esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
## 2 Related Work
### Learning Based Methods for Segmentation and Detection  
Several works have been proposed to address the localization of catheters in medical images. For example, [PERSON] et al. [18] introduced a CNN for catheter segmentation in chest X-ray images with curve fitting being used to connect line segments. [PERSON] at al. [19] proposed to learn end-to-end temporal continuity between frames coupled with flow guided warping techniques for improved segmentation in endovascular interventions. [PERSON] et al. [20] further suggested a method for extracting the needle in X-ray images. However, this method might not be able to accurately track the tools in X-ray due to the flexible nature of the catheter. [PERSON] et al. [21] detect catheter tips using region proposal networks (RPN); however, exact pixel wise locations are required for tracking to be effective. Recently, [PERSON] et al. [22] track catheter robot tips on successive frames via detection and segmentation in natural camera images. A more recent work by [PERSON] et al. [23] segments the catheter tip position in chest X-ray via a fully convolutional network (FCN). [PERSON] et al. [24] and [PERSON] et al. [25] employed similar approaches for catheter segmentation in ultrasound and X-ray images using a UNet [26] architecture. However, existing methods require large amounts of annotated data for supervised training in order to achieve significant improvements in accuracy. Moreover, collection is non-trivial due to privacy restrictions for patient data.  
Recent works [27; 28] have employed conventional data augmentation techniques as a strategy to increase existing data samples to avoid over-fitting and increase performance on limited datasets. In particular, some studies have investigated the effects of geometric transformations, such as translation, reflection, cropping, and the alteration of color schemes, to improve efficiency. [PERSON] et al. [29] suggested using scaling and translations to augment data for mammography lesions detection. Similar data augmentation techniques were employed with elastic deformations for surgical robot segmentation in [22]. However, the direct application of standard data augmentation techniques is often problem specific and it requires careful selection.
## 3 Results and Discussion
### Cooling Energy Saving  
Figure 6 shows the cooling energy saving after introducing the PCM material in the case of four cities. Globally, when we apply between 3 and 7 cm of the thickness of a PCM on the walls of an office building in coastal regions, the cooling energy saving is between 10% and 30%, depending on the region. Indeed, the cooling energy saved is estimated to be about 12.2% in Antsiranana, 12.5% in Mahajunga, 11.8% in Toamasina, and 11.9% in Taolagnaro during dry and rainy seasons. These results show that PCM materials have a more significant effect regarding cooling energy saving in warm tropical climates (e.g., Mahajunga city) than other coastal climate regions.  
In the four cities, the energy saving is very low in the dry season (May-Oct.) and estimated as 18%-37%. In view of all these results, it is interesting to deduce that cooling energy is one of the main components of energy consumption in hot zones. According to EIA [37], globally, the electricity consumed in the residential sector for cooling was estimated at 214 billion kWh in 2018, or about 15% of the total electricity consumption of the residential sector in the world.  
In the next subsection, the effect of different passive strategies on the total electricity consumption in the office will be evaluated.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**No Strategy**} & \multicolumn{2}{c}{**2050**} & \multicolumn{2}{c}{**With Passive Strategies**} \\ \hline
**Cities** & **Parameters** & **Min.** & **Max.** & **Min.** & **Max.** & **Min.** & **Max.** \\ \hline \multirow{2}{*}{Antsiranana} & Temperature (\({}^{\circ}\)C) & 23.1 & 28.5 & 23.5 & 29.0 & 23.0 & 28.2 \\ \cline{2-7}  & Relative humidity (\%) & 56.0 & 73.0 & 43.1 & 52.2 & 58.6 & 74.8 \\ \hline \multirow{2}{*}{Mahajunga} & Temperature (\({}^{\circ}\)C) & 23.2 & 28.6 & 23.4 & 29.0 & 23.0 & 28.2 \\ \cline{2-7}  & Relative humidity (\%) & 55.9 & 74.8 & 42.3 & 46.7 & 56.9 & 77.4 \\ \hline \multirow{2}{*}{Taolagnaro} & Temperature (\({}^{\circ}\)C) & 22.3 & 28.5 & 22.7 & 29.0 & 22.0 & 28.1 \\ \cline{2-7}  & Relative humidity (\%) & 51.6 & 71.6 & 43.5 & 66.7 & 65.3 & 73.1 \\ \hline \multirow{2}{*}{Toamasina} & Temperature (\({}^{\circ}\)C) & 22.5 & 28.4 & 22.8 & 29.0 & 22.0 & 28.2 \\ \cline{2-7}  & Relative humidity (\%) & 72.5 & 76.8 & 44.3 & 60.9 & 72.9 & 77.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average indoor adaptive comfort in the different selected cities with 90% acceptability Range.
# Design and Analysis of a Novel Microspeaker with Enhanced Low-Frequency SPL and Size Reduction
## 4 New Microspeaker Design
### Air Gap Design  
The top plates and the yoke, which are made of SPCC, are the important components of the microspeaker structure. The magnetic flux generated by the permanent magnets is concentrated on the coil through the top plates and the yoke. The air gap refers to the distance between the top plate and the coil, as shown in Figure 16. In microspeaker designs, the distance between the coil and the top plate is the same as that between the magnets and the coil. Additional center top plates, outer top plates, center magnets, and outer magnets can be used if the air gap is decreased, as a decreased air gap can improve the total force. The air gap of 1610_SPK was 0.15 mm. Previously, the achievable minimum air gap was 0.15 mm, owing to the manufacturing technology at that time. During the manufacturing process, the coil is inserted between the center top plate and the outer top plate, and then the air gap is manually checked by human eyes using a tool maker microscope (Mitutoyo).  
Advances in manufacturing technology have enabled the reduction of the air gap in speaker designs, owing to the tightly controlled tolerances of modern jigs and molds. The air gap is reduced using new automated machines that inspect the air gap during manufacturing. Owing to the new automated visual inspection machine (Hirox), the air gap can be reduced from 0.15 to 0.10 mm. Reducing the air gap concentrates more magnetic flux in the coil, resulting in a higher force. In the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **1610_SPK** & **1410_SPK\_3 mag** & **1410_SPK\_5 mag** & **1410_SPK\_2 mag** \\ \hline Magnet Number & 3 & 3 & 5 & 2 (ring shape) \\ Center magnet Volume & 28.14 mm\({}^{3}\) & 37.65 mm\({}^{3}\) & 37.65 mm\({}^{3}\) & 37.65 mm\({}^{3}\) \\ Outer magnet Volume & 39.29 mm\({}^{3}\) & 19.24 mm\({}^{3}\) & 30.26 mm\({}^{3}\) & 34.35 mm\({}^{3}\) \\ Flux density at section 3 & 0.727 T & 0.598 T & 0.588 T & 0.586 T \\ Flux density at section 3 & 0.237 T & 0.365 T & 0.430 T & 0.470 T \\ Flux density at section 3 & 0.238 T & 0.405 T & 0.588 T & 0.598 T \\ Total flux density & 0.560 T & 0.485 T & 0.538 T & 0.551 T \\ Force & 0.388 N & 0.406 N & 0.450 N & 0.462 N \\ \hline \hline \end{tabular}
\end{table}
Table 7: Result of the force due to the changes in magnetic circuit structure designs.  
Figure 15: Magnetic circuit structures: (**a**) 1610_SPK, (**b**) 1410_SPK 3 mag, (**c**) 1410_SPK 5 mag, and (**d**) 1410_SPK_2 mag (ring shape).  
analysis, the coil dimensions were fixed, and the air gap was gradually reduced. The results of the flux density and the force acting on the coil when the air gap was reduced from 0.15 to 0.1 mm are presented in Table 8.  
The results show that reducing the air gap from 0.15 to 0.10 mm increased the force by approximately 8.7%. Subsequently, 1410_SPK with two permanent magnets (a ring-shaped outer magnet and a center magnet) and an air gap of 0.10 mm was subjected to coil design analysis.
# A Stock Prediction Method Based on Heterogeneous Bidirectional LSTM
## 4 Experiment
### Predict the Price for the Next Day  
In this section, we initially use the prices from 10 days to predict the price on the 11 th day. With a random seed number set to 10, a batch size of 500 samples, and an Adam learning rate of 0.59, the BiLSTM model achieves a prediction accuracy of 93.80% on the training set and 92.79% on the test set. This represents a certain degree of improvement in accuracy compared to the classical LSTM. The classical LSTM is less stable in prediction, easily influenced by hyperparameters, and exhibits greater volatility. The BiLSTM, leveraging the advantages of bidirectional transmission, enhances the dependency of information over time, which significantly improves this issue. In the He-BiLSTM model, the prediction accuracy is even higher than that of Bi-LSTM, with the training set reaching an accuracy of 95.41% and the test set reaching 94.23%. The prediction effect diagrams for both BiLSTM and He-BiLSTM are shown in Figure 4.  
Figure 4: Comparison of He-BiLSTM and BiLSTM in S&P 500 forecasting.  
From Figure 4, it is evident that during periods of price volatility, the predictions of He-BiLSTM are closer to the actual values. To compare the predictive capabilities of the two models during periods of stable prices, a section of Figure 4 is enlarged for comparison. The comparative chart is shown in Figure 5.  
Upon comparing the prediction effect diagrams during periods of stable prices, it is observed that the predictions of He-BiLSTM are more accurate than those of BiLSTM. This indicates that He-BiLSTM demonstrates superior robustness compared to BiLSTM, regardless of whether the prices are stable or experiencing sudden changes.  
For the comparison of evaluation metrics for the two models, see Table 1:  
From Table 1, it can be seen that compared to BiLSTM, He-BiLSTM has superior evaluation metrics across the board. On the test set, the MSE of He-BiLSTM is reduced by 38.36% compared to BiLSTM, and the MAE is reduced by 33.42% compared to BiLSTM. In terms of accuracy, He-BiLSTM has improved by an additional 3.87% over BiLSTM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Evaluation Indicators** & **MSE (Train)** & **MSE (Test)** & **MAE (Train)** & **MAE (Test)** & \(\mathbf{R^{2}}\) (Train)** & \(\mathbf{R^{2}}\) **(Test)** \\ \hline BiLSTM & \(9.49135\times 10^{-5}\) & 0.000318619 & 0.00809841 & 0.0136013 & 0.919083 & 0.90357 \\ He-BiLSTM & \(6.12311\times 10^{-5}\) & 0.000196391 & 0.00572986 & 0.00905635 & 0.954146 & 0.942272 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of evaluation metrics for predicting the next day.  
Figure 5: Training and test set local comparison effect diagram.
## 3 Performance Parameters
### Characterization of Residual FEXT  
In this section, we briefly discuss on the stochastic characterization of the residual FEXT for both the DS and the US direction transmissions.  
Considering the DS direction, let \(c_{ij}^{(k,q)}(d_{i})\) be the \((i,j)\) element of \(\mathbf{C}_{k}^{q}\). The residual FEXT term in the \(i\)-th received observable \(r_{i,k}\) is:  
\[x_{F,j,k}^{(q)}(d_{i})=-H_{D,k}(d_{i})\sum_{j=1}^{N}c_{ij}^{(k,q)}b_{j,k}(1- \delta_{ij}). \tag{14}\]  
and \(\delta_{ij}\) is the Kronecker symbol. The \(i\)-th main diagonal term of \(-\mathbf{D}_{k}\mathbf{C}_{k}^{q}\) is such that \(|H_{D,k}(d_{i})c_{ii}^{(k,q)}|\ll H_{D,k}(d_{i})\) so that it is neglected in the numerator in (4). The residual FEXT power is obtained from (14) by first averaging the square modulus of (14) with respect to transmitted symbols \(\{b_{i,k}\}\), \(i=1,\ldots,N\) and then with respect to the FEXT frequency selective terms \(g_{ij}(f_{k})\) and phase terms \(\phi_{ij}\) in (7) thus obtaining:  
\[I_{F,j,k}^{(q)}(d_{i})=|H_{D,k}(d_{i})|^{2}\sum_{j=1}^{N}E\{|c_{ij}^{(k,q)}|^{2 }\}P\times(1-\delta_{ij}). \tag{15}\]where expectation in (15) is taken with respect to selective FEXT and phase terms. In the no-vectoring case (i.e., \(q=1\)) for the DS transmission the FEXT power is:  
\[I_{F,i,k}^{(1)}(d_{i})=\chi_{F}f_{K}^{2}|H_{D,k}(d_{i})|^{2}\sum_{j=1}^{N}l_{ij} e^{2y_{ij}}. \tag{16}\]  
To evaluate the average FEXT in the US case, we need to account for the different distances of the terminals from the Cabinet as well as for the (mandatory) UPBO. It is not difficult to prove that the residual FEXT average power after cancellation of \(q\) order is:  
\[I_{F,i,k}^{(q)}=\sum_{j=1}^{N}|H_{D,k}(d_{j})|^{2}E\{|e_{ij}^{(k,q)}|^{2}\}P_{ j}(1-\delta_{ij}), \tag{17}\]  
and \(e_{ij}^{(k,q)}\) is the \((i,j)\)-th term of the matrix \(\mathbf{E}_{k^{\prime}}^{q}\) which is responsible for the residual FEXT after cancellation when using the \(q\)-th order approximation of the channel matrix inverse; \(P_{j}\) is the power transmitted by the \(j\)-th terminal at distance \(d_{j}\) from the Cabinet. The \(P_{j}\) is set in accordance with the UPBO, which is mandatory to avoid terminals closer to the Cabinet to impair terminal, which are far from the Cabinet. In particular, we have:  
\[P_{j}=\left\{\begin{array}{cc}\frac{|H_{D,k}(d_{UPBO})|^{2}}{|H_{D,k}(d_{j})| ^{2}}P&\text{for}\;\;d_{j}\leq d_{UPBO}\\ P&\text{otherwise}\end{array}\right. \tag{18}\]  
where \(d_{UPBO}\) is the UPBO distance influencing the achievable bit rate on the copper cable. The \(d_{UPBO}\) is selected by the telecom operators after a measurement campaign. The \(P\) is the maximum power that can be transmitted per sub-carrier. Before concluding this Section, in the no-vectoring case (i.e., \(q=1\)) the average US FEXT power is:  
\[I_{F,i,k}^{(q)}=\chi_{F}f_{k}^{2}\sum_{j=1}^{N}\left[|H_{D,k}(d_{UPBO})|^{2}l _{ij}e^{2y_{ij}}\delta_{U,j}+|H_{D,k}(d_{j})|^{2}l_{ij}e^{2y_{ij}}(1-\delta_{ U,j})\right]P_{j}\times(1-\delta_{ij}) \tag{19}\]  
and \(\delta_{U,j}=1\) when \(d_{j}\leq d_{UPBO}\) and 0 otherwise.
## 6 Evaluation  
This area of research typically uses information retrieval systems evaluation methods [30, 31, 32, 33, 34, 35]. A test collection includes a set of service offers \(\mathbb{S}\), a number of service requests \(\mathbb{Q}\) and relevance judgements (RJ). The relevance judgements are identified by how, and to which degree, the offer is relevant to a request by human experts. Evaluation involves the comparison of RJ with a system under evaluation; we simply compare the computed rank with the one induced by the relevant judgement. Therefore, a reliable RJ is critical for a precise and effective evaluation. As these evaluation techniques become ubiquitous, the accuracy and meaningfulness of the result is subjective [36]. Due to conflicts in human judgement,research has been conducted to determine how to obtain reliable relevance judgements [37; 38]. In this section, we first recall some of the ubiquitous IR measurements and then explain the extended version of these metrics that have been adopted in the service discovery domain and, finally, describe our evaluation methodologies towards a trustable evaluation measurement.  
The existing evaluation approaches are based on binary relevance: typically precision and recall. Binary relevance is limited to identifying whether the result is relevant or not. Recall is the proportion of the relevant items that are in the set of returned results, and precision is the proportion of items in the returned results that are relevant [38].  
[PERSON] _et al_. [39] use a more flexible rubric in the form of a fuzzy linguistic variable where matchmaking is the degree of match. The values of this variable are fuzzy terms, such as irrelevant, slightly relevant, somewhat relevant, relevant and very relevant. [PERSON] _et al_. [40] proposed a cumulative gain \((CG)\), at rank \(i\), \(CG_{i}\), to generalize these binary metrics to support the notion of graded relevance. It measures the gain \(g\) the user receives by scanning the top \(i\) items of ranked list \(j\). Gain \(g\) depends on the application. Specifically, \(CG_{i}\) is defined as:  
\[CG_{i}=\sum_{j=1}^{i}g(r_{j})\]  
As shown below, we calculate the gain in our setting based on real quality values rather than on RJ, as in classical IR.
# Implementation of Climate Change Effects on Slope Stability Analysis
## 3 Example of Landslide
### FEM Surface Seepage Model of Slope  
The slope safety factor variation during rainfall was assessed by means of FEM numerical modelling. Variation in surface water content and porewater pressure during infiltration process were analyzed by SEEP/W module of GeoStudio. SLOPE/W is a 2D limit equilibrium modeling program for slope stability that provides a wide range of modeling capabilities, including porewater pressure and rapid drawdown. The program supports a comprehensive list of material models. The main advantage of SLOPE/W over other programs is that this program has built-in unsaturated shear strength models that  
Figure 4: Hydraulic conductivity as a function of matric suction.  
Figure 3: Volumetric water content as a function of matric suction.  
allow modeling of unsaturated soil. However, SLOPE/W does not have the capability to model the progressive failure of a slope due to successive shrink-swell cycles [4]. The SEEP/W is the seepage module of the GeoStudio package and can be used to simulate water flow in saturated or unsaturated soils. Since both the SEEP/W and SLOPE/W are part of the same GeoStudio software package, they allow easy coupling and continuous calculation of the factor of safety for all simulation time steps.  
The geometry of the model is shown in Figure 5. The dimensions of the model are 45 m \(\times\) 20 m, with two soil layers defined, the upper layer being sandy clay and the lower layer being marl. The mesh of the model contains elements of 1 m \(\times\) 1 m. The model boundaries are also defined.  
The upper surface was defined as rainfall infiltration boundary. All rainwater was infiltrated at the beginning of rainfall. The bottom of the model was nearly impermeable, while the right side of the model allowed the water to drain. Figure 5 shows the mesh of the model of the slope with the water loading and boundary conditions defined to simulate the flow of water in the soil down the slope. The infiltration depth is defined by layers geometry, because of the impermeable rocky base. The rainfall intensity defined as Climate change in this paper is set to 139 mm/day [29]. Simultaneously, the simulation lasted for 3 days from the start of rainfall. The blue dashed line indicates the groundwater level. Water drainage is possible on the right side.  
The analysis includes three phases: the first phase, the second phase in which the rain is applied for three days, and the third phase in which the rain stops.
## 3 Results and Discussion
### Diffusion Dialysis  
Diffusion dialysis using the synthesized membranes with different ZIF-8 content was carried out to study the diffusional ion transport process. In Figure 6, it can be seen that the conductivity of the permeate side increases during the experiments, which is caused by the diffusion of ions from the high concentration chamber to the low concentration chamber. However, the rate of conductivity change is different. After 1 h self-diffusion, the conductivity of the M-0 membrane changed from 10 \(\upmu\)s/cm to 275 \(\upmu\)s/cm. The interfacial polymerization between MPD and TMC limited the diffusion process; as a result, the diffusion of NaCl for the M-1 membrane becomes slower, and consequently, the conductivity change reduced. Theoretically, the dehydrated radius of Na\({}^{+}\) is around 0.95 A [21; 23], which is smaller than the ZIF-8 pores (3.4 A). Therefore, a small addition of ZIF-8 to the TFN increased the Na\({}^{+}\) flux. With a higher ZIF-8 loading in the surface selective layer, the Na\({}^{+}\) flux further increases, which may be caused by two reasons. First, increased ZIF-8 loadings introduce more free space that can facilitate the Na\({}^{+}\) migration. More importantly, the structural changes with ZIF-8 incorporation and the possible voids between the organic/inorganic interphase could facilitate the NaCl permeance [17; 33].  
Figure 5: Ion exchange capacity (IEC) and water uptake of the membranes with different ZIF-8 loadings.  
Figure 6: Conductivity change of dilute compartment during diffusion process.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 3 Adaptive Fault Location Algorithm for Active Distribution Systems
### Step 5: Iterative Process to Determine the Fault Current
#### 3.6.1 Update the Voltage and Current Vectors  
As shown in the algorithm presented in Figure 3, the fault is estimated downstream of the first system section and then, the values of voltage and current measured at the local terminal are updated to the next system buses by Equations (24) and (25).  
\[\left[\begin{array}{c}\mathbf{V}_{k+1}^{p,f}\\ \mathbf{I}_{k+1}^{p,f}\end{array}\right]=\left[\begin{array}{cc}\mathbf{d}_{l}&-\mathbf{ b}_{l}\\ -\mathbf{c}_{l}&\mathbf{a}_{l}\end{array}\right]\cdot\left[\begin{array}{c}\mathbf{V}_{k}^{p, f}\\ \mathbf{I}_{k,k+1}^{p,f}\end{array}\right] \tag{24}\]  
\[\mathbf{I}_{k+1,k+2}^{p,f}=\mathbf{I}_{k+1}^{p,f}-\mathbf{I}_{L_{k+1}}^{p,f} \tag{25}\]  
Equation (25) is used for all system nodes, except for the node where the lateral of the DER is connected. For this node, the current \(\mathbf{I}_{k+1,k+2}^{p,f}\) is updated by Equation (26).  
\[\mathbf{I}_{k+1,k+2}^{p,f}=\mathbf{I}_{k+1}^{p,f}-\mathbf{I}_{L_{k+1}}^{p,f}+\mathbf{I}_{g,k+1} ^{p,f} \tag{26}\]  
where \(\mathbf{I}_{g,k+1}^{p,f}\) is the current contribution from the DER to the node \(k+1\), as shown in Figure 10.  
Figure 10: Update of the current for the node where the lateral of the DER is connected.  
The proposed fault location algorithm considers that the DER can be allocated in any node of the system. Therefore, the current contribution from the DER to the node \(k+1\) is calculated from the nodal admittance matrix of the lateral of the DER, as shown by Equations (27) and (28).  
\[\left[\begin{array}{c}\mathbf{V}_{L_{\mathrm{G}}1}^{f}\\ \mathbf{V}_{L_{\mathrm{G}}2}^{f}\\ \mathbf{V}_{L_{\mathrm{G}}3}^{f}\\ \vdots\\ \vdots\\ \mathbf{V}_{L_{\mathrm{G}}k}^{f}\end{array}\right]=\left[\begin{array}{cccccc} \mathbf{Y}_{lg_{11}}&\mathbf{Y}_{lg_{12}}&0&0&\cdots&0\\ \mathbf{Y}_{lg_{21}}&\mathbf{Y}_{lg_{21}}&\mathbf{Y}_{lg_{23}}&0&\cdots&0\\ 0&\mathbf{Y}_{lg_{32}}&\mathbf{Y}_{lg_{33}}&\mathbf{Y}_{lg_{23}}&\cdots&0\\ 0&0&0&\ddots&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\mathbf{Y}_{lg_{3k-1}}&\mathbf{Y}_{lg_{3k}}\end{array}\right]^{-1}\left[ \begin{array}{c}\mathbf{y}_{DER}\cdot\mathbf{E}\\ 0\\ 0\\ \vdots\\ \mathbf{y}_{lg_{3k+1}}\mathbf{V}_{k+1}^{f}\end{array}\right] \tag{27}\]  
\[\mathbf{f}_{g,k+1}^{f}=\mathbf{y}_{lg_{3k+1}}\left(\mathbf{V}_{LG_{\mathrm{G}}}^{f}-\mathbf{V }_{k+1}^{f}\right) \tag{28}\]  
with,  
\[\mathbf{Y}_{\mathrm{lg_{ii}}}=\sum_{\mathrm{j}\mathrm{i}\mathrm{i}\mathrm{i}}^{ \mathrm{k}}\mathbf{y}_{\mathrm{lg_{ii}}} \tag{29}\]  
\[\mathbf{Y}_{\mathrm{lg_{ii}}}=-\mathbf{y}_{\mathrm{lg_{ii}}} \tag{30}\]