esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 2 Literature Review
This technique uses a series of Recurrent Neural Networks (RNNs) to process the audio and it is advantageous to assess the error following each RNN in order to produce a compound loss that represents the quality of the reconstruction after each layer. RNNs have two directions. A particular kind of residual connection, in which two RNNs operate simultaneously, is used to build each RNN block. The bypass-connected layer input and the element-wise multiplication of the two RNNs are combined to create the output of each layer. Unlike isolating known sources [26], the outputs in this scenario are permutation invariant; thus, voices can transition across output channels, particularly during transitory silent periods. So, this series RNN-based audio separation model is employed.
## 2 Results & Discussion
### Biological Studies
#### 2.2.3 Antiproliferative Activity
Comparison of the antibacterial, antifungal, and antiproliferative activities between benzothiazeppines and pyrimidine derivatives indicated that both benzothiazeppines and pyrimidine derivatives showed comparable antibacterial activities; however, the antifungal activity was improved in benzothiazeppines compared to pyrimidine derivatives (**21**, MIC 19.01 \(\upmu\)M vs. **31** MIC 45.37 \(\upmu\)M). On the other hand, the pyrimidine derivative **35** (IC\({}_{50}\) = 5 \(\pm\) 1 \(\upmu\)g/mL) displayed excellent antiproliferative activity in the series. The benzothiazeppines and chloropyrazine-tethered pyrimidine hybrids displayed excellent safety profiles against the LO2 cell line. Overall, the replacement of the 1,5-benzothazepine ring with the pyrimidine scaffold improved the antiproliferative activity and reduced the antimicrobial activity in comparison to the benzothiazep series. The SAR features of the target compounds are displayed in Figure 3. A summary of the potential compounds from this study with significant antibacterial, antifungal, and antiproliferative activities among **22-40** is depicted in Figure 4.
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Opportunistic Routing Protocols
#### 5.2.3 ORR  
In 2017, the same authors proposed another opportunistic routing protocol, named ORR [13]. It is also based on the ORW mechanism, but with a number of improvements. It uses a new metric called the 'Forwarder Score', which includes Residual Energy for calculating EDC. A node with a higher remaining energy is chosen as a forwarder node. ORR controls the number of forwarders based on the forwarding cost estimation. First, the ORR calculates the optimal number of forwarders based on the forwarding cost estimation. The optimal number of forwarders can differ according to the network type, so it needs to be calculated online during operation. Forwarding cost estimation considers the expected sender wait time and expected amount of redundant packets generated. Second, residual  
Figure 14: Packet transmission in ORW [11].  
Figure 15: Packet transmission in ORD [12].  
energy is considered when nodes select their forwarder sets. In ORW, forwarders are selected based on the expected wait time, so nodes with a large number of neighbors have a higher chance of becoming candidate forwarders. Thus, traffic load is often concentrated within a small number of nodes, draining their energy faster than other nodes. On the other hand, in ORR, nodes with a larger residual energy become selected forwarders more often.  
However, the routing metric, 'forwarder score', is recursively computed at the sink. After computing, the sink broadcasts the forwarder score to all nodes, which may lead to congestion in a large network with a high density of nodes. In addition, the ORR updates the forwarder score periodically, and this updating's overhead is expensive. On the other hand, to keep updating FS (Forwarder Score), residual energy is needed, and for this, each node broadcasts a control packet concerning their current energy status, which also adds to the load and contributes to extra overhead.
# Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder
## 3 Methodology
### The Proposed Weighting Strategy  
In the autoencoder-based method \(\mathbf{h}_{\text{loc}}^{n,i}\) is designed as the high-order feature with \(K_{\text{loc}}<M_{\text{loc}}\), which is automatically extracted. In [15], the simple average strategy is used to combine the local features into a final representation. Although sometimes the average strategy is appropriate to use, often it will have a bad influence on the final signal representations of the point machine, as different subsequences contain typically very different amounts of information. For example, the subsequences related to the switch phase may contain less useful information then the other stages, as the current signal remains at an almost constant level. To obtain more effective final feature representations, we proposed a weighting strategy as depicted in Figure 3 and explained below.  
For a certain subsequence \(\mathbf{s}_{n}^{i}=[s_{n}^{i,1},s_{n}^{i,2},\cdots,s_{n}^{i,M_{\text{loc}}}]\), the segment variance is calculated as  
\[v_{n}^{i}=\frac{\sum_{j=1}^{M_{\text{loc}}}(s_{n}^{i,j}-\mu_{n}^{i})^{2}}{M_{ \text{loc}}},\]  
where \(\mu_{n}^{i}=\frac{\sum_{j=1}^{M_{\text{loc}}}s_{n}^{i,j}}{M_{\text{loc}}}\). Then, we create a variance dataset \(\mathbb{S}_{\text{Var}}\) defined by \(\mathbb{S}_{\text{Var}}=\{\mathbf{v}_{1},\mathbf{v}_{2},\cdots,\mathbf{v}_{N}\}\), where \(\mathbf{v}_{n}=\{v_{n}^{1},\
u_{nr}^{2},\cdots,v_{n}^{\Omega_{\text{av}}}\}\).  
Then, the variance dataset \(\mathbb{S}_{\text{Var}}\) is equally divided into \(L\) bins, where \(\mathbb{S}_{\text{Var}}=\{\mathbb{S}_{\text{Var}}^{1},\mathbb{S}_{\text{Var}}^ {2},\cdots,\mathbb{S}_{\text{Var}}^{L}\}\). We define a parameter \(\rho\) to describe the bin frequency, where \(\rho_{l}\) denotes the number of variances located in \(\mathbb{S}_{\text{Var}}^{l}\).  
A weighting function is designed as  
\[\omega(\mathbf{s}_{n}^{i})=\frac{2}{1+e^{\rho_{l}}},\text{ if }v_{n}^{i}\in \mathbb{S}_{\text{Var}}^{l}.\]  
Figure 3: The proposed weighting strategy.  
Here, \(\alpha\) is defined as the tuning parameter. Then, the final hidden representation of a certain current signal \(\mathbf{s}_{n}\) is calculated as  
\[\mathbf{h}^{n}=\frac{\sum_{i=1}^{Q_{n}}\omega(\mathbf{s}_{n}^{i})\mathbf{h}_{ \text{loc}}^{n,i}}{Q_{n}}.\]  
By aid of this analysis, it is clear that the high-order representation \(\mathbf{h}^{n}\) is captured from \(\mathbf{s}_{n}\) by the locally connected autoencoder and the weighting strategy.  
For the sake of high robustness and easy implementation, we employ the local outlier factor (LOF) algorithm for fault diagnosis using \(\mathbf{h}^{n}\). By conducting a representation dataset \(\mathbb{H}=\{\mathbf{h}^{1},\mathbf{h}^{2},\cdots,\mathbf{h}^{N}\}\), the radius of the smallest hypersphere is calculated that contains the \(k\) nearest neighbors. The local density is then computed, where a fault signal will have lower local density. One can refer to the work [26] for detailed steps.
## 4 Results and Discussion
### Preliminary User Evaluation  
Formally assessing the various educational use cases of the RSC is one plan for ongoing work. However, we conducted an informal survey to gather preliminary user impressions during a two-hour student design competition event held in May 2023 at the University of Tartu. We displayed the prototype RSC, and eight (\(N=8\)) students shared their feedback using a Google form after briefly interacting with the prototype (max 5 min). Overall, the feedback was positive, with many supportive comments; for example, students said, \"I liked the prototype and I would like to see its further development\" and \"Very promising prototype with some unfortunate glitches\".  
Initial comments touching on the aesthetic included descriptions such as \"tiny\", \"cute\", \"small robot tutor\", \"geeky\", and \"interesting\". Participants also ranked their preferred interaction modes, with speech and touch as top priorities, followed by gestures, with web/text/chat-based in last place. Their main concerns were privacy and affordability, in line with requirements (Req 3, 6). Participants suggested improving the RSC with remarks such as \"make it portable by adding batteries and [add] touch sensors for more interaction\" and \"use better voice generator [for] more input and output options\". One participant shared their screen preference: \"I would love to see a bigger screen on it...\", while another noted they would prefer a more compact, flipper-free design: \"I actually don't think the arms are necessary, and it could be made more compact without them\".  
As we continue to develop the RSC, we are conducting ongoing cross-cultural user evaluations at partner universities to validate the use cases of the RSC. The successful integration and utility of the Robot Study Companion (RSC) hinge on its acceptance and usability, which require comprehensive evaluation through user studies, planned as part of our future research. In designing the RSC, we adhered to the Technology Acceptance Model (TAM) principles [96; 97], focusing on perceived ease of use and usefulness--key factors for user adoption. Although empirical validation of the RSC's acceptance and effectiveness is pending, we expect these design considerations to promote platform adoption among university students. We foresee the RSC's utilisation in university settings in individual and group exam-study sessions, in laboratories to store and communicate information, and as a motivational reminder for students during study sessions.
# Precise Channel Estimation Approach for a mmWave MIMO System
## 5 Algorithm Description
```
Require : Subsampled matrix \(\text{H}_{\Psi}\), side information matrices \(\text{A}_{\text{R}}\) and \(\text{A}_{\text{T}}\), and the set of indices of observed entries in \(\Psi\) Input : \(\text{H}_{\Psi}\), \(\text{A}_{\text{R}}\), \(\text{A}_{\text{T}}\), \(\rho\), \(\gamma\), \(\Gamma_{\text{H}}\), \(\Gamma_{\text{Z}}\) and \(\text{I}_{\text{max}}\) Output : Estimated output channel matrix \(\widehat{\text{H}}=\text{H}^{(\text{I}_{\text{max}})}\) Initialization: \(\text{H}^{(0)}=\text{P}_{1}^{(0)}=\text{P}_{2}^{(0)}=\text{I}^{(0)}=\text{D}^{ (0)}=\text{J}^{(0)}=\text{P}_{1}^{(0)}=\text{P}_{2}^{(0)}=\overline{\text{I}} ^{(0)}=\text{D}^{(0)}=\overline{\text{J}}^{(0)}=0\) Step 1 : forl =0,12...Imax-1 do Step 2 : Update \(\text{H}^{(\text{I}+1)}\) by using Equation (23). Step 3 : Update \(\overline{\text{P}}_{1}^{(1+1)}\) and \(\overline{\text{P}}_{2}^{(1+1)}\) by using Equations (16) and (17). Step 4 : Update \(\overline{\text{I}}^{(1+1)}\) by using the Equation (18). Step 5 : Update \(\overline{\text{D}}^{(1+1)}\) by using the Equation (19). Step 6 : Update \(\overline{\text{J}}^{(1+1)}\) by using the Equation (20). Step 7 : Update \(\left(\text{P}_{1}^{(1+1)},\text{P}_{2}^{(1+1)},\text{I}^{(1+1)},\text{D}^{ (1+1)}\text{ and }\text{J}^{(1+1)}\right)\) by using Equation (21), where \(\gamma\) is the relaxation factor. Step 8 : meets stopping criteriondo  i = 1 + 1  repeat step 2 to 6. Step 9 : enddo Step10 : endfor
```  
**Algorithm 1**********Ex-ADMM based mmWave MIMO Channel Estimation Scheme
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 3 Unmanned Aerial Vehicles (UAVs)
### UAV Characteristics
#### 3.3.3 Range and Altitude  
UAV's range refers to the distance from where it can be controlled remotely. The range differs from a few meters for small drones to hundreds of kilometers for larger drones. On the other hand, altitude refers to the height at which a drone can fly. Usually, aerial platforms are divided into two categories on the basis of altitude.  
* Low altitude platforms (LAPs): LAPs are usually deployed to support cellular communication as they offer fast-deployment and cost-effectiveness. In addition, LAPs offer line-of-sight (LoS) path which can substantially improve the communication performance [54].
* High altitude platforms (HAPs): HAPs like balloons are also used for cellular connectivity. HAPs offer wide coverage as compared to LAPs. HAPs deployment is complicated and they are mostly considered as a vehicle to support Internet connectivity. Table 4 presents UAV categories based on altitude. Table 5 summarizes a comparison of different UAV types considering three different parameters. Figure 5(a)-d show different UAV projects implemented in different countries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Endurance (h)** & **Flight alt. (m)** & **Range (km)** & **Mass (kg)** \\ \hline Low altitude deep penetration (LADP) & 0.5–1 & 50–9000 & \textgreater{}250 & 250–2500 \\ Low altitude long endurance (LALE) & \textgreater{}24 & 3000 & \textgreater{}500 & 15–25 \\ Medium altitude long endurance (MALE) & 24–48 & 3000 & \textgreater{}500 & 1000–1500 \\ High altitude long endurance (HALE) & 24–48 & 20,000 & \textgreater{}2000 & 2500–5000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: UAV categorization on altitude [55].
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 3 Neural Network Architectures  
Two main frameworks are typically employed for audio data classification. The first involves extracting voice quality features and applying them to deep neural networks (DNNs). Although this approach allows for an intuitive interpretation of the results, it  
Figure 3: Representation of MFCCs and delta MFCCs in audio.  
may not fully capture the complex characteristics inherent in speech signals. The second framework converts audio data into spectrogram-based images using feature extraction techniques and applies them directly to DNNs. This method preserves both temporal and frequency information, enabling the model to learn intricate patterns within the speech signals. However, this method requires significant computational resources and can complicate the interpretation of results.  
Convolutional neural networks (CNNs) were developed primarily to solve image-related problems and have demonstrated strong performance in various speech-related tasks, such as automatic speech recognition, speech emotion recognition, and speaker recognition [29,30]. Recent developments in computer vision have led to the introduction of various techniques, among which the vision transformer (ViT) has gained attention for its exceptional image classification performance. Unlike traditional CNNs, ViTs can capture global information and context by processing the entire input holistically, which has resulted in attempts to use ViTs for classifying speech images [15,31].  
The image-based approach effectively preserves the time frequency information in speech and captures subtle variations in speech patterns across age groups. In this study, three types of CNN-based models and transformer-based ViTs were trained, and their performances were compared. Detailed descriptions of the models are provided below.
## 3 Quality of Service
### Requirements  
Industrial automation applications can be divided into two main categories of systems [32]: close-loop1 and open-loop systems2 as defined in [33]. The applications based on either close or open-loop systems have different QoS requirements, as close-loop systems monitor discrete operations, such as actuator control, and open-loop systems monitor continuous processes, such as cooking raw material.  
Figure 11: 802.15.4e FA MAC superframe structure [29].  
In close-loop applications, the sensors generate traffic that needs to be transmitted timely, reliably and accurately [33] to the control system, whereas in open-loop applications, real-time processing is typically not required, but the energy consumption is a crucial point. An additional classification [35] of the industrial applications refines these categories and is based on the degree of the required real-time guarantees. The classification is composed of four classes presenting increasing real-time requirements that are summarized in Table 2. We next focus on the support of real-time traffic and the reliability offered by the selected specifications.
## 3 Interpolation Technique of MoM Matrix  
According to the previous statement, Equation (4) are actually functions of frequency point \(f\):  
\[\sum_{n=1}^{N}Z_{mn}(f)I_{n}(f)=V_{m}(f),m=1,2,\cdots,N \tag{7}\]  
If the wideband response is of interest, it will take more time to solve EFIE and MFIE at each \(f\) using the MoM. \([f_{1},f_{h}]\) is the frequency variation range. The object is discretized at the highest frequency \(f_{h}\) using triangular meshes. \(\lambda_{h}\) is the wavelength. \(Z_{mn}(f)\) of EFIE and MFIE are:  
\[Z_{mn}^{E}(f_{r})=\int_{S_{m}(\lambda_{h})}ds\int_{S_{n}(\lambda_{h})}ds^{ \prime}\left[\overset{\rightarrow}{f}_{m}(\overset{\rightarrow}{r})\cdot \overset{\rightarrow}{f}_{n}(\overset{\rightarrow}{r}^{\prime})k_{r}-\
abla _{h}\cdot\overset{\rightarrow}{f}_{m}(\overset{\rightarrow}{r})\
abla^{ \prime}{}_{h}\cdot\overset{\rightarrow}{f}_{n}(\overset{\rightarrow}{r}^{ \prime})\frac{1}{k_{r}}\right]\frac{e^{-jk_{r}R}}{R}\frac{j\eta\lambda_{h}^{2}}{4\pi} \tag{8}\]\[Z_{mn}^{M}(f_{r})=\left[\int_{S_{m}(\lambda_{k})}ds\overrightarrow{J}_{m}( \overrightarrow{r})\cdot\overrightarrow{J}_{n}(\overrightarrow{r})-\frac{1}{2 \pi}\int_{S_{m}(\lambda_{k})}ds\overrightarrow{J}_{m}(\overrightarrow{r}) \cdot\hat{n}\times\int_{S_{n}(\lambda_{k})}ds^{\prime}\
abla_{h}\frac{e^{-jk,R }}{R}\times\overrightarrow{J}_{n}(\overrightarrow{r}^{\prime})\right]\frac{ \eta\lambda_{k}^{2}}{2} \tag{9}\]  
where \(k_{r}=2\pi f_{r},f_{r}=f/f_{h}\). Therefore, the range of \(f_{r}\) is \([f_{l},/f_{h},1]\). The following changes:  
\[z_{mn}^{K}(f_{r}):=\frac{Z_{mn}^{K}(f_{r})}{\lambda_{h}^{2}} \tag{10}\]  
where, the superscript \(K\) represents \(E,M,C\).  
The phase term \(e^{-jk,R}\) can cause the fluctuation. The matrix elements are corrected by:  
\[\bar{z}_{mn}^{K}(f_{r})=\left\{\begin{array}{ll}z_{mn}^{K}(f_{r})f_{r}e^{jk_ {r}R_{mn}}&S_{m}\cap S_{n}=0\\ z_{mn}^{K}(f_{r})f_{r}&S_{m}\cap S_{n}\
eq 0\end{array}\right. \tag{11}\]  
\(S_{m}\) and \(S_{n}\) are triangles, and the relationship between them can be found in [23]. Thereby, \(\bar{z}_{mn}^{K}(f_{r})\) becomes a quadratic polynomial.  
The cubic polynomial interpolation/extrapolation method is mainly adopted to generate the corrected matrix at \(y_{v}=f_{v}/f_{h}(f_{v}\in[f_{l},f_{h}];v=0,1,2,3)\), where \(f_{v}\) are the optimal points. The specific formula for each \(f_{r}\) is as follows [23]:  
\[\bar{z}_{mn}^{S}(f_{r})=\sum_{v=0}^{3}\bar{z}_{mn}^{S}(y_{v})\phi_{v}(f_{r}) \tag{12}\]  
where,  
\[\phi_{k}(t)=\prod_{l=0,l\
eq k}^{l=3}\left(\frac{t-t_{l}}{t_{k}-t_{l}}\right) \tag{13}\]
## 3 Management of Electrical Grids by Means of WSANs
### Main Characteristics of a WSAN  
The development since last years of smart sensors, thanks to the spread of Micro Electro Mechanical Systems (MEMS), has contributed in a major way to the recent expansion of Wireless Sensor and Actuator Networks. WSANs are normally composed of a large number of nodes, which are low power devices generally equipped with one or more sensing units (composed of sensors and, eventually, analog to digital converters), an actuator, a processing unit that includes a limited memory, a radio transceiver, and the power supply. Figure 4 shows the basic architecture of a typical node of a WSAN.  
Figure 4: Basic architecture of a WSAN node.  
Taking into account the special characteristics of the operating scenarios of WSANs, the most important design factors are listed below [21].  
* Hardware constraints: The size of nodes, as well as their weight, must be as small as possible.
* Power consumption: This is one of the main constraints for a sensor/actuator node. Batteries are the principal power source for nodes, along with (but not necessarily) energy harvesting solutions. Therefore, the power consumption must be dramatically minimized.
* Fault tolerance: The correct operation of a WSAN must not be disturbed by the failure of one or more sensors.
* Scalability: It is necessary to develop management techniques to deal with networks containing up to hundreds or thousands of nodes (each node supporting a sensor and/or an actuator). The scalability is directly linked with the network topology.
* Transmission media: Communication among nodes is implemented by means of wireless technologies, and messages are broadcasted directly to the medium. Thus, security and privacy mechanisms must be implemented.
* Operation environment: Sensor/actuator nodes must be able to work in harsh environments, in terms of extreme temperatures, high humidity levels, electromagnetic interferences, vibrations, _etc._  
The main characteristics of WSANs are exhaustively surveyed in [22], including the types of sensor networks, applications, operative systems, standards, network services or communication protocols. On the other hand, the most relevant experimental WSANs deployed over the World are reviewed in [23]. These test platforms are decisive to enhance the performances of WSANs from several points of view: nodes technology, communication protocols or energy saving, among others.  
To conclude, it is important to highlight the application of WSANs to manage different issues of the electrical grid at different levels, in the frame of a Smart Grid scenario [24; 25; 26]. The next sections will deal more in detail with the use of WSANs to achieve intelligent electrical grids.
# Experimental Evaluation of the Durability of Concrete Repair Materials
## 2 Materials and Methods
### Test Specimens  
For the strength tests, cylindrical (\(\mathcal{O}100\times 200\) mm) and cuboid (100 \(\times\) 100 \(\times\) 400 mm) specimens were used, with 50 mm deep notches representative of inactive cracks found in concrete, as shown in Figure 1[23]. The values of the notch cracks, 2 and 5 mm, are arbitrarily set in consideration of actual crack conditions. As discussed in Section 2.1, concrete mixtures with target compressive strengths of 20 and 40 MPa were employed. Specimens were repaired with epoxy, impregnant, or epoxy/impregnant, and unrepaired notched specimens were retained as control. Three specimens were prepared for each combination of variables, giving a total of 96 specimens.  
For the concrete durability tests, only the cylindrical specimens were used. Specimens were repaired with epoxy, impregnant, or epoxy/impregnant; unrepaired notched specimens were retained as control. Notch widths of 2 and 5 mm, and compressive strengths of 20 and 40 MPa were used. Tests were conducted to simulate carbonation, chloride attack, and freeze-thaw damage. Three specimens were prepared for each combination of variables, giving a total of 144 specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Target Strength (MPa)**} & \multirow{2}{*}{**Water/Cement Ratio (\%)**} & \multicolumn{4}{c}{**Unit (kg/m3)**} \\ \cline{3-6}  & & **Water** & **Cement** & **Sand** & **Gravel** \\ \hline
20 & 56 & 170 & 305 & 906 & 924 \\
40 & 36 & 155 & 425 & 813 & 958 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete mixtures used for the experiments. The target compressive strengths were for samples at 28 days.  
Figure 2 illustrates epoxy, impregnant, and epoxy/impregnant repairs. Epoxy repairs fill cracks; impregnant repairs apply a coating to the concrete and crack surfaces; epoxy/impregnant repairs fill the crack and protect the surface. The epoxy used in this study was a low-viscosity type mainly used for repairing cracks in concrete social infrastructure, such as bridges, tunnels, and dams. It exhibits high physical strength when hardened. Crack injection was performed using a syringe. Impregnant forms additional hydrates and increases the density of micropores inside the concrete by reacting with Ca(OH)\({}_{2}\), thus improving the durability. Impregnant was applied to the surface using a brush.  
Figure 1: (**a**) Cylindrical and (**b**) cuboid specimens with 50 mm deep notches representative of inactive cracks found in concrete.  
Figure 2: _Cont._