esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Deep Neural Network Based on Alignment
### Front-End Network Architecture  
For deep speaker verification, some simple architectures with only dense layers [4] have been proposed. However, lately, employed deep neural networks such as Residual Convolutional Neural Networks (CNN) [5], but, in text-dependent tasks, it has not achieved the same good results as previous simple approaches.  
In our network, we propose a straightforward architecture with only a few layers that include the use of one-dimensional convolution (1D convolution) layers instead of dense layers or 2D convolution layers as in other works. Our proposal is to operate in the temporal dimension to add context information to the process and at the same time the channels are combined at each layer. The context information that is added depends on the size of the kernel \(k\) used in the convolution layer.  
To use this type of layer, it is convenient that the input signals have the same size. For this reason, we apply a transformation to interpolate the input signals to have all of them with the same dimensions \((D^{0}\times T)\) where \(D^{0}\) is the acoustic feature dimension of the Mel-Frequency Cepstral Coefficients (MFCC) with their first and second derivatives, and \(T\) is the temporal dimension.  
The operation of the 1D convolution layers is depicted in Figure 2, and the layer input and its context, the previous frames and the subsequent frames are multiplied frame by frame with the corresponding weights. The results of this operation for each frame are linearly combined to create the output signal. Using this kind of layer in the network architecture, the acoustic feature dimension of the input signal changes after each convolution layer. As we can see in Figure 2, each convolution layer will have feature vectors of dimension \(D^{(l)}\) as input and \(D^{(l+1)}\) as output. The resulting dimension of the processed signal through the network will be \(D=(D^{(0)},D^{(1)},...,D^{(l)},...,D^{(L)})\), where \(L\) is the total number of front-end layers. The temporal dimension \(T\) is kept constant in these layers since we define a zero padding for the convolution.  
Figure 2: Operation with 1D Convolution layers, (**a**) general pipeline of this operation; (**b**) example of how k context frames from input are multiplied by the weight matrix W and the output is equivalent to a linear combination of convolutions.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 3 Methodology for the Quality Assessment Metrics Evaluation
### Performance Indexes  
We present the performances of the different quality metrics for each database with four different indicators. Before computing these indicators, a non linear regression is applied to the quality scores thanks to a logistic function:  
\[\widetilde{Q}_{i}=a+\frac{b}{1+e^{-\frac{(Q_{i}-c)}{d}}} \tag{31}\]  
where \(Q_{i}\) is the score of the quality metrics on the image \(i\) and \(\widetilde{Q}_{i}\) the mapped quality score. Parameters \(a\), \(b\), \(c\) and \(d\) are determined by the regression conducted by the lsqcurvefit function of Matlab.  
The four performance indicators are given below:  
* the Pearson correlation coefficient (PCC) (cf. Tables A1 and A2) measures the linearity between two variables: \[\text{PCC}_{S,Q}=\frac{\text{cov}(S,Q)}{\sigma_{S}\sigma_{Q}}\] (32) where, \(S\) corresponds to the subjective scores (MOS), \(Q\) to the predicted quality score, \(\text{cov}(S,Q)\) is the covariance of \(S\) and \(Q\) and \(\sigma_{S}\) (resp. \(\sigma_{Q}\)) is the standard deviation of \(S\) (resp. \(Q\)).
* the Spearman rank Order Correlation coefficient (SROCC) (cf. Tables A3 and A4) measures of the monotony between two variables. Raw scores \(S\) and \(Q\) are first converted to ranks \(rg_{S}\) and \(rg_{Q}\). The SROCC corresponds to the PCC of these two new variables: \[\text{SROCC}_{S,Q}=\frac{\text{cov}(rg_{S},rg_{Q})}{\sigma_{rg_{S}}\sigma_{ rg_{Q}}}\] (33)
* the Outlier Ratio [49] (OR) (cf. Tables A5 and A6) represents the quality metrics consistency. It represents the number of outlier point to total points \(N\): \[\text{OR}=\frac{\text{Total }of\text{ }Outlier}{N}\] (34)  
Figure 7: Characteristics of the 4 Kdtb images: (**(a)** The dynamic range, **(b)** key, **(c)** spatial Information, **(d)** Colorfulness.  
An outlier is defined as a point for which the error exceeds the 95 percent confidence interval of the mean MOS value as defined in [43].
* The Root Mean square error (RMSE) (cf. Tables 12 and 13) measures the accuracy of the quality metrics: \[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(S(i)-Q(i))^{2}}\] (35)
## 2 Methodology
### Modeling Collision Likelihood via Decision Trees  
Similar to the dataset used for interpolator evaluation, the dataset used to develop the collision likelihood model was also based on the aforementioned friction model. However, in this case, all the road footage available (18, 19, and 25 January and 4 February 2022) was converted into friction coefficients to maximize the training data available. Following the conversion process, the road sections with friction values were divided into equal-length segments, where the friction value of a segment is the average of all overlapping friction values. In addition to friction, other road segment characteristics were also assigned, including elevation, AADT, slope, and x and y coordinates. Next, a binary classification was given to each segment identifying whether a collision had occurred based on traffic safety records provided by the City of Edmonton. Note that this process was performed independently for each day.  
The decision tree model [23] was chosen to model the relationship between collision occurrence and the selected independent variables. Compared to other ML models, decision trees are highly interpretable. Post training, the user can examine the internal logic of the model to see how the predictions are made, which is impossible with more complex algorithms. Moreover, the model algorithm itself is nonparametric, giving it an edge over traditional SPF models that make a distribution assumption. The algorithm iterates through each input variable and evaluates its ability to reduce error. This evaluation process consists of classifying samples using a true or false condition based on a particular input variable and then calculating the Gini impurity (Equation (9)) to evaluate how well that condition separated the data. Of the available input variables, the one with the lowest Gini impurity is selected as the first node in the tree. Next, additional conditions are placed based on the previous decision to determine which input variable should be used as the next node to reduce impurity. This splitting process is repeated until the impurity is zero or a stopping condition is met. In this study, the stopping condition is based on the number of nodes. Training and validation accuracies are evaluated simultaneously as the number of tree nodes increases. The optimal number of nodes is the point where the validation accuracy peaks. In cases where multiple configurations produce the same accuracy, the structure with the least number of nodes was selected.  
\[\text{Gini Impurity}=1-\sum_{i=1}^{k}p_{i}^{2} \tag{9}\]  
where \(k\) is the number of classes and \(p_{i}\) is the proportion of the \(i\) label.  
A dataset split of 80% training and 20% validation was used during model development.
## 6 Discussion  
Following RQ1, we found that Google Cloud Run offers more memory and CPU options and supports more requests per container instance than AWS App Runner. Microsoft Container Apps has two pricing options and supports up to 300 instances per service. Ultimately, the choice of service will vary based on the individual needs of the user. Cloud Run may be the preferable option for those requiring greater memory and CPU capabilities. Regarding RQ2, as a beginner cloud service user, we found that Google Cloud has the most detailed and user-friendly documentation for their serverless service, with several third-party resources available online. AWS App Runner's documentation may be challenging for beginners, but there are still online resources available. Azure has a straightforward cloud console and clear documentation, but fewer online resources compared to the other two services. In accordance with RQ3, overall, the findings of the analysis revealed that Google's Cloud Run exhibited superior performance and usability compared to AWS's App Runner and Microsoft Azure's Container Apps on this particular application. Cloud Run demonstrated the lowest latency at 50 ms and a faster response time of 63.7 ms for distance matrix queries. These results provide valuable insights for individuals seeking to select an appropriate cloud service for similar containerized web applications. Google Cloud Run might be the best service to host a Django containerized web application with Google Maps and distance matrix API usage.
## 2 Background
### IoMT  
The convergence of medical devices and applications that can be connected to medical information technology systems utilizing networking technology is known as the Medical Internet of Things. The IoMT connects patients with doctors through a secure network and allows for the transmission of medical data, thereby reducing unnecessary hospital visits and burdens on the medical system. The IoMT market involves the production of smart devices such as wearables or medical monitoring devices, which can provide real-time location tracking or telemedicine services in home, community, or hospital environments.
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 3 The Next Generation PaaS: Architecture, Concepts, Processes, and Workflows
### Reusable Functional Blocks  
RFBs are a core concept of NGPaaS, as they have a significant influence in its architecture and workflows. RFBs are a logical representation of functions that can decompose a complex system into simple sub-functions. In a sense, RFBs are a generalisation of the Virtual Network Function (VNF) concept proposed by ETSI [28]. There are however some core differences between ETSI VNFs and NGPaaS RFBs. The main difference is that RFBs are recursive and thus can be arbitrarily decomposed into other RFBs, while VNFs in the ETSI model cannot be decomposed into other VNFs (the composition stops at the level of VNF Component). In addition, RFBs can be mapped into both software and hardware execution environments [28], while VNFs can only be mapped into Virtual Machines (or Containers) in traditional cloud infrastructures. RFBs can also be paired with metadata during their composition, which allows for high degrees of configuration, with reduced complexity. Figure 2 illustrates the RFB concept. Additionally, NGPaaS is a higher-level orchestration framework when compared to ETSI NFV, as it facilitates the deployment and management of multiple heterogeneous platforms. For example, it could be the case that an Open Source MANO (OSM) platform, a reference implementation of ETSI NFV, is deployed and managed by NGPaaS. A detailed comparison between the ETSI NFV and NGPaaS proposal is also provided in reference [3].  
Figure 1: Simplified view of the NGPaaS architecture.  
Figure 2: The concept of Reusable Functional Blocks.  
The figure shows how an RFB parent is decomposed into two RFB children, each of which is mapped to the deployment of a micro-service into the execution environment. For these RFBs to be properly configured, they are each paired with some metadata. The NGPaaS RFB model supports inheritance from an RFB parent to its children; this means that, unless overwritten by an RFB child, metadata from the parent will be propagated to all its children.
## 3 Processing Method
### The Proposed Method  
According to the analysis in Section 3.1, the residual errors include the channel amplitude phase error, the phase error caused by the CTB measurement error, and the motion error. The channel amplitude error can be estimated and compensated by a simple channel equalization. According to the analysis in [23], its corresponding phase error caused by the CTB measurement error can be considered constant in the local area, although it is range variant over the entire scene. Therefore, after compensating for the channel amplitude error, the signal of the \(m\)th channel in the local region can be expressed as  
\[s_{m}(\eta)=\exp\bigl{(}j\phi_{m}^{\prime}\bigr{)}\exp\biggl{(}-j\frac{4\pi \Delta R_{m0}(\eta)}{\lambda}\biggr{)}s_{m}^{\prime}(\eta) \tag{8}\]  
The traditional TDCM can be used to estimate the channel phase error, but unlike the spaceborne multi-platform distributed SAR, the channels of the distributed SAR on a single airborne platform have consistent residual motion error among channels due to the steel connection. The schematic diagram of the coupling of the motion error and the CTB measurement error is shown in Figure 3. Figure 3 indicates the CTB measurement error between channels, where the red dashed line indicates the ideal trajectory and different shapes indicate different channels. The presence of the CTB measurement error shifts non-reference channels from the ideal trajectory. Figure 3 indicates the motion error of a single channel with the solid blue line indicating the actual trajectory and different colors representing different slow time samplings. Figure 3 shows the actual arrangement of the sampling points in space after coupling the two types of errors.  
The proposed method is based on the TDCM, considering both channel phase error and motion error, and the main steps for error estimation are as follows.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Method
#### 3.2.4 Nash-Sutcliffe (NSE) Efficiency Coefficient  
The NSE is a core evaluation index for hydrological models. It reflects the model's ability to fit hydrological processes and is widely used to assess model accuracy. The closer its value is to 1, the higher the reliability of the model. In this study, it was used to evaluate the differences between TWSC results from Swarm and GRACE/GFO solutions. The specific calculation formula is as follows [26]:  
\[NSE=1-\frac{\sum\limits_{i=1}^{n}{(x_{i}-y_{i})}^{2}}{\sum\limits_{i=1}^{n}{(x _{i}-\overline{x})}^{2}} \tag{17}\]  
where \(x\) and \(y\) represent the TWSC results from Swarm and GRACE/GFO solutions, respectively; \(n\) represents the number of \(x\) and \(y\); \(x_{i}\) and \(y_{i}\) represent the \(i\)th value of \(x\) and \(y\), respectively; and \(\overline{x}\) represent the average values of \(x\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Description** & **GRACE-DSI** \\ \hline D0 & Near normal & GRACE-DSI \(>-0.5\) \\ D1 & Light drought & \(-0.8\leq\) GRACE-DSI \(<-0.5\) \\ D2 & Moderate drought & \(-1.3\leq\) GRACE-DSI \(<-0.8\) \\ D3 & Severe drought & \(-1.6\leq\) GRACE-DSI \(<-1.3\) \\ D4 & Extreme drought & \(-2.0\leq\) GRACE-DSI \(<-1.6\) \\ D5 & Exceptional drought & GRACE-DSI \(<-2.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Drought category according to GRACE-DSI.
## 2 Method
### Acoustic Source Excitation  
To build the required control points, a set of transducers generating plane waves are uniformly arranged at a circular boundary enclosing the control points (Figure 1c). The technique of inverse filtering is adopted to acquire the operating parameters of each transducer.  
For small amplitude wave and linear transmission medium, the temporal relationship between the driving signal and pressure of each control point can be expressed as  
\[p_{q}(t)=\sum_{l=1}^{L}h_{lq}(t)*e_{l}(t),\ (1<l<L), \tag{10}\]  
where \(*\) refers to convolution, \(e_{l}(t)\) is the input signal of \(l\)th transducer, \(L\) is the total number of the employed transducers, \(h_{lq}(t)\) is the time impulse response function for transducer \(l\), and control point \(q\). As the transmission is supposed to be time-invariant, Equation (10) can be further written with the matrix format in the frequency domain  
\[\mathbf{P}=\mathbf{H}\mathbf{E}, \tag{11}\]  
where \(\mathbf{P}\) is the column vector of the transformed pressure, \(\mathbf{H}\) with \(L\times Q\) items is the propagation matrix indicating the propagation from each transducer to the control points, and \(\mathbf{E}\) is the Fourier transformation of the input signals on the transducers.  
When \(\mathbf{H}\) is well-conditioned, the excitation signals of each transducer can be easily calculated by the inverse matrix. However, as all of the inverse problems, the ill-conditioned inversed matrix may result in considerable errors [54]. To avoid this problem, singular value decomposition is applied to the propagation matrix before the inversion calculation,  
\[\mathbf{H}=\mathbf{U}\mathbf{D}\mathbf{V}^{\mathrm{T}}, \tag{12}\]  
where \(\mathbf{D}\) is a \(L\times L\) diagonal matrix with eigenvalues \(\varsigma_{i}\), \(\mathbf{U}\) is a \(Q\times L\) unitary matrix, and \(\mathbf{V}\) is a \(L\times L\) unitary matrix. Regularization of \(\mathbf{H}\) is followed by a simple rules: \({\varsigma_{i}}^{-1}\) is replaced by zero when the eigenvalues \(\varsigma_{i}\) become very small. Finally, a noise-filtered approximation \(\widetilde{\mathbf{H}}^{-1}\) of the inverse propagation operator \(\mathbf{H}^{-1}\) can be obtained  
\[\widetilde{\mathbf{H}}^{-1}=\mathbf{V}\widetilde{\mathbf{D}}^{-1}\mathbf{U}^{ \mathrm{T}}=\mathbf{V}\left[\begin{array}{cccccc}{\varsigma_{1}}^{-1}&\cdots& \cdots&\cdots&\cdots&0\\ \vdots&\ddots&&&&\vdots\\ \vdots&&{\varsigma_{g}}^{-1}&&\vdots\\ \vdots&&&0&&\vdots\\ \vdots&&&&\ddots&\vdots\\ 0&\cdots&\cdots&\cdots&\cdots&0\end{array}\right]\mathbf{U}^{\mathrm{T}}, \tag{13}\]  
where \(\varsigma_{1}\cdots\varsigma_{g}\) is arranged in descending order according to their absolute value.  
In the frequency domain, the excitation signals of the employed transducers are conveniently determined by  
\[\mathbf{E}=\widetilde{\mathbf{H}}^{-1}\mathbf{P}. \tag{14}\]  
The time-dependent excitation signals can be acquired by applying inverse Fourier transform. With the corresponding excitation signal for each transducer, any required pressure field can be synthesized in the target region.
# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
## 4 Transient Contents Caching in NDN  
The lifetime of transient contents can largely vary from some milliseconds to minutes or hours, depending on the application domain and the characteristics of the source. Therefore, the first research works in this field were centered to verify if NDN caching is still useful in presence of transient contents, especially those that expire very quickly, like in the context of IoT and wireless sensor networks [42, 43]. Since these environments are part of the complex IoV vision, we provide a basic review of the main research results obtained in this area.
## 3 Proposed Fault Detection and Classification Algorithm
### S-Index  
In the first step of the algorithm, the signal of the current is decomposed by the use of the \(S\)-transform to evaluate an output matrix. The sum absolute values (SAVs) are calculated from this matrix for a duration of a quarter cycle with the help of window moving at a step of one sample. The maximum values of the SAVs over a quarter cycle with the window over one sample are designated as the S-index. The discrete version of the \(S\)-transform for a discrete function \(h[kT]\) is given in the detailed relation below (let \(f\to n/NT\) and \(\tau\to jT\)) [20].  
\[S\left[jT,0\right]=\frac{1}{N}\sum_{m=o}^{N-1}H\left[\frac{m}{NT}\right] \tag{1}\]  
A complex \(N\times M\) matrix, which is called the \(S\)-matrix, is evaluated as the output of the \(S\)-transform. Every row and column in this S-matrix indicates a definite frequency and time, respectively [21].
# An Efficient UV-C Disinfection Approach and Biological Assessment Strategy for Microphones
## 3 Results
### Assessment of Virus Extractability  
Microphones have complex shapes and are fabricated from various materials, such as plastics and metals, which are also located on places that are difficult to reach in terms of cleaning and possible exposure to the UV-C irradiation. One of the main parts that was foreseen as a potentially problematic area was the inner foam, which resides underneath a metal grill at the microphone head. The foam has a porous structure and, hence, aerosols or liquids that penetrate into the foam and dry out would possibly be shielded from UV-C irradiation rendering the treatment less effective. Therefore, we started by investigating the disinfection of the foam, which also allowed us to set up the basic methodology.  
For the assessment of the efficacy of UV-C treatment, we used the M13 bacteriophage as it offers a straightforward readout in terms of infectivity. As described in the methods section, 10 \(\mu\)L of a M13 suspension with 10\({}^{13}\) PFU/mL was used to contaminate the foam (Shure SM58). It was envisioned that shaking and agitation of the foam in water would allow us to extract the virus from the foam. Different mixing times were investigated by taking aliquots at different time points and assessing the concentration of the infectious viral particles in the solutions through viral titration. The detected viral concentration was then compared with the concentration measured for a control sample in which 10 \(\mu\)L of M13 at 10\({}^{13}\) PFU/mL were directly transferred to 10 mL MilliQ water, serving as the 100% recollectable concentration. The intention was not to have a method that extracts 100% of the applied viruses, but to have enough virus extracted to perform reliable disinfection measurements with a minimal number of dilution steps. It was found that after 2 h of mixing, about 77% of the virus could be recovered from the foam, whereas after 15 h this number increased to 81%.
## 7 Hybrid Communication Systems: VLC with Other Communication Technologies
### Network Architecture for Underwater VLC  
Underwater VLC networks require specialized architectures to address dynamic channel conditions and mobility challenges. Recent advances demonstrate that multi-hop relay networks extend coverage by 35% compared to single-hop links, leveraging collaborative beamforming to mitigate alignment issues in turbulent waters [79]. For mobile AUV swarms, mesh topologies with adaptive routing (depth-based path selection) reduce latency by 33% while supporting 50+ nodes, as validated in [80] 3D simulations. Conversely, star topologies remain optimal for static sensor networks, achieving 8 ms latency for 20-node deployments but suffering coverage limitations beyond 150 m [76].  
Hybrid MAC protocols are critical for scalability. A TDMA/CSMA hybrid (tested in [49]) achieves 82% channel utilization by dynamically allocating slots based on node priority and channel state information (CSI). For reliability, QoS-aware routing protocols like [82] turbulence-adaptive algorithm reduce packet loss by 40% in high-salinity environments by bypassing degraded links. Field tests of a five-node network (three AUVs + two sensors) demonstrated 1.8 Gbps aggregate throughput using these methods, with 92% reliability over 3-month sea trials [83].  
Challenges persist in cross-layer integration and energy efficiency. Ref. [84] works on energy-balanced routing, showing that integrating VLC with acoustic wake-up signals cuts idle power consumption by 60%, while [85] machine learning-based topology optimization extends network lifetime by 30%. Future directions include RIS-assisted reconfigurable networks (theoretically doubling coverage [26]) and federated learning for distributed channel prediction.