esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Active volcanic calderas often experience significant ground deformation, characterized by uplift and subsidence, typically with a radial or elliptical pattern centered on the caldera. However, the detection of small second-order anomalies within the main deformation pattern can provide critical insight into impending eruptions and/or hazardous structural and volcano-tectonic features. In this study, we present a simple but novel method for detecting and interpreting second-order deformation anomalies at Campi Flegrei caldera by filtering the primary, radial deformation signal observed during volcanic unrest phases. For this purpose, we used a procedure based on the polynomial fit of vertical displacement data, assuming that they depend only on the distance from the deformation center. By subtracting the best fitting radial deformation from the observed vertical displacement, we generated an anomaly map that highlights sectors with unexpected deformation patterns. We applied the proposed procedure to analyze the ground deformation at the Campi Flegrei caldera (Italy) that occurred from 2016 to 2021, by using MT-InSAR data from Sentinel-1. Coeval GPS datasets were also used for cross-checking the obtained results. The results of this analysis show a pronounced NE-SW alignment that separates sectors with different uplift trends. It highlights a primary volcano-tectonic structure potentially linked to high seismic hazard. This method, after further investigation, can provide a valuable tool for improving hazard assessment and understanding the structural dynamics of calderas during unrest phases, with implications for improving preparedness in densely populated volcanic regions.  
c calderas unrest; ground deformation monitoring; volcanic hazard; MT-InSAR
## 4 Results
### Assessing the Calibrated InSAR Result by Leveling  
The leveling data form an independent dataset that can assess the GPS-calibrated InSAR result. Figure 11a shows the differences between the vertical velocities from InSAR and from leveling evaluated at the benchmarks in Figure 2. The InSAR velocities were consistent with those from leveling in most parts of Yunlin, except for Southwestern Yunlin. Figure 11b shows the scatter plot for the velocities from InSAR and leveling at the benchmarks. If the two sets of velocities were consistent with the results, the results are consistent with the results.  
Figure 8: Monthly rainfalls in Yunlin, based on the data at 13 rain gauge stations in Yunlin provided by the Central Weather Bureau of Taiwan. To get such data, visit [[http://e-service.cwb.gov.tw/HistoryDataQuery/index.jsp](http://e-service.cwb.gov.tw/HistoryDataQuery/index.jsp)]([http://e-service.cwb.gov.tw/HistoryDataQuery/index.jsp](http://e-service.cwb.gov.tw/HistoryDataQuery/index.jsp)) [29].  
Figure 9: The paired SAR images forming the interferograms for determining displacements in the wet and dry seasons.  
at the benchmarks, they would be on a line with a gradient of one (the gray line) in Figure 11b. In reality, inconsistencies existed between the two sets of velocities (the cyan line shows the linear relationship from the least-squares fitting to the two velocity sets). The RMS difference between the un-calibrated InSAR velocities and leveling velocities was 16.0 mm/a and reduced to 9.4 mm/a after the GPS calibration.  
Figure 10: LOS displacements from the calibrated InSAR result and GPS measurements (point values with station names) in the (**a**) wet season (from April 2016 to October 2016) and (**b**) dry season (from October 2016 to April 2017). The anomalously large displacements in Figure 10a in Eastern Yunlin could be due to noises in the SAR images that are not removed in our data processing.
## Appendix A  
### Pseudocode of K-Nearest Neighbors Algorithm  
```
1:Input:\(D_{\text{train}}\): Training dataset; \(D_{\text{test}}\): Test dataset; \(k\): Nr. of neighbors; \(dist\_metric\)
2:Output:\(D_{\text{predicted}}\): Predicted labels for the test dataset
3:Program Body
4:for each \(x_{\text{test}}\) in \(D_{\text{test}}\)do
5: let \(distances\) be an empty list
6:for each \(x_{\text{train}}\) in \(D_{\text{train}}\)do
7: let \(distance\) be \(dist\_metric(x_{\text{test}},x_{\text{train}})\);
8: let \(label\) be the label of \(x_{\text{train}}\); append \((distance,label)\) to \(distances\)
9:endfor
10: sort \(distances\) by \(distance\) in ascending order; let \(k\_nearest\) be the first \(k\) elements from \(distances\); let \(label\_counts\) be an empty dictionary
11:for each \((distance,label)\) in \(k\_nearest\)do
12:if label is not in \(label\_counts\)then
13: let \(label\_counts[label]\) be 0
14:endif
15: increment \(label\_counts[label]\)
16:endfor
17: let \(predicted\_label\) be the label with the highest count in \(label\_counts\)
18: assign \(predicted\_label\) to \(x_{\text{test}}\) in \(D_{\text{predicted}}\)
19:endfor
20:Return \(D_{\text{predicted}}\)
```  
**Algorithm A1** K-Nearest Neighbors Algorithm [50]
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 3 Methodology
### Global-Local Interaction Learning Module  
The exclusive use of either CNNs or Transformers as the encoding mechanism often fails to strike an optimal balance between extracting global features and capturing local detail features. Furthermore, existing medical segmentation networks that utilize hybrid CNN-Transformer architectures tend to merely fuse features from different encoders and directly employ them in the decoding phase, thereby overlooking the potential synergistic interactions among the encoders. To address this limitation and effectively harness the strengths of both CNNs and Transformers, the GLIL module has been developed. This module facilitates the exchange of information between the CNN and Transformer, promoting interactive learning. The GLIL module comprises two parallel spatial attention modules [41]: one extracts global information from the Transformer's output features and the other captures detailed local information from the CNN's output features. For an input feature F within the dimensions \(\mathbb{R}^{H\times W\times C^{\prime\prime}}\), the spatial attention module conducts average pooling and max pooling along the channel dimension to generate compressed feature maps \(F_{avg}\) and \(F_{max}\), respectively, both in \(\mathbb{R}^{H\times W\times 1}\).  
\[F_{avg}=AvgPool(F) \tag{4}\]  
\[F_{max}=MaxPool(F) \tag{5}\]  
Figure 2: The DEIF-Net architecture is composed of parallel encoders and GLIL and GLFF modules, as well as skip connections.  
These compressed maps, \(F_{mag}\) and \(F_{max}\), are then concatenated along the channel dimension to form \(F_{concat}\in\mathbb{R}^{H\times W\times 2}\). Next, a convolutional operation followed by a sigmoid activation function \(\sigma\) is used for deriving the attention weights \(\omega\in\mathbb{R}^{H\times W\times 1}\) for the input feature map \(F\).  
\[F_{concat}=Concat[F_{avg},F_{max}] \tag{6}\]  
\[\omega=\sigma(Conv(F_{concat})) \tag{7}\]  
This architecture allows for the parallel extraction of global information \(\omega_{t}\) from the Transformer and local detail knowledge \(\omega_{c}\) from the CNN. To facilitate effective interaction between the Transformer and CNN, the global information is utilized for modulating the features \(F_{c}\) extracted by the CNN while the local detail knowledge is used for modulating the features \(F_{t}\) extracted by the Transformer. Additionally, residual connections are employed to enhance the stability of the interactive learning process.  
\[F_{c}=F_{c}+\alpha\times(F_{c}\otimes\omega_{t}) \tag{8}\]  
\[F_{t}=F_{t}+\beta\times(F_{t}\otimes\omega_{c}) \tag{9}\]  
Through the implementation of the GLIL module, the output features of the Transformer and CNN are enabled to mutually enhance their learning and propagate effectively to the subsequent layer of the encoder. This method significantly enriches the interaction between the Transformer and CNN encoders, facilitating a more integrated and effective use of their respective advantages within the network.
## 7 Experimental Results and Analysis
### Safety Discussion
#### 7.3.3 Side Channel Attacks  
A side channel attack is a type of attack that leverages unintentional information leakage in a cryptographic device or implementation to extract secret information, such as keys or plaintexts. These channels can include power consumption, electromagnetic radiation, acoustic emissions, and temporal information. Side channel attacks can be effective against many cryptographic implementations, including both symmetric and public key cryptosystems. One common type of side channel attack is a time-based analysis, in which a certain amount of time is required to encrypt and decrypt data during encryption. This time is dependent on factors such as the encryption algorithm and key length. An attacker can use this time information to infer the key and gain further details about the encrypted data. By analyzing the encryption and decryption times of the algorithm under a large number of different plaintexts, the attacker can analyze the relationship between the plaintext and ciphertext to find features for algorithm cracking.  
Cryptographers have taken several countermeasures against edge channel attacks, such as adding noise, increasing randomness, and increasing the algorithm's complexity. For this chunked disordered encryption algorithm, the randomness of the length of its sharing code and its disorderliness increases the difficulty of the attack in time-consuming  
Figure 20: Difficulty of brute-force cracking.  
analysis to a certain extent. At the same time, we can further verify the algorithm's security by taking different lengths of sharing codes to test its encryption and decryption time.  
The experimental results show that the encryption and decryption time of this chunking disorder algorithm is weakly correlated with the length of the sharing code, and the encryption and decryption time is random and fluctuates within a specific interval, which means that it is difficult to crack the encryption and decryption time of the algorithm by using time-consuming analysis attacks. Therefore, the present chunking scrambling algorithm strongly resists edge channel attacks. Figure 21 illustrates encryption and decryption times for different lengths of sharing codes.
# A Simplified CityGML-Based 3D Indoor Space Model for Indoor Applications
## 3 Simplified 3D Indoor Space Model (3 DSISM) Based on CityGML
### LOD of the 3 DSSISM  
#### 3.1.1 Geometry Model for Indoor Spaces  
CityGML adopts the objects of GML3 [49] geometric model based on ISO19107 \"spatial model\" [50] to describe the geometric properties of city model features using Boundary Representation (Brep). To describe the geometric properties of 3D indoor spaces, a subset of the CityGML geometric model can be used, as shown in Figure 5. A zero-dimensional object is a point (_Point_), a one-dimensional object is a curve (_Curve_), a two-dimensional object is a surface (_Surface_), and a three-dimensional object is a solid (_Solid_). A solid is bounded by surfaces and a surface is enclosed by curves. Curves are not allowed to cross and two curves intersect at a common point. A curve is defined as a straight line using GML3 class _LineString_. A surface is defined as a plane, represented by a polygon, which is topologically closed. The ring (_Ring_) is the boundary of a polygon, and the geometry is a closed linear ring (_LinearRing_). A polygon consists of an outer ring and zero to multiple inner rings.
# Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
###### Abstract  
Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions, on how to make the most out of the state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world.  
remote sensing; model; hydrological prediction; climate change; land use change; evapotranspiration 2021
## 2 Categorization Approach
### Proposed Data Lifecycle Model  
The data lifecycle model developed and adopted in the context of this paper is depicted in Figure 1.  
Based on the data lifecycle stages of the USGS model, our model has three constituent elements:  
1. Data Collection and Internet-of-Things (IoT): The first element is responsible for directly or indirectly collecting existing data and/or generating new data. Furthermore, it encompasses managing the sources (e.g., data from sensors, databases, historical data) for the data collection. This element also underscores the vital importance of IoT technologies in data collection and in  
Figure 1: Proposed data lifecycle model.  
PA as a whole. The term IoT refers to interrelated computers and everyday objects, which can transmit and receive data over a network and often incorporate ubiquitous intelligence [16].
2. Data Analysis and Artificial Intelligence (AI): The second element involves processing of data from various sources as well as extracting valuable knowledge and generating added value from data. It also refers to transforming raw data to a more sophisticated form in order to facilitate subsequent analysis and/or integration. AI has a central role in this element and covers a range of different techniques (e.g., computer vision, fuzzy logic, evolutionary algorithms, machine learning, semantic processing).
3. Data Storage and Distribution: The third element refers to the necessary processes and resources for permanently or temporarily storing sets of data as well as for providing end-users with open or restricted access to data.  
Asuring quality, privacy, and security in all data lifecycle stages are top priorities for PA. Therefore, a cross-cutting element titled \"Security and Quality Assurance\" is included in our proposed model. We followed a compact approach for the model so that it can easily be tailored to the needs of academic users from different scientific fields as well as of various other stakeholders. This compact approach offers compatibility and easy mapping with respect to the main existing data lifecycle models, as studied and presented in Section 2.1, while offering better generalization compared to more finely tuned approaches.
# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 3 Proposed Algorithm  
### Problem Statement  
Most of the routing protocols focus on improving the performance of the sensors network in terms of the QoS at the expense of the routing processing time. Accordingly, the long processing time needed to select the best route would consume a considerable amount of energy. The power of sensors is mainly supplied by batteries which would be depleted with increasing the number of routing requests and their processing time that depends primarily on the routing algorithm complexity. Sensors would turn into being faulty nodes when their power is drained. Accordingly, this degrades the performance of the network regarding the data throughput and communication delay between the end nodes and hence alternative routes should be sought. To avoid having dead nodes particularly during the data communication period, routes should be selected to go through nodes with enough energy.
# The Effects of Train Passes on Dwell Time Delays in Sweden
## 3 Modelling with Odds Ratios and Logistic Regression  
In this study, probability and odds ratios were used to investigate the tendency of combined dwell delays that occurred due to different types of train passes. Probability is the proportion of the number of instances that meet the criterion, divided by the total number of instances. Since the probability is in the range between 0 and 1, it was interpreted in terms of percentage in this study.  
\[\pi_{i}=\frac{\text{Number of instances with an event occurring}}{\text{Total number of instances}}\times 100\% \tag{4}\]  
where \(\pi_{i}\) represent the probability of the event \(i\) occurring  
Odds are a way of using probability to estimate the chance that an event occurs divided by the probability that the event does not occur.  
\[\text{Odds }=\frac{\text{Probability of an event occurring}}{\text{Probability of an event not occurring}}=\frac{\pi_{i}}{1-\pi_{i}} \tag{5}\]  
In this study, the odds ratio was used as a comparison between two events. Specifically, we compared the odds of delays for cancelled or unscheduled passes with the odds of delays due to passes that are carried out as scheduled:  
\[\text{Odds ratio }=\frac{\text{Odds}1}{\text{Odds}2}=\frac{\frac{\pi_{i}}{1- \pi_{i}}}{\frac{\pi_{i}}{1-\pi_{i}}} \tag{6}\]  
It is also important to note that odds and odds ratio do not have units, just like percent.  
The logistic regression model is a statistical modelling technique that estimates the probability of a dichotomous outcome event being related to a set of explanatory variables. By convention, the dependent variable is designated as being positive when delays increase and negative when not with the scores of 1 versus 0, respectively, for coding of the dependent variable in computerised data sets. The logistic regression model can also be written as  
\[\log\biggl{(}\frac{\pi_{i}}{1-\pi_{i}}\biggr{)}=\beta_{0}+\beta_{1}x_{1}+ \cdots+\beta_{p}x_{p} \tag{7}\]  
Notice that the left-hand side of the equation is the log odds of a delay occurring.
# The Influence of Laser Linewidth on the Brillouin Shift Frequency Accuracy of BOTDR
## 4 Experiment Results and Discussions
### BFS Accuracy Evaluation  
For further evaluating the influence of the laser linewidth on the BFS accuracy, the measured BFS over the heated fiber were specially extracted and are shown in the inset of Figure 12a-e, eliminating the rise and fall edge of BFS change. The root-mean-square errors (RMSEs) of the extracted BFS distribution were calculated and plotted versus the laser linewidth and pulse width, as shown in Figure 12f.  
Figure 12: Measured BFS distribution when the linewidth is: (**a**) 1.05 MHz, (**b**) 101 kHz, (**c**) 10.2 kHz, (**d**) 3.1 kHz, and (**e**) 98 Hz; (**f**) BFS root-mean-square errors (RMSEs) versus laser linewidth and probe pulse width.  
From Figure 12f, it can be seen that the RMSE of BFS was related with both the laser linewidth and the probe pulse width. It decreased obviously when the pulse width was extended from 12 ns to 42 ns. When the pulse width was fixed, the RMSE of BFS decreased with the laser linewidth narrowing from 1.05 MHz to 3.1 kHz, which was in good agreement with the numerical simulations. However, it unexpectedly increased as the laser linewidth narrowed from 3.1 kHz to 98 Hz, which was inconsistent with the simulation results. We contribute this exception to the coherent Rayleigh noise (CRN). The CRN increases sharply as the laser linewidth becomes narrower [33]. Additionally, it has been proven that the CRN cannot be reduced by signal averaging [34]. Hence, the accuracy of BOTDR will decrease once the linewidth of the seed laser becomes narrow to a certain value, such as 98 Hz in this paper, because the extremely-narrow laser linewidth enhances the CRN largely and further results in sharp SNR deterioration of the BOTDR.  
Based on the above analysis, it was demonstrated that the BFS accuracy improves when the laser linewidth narrows. However, the BFS accuracy will deteriorate when the laser linewidth is so narrow that the CRN is enhanced sharply. The measured results of BFS RMSE and BGS width (\(\mathbf{W_{\text{BGS}}}\)) by respectively utilizing five different-linewidth lasers as the seed source are summarily listed in Table 2, where the probe pulse width was 12 ns, 22 ns, 32 ns, and 42 ns with the same peak power of 23.09 dBm.
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 7 Data Analysis Methodology  
Statistical data was collected from patients admitted to the hospital for Type 2 diabetes-related complications associated with diabetic foot ulcers (DFUs). To meet the inclusion criteria, patients must have diagnostic codes corresponding to the pathophysiology of DFUs, as well as discharge diagnoses and soft tissue lesion classifications, in accordance with the International Classification of Diseases, 10 th edition [49].  
Key risk factors include age, gender, residential area, history of diabetes, and the presence of conditions such as retinopathy, neuropathy, nephropathy, peripheral artery disease, and hypertension. Biological markers such as glycated hemoglobin, white blood cell count, and fibrinogen levels are also recorded. DFU disease progression and severity is diagnosed and categorized following systems such as the Wagner-Meggitt classification [50] or the SINBAD classification (Site, Ischemia, Neuropathy, Bacterial infection, Area, Depth) [51]. Grading systems, including the Society for Vascular Surgery Lower Extremity Threatened Limb Classification [52] and the Saint Elian Wound Score System, may also be utilized [53].  
Statistical analysis can be performed using the Statistical Package for Social Sciences (SPSS) by IBM or the Statistical Analysis System (SAS) developed by SAS Institute in North Carolina, USA. This analysis reveals the incidence of biofilm formation in wounds and its impact on wound healing, underscoring the need to understand the biofilm-forming mechanisms of the most common pathogens.
# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
## 4 Simulation Results and Discussion
### Discussion  
In this part, the system link budget will be analyzed. Investigating the maximum detection range of this system as a function of the coherent integration time is the main objective of this analysis.  
According to the bistatic radar function, the SNR for the echo signal is  
\[SNR_{in}=\frac{\rho G_{r}\lambda^{2}\sigma\eta}{(4\pi)^{2}R_{R}^{2}P_{n}} \tag{32}\]  
where Table 3 provides the parameters of the radar system. Next, we take the signal processing gain into consideration. The whole signal processing gain \(G_{sp}\) can be written as:  
\[G_{sp}=BT_{c}\cdot\frac{T_{coh}}{T_{c}} \tag{33}\]  
where \(BT_{c}\) and \(\frac{T_{coh}}{T_{c}}\), respectively, denote the range and azimuth processing gain. After the two-dimensional integration processing, the SNR of the echo signal is:  
\[SNR_{out}=SNR_{in}\cdot G_{sp}=\frac{\rho G_{r}\lambda^{2}\sigma\eta}{(4\pi)^ {2}R_{R}^{2}P_{n}}BT_{coh} \tag{34}\]  
We set the minimum SNR for the target detection is \(SNR_{\text{min}}=8\) dB and the coherent time \(T_{coh}=3\) s. Subsequently, the detection of the GNSS_based passive radar system is \(3.5\) km.  
A large-scale antenna with long integration interval is highly recommended to further enhance the detection range of the GNSS_based radar system. Moreover, any point on the earth is illuminated by more than 32 satellites simultaneously, the detection range can also be enhanced by utilizing multiple satellites. Research on the exploitation of multiple satellites to increase the detection range is our future work.