esa-sceva/satcom-chunk-collection huggingface dataset:
# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 3 Materials and Methods  
### CMR Dataset  
This study utilizes a dataset of CMR HASTE anatomical sequences acquired in the axial, sagittal, and coronal planes during routine clinical practice at Hospital of Braga (HB; Portugal) between 2018 and 2019. Retrospectively collected with ethical approval from HB's Ethics Committee (ref. 180/2023), the dataset includes 691 cases, totaling 35,071 DICOM images. All images were annotated by one of two radiologists regarding the presence of ECFs. When present, the findings were coarsely segmented by the expert. The images reveal a wide variety of findings with different prevalence (Figure 2), including liver nodules, spleen nodules, vesicular lithiasis, lung nodules, pleural effusions, bronchiectasis, non-specific lung alterations, mediastinal changes, hiatal hernias, breast nodules, aortic aneurysms, bone nodules, ascites, adrenal nodules, breast implants, and structural anomalies. Table 1 summarizes some key dataset statistics, namely the proportion of normal and abnormal samples at the patient, sequence, and image levels.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Patient Level**} & \multicolumn{3}{c}{**Sequence Level**} & \multicolumn{3}{c}{**Image Level**} \\ \cline{2-10}  & **\#** & **\%Normal** & **\%Abnormal** & **\#** & **\%Normal** & **\%Abnormal** & **\#** & **\%Normal** & **\%Abnormal** \\ \hline Coronal & - & - & - & 690 & 61.59 & 38.41 & 11,361 & 90.27 & 9.73 \\ Axial & - & - & - & 690 & 62.46 & 37.54 & 12,086 & 87.78 & 12.22 \\ Sagittal & - & - & - & 691 & 63.24 & 36.76 & 11,624 & 90.80 & 9.20 \\ \hline All & 691 & 49.64 & 50.36 & 2071 & 62.43 & 37.57 & 35,071 & 89.59 & 10.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CMR dataset statistics at the patient, sequence, and image levels.
## 2 Network Model
### LIF Model with Adaptive Threshold to Update the Electric Potential of Neurons  
#### 2.2.1 Neuron Model  
In the LIF model, the cell membrane continuously exchanges ions inside and outside the membrane. When there is only one input, the voltage will leak and slowly fall back to a resting state. The LIF model believes that it first drops below the resting membrane potential, and then rises to the resting potential. This model equates neurons as an RC circuit composed of capacitors (representing neuron cell membranes) and resistors (representing ion channels), as shown in Figure 4a.  
Figure 4b depicts the relationship between the neuron's membrane potential u and the input current I. The transformation process of membrane potential u is described by the following first-order differential equation:  
\[I_{i}(t)=\frac{u_{i}(t)-u_{reset}}{R_{m}}+C_{m}\frac{du_{i}(t)}{dt} \tag{2}\]  
The standard LIF differential model can be obtained by deforming:  
\[\begin{split}&\tau_{mem}\frac{du_{i}(t)}{dt}=-(u_{i}(t)-u_{reset})+R_{m}I_{i}(t),u_{i}(t)<u_{th}\\ & u_{i}(t)=u_{reset},u_{i}(t)\geq u_{th}\end{split} \tag{3}\]  
where \(u_{i}(t)\) represents the cell membrane potential of the neuron \(i\) at time \(t\), \(u_{reset}\) represents the resting membrane voltage, and \(\tau_{mem}=R_{m}C_{m}\) is the cell membrane time constant. When the cell membrane potential \(u_{i}\) exceeds the threshold \(u_{th}\), the neuron emits pulses and resets to \(u_{reset}\).
###### Abstract  
Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE). In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs) and the pico Cell Range Expansion (CRE) are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC) technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.  
H tNets; eICIC; energy efficiency; stochastic geometry +
Footnote †: journal: Sensors
## 3 VoLTE Call Failure Use Cases  
In this section, by using the available dataset, we will discuss three different call failures use cases where failures in Diameter and Real-time Transport Protocol (RTP) protocols lead to a SIP failure response.  
1. **Bearer Assignment Failure**: This use case includes failure that occurs in the bearer assignment procedure leading to Diameter protocol failure, and then SIP protocol failure. As shown in Figure 1, the invite message was sent from the S-CSCF network node towards P-CSCF as a standard procedure to make a call. The P-CSCF sent an Authentication Authorization Request (AAR) towards PCRF through the Diameter Routing Agent (DRA) to assign the Quality of Service Class Identifier (QCI) bearer to perform the call on the VoLTE Domain. However, DRA responded with a _DIAMETER UNABLE TO DELIVER_ error leading to a successive _SIP 503 SERVICE UNAVAILABLE_ error originating from P-CSCF and sent towards S-CSCF. The root cause was identified as the originating number going out of coverage during performing a call. This leads to a termination request sent from the Packet Data Network Gateway (PDN-GW), deleting the subscriber binding information. As a result, the DRA could not find any binding information for the user and replied with an _Diameter UNABLE-To-DELIVER_ error, as mentioned previously.
2. **Media Failure**: This use case shows a failure that occurred in the opening media channel procedures for a VoLTE call leading to RTP protocol failure then, consecutively, a SIP protocol failure. As shown in Figure 2, the _RTP Timeout_ error in the RTP protocol leads to a _CANCEL SIP_ message containing _RTP Timeout_, which was sent from the handset side towards P-CSCF, leading to call failure. The _RTP Timeout_ error shows that there was no response received from the IMS Media Gateway side, which led to this call failure.
3. **Charging Failure**: Figure 3 shows a call SIP/Diameter trace for a charging procedure where a failure occurs in the Diameter protocol, leading to the SIP protocol failure. Before the VoLTE call starts, the Application Server (AS) sends an accounting request to the Charging Data Function (CDF) to open a CDR for the call. This CDR will be used afterward in charging the customer. However, in this use case, the CDF responded with the _DIAMETER UNABLE TO DELIVER_ error. This leads to a _SIP 406 NOT ACCEPTABLE_ error (permanent problem with charging). After investigating the root cause of the failure, it is discovered that the diameter peers between the charging nodes and DRA were down.  
From the previously mentioned use cases and according to [13; 14; 15], we conclude that any failed VoLTE call necessarily leads to a SIP protocol failure response. As a result, monitoring any other protocol (e.g., Diameter, RTP) will show the failures that occur in this protocol. However, by monitoring the SIP protocol, it shows the failures occurring in all the protocols. Therefore, we will use the SIP protocol traces to train the proposed failure prediction ML models, as shown later.  
Figure 1: Bearer assignment failure flow.
## 3 Results
### The Duration of Data Collection  
Aside from feature selection and prediction outcomes, another interesting consideration is that different prediction outcomes require different durations of data collection. Since most studies only looked at a single type of outcome, this was not typically investigated in a direct manner. [PERSON] et al. (2018) did look at two outcomes. First to detect current depression (binary), and second to predict current severity score (continuous). They compared algorithms using 3 and 7 days' worth of data to investigate if a longer period was beneficial. While the prediction of depression showed no difference, the severity score was predicted significantly better with 7 days' worth of data. This implies that more complex predictions (continuous score as opposed to binary classification) may particularly benefit from the additional measurements. As mentioned before, [PERSON] et al. (2018) also looked at two types of predictions and found that predicting the onset of depression required 7 weeks' worth of data, whereas predicting worsening symptoms only required a minimum of 2 weeks. In a similar comparison, [PERSON] et al. (2018) found that predicting future depression was accurate after only 2 weeks, whereas suicidal ideation was not accurately predicted until 7 weeks' worth of data were available. The difference in time needed to predict future depression is likely due to the different model training methods and feature selection used, which both heavily influence the data requirements. In the current review, not enough studies focus on this aspect to draw a conclusion on how much data is required for specific types of predictions. Nonetheless, it is advisable to remember that some predictions and algorithm training methods benefit from longer periods of data collection. A practical consideration when making such a model should, therefore, be whether that duration is suitable for the model's intended application.
## 4 Results
### Comparison to Conventional Approaches  
As mentioned in Section 3.3 (Table 5), two additional conventional model architectures were utilized to prove the superior performance of the proposed methodology. In these models, RF was chosen as the ML model for classification. Figure 11 displays the CMs of the four model architectures. It is evident that the proposed method achieved an OA that was approximately 10% and 9% higher compared to the conventional feature stacking approach without and with PCA, respectively. The results demonstrated that the proposed methodology displayed improved discrimination across all targets by correctly classifying a higher number of validation samples in each class (identified by the main diagonal elements) compared to the other methods. This indicates that the proposed  
Figure 10: Comparison of crop type classification maps of the study site derived from the proposed methodology (Pa-PCA-Ca) compared to the Pa-Ca structure.  
method effectively accounted for both intra-class and inter-class variabilities of different crop types, resulting in its superior performance.
## 2 Materials and Methods
### Image Analysis with THz CSR  
The CSR beam extracted from the sapphire window was vertically deflected and converged by a parabolic mirror with a focal length of 153 mm and a diameter of 50 mm. The profile was almost a perfect TEM00 mode, and the beam size was 5.9 mm in full width at half maximum. The CSR was stable, and the fluctuation of CSR intensity was 3%. Two-dimensional imaging with THz CSR can be performed by translating a sample on a horizontal plane at the focal point of the CSR beam. A schematic layout of the two-dimensional imaging system is shown in Figure 2. To limit the irradiated area of the CSR beam, a conical horn, which had a circular output with a diameter of 2 mm, was set at a position 2 mm above the sample. The sample was fixed on an X-Y axis translation stage, in which the effective movable areas were 100 and 50 mm in the horizontal (X) and vertical (Y) directions, respectively. In the THz imaging experiments, this translation stage was moved by a raster scan at intervals of 0.2-0.5 mm.  
The CSR intensity was measured using a Schottky D-band diode detector with a square antenna (Millitech Inc., DXP-06, Northampton, MA, USA), which was set at a position 2 mm away from the sample. This detector had the maximum sensitivity at a frequency of 0.10 THz [11]. To prevent detection of the scattered light, a metal aperture with an opening of 1 \(\times\) 2 mm\({}^{2}\) was placed on the square antenna. The spatial resolution of the CSR imaging system with the D-band diode detector was evaluated to be 1.6 mm using a knife-edge method. The signal of the detector was measured using an oscilloscope (Tektronix, TDS3054, Beaverton, OR, USA). The measurement was performed four times for each measurement point, and the average value was used to evaluate the transmission.  
Figure 1: Hematoxylin and eosin-stained liver samples (**a**,**b**).  
Figure 2: Schematic layout of the (**a**) terahertz imaging system and (**b**) infrared free-electron laser imaging system.
###### Abstract
To achieve pixel-wise classification, the fully connected layer is replaced by a fully convolutional layer in FCNs [19; 20]. Another popular approach is the U-Net, which was developed based on the encoder-decoder structure [13; 19; 20]. In U-Net, the encoder compresses the input into a feature-space (also called latent-space) representation and the decoder predicts the network's output from the feature-space representation. Some existing methods based on the U-Net have been proposed to analyze mammogram, computed tomography, and Magnetic Resonance Imaging (MRI) images. From the view of segmentation output, image segmentation can be categorized into semantic segmentation and instance segmentation [11; 17]. Semantic segmentation achieves pixel-level classification with a set of object categories such as buildings, vehicles, and humans for all image pixels. Compared with semantic segmentation, instance segmentation can not only assign the pixel-level label information but also identify each individual region on the basis of specific categories in an image. Actually, the skin image shown in Figure 1 may have several psoriasis regions, and physicians often prefer to observe and evaluate the status of psoriasis regions for further treatment research in real applications [3]. This means that detecting and identifying each psoriasis regions in a skin image is helpful in conducting further treatment research for physicians. Although the U-net can achieve semantic segmentation, it cannot distinguish different regions of the same category. This means that an instance segmentation scheme is suitable for psoriasis image segmentation compared with semantic segmentation. So far, most existing methods have been designed to deal with psoriasis skin images captured under a simple background or without a background, i.e., constrained psoriasis images [1; 3; 21]. Furthermore, images captured by smartphones or digital cameras may have a poor visual quality to factors such as motion blurring, image resolution, noise, etc. Unfortunately, the psoriasis images captured by smartphones or digital cameras in outpatient rooms may also have a complex background. This means that the captured clinical psoriasis images are often unconstrained in real applications. However, the existing image segmentation algorithms may not be robust to deal with unconstrained psoriasis images well. Therefore, these motivate us to develop a robust instance segmentation scheme to deal with the clinical unconstrained psoriasis skin images in dermatology. The rest of this paper is organized as follows. Section 2 describes the proposed instance segmentation scheme systematically. In Section 3, we elaborate each part of the proposed instance segmentation scheme based on transfer learning. Section 4 demonstrates the experimental results. Finally, Section 5 concludes this paper.
# A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
## 3 Materials and Methods
### Software  
The fundamental pillar of the proof of concept presented here is based on the fact that obstructive sleep apnea comes from the absence or weakening of electrical stimuli from the brain, which causes the tongue muscles to relax, which, in turn, causes them to obstruct the upper airway; this interrupts normal respiratory function.  
These electrical signals can be detected by surface electromyography in the largest muscle of the tongue, the genioglossus.  
After being acquired through the EMG sensor, these signals can be processed so that when this absence/weakening of the detected signals occurs, it is understood as the beginning of a potential apneic event. In this way, and with this knowledge, solutions can be developed to try to overcome these effects by trying to solve the problem. As described in the literature, and discussed earlier, electrostimulation is an effective way to stimulate the tongue muscles, promoting its normal functioning. Furthermore, it would be important to have access to the data so that the medical specialist can adopt a more incisive and personalized therapy for each patient. These data, which are obtained through the microcontroller, could be stored in a database so that, when necessary, they could be easily visualized.  
All the software was developed based on the flowchart in Figure 1, and was divided into 5 main parts, as follows:  
* Phase of the charging verification, represented in the Figure with number 1;
* Phase of the acquisition of the humidity and temperature values, represented in the Figure with number 2;
* Phase of the system initialization and parameter verification, represented in the Figure with number 3;
* Phase of reading and processing the EMG sensor values, represented in the Figure with number 4;
* Phase of stimulation and sending the values to the database, represented in the Figure with number 5.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 3 Proposed Method
### Loss Function  
The loss function for the proposed CNNs is divided into two separate loss functions: one for learning the semantic segmentation mask reconstruction and the other for multi-label classification. The loss for semantic segmentation reconstruction is the mean absolute error (MAE), which is expressed as follows:  
\[SegLoss=\frac{1}{r^{2}hw}\sum_{x=1}^{rw}\sum_{y=1}^{rh}|S_{x,y}^{GT}-S_{x,y}^ {Const}|, \tag{5}\]  
where \(S_{x,y}^{GT}\) is the ground-truth pixel in the semantic segmentation mask and \(S_{x,y}^{Const}\) is the pixel in the constructed semantic segmentation mask. The other loss is the multi-label binary cross-entropy (BCE) loss for \(N\)-classes of the dataset. This classification loss can be defined as  
\[ClassLoss=\sum_{i=1}^{N}[-p_{i}log(q_{i})+(1-p_{i})log(1-q_{i})], \tag{6}\]  
where \(p\) is the ground-truth label probability (0 or 1) and \(q\) is the predicted label probability. The multi-label binary cross-entropy loss is used to predict the objectness of each class independently with sigmoid activation which outputs a value in the range of 0 to 1 equivalent to the probability of the existence of each object. The employment of MAE for segmentation map regression and the multi-label classification for mask filtration improves the speed, it lowers the number of computations needed for obtaining the segmentation map as it constructs the segmentation map directly like an image with a single channel instead of predicting N channels image (where N is the number of classes) which is computationally expensive since the final segmentation maps have the same size of the input image which is relatively a high-resolution image (\(1024\times 512\) in case of cityscapes). Additionally, mask filtration using multi-label classification (NLF) is much cheaper than constructing the N segmentation masks.
## 2 Background and Example
### The Example of Administrative Information in the XML  
To facilitate the use of calibration certificates, electronic calibration certificates are increasingly being introduced into practice. The DCC provides a standardized structure for calibration certificates, which can be easily read and interpreted by both humans and machines. The DCC can be used in various settings, including industrial manufacturing, testing and calibration laboratories, and scientific research. Many calibration laboratories use Microsoft Excel for data acquisition, result calculation, and report generation. However, programming skills are required to generate a validated DCC. As shown in Figure 3, the XML is designed for machine-readable administrative information and can be created and edited using XMLSpy software based on the PTB's published information. To create an element, the user needs to use a start tag and an end tag. The root element is a mandatory component for every valid DCC file, and all other elements are either direct or indirect children of this root element. There can be multiple child elements under a given element, and those elements that do not have any child elements are referred to as \"data\" elements [28]. For example, <model> represents the start tag, and </model> represents the end tag, which contains a forward slash after the opening angle bracket. The entire content of the element must be enclosed between these tags. In XML, <model> and </model> represent the information related to the model content, while Model: is the actual XML content within the XML content. Additionally, PMM-C Ultra is a value container that can store information such as a quantity value, unit, comment, and more within an element. The administrative information included in the XML pertains to the high-precision CMM used in the study. The CMM was manufactured by Leitz, with a model of PMM-C Ultra and a serial number of 571. To ensure consistency during the measurement process, the temperature of the environment was kept at 20 \(\pm\) 1 \({}^{\circ}\)C, and the relative humidity was maintained at 45 \(\pm\) 10%.
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
## 5 Simulation Results and Discussion  
To explore the performance of the proposed approaches, we chose to use MNIST [77] and Fashion MNIST [78] datasets. Each of these datasets consists of a training set of 60,000 samples and a test set of 10,000 samples, and each dataset has 10 classes. Each sample is a 28 \(\times\) 28 grayscale image in the range [0, 255]. It is important to mention that to test the actual recognition capability of both presented approaches, no image preprocessing was performed. All images were taken as they are. For CNNMVN, these images were transformed according to (1), and for MLMVN, as a CNN in the frequency domain, essential frequencies were extracted according to the \"diamond\" rule described above.  
The main goal was to understand how the neural networks learn according to the proposed approaches and define possible ways for improving the learning algorithm. Considering that the learning algorithms for both approaches are based on the heuristic assumption that the error of the network should be equally shared among all the neurons that participated in its formation, we made some changes to the learning process. In the next two sections, these changes are considered, and the corresponding experimental results are given.