esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### System Architecture  
The lung diagnosis image-based system is composed from multiple modules and autonomous functions. Each of these help in obtaining the result focused on the diagnosis of the types of lung tumors. The process begins with defining the affected area as the initial region of interest (ROI) considered by the user based on his experience. Inside this selected mask, three parameters are computed that describe the lung vascularization: blood vessels area related to the ROI (mask area), blood vessels tortuosity index, and blood vessels orientation. The three vascularization parameters--blood vessel area, tortuosity index, and orientation--were selected based on their strong correlation with malignancy indicators. Malignant tumors tend to exhibit: (1) irregular and increased vascularization (evaluated through blood vessel area); (2) highly convoluted and disorganized blood vessel paths (measured by tortuosity index); and (3) altered blood flow orientation due to chaotic angiogenesis (quantified by vessel orientation patterns) [5].  
These features have been widely recognized in tumor angiogenesis studies and provide a comprehensive assessment of malignancy, improving diagnostic accuracy. The chosen parameters are transmitted as inputs to a feed-forward neural network to be analyzed which will provide an indication regarding the malignancy character of the analyzed lung lesion as output. A feed-forward neural network (FNN) was selected instead of a convolutional neural network (CNN) because the input features consisted of extracted numerical vascularization parameters rather than raw image data. CNNs are typically used for pixel-level feature learning, whereas our approach relies on precomputed vascularization metrics (blood vessel area, tortuosity index, and orientation), which are better suited for structured data analysis using an FNN. Figure 1 presents the entire system architecture.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 2 Materials and Methods
### UAV Modeling  
#### 2.4.1 Kinematic Model  
Assuming that the UAV flies at a constant speed in the mission area, \(U_{i}\)\(i=\{1,2,3,\ldots,N\}\) represents the \(i\)-th UAV platform, and the motion model for \(U_{i}\) is expressed as:  
\[\begin{array}{l}\dot{x}_{i}=v_{i}\cos\theta_{i}\cos\varphi_{i}\\ \dot{y}_{i}=v_{i}\cos\theta_{i}\sin\varphi_{i}\\ \dot{z}_{i}=v_{i}\sin\theta_{i}\\ \dot{\varphi}_{i}=\eta_{i}\varphi_{\max}\\ \dot{\theta}_{i}=\eta_{i}\theta_{\max}\end{array} \tag{13}\]  
where \([x_{i},y_{i},z_{i}]^{\text{T}}\) represents the coordinates in the mission area; \(v_{i}\), \(\varphi_{i}\), and \(\theta_{i}\) are the velocity, yaw angle, and pitch angle of \(U_{i}\), respectively; \(\eta_{i}\in[-1,1]\) is the angular rate of \(U_{i}\), \(\varphi_{\max}\); and \(\theta_{\max}\) is the maximum yaw and pitch angle.  
The state equation of UAV can be expressed as:  
\[s_{i}(k+1)=s_{i}(k)+f(s_{i}(k),u_{i}(k),\Delta t) \tag{14}\]  
where \(s_{i}(k)=\left[\widetilde{s}_{i}(k)\quad\varphi_{i}(k)\quad\theta_{i}(k)\right] ^{\text{T}}\) is the state of \(U_{i}\) at time \(k\), \(\widetilde{s}_{i}(k)=\left[x_{i}\quad y_{i}\quad z_{i}\right]^{\text{T}}\) is the coordinate of \(U_{i}\) at time \(k\), \(u_{i}(k)=\left[v_{i}(k)\quad\varphi_{i}(k)\quad\theta_{i}(k)\right]^{\text{T}}\) represents the control input of \(U_{i}\), \(\varphi_{i}(k)\) represents the heading angle at time \(k\), \(\theta_{i}(k)\) is the pitch angle at time \(k\), \(f(s_{i}(k),u_{i}(k),\Delta t)=\left[\begin{array}{l}v_{i}(k)\cos(\varphi_{ i}(k)+\eta_{i}(k)\Delta t)\Delta t\\ v_{i}(k)\sin(\varphi_{i}(k)+\eta_{i}(k)\Delta t)\Delta t\\ \varphi_{i}(k)+\eta_{i}(k)\Delta t\\ \theta_{i}(k)+\eta_{i}(k)\Delta t\end{array}\right]\) is the state change at time \(\Delta t\), and \(\eta_{i}(k)\) is the angular rate at time \(k\).  
The process of UAV motion planning satisfies the limited constraints, such as UAV operation, connectivity maintainence, and collision avoidance. The constraint equations are given as follows:  
\[\left\{\begin{array}{l}s_{i}(k+t+1)=s_{i}(k+t)+f(s_{i}(k+t),u_{i}(k+t), \Delta t)\\ b\leq\|\widetilde{s}_{i}(k+1)-\widetilde{s}_{n}(k+1)\|\leq d_{c}\\ v_{\text{min}}\leq\
u_{i}\leq v_{\text{max}}\\ \varphi_{\text{min}}\leq\varphi_{i}\leq\varphi_{\text{max}}\\ \theta_{\text{min}}\leq\theta_{i}\leq\theta_{\text{max}}\end{array}\right. \tag{15}\]  
where \(d\) represents the maximum communication range between UAVs; \(b\) represents the minimum safe distance for collision avoidance between UAVs; and \(\varphi_{\text{min}}\) and \(\theta_{\text{min}}\) correspond to the minimum angular velocities of the yaw and pitch angles, respectively.
## 3 Preliminaries  
### Fully Homomorphic Encryption  
Homomorphic encryption has been an active area of research for over 30 years, with the first reasonably implementable fully homomorphic encryption scheme being proposed by [PERSON] in 2009 [4]. In this paper, we adopt the Cheon-Kim-Kim-Song (CKKS) encryption scheme proposed by [PERSON] et al. [23], which is considered the most suitable for machine learning applications due to its support for floating-point operations. The CKKS scheme consists of the following seven components.  
* _KeyGen_(\(\lambda\)): Given the security parameter \(\lambda\), choose \(M=M(\lambda,Q)\), choose an integer \(P=P(\lambda,Q)\)
* Sample \(s\leftarrow\chi_{s}\), output \(sk=(1,s)\).
* Sample \(a\leftarrow\mathcal{R}_{Q}\) and \(e\leftarrow\chi_{e}\), output \(pk=\left(b=\left[-a\cdot s+e\right]_{q},a\right)\).
* Sample \(a^{\prime}\leftarrow\mathcal{R}_{PQ}\) and \(e^{\prime}\leftarrow\chi_{e}\), output \(evk=\left(b^{\prime}=\left[-a^{\prime}s+e^{\prime}+Ps^{2}\right]_{PQ^{\prime}} a^{\prime}\right)\)
* \(Enc(pk,m)\): For \(m\in\mathcal{R}\), sample \(r\leftarrow\chi_{r}\) and \(e_{0},e_{1}\leftarrow\chi_{e},\text{output}=\left[r\cdot pk+(m+e_{0},e_{1}) \right]_{q}\).
* \(Dec(sk,c)\): For \(c=(b,a)\in\mathcal{R}_{q}^{2}\), output \(m=\left[b+as\right]_{q}\).
* \(Add(c,c^{\prime})\): Given two ciphertexts \(c,c^{\prime}\in\mathcal{R}_{q}^{2}\), output \(c_{\text{add}}=c+c^{\prime}\bmod q,c_{\text{add}}\ \in\mathcal{R}_{q}^{2}\).  
\(Mult(c,c^{\prime})\): Given two ciphertexts \(c=(b_{1},a_{1}),c^{\prime}=(b_{2},a_{2})\in\mathcal{R}_{q}^{2}\), let \((d_{0},d_{1},d_{2})=\left[(b_{1}b_{2},a_{1}b_{2}+a_{2}b_{1},a_{1}a_{2})\right]_{q}\), output \(c_{mult}=\left[(d_{0},d_{1})+\left\lfloor\,P^{-1}d_{2}\cdot evk\,\right\rceil \right]_{q}\).
* \(ReScale_{l\to l-1}(c)\): Given \(R\) a ciphertext \(c\in\mathcal{R}_{q_{l}}^{2}\) at level \(l\), output \(c^{\prime}=\left[\,\left\lfloor\,\frac{q_{l-1}}{q_{l}}c\,\right\rceil\right]_{ q_{l-1}}\).  
The CKKS scheme uses large integers which require a high computational complexity. To reduce this complexity, [PERSON] et al. [24] proposed a variant of the CKKS scheme called RNS-CKKS. In this variant, large integers are split into several small integers, and the addition and multiplication operations on the original large integer are equivalent to the corresponding operations on the small integers in the residue number system.
# Quaternary Segmentation Characteristics of the Hunhe Fault, Northeast China
## 4 Results
### Shenyang Segment  
* 1 The fault structure and deformational characteristics  
Distributed along the northeastern edge of the Lower Liaohe Basin, this fault has a length of 60 km and consists of 2-3 branching segments that are arranged in a parallel manner (Figure 3). It exhibits good continuity and a compact structure, with branch spacing of 1-4 km, forming a step-like, narrow fault depression zone. The fault strikes NE-NEE, with an overall northern inclination and a steep dip.
* 2 Transverse faults and obstruction structures  
The western end is adjacent to the Yilan-Yitong fault and intersects with the Lower Liaohe section of the Tan-Lu fault zone. Located on the northeastern edge of the Lower Liaohe Basin, the segment exhibits a certain degree of structural tiering in the horizontal direction with the Fushun segment, as well as a notable vertical drop.
* 3 Geological and geomorphological characteristics  
The fault is situated between the new and old alluvial fans of the Hunhe River and the mountain front sloping plain. Small gullies related to faults are visible (Figure 4). The Quaternary faulting is considerable, with the Quaternary strata being continuous and complete, having a thickness of 20-100 m. The strata deepen rapidly from NEE to SWW, with a notable gradient change. The fault cuts into the upper parts of the MIS 20-103-MIS6-19 strata but does not affect the near-surface MIS 2-5-Q4 strata.  
Figure 3: Detailed structure and seismic tectonic map of the Hunhe fault in the Shenyang segment.  
4 Seismic features  
Located within the North China Basin's Lower Liaohe fault depression, both the crystalline basement and sedimentary cover are well developed, controlling the distribution of the Archean and Neogene strata, with striped Paleozoic-Mesozoic strata along the fault zone. In the context of neotectonic movement, the overall subsidence in the Lower Liaohe depression area is significant, with relatively thick Quaternary sediments. The edge of the Lower Liaohe Basin where the fault is located exhibits an inclined subsidence, while the differential subsidence on both sides of the fault is relatively insignificant. The fault zone is large in scale and has a complex and variable morphology, characterized by high seismic intensity and frequency. A magnitude 5 earthquake occurred along the fault in 1765, along with other earthquakes ranging from ML 4.3 to 4.0. Currently, smaller earthquakes are frequent, forming a banded or clustered distribution.  
5 Fault activity  
Fault activity exhibits multi-phase activity with a significant extension during the Yanshan period and even into the Paleogene. In the Neogene to Quaternary periods, the activity is inherited but somewhat diminished in intensity, with the most recent activity occurring during MIS 20-103-MIS6-19, characterized by relatively high levels of activity.
## 3 Testing Results Analysis  
### Analysis of Experimental Results of Upper Step Specim  
#### 3.1.1 Space-Time Evolution Process of Surrounding Rock Deformation and Fracture  
Figure 6 is the experimental stress-time curve of the upper step specimen. Figure 8 is the total displacement cloud diagram of the upper step specimen. In general, before the buried depth of 1150 m, the average and maximum values of the total displacement were mainly concentrated in the left arch roof and the inner wall of the arch footing, and the total displacement of the remaining positions was small (Figure 8A-G). When the buried depth was 1050 m, the first closed fracture ring was generated (Figure 7F). With the further increase of the buried depth (1150-1250 m), the multiple fracture zones of the specimen were superimposed on each other, and a fracture zone with a certain width was formed on the surface of the tunnel surrounding rock (Figure 7G-H). Before the rockburst (Figure 7I), the fracture zone of the surrounding rock was characterized by banded and interval distribution. The average and maximum values of total displacement were mainly concentrated at the right arch roof and arch footing (Figure 8I). During the formation of the fracture zone, the mechanical properties of the surrounding rock deteriorated, and a considerable part of the elastic strain energy of the surrounding rock was consumed, thereby reducing the energy accumulation degree of the surrounding rock. When the buried depth was 1350 m, the depth of the stress concentration area transferred from the excavation contour to the deep part of the surrounding rock was further increased, and finally a slight rockburst failure occurred (Figure 7I). At this time, the average and maximum values of displacement reached the peak (Figure 8J). During the rockburst, only the shedding of the specimen epidermis and the ejection at the right arch foot were seen, the ejection speed was slow, and no obvious pit was formed.  
Figure 6: Experimental stress–time curve of upper step specimen.
## 2 Contextualization  
Currently, there are multiple applications for extracting and analyzing data in real-time, each of which having their specific features and purpose. However, these applications have a common feature, namely the processing of data in real-time to facilitate the extraction of useful knowledge for companies and consequent decision making. As companies keep producing large amounts of data over time, they have started to express the desire for real-time processing applications to process complex events in real-time, improving their decision-making process.  
This article intends to analyze a set of applications that have the ability to process events in real-time, and to make a comparative analysis of each application. Furthermore, CEP in real-time is not easy to implement and adhere to, largely due to its complexity and variety of data. Due to these challenges, new applications began to appear, making this adoption simpler and easier to implement. This article will analyze various technologies for CEP in real-time, their different characteristics, and in the end, propose an architecture for CEP in real-time that will combine some of the technologies that were analyzed in this article.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 3 MC-BDP Reference Architecture
### MC-BDP Reference Architecture for Big Data Stream Processing  
MC-BDP is an evolution of the PaaS-BDP architectural pattern originally proposed by the authors. While PaaS-BDP introduced a framework-agnostic programming model for batch and stream processing and enabled different frameworks to share a pool of resources [65], MC-BDP focuses on stream processing and expands the previous model by explicitly prescribing a multi-tenant environment where nodes are deployed to multiple clouds [57]. Therefore, the rationale for proposing a multi-cloud model is to mitigate the risk of vendor lock-in, perceived as a major obstacle to the adoption of cloud computing.
## 2 Data and Methods
### CNN-LSTM Neural Network  
Convolutional neural network (CNN) is capable of extracting the spatial features of an element with its surrounding units by applying a convolutional algorithm within a certain coverage area. Long short-term memory (LSTM) is a recurrent neural network, that is able to link the present PM\({}_{2.5}\) to the past information of itself. Compared to the BP algorithm, LSTM does not need meteorological data to predict future PM\({}_{2.5}\) concentrations, but depends on the long-term correlation of the PM\({}_{2.5}\) sequences, which have been extensively studied by [PERSON] et al. [33] for typical megacities. In this study, with the aim to explore the spatial features and the autocorrelation of the PM\({}_{2.5}\) timeseries, a hybrid algorithm of CNN-LSTM was used to design the prediction model for IMFs from EEMD. By doing so, the timeseries of PM\({}_{2.5}\) in a certain area was regarded as a 3D image, which enables CNN to capture the spatial information. Then, the extracted information from CNN was transformed into a vector in time, used by LSTM prediction. The detailed algorithm of CNN and LSTM can be found in Appendix C.
For example, the biological toxicity of C\({}_{4}\)F\({}_{7}\)N is still debatable, and its GWP is very high (about 2400). The high liquefaction temperature of C\({}_{5}\)F\({}_{10}\)O limits its practical applications since it requires too much buffer gas. Lastly, HFO-1234 ze (E) and HFO-1336 mzz (Z) were the HFO gases suggested as insulating medium. HFO-1234 ze (E) has a lower insulating strength than SF\({}_{6}\) and a lower liquefaction temperature (\(-19.4\ ^{\circ}\)C at 0.1 MPa) [17]. The dielectric strength of HFO-1336 mzz (Z) is twice that of SF\({}_{6}\), but it has a very high boiling point of 33.4 \({}^{\circ}\)C [18]. In this paper, we suggest transhexafluoro-2-butene (HFO-1336 mzz (E)/(C\({}_{4}\)H\({}_{2}\)F\({}_{6}\)) as a promising dielectric gas. HFO-1336 mzz (E) is a common refrigerant because it has good thermal stability, is colorless, and has no fire or explosion risks. HFO-1336 mzz (E) has a very short atmospheric lifespan (days to weeks) and a low GWP of just approximately 18. Since no chlorine nor bromine is present, the ODP value is 0. Because its molecules have such strong electronegative characteristics, they provide a great insulating performance, 1.2 to 1.4 times higher than benchmark SF\({}_{6}\). HFO-1336 mzz (E) has the drawback of a high liquefaction temperature, about 8 \({}^{\circ}\)C, limiting its application as a dielectric gas. To compensate for the drawback of its high boiling point, the proposed gas should be mixed with a buffer gas, such as N2 or CO\({}_{2}\), with temperatures of \(-196\ ^{\circ}\)C and \(-79\ ^{\circ}\)C, respectively [19]. Related investigations on the insulation capabilities of HFO-1336 mzz (E) are currently lacking. [PERSON] et al. 2015 executed computational screening of several insulating gases, and among them, HFO-1336 mzz (E)'s dielectric characteristics were estimated to be almost 1.8 times those of SF\({}_{6}\)[20]. [PERSON] et al. 2020 tested a number of new environmentally beneficial gases. They discovered that the insulating strength of HFO-1336 mzz (E) and CO\({}_{2}\) or N\({}_{2}\) is comparable to that of mixtures of C\({}_{5}\)F\({}_{10}\)O/CO\({}_{2}\) and C\({}_{4}\)F\({}_{7}\)N/CO\({}_{2}\)[21]. Its synergistic interactions with various buffer gases and its breakdown characteristics in inhomogeneous electric fields remain unknown. The molecular structure and chemical properties of HFO-1336 mzz (E) gas are shown in Figure 1 and Table 1, respectively.
# Analysis of the Impact of Vibrations on the Driver of a Motor Vehicle
## 2 Measurement Process
### Measurement System  
For the purposes of the studies in question, a hardware and software platform comprising the Sirius data acquisition card (Figure 2) and the Dewesoft software working together with an S-box measuring computer was used. The measurements were performed in the Dewesoft software, supporting the Sirius data acquisition cards. Two 8-channel cards featuring the ACC/STG inputs were used in the tests.  
The measurement chain, installed on board the test vehicle, has been depicted in Figure 2. Pre-configured for the tests, the system enabled recording of the following:  
- Video camera--25 fps synchronously--at the vehicle front;  
- GPS signal for vehicle speed and acceleration at 10 Hz;  
- General vibrations at the driver's seat using a PCB 356B51 accelerometer (in three directions: X, Y, Z) at 5000 Hz;  
- Local vibrations at the steering wheel using a Dytran Piezo 3053M10 accelerometer (in three directions: X, Y, Z) at 5000 Hz;  
- Local vibrations at the steering wheel using a Dytran MEMS 7600B3 accelerometer (in three directions: X, Y, Z) at 5000 Hz.  
The accelerometer readings were checked for correctness by means of a manual PCB calibrator.  
\begin{table}
\begin{tabular}{c c c} \hline
**Vibration Type** &
\begin{tabular}{c} **Quantity Characteristic of Mechanical** \\ **Vibrations in the Working Environment** \\ \end{tabular} & **Permissible Limit** \\ \hline \multirow{2}{*}{Hand-arm vibration} & Daily vibration exposure (A8) & 2.8 m/s\({}^{2}\) \\ \cline{2-3}  & Short-term vibration exposure (a\({}_{\text{brx},\text{20 min}}\)) & 11.2 m/s\({}^{2}\) \\ \hline \multirow{2}{*}{Whole-body vibration} & Daily vibration exposure (A8) & 0.8 m/s\({}^{2}\) \\ \cline{2-3}  & Short-term vibration exposure (a\({}_{\text{brx},\text{20 min}}\)) & 3.2 m/s\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 2: Permissible exposure limits (PEL) of mechanical vibration.  
Figure 2: Testing equipment: (**a**) S-box and SIRIUS data acquisition card, (**b**) test vehicle; (**c**) sensor installed on the steering wheel, and (**d**) vibration measuring pad on the driver’s seat.
## 4 Discussion
For example, comparing multi temporal measurement data to study the change in terrain with time, more attention is paid to the relative change between data, and the quality of multi temporal data can be improved through cooperative registration;
3. GCP distribution experiments show that the uniform distribution of GCPs is crucial when using more than one GCP. Figure 10b uses nonlinear curve fitting. In [PERSON]'s study, linear fitting was used to determine that, on average, the vertical accuracy decreased by 0.09 m when the distance from the nearest GCP increased by 100 m [45]. In the experiment with four GCPs, the accuracy was improved after moving the GCP slightly towards the center of the study area compared with placing the GCP at the edge of the study area, which is similar to the study of [PERSON]. [PERSON]'s study concluded that the best horizontal accuracies are achieved by placing GCPs around the edges of the study area, but it is also essential to place GCPs inside the area with a stratified distribution to optimize vertical accuracy [28];
4. The DSM vertical _RMSE_ and DOM horizontal _RMSE_ obtained by direct georeferencing with the GSD set to 1.7 cm/pixel were 0.087 and 0.041 m, respectively. Without GCPs, the accuracy of the results was highly dependent on the accuracy of the image position data. The following measures can be taken to improve the accuracy of results obtained without GCPs. UAV cross flights and imagery with large overlap can provide redundant data and improve the reliability of image matching, which requires high computing power [1; 39]. The addition of oblique images helps to accurately estimate the internal and external orientation elements in the process of bundle adjustment, extract vertical features such as building sidewalls, and obtain the best vertical accuracy [13]. A more accurate GCP measurement method can be used, rather than simply increasing the number of GCPs. Another recommendation is to use a tripod-mounted prism instead of a pole-mounted prism, as well as the RTK static measurement method when time permits. If inaccurate coordinates are introduced when measuring GCPs, a more complex error surface will be introduced, as opposed to reducing the initial deformation [14]. The direct georeferencing technology integrated by UAV and GNSS RTK has advantages in monitoring locations with large ranges or difficult access. However, further development is needed. High-quality optical lenses and multi-frequency GPS can obtain higher quality images and positioning accuracy.
## 3 Enhanced Water Quality Index
### Weight Assignment  
This step involves assigning weights to each parameter. Previously, WQI calculation involved assigning unequal weights to parameters [6] or giving equal weights or no weights [7]. Usually, this is accomplished by assigning a 1-5 range to the variables. The high priority variables are given a weighting of five and low priority variables a value of one. Then, relative weights are computed. This method is known as the ranking method. Other weighting techniques are expert opinion, fuzzy interference and the Delphi method. Such weighting methods can introduce bias in the method and are dependent on the inclusion of all the selected parameters. Any missing parameter will directly affect the resultant WQI value. In our index, this process is replaced by a semi-supervised technique that involves first clustering the data and then applying an algorithm. The K-means clustering method is applied on the training data. The Elbow method is used to obtain the number of K. Once the training set is clustered, the tree-based feature importance scores are calculated. There are five tree-based feature importance methods in machine learning, i.e., XGBoost (XGB), Random Forest (RF), LightGBM (LGBM), CatBoost (CatB) and AdaBoost (AdaB) are taken to obtain weights for the parameters, and then the relative weights are computed. The final weights with the highest accuracy are used to calculate the EWQI on the test set. Equation (2) shows the formula for the relative weight of the parameter \(n\) where \(S_{n}\) = Score of the \(n\)th parameter.  
\[W_{n}=\frac{S_{n}}{\sum_{i=1}^{n}S_{i}} \tag{2}\]