esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
###### Abstract  
Despite the great success of speaker recognition models based on deep neural networks, deploying a pre-trained model in real-world scenarios often leads to significant performance degradation due to the domain mismatch between training and testing conditions. Various adaptation methods have been developed to address this issue by modifying either the front-end embedding network or the back-end scoring model. However, existing methods typically rely on knowledge of the network, scoring model, or even the source data. In this study, we introduce a knowledge-free adaptation approach that only necessitates the unlabeled target data. Our core concept is based on the assumption that domain mismatch primarily stems from distributional distortion in the embedding space, such as shifting, rotation, and scaling while maintaining inter-speaker discrimination for data from unknown domains. Building on this assumption, we propose clustering LDA (C-LDA), a full-rank linear discriminant analysis (LDA) based on agglomerative hierarchical clustering (AHC) to compensate for this distortion. This approach does not need any human labels and does not rely on any knowledge of the model in the source domain, making it suitable for real-world applications. Theoretical analysis indicates that with cosine scoring, C-LDA is capable of eliminating distributional distortion related to global shift and within-speaker covariance rotation and scaling. Surprisingly, our experiments demonstrated that this simple approach can outperform more complex methods that require full or partial knowledge, including front-end approaches such as fine-tuning and distribution alignment, and back-end approaches such as unsupervised probabilistic linear discriminant analysis (PLDA) adaptation. Additional experiments demonstrated that C-LDA is insensitive to hyperparameters and works well in both multi-domain and single-domain adaptation scenarios.  
domain mismatch; unsupervised adaptation; speaker recognition Article
## 3 Results
### Functional Suitability
#### 3.1.3 Compliance with Sustainability Requirements  
The requirements of different sustainability programs reveal light interpretations of climate adaptation measures. This section analyses climate adaptation aspects included in the sustainability certification programs for neighbourhoods.  
BREEAM (Building Research Establishment Environmental Assessment Methodology) defines the standards for communities expressing the following climate adaptation sustainability topics [15]:  
* rainwater assessment;
* site-specific flood risk assessment and management ensuring water run-off capabilities to deal with a 100-year flood event;
* microclimate simulations illustrating the effect of urban morphology on air temperature, solar exposure, air movement, air pollution, and acoustics;
* a climate adaptation plan considering the predicted climate change impact;
* green infrastructure and high quality of open space;
* ecological plans creating aesthetic value, ensuring social wellbeing, and adaptation to climate change.  
LEED (Leadership in Energy and Environmental Design) for neighbourhood includes aspects relevant to climate change such as [16]:  
* floodplain avoidance;
* rainwater management;
* green infrastructure;
* heat island reduction;
* ecological services considering habitats and wetlands.  
However, the LEED framework is focused more on buildings rather. than neighbourhoods, and excludes climate adaptation as a separate assessment field.  
The DGNB (Deutsche Gesellschaft fur Nachhaltiges Bauen) for new urban quarters defines the categorisation of quality aspects. Each category includes the climate adaptation measures listed below [17]:  
* ecological qualities: climate change in the district and water and soil protection;
* economic qualities: life cycle costs and economic effects on the community;
* social-cultural qualities: environmental impacts;
* technical qualities: rainwater management;
* process qualities: participation.  
The DGNB differs from BREEAM and LEED by adding social and economic aspects of climate adaptation planning, such as life cycle costs and financial effects on the community. However, some aspects--such as participation and the estimation of economic effect on the community--are too complex for current climate adaptation software tools.  
Table 4 summarises the main climate adaptation aspects covered in the sustainability certifications and considers which aspects can be analysed with different software tools. These results show that GreenScenario and AST tools address the largest number of requirements. This evaluation considers only the ability of each piece of software to cover each aspect from different sustainability certifications. However, wider research could be performed to evaluate how well each piece of software can perform analysis on each aspect considering sustainability levels.
# Improved Acoustic Emission Tomography Algorithm Based on Lasso Regression
## 2 Conventional Algorithm of AE Tomography
AE tomography can raise the accuracy of the source location and emission time by increasing node and relay points. The relay points in each cell are described in Figure 2. An orthogonal straight line to the edge is drawn at each division point. Relay points are constructed at the intersections of the straight lines and the edges. It is essential to determine the appropriate density of relay points before calculation, since the density of relay points and calculation cost are proportional. The average of the potential emission time \(O^{\prime}_{k}\) at the source location is shown as:  
\[O^{\prime}_{k}=\frac{1}{n}\sum_{i=1}^{n}P^{\prime}_{ik}. \tag{3}\]  
Figure 1: Flowchart of AE tomography. This value is adopted for the reconstruction algorithm in the following part. The velocity distribution is identified using the difference between the theoretical and observed travel times. Typically, it is determined that the averaged potential emission time at the nodal or relay point of the minimum variance is chosen as the source location. The theoretical arrival time \(A_{i}^{\prime}\) of receiver \(i\) shown as follows:  
\[A_{i}^{\prime} =O_{k}^{\prime}+\Delta T_{ik}. \tag{4}\]  
It is noted that \(\Delta T_{ik}\) is the travel time from the receiver to the nodal and relay point selected as the source location correspondingly. The traditional algorithm SIRT adopts iterates through comparative measurement data to generate a velocity distribution representation of an interior physical structure based on the analysis in AE tomography. The equation of the SIRT algorithm is shown as follows:  
\[S_{j}^{(I)}=S_{j}^{(I-1)}+\sum_{i}\Bigl{(}T_{oi}-\Delta T_{i}^{(I-1)}\Delta I _{ij}/L_{i}\Bigr{)}/\sum_{i}l_{ij}, \tag{5}\]  
where \(I\) is the iteration times and \(j\) is the cell number, \(i\) is the number of ray, \(S_{j}^{(I)}\) means the slowness of cell \(j\) in iteration \(I\), \(T_{oi}\) is the observed travel time, \(\Delta T_{i}^{(I-1)}\) is the theoretical travel time of ray \(i\) for the \((I-1)\)th iteration, \(l_{ij}\) is the ray length of \(i\) in \(j\) cell, and \(L_{i}\) is the ray length between the source location to the receiver [14]. It is obvious that the SIRT algorithm needs lots of event data for iterate calculation. However, these algorithms have their own restrictions. For instance, they need sufficient event data for velocity reconstructions. This research utilized Lasso regression for AE tomography to eliminate these deficiencies.
# Spatio-Temporal Distribution of Ground Deformation Due to 2018 Lombok Earthquake Series
## 3 Results
### Average Magnitude of Displacement at Local Scale  
We estimated displacements on a local scale: Mataram City, Pamenang, Tampes, Sukadana, Sembalun, and Belanting (Figure 6). Mataram City appeared to be a modest average in subsidence and uplift, ranging from \(-0.053\) m to \(0.025\) m. The most extreme displacements were found at Tampes, Pamenang and Sukadana. In Tampes, the highest uplift of \(0.530\) m was caused by a mainshock earthquake series of \(7.0\) Mw on \(5\) August 2018, while the deepest subsidence of \(-0.121\) m was caused by the aftershock earthquake series of \(6.2\) Mw on \(9\) August 2018 (Figure 6).  
Pamenang was also affected by deformation since it experienced the deepest subsidence of \(-0.225\) m due to the aftershock earthquake of \(6.2\) Mw on \(9\) August 2018. Another subsidence was caused by the foreshock earthquake of \(6.4\) Mw on \(29\) July 2018 reaching \(-0.083\) m. The uplift in this area reached \(0.387\) m. This was caused by the mainshock earthquake of \(7.0\) Mw on \(5\) August 2018. The other area with extreme uplift in this study was Sukadana. It reached \(0.374\) m following the mainshock earthquake of \(7.0\) Mw on \(5\) August 2018. Even if the subsidence in Sukadana was not the greatest, it still reached \(-0.105\) m and it was caused by the \(6.2\) Mw earthquake on \(9\) August 2018.  
Figure 5: Spatial distribution of vertical displacement due to earthquakes on \(29\) July, \(5\) August, \(9\) August, and \(19\) August 2018.  
Sembalun is located next to the Rinjani volcanic complex. It is a relatively flat area surrounded by mountainous area. Two major subspaces occurred due to the foreshock earthquake of 6.4 Mw on 29 July 2018 and the aftershock earthquake of 6.2 Mw on 9 August 2018. The subsidences were measured consecutively \(-0.095\) m and \(-0.090\) m. This was the only area having two nearly identical subsidence due to two different earthquakes.  
Belanting showed an unique characteristic. The highest uplift due to the earthquake of 6.9 Mw on 19 August 2018 was found in this area. It reached 0.265 m. The deepest subsidence there was measured \(-0.110\) m.
## 3 Design and Performance of Low-Frequency Low-Reflection BSI Tunnels  
Next, we designed a low-frequency low-reflection BSI tunnel, which consisted of a pair of ultrathin AMs attached symmetrically on both sides. The schematic diagram of the  
Figure 2: (**a**) Schematic of designed AM composed of 6 unit cells. (**b**) Theoretical continuous (blue solid line) and discrete (red open circles) phase delays of the AM. Schematic of theoretical propagation paths of the reflected waves (blue solid arrows) created by the AM for the incident waves (red solid arrows) with the angles (**c**) \(\theta_{\rm i}=90^{\circ}\), (**d**) \(30^{\circ}<\theta_{\rm i}<90^{\circ}\), (**e**) \(\theta_{\rm i}=30^{\circ}\), (**f**) \(0^{\circ}<\theta_{\rm i}<30^{\circ}\), (**g**) \(\theta_{\rm i}=0^{\circ}\) and (**h**) \(-90^{\circ}\leq\theta_{\rm i}<0^{\circ}\).  
proposed tunnel is shown in Figure 3a, in which a cylindrical sound source was separately placed at the point O or O\({}^{\prime}\) in the following studies. The corresponding settings of the numerical models are shown in Table 1. Figure 3b,c show the simulated transmittance and reflectance spectra through the tunnel, respectively. We can see that the transmittance spectra were almost the same for both cases, and the transmittances were below \(-10\) dB in the range 337-356 Hz (black shaded region), with a minimum value of \(-27\) dB at 343 Hz, showing a typical characteristic of low-frequency BSI. Beyond that, the thickness \(h\) of the AM was 28.5 mm, which is equal to \(\lambda/35\) at the valley of spectra, exhibiting an ultrathin characteristic of the AM. However, it is worth noting that the incident sound energy was not reflected totally for both cases (shown in Figure 3c), and the reflectance spectra were different in the working band, in which the reflectances excited by the cylindrical sound source at O\({}^{\prime}\) were obviously lower than those at O, a result that may have arisen from different mechanisms of sound modulations on both sides.
# Dual-Arm Peg-in-Hole Assembly Using DNN with Double Force/Torque Sensor
## 3 Methodology
### Deep Neural Network Definition  
Deep Neural Networks can map the force/torque signals to the defined error position class. These classes can also map the corresponding robot actions. Contact states are defined as quasi-static motion, which means that velocity of the motion is not taken into account.  
For this investigation, the contact states that are under analysis are those that occur during the first assembly stage: the initial approach between peg and hole. Although the error position during this stage is unknown, a set of error positions can be defined to convert the case into a classification problem.  
The sensors deliver x, y, z components for the force and momentum. Instead of using separate sensor signals, a single F/T vector of 12 features is proposed as shown next:  
\[FT=[f_{x1}\ f_{y1}\ f_{z1}\ m_{x1}\ m_{y1}\ m_{z1}\ f_{x2}\ f_{y2}\ f_{z2}\ m_{x2}\ m_{y2}\ m_{z2}] \tag{8}\]  
where \(FT\) represents the force/torque vector, and the sub-indexes \(x1\), \(x2\) and so on, represent the component from sensor 1 or sensor 2.  
Then, a DNN model as expressed in the next equations is defined experimentally by changing parameters and hyper-parameters to reach an acceptable accuracy during the training and testing.  
\[y=\sigma(\sum_{i=0}^{n}W_{i}X_{i}) \tag{9}\]  
\[\sigma=\frac{e^{z}-e^{-z}}{e^{z}+e^{-z}}=\tanh \tag{10}\]  
\[Loss(\hat{y},y)=-\sum_{i=1}^{2}y_{i}\log{(\hat{y}_{i})} \tag{11}\]  
\[S(y)_{i}=\frac{\exp{(y_{i})}}{\sum_{j=1}^{n}\exp{(y_{i})}} \tag{12}\]  
where \(X_{i}\) corresponds to the number of features or inputs, \(y\) correspond to the number of outputs or classes, \(h\) correspond to the number of hidden layers, and \(n\) to the number of hidden units.  
In order to pre-process the data set for training the DNN, a standardization process corresponding to the following equation is executed.  
\[\hat{X}=\frac{X-\mu}{\sigma} \tag{13}\]  
where \(\hat{X}\) is the normalized sensor features, \(X\) is the input feature, \(\mu\) the mean of each feature in the data set, and \(\sigma\) the standard deviation of each feature in the data set.  
A second DNN model is designed to classify basic contact states such as orthogonal contacts in all axes \(X\), \(Y\), \(Z\). That means that the second DNN model will have the same 12 input features and 5 outputs corresponding to the actions: move to \(+X\), move to \(-X\), move to \(+Z\), move to \(-Z\), and move to the insertion position \(-Y\). This model classifies the contact states during the insertion step. In any case, a small deviation causes a contact state force \(f>f_{limit}\) that commands the robot to stop insertion and align according to the second DNN model output.  
The trained DNN model helps the system classify the contact states caused by the error position. This classification selects the relative position to which the robot should move next. Other algorithms are also trained and tested with the same data set to compare the performance.
###### Abstract  
Although the association between left ventricular dilation and mitral annulus dilation is well understood, the potential variation in the size of the mitral annulus during dilation of the left atrium is currently unknown. In order to investigate the link between the two variables, we used multidetector computed tomography (MDCT) and looked at patients who had a dilated left atrium, assessing if the mitral valve also dilates. **Materials and Methods:** The study included 107 patients with paroxysmal and persistent atrial fibrillation, in whom catheter ablation was performed using pulmonary vein isolation \(\pm\) atrial substrate modification. Eighty patients were male (74.8%), with a mean age of 55.8 years (\(\pm\)9.87 with a minimum age of 26 years and a maximum age of 79 years), of which 57.1% had paroxysmal AF and the rest had persistent fibrillation. All the patients underwent multiple-detector CT (MDCT) with contrast medium before the ablation. CT images were integrated into the three-dimensional mapping system CARTO 3, after which the diameters of the mitral annulus, area, and circumference were measured. Left atrial size was evaluated by measuring the diameters, area, and volume. **Results:** The left atrial area was 247 \(\pm\) 65.7 cm2 and the left atrial volume was 139 \(\pm\) 56.3 mL. The transverse mitral annulus (MA) was 29.9 \(\pm\) 5.3 mm and the longitudinal diameter was 41.9 \(\pm\) 7.6 mm. The MA circumference and area were 15.0 \(\pm\) 3.5 cm and 14.2 \(\pm\) 4.6 cm2, respectively. The following statistically significant correlation was identified between the dimensions of the mitral annulus and the diameters of the left atrium: the transverse mitral annulus correlates with the antero-posterior (AP) LA diameter (R = 0.594, \(p<\) 0.01) and the longitudinal MA diameter correlates with the latero-lateral (LL) LA diameter (R = 0.576, \(p<\) 0.01). Furthermore, the MA area correlates with the LA volume (R = 0.639, \(p<\) 0.001). **Conclusions:** The volume of the left atrium correlates with the area of the mitral annulus. In patients with paroxysmal and persistent AF, an increase in left atrial dimensions is further associated with an increase in mitral valve dimensions.  
left atrium; mitral valve; mitral annulus; dilation +
Footnote †: journal: Accepted: 23 November 2021
## 2 Materials and Methods  
### Laser Cutting Experiments  
The series of experiments were performed with a 4-kW multimode fiber laser (IPG Photonics, YLS-4000, Burbach, Germany) with an emission wavelength of \(\lambda\) of 1070 nm, a beam quality factor M\({}^{2}\) of 8.5, a beam parameter product of 2.9 mm\(\times\)mrad, and a fiber core diameter of 100 \(\upmu\)m. The laser beam was collimated and focused by a cutting head (Precitec, HP SSL, Gaggenau, Germany) with a focal length of the collimator lens of 100 mm and a focal length of the processing lens of 200 mm, resulting in a focal diameter of 200 \(\upmu\)m.  
The cutting head was moved by a direct linear drive over the sheet on a conventional 2D flatbed. A nozzle with a diameter of 2.0 mm was mounted, and a stand-off distance of 1.0 mm was adjusted.  
Figure 1 shows the applied detection setup, which was intended to be an integrated retrofit between the collimator and processing lens, consisting of a dichroic mirror, an aperture, and a lens for focusing with a focal length of 200 mm for a 1:1 projection to the camera image. The dichroic mirror was designed to reflect the visible spectral components of the thermal process radiation towards the high-speed camera while transmitting the wavelength of the fiber.  
The primary laser beam, represented by a red line, was collimated, transmitted through a dichroic mirror, and focused by the process lens onto the sheet. The thermal process radiation from the cutting kerf, represented by a green line, propagates omnidirectionally from the process zone, partly also into the direction of the cutting head through the nozzle, where it was collimated by the processing lens. The reflected radiation was separated optically by a dichroic mirror, whose dielectric coating was transmissive for the emission wavelength of the laser and reflective for the visible thermal radiation from the process zone. The thermal radiation was then guided to the high-speed camera, where it was recorded. The intention of this monitoring setup was to monitor the thermal radiation in the visible spectrum during the cutting process in situ from the perspective of the cutting head.  
The cuts were performed in stainless steel with a sheet thickness of 1.5 mm, zinc-coated steel with a sheet thickness of 1.0 and 3.0 mm, and aluminum with a sheet thickness of 1.0, 3.0, and 5.0 mm, respectively. To measure the signal characteristics associated with the melt pool, a complete cut was performed for each material and sheet thickness. Low cut qualities and incomplete cuts were enforced by too-high feed rates and too-low laser power. The laser power varied between 2.0 and 4.0 kW, and the feed rate was between 30 and 300 mm/s. The focus position was set to the bottom of the sheet, and nitrogen was used as the assist gas in all experiments. The variation of the cut parameters to distinguish between complete and incomplete cuts is shown in Table 1.  
Figure 1: Schematic illustration with a photo of the setup of the monitoring system with the cutting head and the high-speed camera.
## 5 Simulation Experiments
### Average Delay (AD)  
We have already mentioned that broadcasting for route-request is completely eliminated in SD-EAR. Therefore, the delay in route discovery is reduced by a great extent in our proposed protocol. Moreover, link breakages are also repaired proactively. This saves the time span that would have been required to identify a broken link and initiate route-rediscovery to repair the link. Therefore, the average delay in SD-EAR is much smaller than that of its competitors, as shown in Figures 14 and 19.
## 4 Data-Driven Prediction Model
### Prediction of Palm Oil Using Unsupervised ML  
Conventional clustering methods are unsupervised, signifying the absence of outcome variables or relationships between the dataset observations [77]. Generally, it is easier to spot patterns and features when the palm oil data are clustered. There are numerous cluster approaches, e.g., hierarchical clustering, comprising Unweighted Pair Group Methods with Arithmetic Mean (UPGMA) and Neighbour-Joining (NJ). These two methods are widely utilised in genotype predictions of palm oil. The UPGMA is a unique high-dimensional data visualisation technique with a quick hierarchical clustering system, which provides a simple approach to constructing the distance matrix of the phylogenetic tree [78]. The method implicitly assumes a constant substitution rate with time and phylogenetic lines. Meanwhile, the NJ method is expressed as an algorithm involving knowledge of the distance between each pair of taxa to shape a tree [79]. The bottom-up (agglomerative) clustering approach creates phylogenetic trees based on DNA or protein sequence data [80]. A recent review of hierarchical clustering in palm oil genotype prediction proposed UPGMA and NJ to cluster the DNA of palm oil [81].  
Bayesian network (BN) clustering is a prediction model where the division of items into subsets becomes a probability model parameter for the data. However, this model is subjected to presumptions and clustering references derived from post distribution properties. Notably, NJ and Bayesian methods were effective clustering and model-prediction tools. A study proposed these methods to predict the leaf genotype of the Acrocomia aculeata species [74]. Meanwhile, other clustering algorithms, such as k-means, are used in phenotype predictions of palm oil. K-means clustering is an unsupervised classification technique derived from signal processing. This model seeks to divide \(n\) observations into \(k\) clusters, where each observation is a cluster prototype of the cluster of the nearest mean.  
Table 3 indicates that the clustering of phenotype and environment datasets is less, compared to genotype. This result demonstrates that k-means were applied less often to the phenotype dataset.