esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 2 Related Work
### Federated Learning in IoT  
The above discussion is based on a single node and ignores the variations between different nodes, which are characteristic of the FloT. This oversight results that current anomaly detection methods suffer from insufficient adaptability and low coordination. [PERSON] et al. [24] combined federated learning and LSTM to train a global model in multiple node networks. In federated learning, the parameter aggregator collects theparameters of the trained model from each distributed training node and sets the average value of each parameter to be the parameter value in the converged model. Therefore, traditional federated learning can be abbreviated as Fed-Avg. But statistical heterogeneity due to the non-IID distribution of data across IoT devices often leads to the local models trained solely on their private data performing better than the global shared model. To solve this problem, [PERSON] et al. (2020) presented a systematic learning-theoretic study of personalization in learning and proposed and analyzed three algorithms. [PERSON] et al. (2020) presented a flexible personalized federated learning framework that achieves strong performance across various non-IID settings. However, the federated learning approaches proposed in these studies focus only on general IoT applications and do not consider the specific requirements of the FloT nor conduct experimental analysis within the FloT context.  
It can be seen that most of the current research focuses on the improvement of a single detection model or cooperative training among nodes in IoT. There is still a lack of anomaly detection methods that combine financial application scenarios and implement improvement and improvement from the model's own performance and training cooperation in the FloT.
## 1 Introduction  
Land cover information is of high value for applications in the Arctic. More than 65% of land area north of 60 degrees latitude is underlain by permanently frozen ground. Information on surface properties serves as an indicator or modeling input for the extraction of permafrost properties [1]. This also applies to assessments of carbon stores and fluxes. Permafrost is expected to change with climate change in the upcoming decades [2]. It has been pointed out by a range of studies (e.g., [PERSON] et al. [1] and [PERSON] et al. [3]) that there is a need for accurate land cover description in the northern high latitudes. A suitable circumpolar map does not yet exist to date, although a range of studies demonstrated the suitability of satellite data for this purpose on local and regional scales.  
A first review on traditional Arctic land cover maps as a baseline for the development of a circumpolar map was already published in 1995 by [PERSON] et al. [4]. The Circum-Arctic Vegetation Map (CAVM) has in the following been developed using satellite data (Advanced Very High Resolution Radiometer (AVHRR)) and published [5]. This map represents vegetation communities and is to date the only consistent map across the Arctic with the necessary detail for many applications. It is limited to the area north of the treeline and has a 1-km spatial resolution. This is insufficient for a range of applications, as this cannot capture the heterogeneous nature of Arctic land cover (e.g., [6; 7]). Recent globally-available land cover maps offer higher spatial detail (300-500 m), but low thematic content.  
A new land cover classification should be compatible with global maps and applicable for varying purposes. Local, regional and globally-developed datasets need to be reviewed and discussed in this context.  
This paper presents the techniques used for local to regional, as well as national-scale land cover mapping and discusses their varying applications in the Arctic. This includes the use of classifications for soil carbon and carbon flux upscaling, permafrost features, as well as transition monitoring. To illustrate the need for consistent and higher thematic detail in land cover products, we have compared the classes of currently available global land cover maps to the CAVM, a widely-accepted map for coarse resolution land cover assessment [8]. The purpose of this paper is to identify the required thematic detail in high latitude permafrost regions and suitable data sources, as well as to establish related problems in current global maps and potential methods to solve them.
## 3 Geological Characteristics of Shales in the Yanchang Formation
### Shale Oil and Gas Occurrence Characteristics and Contents  
3.6.1. Hydrocarbon Generation Simulation Experiment  
To study the coexisting characteristics of shale oil and gas in the Yanchang Formation, a hydrocarbon generation simulation experiment was conducted using outcrop samples (Ro = 0.5%, TOC = 3.3%) of the Chang 7 shale from the Hejiafang area. The experimental pressure was 50 MPa, and the temperature ranged from 336 \({}^{\circ}\)C to 600 \({}^{\circ}\)C. The experiment showed that, as the maturity increased, the proportion of gaseous hydrocarbons generated by the shale gradually increased. When the maturity was around 0.6%, the shale primarily generated liquid hydrocarbons, with gaseous hydrocarbons accounting for less than 0.4% of the mass. When the maturity increased to 0.8%, the proportion of gaseous hydrocarbons rose to 2.82%, with methane accounting for about 1.26% and C\({}_{2-5}\) components for about 1.56%. The mass percentage of liquid hydrocarbons was about 97.18%, with light hydrocarbons (C\({}_{6-14}\)) accounting for about 13.04% and C\({}_{14+}\) components accounting for 84.14% of the total. When the maturity increased to 1.2%, the proportion of gaseous hydrocarbons rose to 20%, with methane accounting for about 5.95% and C\({}_{2-5}\) components for about 14.05%. The mass percentage of liquid hydrocarbons was about 80%, with light hydrocarbons (C\({}_{6-14}\)) accounting for about 20.83% and C\({}_{14+}\) components accounting for 59.17% of the total. The hydrocarbon generation simulation experiment indicated that the Yanchang Formation shales are generally in a stage of orderly coexisting oil and gas, with the proportion of gaseous hydrocarbons increasing as maturity increases (Figure 12).  
Figure 11: Comparison of porosity of shales in the Yanchang Formation before and after oil extraction.
# Real-Time Prediction of Operating Parameter of TBM during Tunneling
## 3 Data Acquisition and Preprocessing
### Data Augmentation  
The size of borehole datasets obtained from projects is considerably smaller than the size of TBM operating datasets (Table 2). In other words, the tunnel sections positioned between the boreholes have none of the borehole information. For this reason, the loss of the TBM operating dataset between boreholes is inevitable due to the mismatch between geological information and the TBM operating dataset. Therefore, additional processing is a prerequisite to match the borehole and the TBM operating dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Project**} \\ \cline{2-3}
**List** & **A-1** & **B-2** \\ \hline TBM type & EPB shield & Slurry shield \\ Excavation diameter & 3.40 m & 353 m \\ Maximum thrust & 9600 kN & 12,000 kN \\ Maximum cutterhead revolution per minute & 90 rev/min & 4.6 rev/min \\ Maximum cutterhead power & 630 kW & 440 kW \\ Maximum cutterhead torque & 1250 kN\(\cdot\)m & 1410 kN\(\cdot\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of shield tunnel boring machine (TBM) applied to projects A-1 and B-2.  
Figure 2: ARIMAX-based algorithm for constructing model.  
In this study, a weight parameter expressed by a distance for the n-th segmental lining was introduced (Figure 3). The two weight parameters can be quantified as the distance between two adjacent boreholes from the n-th segmental lining position. The weight parameter makes it possible to retrieve both boreholes' information for the n-th segmental lining. Using this parameter, we augmented the dataset with output (thrust) and input parameters, as summarized in Table 3.
# Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems
## 6 Experimental Results  
In this section, we will show the experiments carried out to compare the number of steps required to converge to a stationary state using probabilistic and possibilistic Markov chains induced from the indistinguishability operators given in (6) and (8).  
The robots must perform the task according to the stimulus defined in Section 5 under different configurations of the system: different positions of the objects, parameters of the possibility response functions (\(\theta\) and the power \(n\)), and number of tasks. All the experiments have been carried out using MATLAB with different synthetic environments. Figure 1 shows the six types of environment used during the experiments, depending on the position of the tasks: randomly placed (Figure 1a), tasks grouped into two clusters (Figure 1b), tasks grouped into four clusters (Figure 1c), tasks grouped into six clusters (Figure 1d), tasks grouped into eight clusters (Figure 1e), and tasks grouped into 10 clusters (Figure 1f). This section extends the previous work given in [12] in order to consider clustered tasks.  
As pointed out in Section 2, the threshold value \(\theta\) must depend on the position of the tasks. During the performed experiments, \(\theta\) will depend on the maximum distance between tasks as follows.  
\[\theta=\frac{d_{max}}{nTH}, \tag{13}\]  
where \(d_{max}\) is the maximum distance between two objects and \(nTH\) is a parameter of the system. In our simulations, \(d_{max}\) is constant and equal to 800.5 units. In order to see the impact of the parameter \(nTH\) on the transition possibility (\(p_{ij}\)) from a task \(t_{i}\) to the task \(t_{j}\), Figure 2 shows the values of \(p_{ij}\) using the indistinguishability operators OPRF (Figure 2a) and EPRF (Figure 2b) with \(nTH=2,4,8\) and the power value \(n=2\). It should be noted that if the distance is equal to 0 (\(d(t_{i},t_{j})=0\)) then \(t_{j}=t_{i}\) and \(p_{ij}=p_{ii}\) is the possibility of remaining in the current task.  
Markov chains results to be comparable. To make this conversion, we use the transformation proposed in [22], where each element of \(M\) is normalized (divided by the sum of all the elements in its row) meeting the conditions of a probability distribution.
## 2 Materials and Methods
### Performance Function  
In general, the performance function in the structural reliability analysis is defined as follows [10]:  
\[\mathrm{g}=\mathrm{R}-\mathrm{S}, \tag{7}\]  
\(\mathrm{R}=\mathrm{r}(\mathrm{r})\)--is resistance function, \(\mathrm{r}\) represents set of resistance variables  
\(\mathrm{S}=\mathrm{s}(\mathrm{s})\)--is resistance function, \(\mathrm{s}\) represents set of load variables  
In the case of a shallow foundation, \(\mathrm{R}\) and \(\mathrm{S}\) can be expressed as follows [12]:  
\[\mathrm{S}=\mathrm{V}_{\mathrm{d}}=1.35\cdot\mathrm{V}_{\mathrm{g}}=1.35\cdot \mathrm{X}_{2}, \tag{8}\]  
\[\mathrm{R}=\mathrm{R}_{\mathrm{d}}=\mathrm{A}^{\prime}\cdot\left(\mathrm{c}^{ \prime}\mathrm{N}_{\mathrm{c}}\mathrm{b}_{\mathrm{c}}\mathrm{s}_{\mathrm{c}} \mathrm{i}_{\mathrm{c}}+\mathrm{q}^{\prime}\mathrm{N}_{\mathrm{q}}\mathrm{b}_{ \mathrm{q}}\mathrm{s}_{\mathrm{q}}\mathrm{i}_{\mathrm{q}}+0.5\gamma^{\prime} \mathrm{B}^{\prime}\mathrm{N}_{\gamma}\mathrm{b}_{\gamma}\mathrm{s}_{\gamma} \mathrm{i}_{\gamma}\right), \tag{9}\]  
where:  
\(\mathrm{N}_{\mathrm{c}}\), \(\mathrm{N}_{\mathrm{q}}\), \(\mathrm{N}_{\gamma}\)--are bearing capacity factors  
\(\mathrm{b}_{\mathrm{c}}\), \(\mathrm{b}_{\mathrm{q}}\), \(\mathrm{b}_{\gamma}\)--are the base inclination factors  
\(\mathrm{s}_{\mathrm{c}}\), \(\mathrm{s}_{\mathrm{q}}\), \(\mathrm{s}_{\mathrm{\gamma}}\)--are the shape factors  
\(\mathrm{i}_{\mathrm{c}}\), \(\mathrm{i}_{\mathrm{q}}\), \(\mathrm{i}_{\mathrm{v}}\)--are the inclination factors  
\(\mathrm{q}^{\prime}\)--is the design effective overburden pressure at the level of foundation base  
\(\mathrm{B}^{\prime}\)--is the effective foundation width  
Equations for the \"N\", \"b\", \"s\" and \"i\" coefficients used for performance function definition were taken from Eurocode 7 [12]. By including Equations (8) and (9) in 7 the following expression for the performance function in \(\mathrm{X}\) space yields:  
\[(\mathrm{X}_{1},\mathrm{X}_{2},\mathrm{X}_{3})=\mathrm{B}^{2}\mathrm{X}_{3} \bigg{\{}\mathrm{d}\cdot\mathrm{e}^{\pi\tan\mathrm{X}_{1}}\tan^{2}\left(45^{ \circ}+\mathrm{X}_{1}/2\right)\left(1+\sin\mathrm{X}_{1}\right)+0.7\mathrm{B} \bigg{(}\mathrm{e}^{\pi\tan\mathrm{X}_{1}}\tan^{2}\left(45^{\circ}+\frac{ \mathrm{X}_{1}}{2}\right)-1\bigg{)}\tan\mathrm{X}_{1}\bigg{\}}-\mathrm{X}_{2}, \tag{10}\]  
To simplify the reliability analysis, random variables \(\mathrm{X}_{1}\), \(\mathrm{X}_{2}\) and \(\mathrm{X}_{3}\) are transformed from the space of real random variables (\(\mathrm{X}\) space) to the standard normal space (\(\mathrm{U}\) space) using the Rosenblatt transformation [29]. By applying the transformation, the performance function in \(\mathrm{U}\) space yields:  
\[\mathrm{g}(\mathrm{U}_{1},\mathrm{U}_{2},\mathrm{U}_{3})=\mathrm{B}^{2}( \mathrm{\mu}_{3}+\sigma_{3}\mathrm{U}_{3})\bigg{\{}\mathrm{d}\cdot\mathrm{e}^ {\pi\tan\mathrm{(\mu}_{1}+\sigma_{1}\mathrm{U}_{1})}\tan^{2}\left(45^{\circ}+ (\mathrm{\mu}_{1}+\sigma_{1}\mathrm{U}_{1})/2\right)\left(1+\sin(\mathrm{\mu }_{1}+\sigma_{1}\mathrm{U}_{1})\right)+ \tag{11}\]
## 3 Proposed Method  
The overall structure of the proposed LPS-Net is constituted by four recurring enhancement stages, as shown in Figure 2. At each stage, a pair of intricately designed components, i.e., the Dual-Attention Enhancement Block and the Mirror Large Receptiveness Block, is utilized to, respectively, augment color and restore image details. Specifically, the incoming features are fed into these two blocks on parallel branches. Then, at the end of each stage, the Gated Fusion Unit adaptively merges features from the DAEB and MLB, forming a unified representation. Throughout this procedure, the DAEB alleviates color distortion resulting from different degrees of light attenuation in different color channels. Concurrently, the MLB, with its large field of view, gradually reinstates image details at every stage, leveraging its ability to garner more contextual data to correct Gaussian blur caused by light scattering and other texture distortions. Feature maps from these two branches are then fused by the GFU in order to achieve effective image color and detail rendering simultaneously.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 4 Modelling of Quadcopters
### Motion Equations
#### 4.5.2 Dynamics  
The quadcopter's motion is described by the dynamics equation defined in (6), which is generic and can be used for any rigid body [120; 126; 127; 129; 130; 131; 132; 133].  
\[M_{B}\vartheta+C_{B}(v)v=\Lambda \tag{6}\]  
where \(M_{B}\) is the system's inertia matrix given in (7), \(C_{B}(v)\) is the Coriolis Centripetal matrix given in (8), \(v\) is the generalized velocity vector given in (9) and containing the linear and angular velocities in (m/s) and (rad/s), respectively, \(\vartheta\) is the generalized acceleration vector containing the linear and angular accelerations in (m/s2) and (rad/s2), respectively, and \(\Lambda\) is the vector containing the force and torque terms in (N) and (Nm), respectively.  
\[M_{B}=\begin{bmatrix}m&0&0&0&0&0\\ 0&m&0&0&0&0\\ 0&0&m&0&0&0\\ 0&0&0&I_{xx}&0&0\\ 0&0&0&0&I_{yy}&0\\ 0&0&0&0&0&I_{zz}\end{bmatrix} \tag{7}\]  
\[C_{B}(v)=\begin{bmatrix}0&0&0&0&mw&-mv\\ 0&0&0&-mw&0&mu\\ 0&0&0&mv&-mu&0\\ 0&0&0&0&I_{zz}r&-I_{yy}q\\ 0&0&0&-I_{zz}r&0&I_{xx}p\\ 0&0&0&I_{yy}q&-I_{xx}p&0\end{bmatrix} \tag{8}\]  
\[v=\begin{bmatrix}u&v&w&p&q&r\end{bmatrix}^{T} \tag{9}\]  
where \(m\) is the body mass in (kg) and \"\(I_{xx}\), \(I_{yy}\), \(I_{zz}\)\" are the diagonal terms of the inertia matrix in (Nms2).  
As shown in (7), the inertia matrix is diagonal and constant, following the universal assumptions adopted in this approach to simplify the mathematical complexity. The dynamics equation in (6) is generic and can be applied to any quadcopter, based on the previously discussed assumptions. The vector \(\Lambda\) contains the specific forces and torques that must be incorporated into the model depending on the type of quadcopter and the mission requirements. By choosing a generic model, we provide a flexible framework that researchers can build upon and customize for various applications.
###### Abstract  
The airborne transient electromagnetic (TEM) method is widely used in land applications but faces challenges in marine applications due to the strong masking effects of seawater. The accuracy of the inverse Laplace transform algorithm strongly affects the applicability of the 2.5D TEM method in marine research; thus, an appropriate transmitted waveform is required. To resolve these issues, a trapezoidal wave was utilized as the waveform of the current source, owing to the substantial energy contribution of the low-frequency range. Further, the Talbot algorithm was selected for the inverse Laplace transform as it can yield an accurate response with smaller summation terms than the commonly used Gaver-Sethfest (G-S) algorithm. On this basis, a rugged seabed and a subsea tunnel were also investigated. The voltage response is obtained when the flight heights of the loop source change. The results confirmed that the Talbot algorithm with a trapezoidal waveform is more reliable and robust for complex marine geological models and is expected to provide an effective approach for marine explorations.  
Keywords:airborne transient electromagnetic method (TEM); inverse Laplace transform algorithm; marine investigation; Talbot algorithm; trapezoidal waveform +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Stereographic Method for CBH Estimation  
Obtaining CBH from paired sky images has been done by several authors in the past. For instance, the method proposed in [46] generated blocks of clouds which are computed to make a 3D reconstruction of the clouds. Then, the authors were able to obtain the height of the clouds by using geometric computation. On the other hand, the method in [66] used the cross correlation of non-projected saturation images to find all possible combinations that yield feasible heights, selecting the most correlated one (or the one with the minimum error).  
In the GRACIOSA project, a pure geometric method based on the relative position of the sky-imagers and the clouds was implemented. First, the algorithm looks for the same cloud feature in the images coming from the two sky-imagers. This task can be extremely difficult due to the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Definition** \\ \hline Mean Squared Error & \(\text{MSE}=(1/n)\frac{\sum_{t}(A_{t}-F_{t})^{2}}{\sum_{t}(A_{t}-F_{t})^{2}}\) \\ \hline Normalized RMS Error & \(nRMSE\ =\sqrt{MSE/\rho},\rho=(A_{max}-A_{min})\) \\ \hline Explained Variance & \(R^{2}=1-\frac{\sum_{t}(A_{t}-F_{t})^{2}/\sum_{t}(A_{t}-\mu)^{2}}{\sum_{t}(A_{ t}-\mu)^{2}}\) \\ \hline Mean Absolute Error & \(MAE=(1/n)\frac{\sum_{t}(A_{t}-F_{t})}{\sum_{t}(A_{t}-F_{t})/A_{t}|}\) \\ \hline Mean Absolute Percent Error & \(\text{MAPE}=\frac{100\%}{n}\sum_{t}(A_{t}-F_{t})/A_{t}|\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Regression Metrics: A actual, F forecast, \(\mu\) mean.  
Figure 15: Sub-images of circumsolar region: (**a**) low exposure and (**b**) raw fused.  
chaotic nature of clouds and slight changes in image properties such as luminosity. But fortunately, a Scale-Invariant Feature Transform (SIFT) algorithm [67], can handle this situation. This algorithm performs excellently, identifying features in a constantly changing shape (as clouds), since it considers possible changes in scale and orientation. SIFT is applied to pairs of simultaneous images from both cameras (Figure 16). Once the features from both images have been paired up, the best matches are selected to continue the calculations. Valid features are then transposed from the image (pixels) to real space (azimuth and zenith). With real space coordinates defined and the projection matrix of the lenses known, geometric computation is used to obtain the length of the vectors containing each feature and the geographical position of each camera in real space, from which the height of the evaluated feature can be derived.
## 3 Results  
Regarding the self-perceived referred pain, 43% of the subjects did not report any pain or discomfort following PPT of any of the TrPs locations on the sternocleidomastoid belly, while 57% of the subjects referred pain in the head and neck. A total of 80% of the latter reported a VAS 3 in at least one of the TrPs locations on the sternocleidomastoid belly, 15% at least a VAS 2 and only 5% a VAS 1. Overall, 244 TrPs locations evocated a referred pain response, of which 127 have been reported evocating a VAS 3 pain intensity, 56 a VAS 2 and 61 a VAS 1. The front cranium belly point resulted in evocating the most referred pain intensity with 46 VAS 3 responses, 11 VAS 2, and 9 VAS 1. The first trigger point situated on the mastoid insertion and the fourth trigger point situated on the sternum insertion did not evocate any VAS 3 or VAS 2 reaction. The frontal region resulted in being the most indicated area as the site of referred pain: 40% and 33% of the subjects reported pain in area 1 and area 2, respectively.  
The instantaneous temperature for each of the six areas of the template of each subject was extracted by the \"ROI Analysis\" at baseline (t0), 2 s before the PPT (t1), and 2 s (t2) and 60 s (t3) after the PPT.  
The temperature measured 2 s before the PPT of each TrPs location (t1) was compared with the baseline temperature (t0) of each of the six areas in the same subject. If at least two areas showed a \(\Delta\)T(t1 - t0) \(\geq\) 0.1 \({}^{\circ}\)C, it meant that the subject did not show basal conditions before the PPT; therefore, the measurement of this TrP location was not processed. After performing this comparison, 98 out of 230 PPT were excluded.  
In the end, the temperature template of the 132 remaining PPT and the correspondent VAS template were superimposed (Figure 3).  
Regarding the thermal IR imaging data analysis, the following parameters computed over each region's temperature (T) were calculated \(\Delta\)T\({}_{1}\) = T(t2) - T(t1) and \(\Delta\)T\({}_{2}\) = T(t3) - T(t1); differences in \(\Delta\)T range \(\geq\) 0.2 \({}^{\circ}\)C have been considered significant. [20] In 77% of the superimpositions, the referred pain area corresponded to a \(\Delta\)T\({}_{2}\)\(\geq\) 0.2 \({}^{\circ}\)C while only the 59% corresponded to a \(\Delta\)T\({}_{1}\)\(\geq\) 0.2 \({}^{\circ}\)C. In 19% of superimpositions, a \(\Delta\)T\({}_{2}\)\(\geq\) 0.2 \({}^{\circ}\)C did not correspond to a referred pain area indicated by the patient, and this percentage lowers to 4% for \(\Delta\)T\({}_{1}\)\(\geq\) 0.2 \({}^{\circ}\)C. None of the areas that reported a VAS 0 or 1 showed a \(\Delta\)T\({}_{1}\)\(\geq\) 0.2 \({}^{\circ}\)C or a \(\Delta\)T\({}_{2}\)\(\geq\) 0.2 \({}^{\circ}\)C.