region-based approach was introduced in [20] by using the fast marching method, which gives a precise segmentation of the nodule and can properly handle juxtapleural and juxtavasular nodules. The main disadvantage of region growing segmentation is the fact that the resulting histograms do not provide any spatial knowledge of the input images.
## 3 Preliminary Process Analysis
### Descriptive Analysis of Variables in the Model
Further analysis of the key variables (\(W_{1}\), \(W_{2}\), \(W_{3}\)) provides deeper insights into their behaviour and relationships:  
* \(W_{1}\) (ink temperature): showed a low coefficient of variation (Co. Var. = 0.22%) and a low standard deviation (Std. Dev. = 0.0622), which indicates a high stability of this variable. This consistency is crucial for maintaining the viscosity and uniform application of the ink. * \(W_{2}\) (dampening solution temperature): showed a relatively higher variability (Co. Var. = 3.39%) and a negative skewness (Skew. = \(-\)0.6523), indicating a tendency towards lower temperature values. This indicates the need for precise control to maintain process stability. * \(W_{3}\) (dampening solution acidity): showed a standard deviation of 0.1367 and a positive skewness (Skew. = 0.4870), indicating a slight tendency towards higher pH values. Such deviations could have an effect on the chemical stability of the process. Furthermore, interrelationships among these variables, especially the strong correlation between \(W_{2}\) and \(W_{3}\), confirm their mutual influence on the overall performance of the process. This underlines the importance of collective regulation of these parameters to ensure optimal process conditions. This descriptive analysis confirms the process stability and provides a robust foundation for the optimization steps in this research. Consistent data from the analysis enabled the development of a reliable, AI-driven Six Sigma model that aims to further reduce variability, improve production performance, and enhance overall process quality.
## 3 Results
### Detection Probabilities
#### 3.2.1 False Detection Probability  
False detection probability means the likelihood of a SU affirming the existence of a PU when there is no other user in the spectrum and the channel is free. It means that PUs are inactive and SUs can use free bands easily. In this way, the system output will increase. The lower the false detection probability, the higher will be the usability of the free band. It also provides an advantage to SUs as they can send their transmissions through the band that is unoccupied by licensed users [24].
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 4 Experimental Planning and Test Bed Design
### Measurements Scenarios  
This section discusses the different scenarios used to perform the measurements. It is divided into two scenarios that are described in the following paragraphs.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### Open Source IoT Technology for Device Access
#### 2.1.1 Short-Distance Communication  
1. Bluetooth technology [13; 14; 15; 16; 17]: This has been developed and applied for many years, and there are many open source Bluetooth platforms. For example, Bluez technology provides a complete Bluetooth system which supports a variety of CPUs, Linux and Android operating systems. Android BLE built into Android implements Bluetooth device access management. Gatt, based on the Go language, is an open source software that implements the GATT framework. Noble implemented the central module of Bluetooth by using Node.js and Bloom implemented a Bluetooth peripheral module based on Node.js.
2. NFC (Near Field Communication) [18]: There are many open source systems that support NFC. For example, Libnfc provides platform-independent, low level NFC SDK, and supports event-based device interaction mechanisms. NFC device read capabilities for the Android platform are also available.
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 3 Integrated Prediction Method Based on the LSTM-SVR Model
### Data Preprocessing  
In order to facilitate analysis, improve training speed, and avoid the impact of dimensional differences between various data on the network model, it is necessary to standardize the data vector using the Z-score method based on various time series data [40] and normalize the input data vector into a standardized sequence with a mean value of 0 and a standard deviation of 1. The standardized expression is as follows:  
\[x_{i}=\frac{x_{i}-\overline{x}_{i}}{\sigma_{i}} \tag{12}\]  
In the formula, \(x_{i}\) represents the input data, \(\overline{x}_{i}\) represents the mean value of the data, and \(\sigma_{i}\) is the standard deviation of the data.
## 3 Materials and Methods
### The Surveying of GCPs and CPs for TLS and MLS  
The GCPs and CPs for TLS used in our study were temporarily stabilized using black-and-white circular scanning targets (Figure 3a). At every second position, three targets for TLS were placed around the scanner and then scanned and used to link the TLS survey to a common coordinate system. In total, 37 scanning targets were used for the TLS monitoring of the city center of Ziar nad Hronom.  
A total of 75 uniquely identifiable features, such as manholes, valves, and curbs, were selected as the CPs for the Lidaretto laser scanner (GEOTECH, Bratislava, Slovakia) (Figure 3b). For the Stonex X120 GO laser scanner, 5 GCPs were evenly distributed across the study area, consisting of black-and-white circular targets with dimensions of 0.3 \(\times\) 0.3 m (Figure 3c). These GCPs were temporarily stabilized and fixed into the ground with nails to prevent dislocation. During the survey with the Stonex X120 GO laser scanner, CPs similar to those from the Lidaretto survey were also used. The GCPs for TLS, as well as the GCPs and CPs for MLS, were determined using the fast static method with the Leica  
Figure 2: Surveying equipment used in the study.  
GS07 GNSS system (Leica Geosystems AG, Heerbrugg, Switzerland) using RTN corrections, connected to the Leica SmartNet service. All GNSS measurements were transformed into the national positional coordinate system Datum of Uniform Trigonometric Cadastral Network (S-JTSK). The measured heights of the points were transformed into the height system Baltic Vertical Datum after adjustment (Bpv) using the digital height reference model DVRM. Both coordinate systems and transformations are mandatory in Slovakia according to the Decree No. 300/2009 of the Office of Geodesy, Cartography and Cadastre of the Slovak Republic (UGKK) [37]. They are implemented using the Departmental Transformation Service (RTS JTSK) of the Geodetic and Cartographic Institute (GKU). The estimated accuracy (2\(\sigma\)) of the GCPs and CPs coordinates was 0.01 m in position and 0.02 m in height. The values mentioned were determined by previously implemented testing of the GNSS equipment on the test base. The testing procedure was performed using a simplified test procedure adapted according to the technical standard STN ISO 17123-8 [38]. The Leica GS07 GNSS consists of a SmartAntena, a Leica CS20 controller, and a telescopic pole. The SmartTrack function ensures support and tracking of signals from GPS, Glonass, BeiDou, Galileo, QZSS, and other systems. All additional specifications of this device are listed in Table 2.
## 3 Results and Analysis
### Response of Kiwifruit Surface Reflectance to Sweetness Content  
The response patterns of the SG, SNV, and MSC spectra at different sweetness levels were illustrated in Figure 3. SG smoothing effectively mitigated the random interference originating from the original spectra, enhancing the overall smoothness of the spectral curves. Subsequently, after SNV and MSC transformations, the curves became more compact, and distinctive patterns emerged in specific spectral bands. For instance, the spectral reflectance curves of kiwifruit in the vicinity of 720-850 nm exhibited gradual decreases and increased with saccharinity variations, particularly evident in the SNV and MSC transformations. In contrast, the spectral reflectance in the ranges of 1560-1960 nm and 2000-2270 nm displayed a discernible regularity, which increased progressively with rising sweetness levels, demonstrating a clear positive correlation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multirow{2}{*}{**No. of Samples**} & \multicolumn{3}{c}{**Sweetness (\%)**} & \multirow{2}{*}{**Coefficient of Variation**} \\ \cline{3-3} \cline{5-6}  & & **Minimum** & & **Maximum** & **Average** \\ \hline T1 & 202 & 4.8 & 11.1 & 7.56 & 20.64\% \\ T2 & 290 & 6.4 & 13.9 & 8.81 & 17.84\% \\ T3 & 215 & 11.2 & 19.4 & 15.73 & 9.86\% \\ T4 & 707 & 4.8 & 19.4 & 10.58 & 35.95\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical description of Kiwifruit samples.
# Homogenization of Ancient Masonry Buildings: A Case Study
## 8 Modal Analysis
### Modal Analysis of the Fine Models of a Building Portion  
The modal analysis of the LW, LTW, and SLTW fine models of the long longitudinal arm is performed, in order to investigate the actual influence of the transverse walls on the dynamic behavior. The corresponding natural frequencies and modal shapes of the first natural mode are shown in Figures 15-17.  
Figure 16: First natural mode of the LTW fine model. Natural frequency \(f=1.38\) Hz. Modal shape in the: (**a**) 3D space; (**b**) \(x-z\) plane; (**c**) \(y-z\) plane.  
Figure 14: (**a**) building portion affected by the dynamic tests of the campaign 2—first, second, third, fourth floor. First vibrating mode: natural frequency \(f=1.54\) and modal shapes in the (**b**) 3D space, (**c**) \(x-z\) plane, and (**d**) \(y-z\) plane..  
Figure 15: First natural mode of the LW fine model. Natural frequency \(f=0.54\) Hz. Modal shape in the: (**a**) 3D space; (**b**) \(x-z\) plane; (**c**) \(y-z\) plane.  
First, it is seen that the first vibrating mode mainly involves the out-of-plane behavior of the structure. The LW model is the less stiff, with a natural frequency \(f=0.54\) Hz. Moreover, pure shear modal shapes are observed in the \(x-z\) and \(y-z\) planes. Concerning this aspect, some considerations about the significant influence of the slabs are reported in Appendix B. However, when the distributed transverse walls are introduced, a significant increasing of the natural frequency reasonably occurs (the natural frequency of the LTW model is \(f=1.38\) Hz), together with a qualitative change of the modal shape in the \(y-z\) plane, in which also a bending behavior of the transverse walls is observed. Finally, the adding of the localized transverse walls further increases the frequency (the natural frequency of the SLTW model is \(f=1.42\) Hz) and, mainly, modifies the modal shape in the \(x-z\) plane, in which localized stiffening effects are evident.  
By comparing the numerical results with the experimental ones of Section 8.1, it is evident how decisive appears the contribution of the transverse walls. The SLTW fine model captures quite well the experimental results, with a percent error in frequency of about 7.8%. However, an evident error is related to the hinge constraints at the lateral sides of the longitudinal walls of the fine model, which could be conveniently substituted by horizontal springs (appropriately calibrated, possibly in further investigations).
# Transparent Redirections in Content Delivery Networks
## 4 Verification
### Forwarder Implementation  
Open vSwitch was chosen as a forwarding element due to its popularity and performance [29]. The forwarder consists of two components. The userspace implementation of the forwarding datapath called ovs-switchd and the datapath kernel module written specifically for the host operating system to achieve high performance [29]. The performance of the Open vSwitch kernel datapath reaches up to 800 Mbit/s [30] on a single UDP flow tested, which can be further enhanced by using a different datapath, e.g., DPDK (Data Plane Development Kit) [31] or a hardware implementation. We modified the Open vSwitch open source software implemented forwarder, which currently handles OpenFlow up to version 1.5. As OpenFlow since version 1.3 provides experimenter headers, this version of the protocol became our choice. We added a new action to the standard list of actions with IDs 32 and 33 (inc_seq and inc_ack) to increment the sequence number and the acknowledge number respectively. Both actions take a single 4 Byte parameter, which represents the value, by which the original value in the header will be incremented. The action was both implemented in user-space and kernel-space to achieve high forwarding performance.  
The full patch is available at the URL ([[https://github.com/rfonco/ovs/compare/v2.8.1...fr6](https://github.com/rfonco/ovs/compare/v2.8.1...fr6) ncc:seq_ack_experimenter]([https://github.com/rfonco/ovs/compare/v2.8.1...fr6](https://github.com/rfonco/ovs/compare/v2.8.1...fr6) ncc:seq_ack_experimenter)).  
The applied patch extends the OpenFlow interface with new actions:  
* inc_seq(increment): increments sequence number by the supplied increment argument. The argument is a 4 Byte unsigned integer
* inc_ack(increment): increments the acknowledge number by the supplied increment argument. The argument is a 4 Byte unsigned integer We created a testbed, where we attached hosts A and B and manually added flow entries, where we incremented the sequence number of the TCP header in the direction from A to B, and we decremented the acknowledge number in the opposite direction. Decrement is not implemented, however, if we increment the number by \(2^{32}-1\), we will achieve a decrement by 1 as the acknowledge number overflows. We achieved a goodput of around 600 Mbit/s on a single-core CPU with a 2.7 GHz frequency in a virtualized environment using iperf [32], which is similar to the forwarding performance achieved in [30]. The measured goodput is shown in Figure 7. The data plane is emulated using the Mininet emulator [33].