esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Experiment
### Results Analysis
#### 3.2.2 Analysis of Jacking Data Fusion Effects
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial Number** & **Standard Load/N** & \multicolumn{2}{c}{**Fusion Method**} & \multicolumn{2}{c}{**JM(Fusion)**} & \multicolumn{2}{c}{**RSGM**} \\
**Number** & **Load/N** & **Mean/N** & **Standard Deviation/N** & **Mean/N** & **Standard Deviation/N** \\ \hline Case 2 & 26,425 & 26,422 & 263 & 26,427 & 382 & 26,421 & 265 \\ Case 3 & 26,381 & 26,375 & 253 & 26,391 & 381 & 26,374 & 264 \\ Case 4 & 23,689 & 23,682 & 324 & 23,695 & 342 & 23,677 & 380 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of measurement results from various measurement methods. Figure 10: Relative frequency distribution of results for JM and JM(Fusion); (**a**) Case 2; (**b**) Case 3; (**c**) Case 4. From Table 7 in conjunction with Figure 11, it can be observed that the measurement method based on the fusion of redundant strain and jacking data has higher measurement accuracy. Under the conditions of each case, the mean of the measurement results from the three measurement methods does not significantly differ from the standard load, but the standard deviation of the fusion measurement method is the smallest. Under the conditions of each case, the minimum and maximum differences between the mean of the fusion measurement results and the standard load are 3 N and 7 N, respectively, while the minimum and maximum differences for the other two measurement results and the standard load are 2 N and 12 N, respectively. The minimum and maximum standard deviations of the fusion measurement results are 253 N and 324 N, respectively, while the minimum and maximum standard deviations of the other two measurement results are 264 N and 382 N, respectively. Under the same conditions, compared to the other two methods, the standard deviation of the measurement results from the fusion measurement method can be reduced by a minimum of 1.1% and a maximum of 33.6%.
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 3 Integrated Photovoltaics Options
### Building-Integrated Photovoltaics and Rooftop Photovoltaics  
#### 3.3.1 Building-Integrated Photovoltaics  
Building-integrated photovoltaics are well-known and have been established PV applications for many years now. They consist of the replacement of conventional building materials with materials incorporating PV technologies so as to have a double function, acting like an energy-producing building component. The generated electricity is consumed close to where it is produced, thus excluding potential grid investments.  
According to a recent study, when considering a building skin to building net surface area ratio of 0.78 and a building skin glazing ratio of 30%, buildings could cover their electricity consumption using building-integrated photovoltaics systems by 2030 in the EU [27].  
More technology details can be found in the dedicated paper in this Special Issue.
## 2 Materials and Methods
### Improved Network Module  
#### 2.2.1. Atrous Bottleneck Pyramid Module (ABPM)  
The original ASPP (Atrous Spatial Pyramid Pooling) module extracts feature map information from different receptive fields by paralleling atrous convolutions with various dilation rates. However, the interval sampling and sparse sampling of atrous convolutions can lead to a lack of correlation between convolution results, causing issues such as the checkerboard effect in image pixels, resulting in discontinuous image features and spatial gaps. A common solution is to perform upsampling before convolution to avoid the generation of artifacts. Inspired by the inverted residual structure in MobileNetV2, which first increases the dimensionality, then applies convolution, and finally reduces the dimensionality, while using depthwise separable convolutions to ensure less computational cost, we design the Atrous Bottleneck Pyramid Module (Figure 2). In this module, atrous convolutions are used for feature extraction in high-dimensional features to increase the correlation between extracted features. Specifically, a \(1\times 1\) convolution is first used to unify the number of channels in the four branches to 256. Then, three of these branches apply depthwise convolutions with dilation rates set to 6, 12, and 18. Respectively, for feature extraction in high-dimensional features that have been expanded six times. Afterward, pointwise convolutions are used for feature extraction in the channel direction and to restore the channel dimensions. Finally, the features from the four branches and the pooled features are concatenated, and a \(1\times 1\) convolution is used to change the number of channels:  
\[\mathbf{F_{ABPM}}=\sum_{i=1}^{3}\delta_{\text{DS}}^{r_{i}}(\delta_{1\times 1} (\mathbf{F_{in}}))+\delta_{1\times 1}(\mathbf{F_{in}})+\delta_{\text{pool- conv}}(\mathbf{F_{in}}) \tag{1}\]  
Among them, \(\delta_{1\times 1}()\) represents a \(1\times 1\) convolution, \(\delta_{\text{pool-conv}}()\) represents pooling followed by a \(1\times 1\) convolution, and \(\sum_{i=1}^{3}\delta_{\text{DS}}^{r_{i}}()\) represents depthwise separable convolutions with dilation rates set to 6, 12, and 18. \(r_{i}\) = 6, 12 and 18. Using ABPM expands the receptive field of the model while preserving important detail information, and at the same time, the parameter count and computational complexity remain largely unchanged due to the use of depthwise separable convolutions with dilation rates.  
Figure 1: Cross-Domain Feature Fusion Network (CDFFNet).
# Temperature Effects on the Natural Frequencies of Composite Girders
## 4 Numerical Simulations for Modal Testing
### Numerical Model  
An FE model of the specimen was constructed using ABAQUS/Standard 2021 (Figure 8). The concrete deck was modeled with eight nodes of solid elements (C3D8R) and a steel girder with eight nodes of shell elements (S8R). The reinforcement of the deck was modeled with two nodes of truss elements (T32D) embedded into the deck elements. A surface constraint (tie condition) was used to bond the top flange of the steel beam and the bottom surface of the concrete slab to obtain a composite action. Support conditions were simulated using a hinge at one end and a roller at the other.  
Linear elastic material models were utilized in the FE analysis. Table 1 lists the elastic moduli of the concrete and steel used in our analysis. The elastic modulus of the concrete deck was determined using Equation (1) from Section 2.1 to account for temperature dependency. Equation (2) was used to calculate the elastic moduli of the steel beams and reinforcing bars. The other material properties used in the FE model are summarized in Table 2. Notably, the specific heat and thermal conductivity of steel are also temperature-dependent. Therefore, Equations (3) and (4) were used to determine these properties, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Temperature** (°C) & **Concrete (MPa)** & **Steel/rebar (MPa)** \\  & **(Equation (1))** & **(Equation (2))** \\ \hline
20 & 19,188 & 205,420 \\
25 & 18,900 & 204,370 \\
30 & 18,612 & 203,320 \\
35 & 18,324 & 202,280 \\
40 & 18,037 & 201,230 \\
50 & 17,461 & 199,130 \\ \hline \end{tabular}
\end{table}
Table 1: Moduli of elasticity of concrete and steel/rebar in the model (Equation (1) for concrete and Equation (2) for steel/rebar).  
Figure 8: FE model of the specimen.  
Concrete is a heterogeneous material composed of mortar and aggregate. The tensile strength of the mortar is lower than that of the aggregate. Therefore, concrete is susceptible to cracking between the mortar and the aggregate. The development of cracks in the concrete deck resulted in a reduction in the moment of inertia of the section. To account for this phenomenon, 70% of the flexural rigidity of the un-cracked condition was applied in the FEA according to the ACI318-19 standard [37].
# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 4 Materials  
### English Corpus (TORGO)  
Two commonly utilized datasets for English dysarthric speech research are the UA-Speech Database [58] and the TORGO database [59]. The former is larger and only contains isolated words, while the latter is composed of both isolated words and continuous speech. Isolated words may be  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **\# of Hidden Layers** & **\# of Neurons** & **Activation Function** & **Optimizer** & **Learning Rate** \\ \hline Values & 1, 2, 3, 4 & 25-100 & Logistic, tanh, ReLU & Adam, SGD, LBFGS & 0.0001–0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The different parameters examined with NN classifiers.  
sufficient to detect dysarthria from speakers with severe or moderate dysarthria but may not be enough input to detect mild dysarthria. Furthermore, prosody is a dynamic aspect of speech which is better represented by continuous speech. Therefore, we only use continuous speech data in our experiments to check the effectiveness of prosody-based measures.  
The TORGO dataset contains 8 speakers with dysarthria, 5 males and 3 females from patients with Cerebral Palsy, and 1 speaker with ALS. Speakers are recruited by a speech pathologist in Toronto Ontario, Canada. While the exact dialect is not mentioned in Reference [59], speakers are likely to have a standard Canadian English dialect. Dialect consistency may be an important factor as different dialects can display different prosodic characteristics. Since we are using prosody-based measures as features, we want the machine learning model to detect prosodic variation between healthy speech and dysarthric speech not between dialects.  
Speakers with dysarthria are assessed by a trained speech pathologist using the Frenchay Dysarthria Assessment [7]. Two speakers are categorized as having severe dysarthria, one speaker with moderate/severe, one moderate, and four mild. Recording from 7 healthy controls, 4 males and 3 females, are also collected. A mixture of short words, non-words, restricted sentences (read speech), and unrestricted sentences (spontaneous speech) are recorded from all speakers, but only restricted sentences are analyzed for our study. Some examples of recorded speech stimuli can be examined in Table 13 from the Appendix A.
# A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
## 4 The Intrinsic Limit Determination Algorithm
### ILD Theorem  
The ILD theorem can now be formulated.  
**Theorem 2** (ILD Theorem).: _Among all possible curves that can be constructed by generating a set of points \(\mathcal{P}\) by flipping all components of \(\mathbf{p}_{S}\) in any order one at a time, the curve \(\mathcal{C}\) has the maximum AUC._  
Proof.: The Theorem is proven by giving a construction algorithm. It starts with one set of points \(\mathcal{P}_{0}\) generated by flipping components of \(\mathbf{p}_{S}\) in a random order. Let us consider two adjacent segments generated with \(\mathcal{P}_{0}\): \(s^{[j]}\) and \(s^{[j+1]}\). In Figure 4A, the two segments are plotted in the case where the angle between them \(\beta^{[j,j+1]}<\pi\). The angles \(\alpha^{[j]}\) and \(\alpha^{[j+1]}\) indicate the angles of the segments with the horizontal direction and \(\beta^{[j,j+1]}\) the angle between the two segments \(j\) and \(j+1\). The area under the two segments and any horizontal line that lies below the segments can be increased by simply switching the order of the two flips, as it is depicted in Figure 4B. Switching the order simply means flipping first the \(j+1\) component and then the \(j\) component.  
It is important to note that in Figure 4A \(\beta^{[j,j+1]}<\pi\), while in panel (B) \(\beta^{[j+1,j]}>\pi\). It is evident that the area under the two segments in panel (B) is greater than the one in panel (A). The parallelogram in Figure 4C depicts the area difference.  
The proof is based on repeating the previous step until all angles \(\beta^{[j,j+1]}>\pi\). This is described in pseudocode in Algorithm 1.  
```
Generate a set of points \(\mathcal{P}_{0}\) by flipping the components of \(\mathbf{p}_{S}\) in a random sequence; whiletruedo \(c=0\); for\(i=1,\ldots,N_{B}\)do if\(\beta^{[i,j+1]}<\pi\)then  Switch points \(i\) and \((i+1)\) in \(\mathcal{P}_{0}\);  c = c + 1;  end if if\(c=0\)then  Exit While loop and end the Algorithm  end if The final set of points \(\mathcal{P}_{0}\) obtained in the loop above will generate the curve \(\mathcal{C}\).  end if
```  
**Algorithm 1**Algorithm to construct the curve \(\mathcal{C}\)  
The area under the curve obtained with Algorithm 1 cannot be made larger with any further switch of points in \(\mathcal{P}\).  
Figure 4: Visual explanation for the ILD Theorem. Panel (**A**): two consecutive segments after flipping components \(j\) and then \(j+1\); Panel (**B**): two consecutive segments after flipping components \(j+1\) and then \(j\); Panel (**C**): parallelogram representing the difference between the area under the segments in panel (**A**) and the area under the segments in panel (**B**).  
Note that Algorithm 1 will end after a finite number of steps. This can be shown by noting that the described algorithm is nothing else than the bubble sort algorithm [24] applied to the set of angles \(\alpha^{[1]}\), \(\alpha^{[2]}\), \(\ldots\), \(\alpha^{[N_{B}]}\). Therefore, this algorithm has a worst-case and average complexity of \(\mathcal{O}(N_{B}^{2})\).
# Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems
## 6 Monostatic Radar Signal Processing
### Ambiguity Functions in the Presence of Noise  
Since the monostatic radar configuration does not require a chaotic synchronization approach, its performance in the presence of noise is robust compared to the bistatic radar system. Consequently, even for the SNR of \(-30\) dB, the sidelobe does not rise above \(-13.33\) dB. Figure 17a,b shows the ambiguity surface of the Lorenz CBFM and Burke-Shaw CBFM waveforms considering the received signal is corrupted with \(SNR=-30\) dB. The noise floor rises uniformly to 0.21 \((-13.5\) dB) across both planes. From Figure 18 it is evident that for \(SNR<-30\) dB, the sidelobes increase significantly, likely masking the targets present in close proximity.  
Figure 15: Ambiguity surface of Monostatic radar for (**a**) the Lorenz CBFM waveform and (**b**) the Burke-Shaw CBFM waveform.  
Figure 16: Ambiguity surface of the monostatic radar for **(a)** the Lorenz CBFM waveform and **(b)** the Burke-Shaw CBFM waveform.  
Figure 17: Ambiguity surface of the monostatic radar for **(a)** the Lorenz CBFM waveform and **(b)** the Burke-Shaw CBFM waveform considering \(SNR=-30\) dB.  
Figure 18: Sidelobe volume of the monostatic radar configuration for the Lorenz CBFM and Burke-Shaw CBFM waveforms plotted against the signal to noise ratio (\(SNR\)). The green square at \(SNR=-30\) dB indicate that for sidelobe levels are at the value of \(-13.33\) dB.
# Study and Investigation on 5G Technology: A Systematic Review
## 4 5G Technologies
### 5G Millimeter Wave (mmWave)
In addition, they cross-checked the mesh-grid array of antennas with the polarized beam for upcoming hardware challenges. In [12], the authors considered the suitability of the mmWave band for 5G cellular systems. They suggested a resource allocation system for concurrent D2D communications in mmWave 5G cellular systems, and it improves network efficiency and maintains network connectivity. This research article can serve as guidance for simulating D2D communications in mmWave 5G cellular systems. Massive mmWave BS may be set up to obtain a high delivery rate and aggregate efficiency. Therefore, many wireless users can hand off frequently between the mmWave base terminals, and it emerges the demand to search the neighbor having better network connectivity. In [62], the authors provided a brief description of the cellular spectrum which ranges from 1 GHz to 3 GHz and is very crowed. In addition, they presented various noteworthy factors to set up mmWave communications in 5G, namely, channel characteristics regarding mmWave signal attenuation due to free space propagation, atmospheric gaseous, and rain. In addition, hybrid beamforming architecture in the mmWave technique is analyzed. They also suggested methods for the blockage effect in mmWave communications due to penetration damage. Finally, the authors have studied designing the mmWave transmission with small beams in nonorthogonal device-to-device communication. This section covered various works done on 5G mmWave technology. The Table 7 shows how different author's worked on the improvement of various parameters i.e., transmission rate, coverage, and cost, with 5G mmWave technology.
# The IoT/IoE Integrated Security & Safety System of Pompeii Archeological Park
## 18 Results
The results obtained for the system under analysis are summarized in Table 1, which also includes the minimum and maximum reference values for comparable systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Actual Value of**} & \multicolumn{2}{c}{**Minimum Value of**} & \multicolumn{2}{c}{**Maximum Value of a**} \\  & \multicolumn{2}{c}{**the System (This Work)**} & \multicolumn{2}{c}{**a Similar System**} & \multicolumn{2}{c}{**Similar System**} \\ \hline Event Processing Capacity & 8500 & 100 & 10,000 \\ \hline Average Response Time to & \multirow{2}{*}{\(1\) s} & \multirow{2}{*}{\(2\) s} & \multirow{2}{*}{\(30\) s} \\ an Event (MTTR) & & & \\ \hline Maximum Supported Load & 2200 & 50 & 2000 \\ \hline Accuracy of Intelligent & \multirow{2}{*}{\(98.8\%\)} & \multirow{2}{*}{\(80\%\)} & \multirow{2}{*}{\(99\%\)} \\ Analysis & & & \\ \hline Input Latency & \(7\) ms & \(5\) ms & \(1\) s \\ \hline Processing Latency & \(5\) ms & \(5\) ms & \(1\) s \\ \hline Notification Latency & \(80\) ms & \(100\) ms & \(1\) s \\ \hline End-to-End Latency & \(92\) ms & \(110\) ms & \(3\) s \\ \hline Jitter & \(1\) ms & \(1\) ms & \(5\) ms \\ \hline MTBF & \(85\),\(000\) h & \(20\),\(000\) h & \(100\),\(000\) h \\ \hline MTTR & \(1.5\) h & \(2\) h & \(6\) h \\ \hline Availability & \(99.998\%\) & \(99.5\%\) & \(99.9\%\) \\ \hline Component Error Rate & \(0.15\%\) & \(0.1\%\) & \(1\%\) \\ \hline Accuracy & \(99.71\%\) & \(95\%\) & \(99\%\) \\ \hline Precision & \(99\%\) & \(90\%\) & \(99\%\) \\ \hline Recall & \(98.8\%\) & \(90\%\) & \(99\%\) \\ \hline F1-score & \(98.9\%\) & \(90\%\) & \(98\%\) \\ \hline False Positive Rate & \(0.15\%\) & \(0.1\%\) & \(0.5\%\) \\ \hline False Negative Rate & \(1.2\%\) & \(1\%\) & \(5\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of performance analysis of the integrated system. As observed from the presented data, the integrated system exhibits performance levels that are consistent with--and, in several key parameters, superior to--the best benchmark values for similar systems, whether these correspond to upper or lower bounds depending on the performance indicator. This confirms both the efficiency and the effectiveness of the implemented solution in ensuring robust and reliable security management within the site.
## 4 Rare Earth Flotation from Secondary Resources
### Solvent Sublation  
Solvent sublimation (Figure 3b) is an advanced flotation technique combining the principles of ion flotation and solvent extraction. This technique consists of two liquid components: (i) aqueous phases containing REE ions, and (ii) an organic phase. It should be noted that the organic substances used in this process must have a lower density than water to facilitate separation. They also need to have low solubility and a high ability to accumulate sublate, so isooctyl alcohol and 2-octanol are usually used [138; 139; 144; 145].  
Separation occurs when sublate-bearing gas bubbles (usually using nitrogen gas) float and transfer rare earth ions to the organic phase. The gas flow rate used in this process must be very low (~3.3 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/h) to ensure that the bubbles do not disturb the organic phase. Similar to ion flotation, NaDDS is frequently used as the surfactant in solvent  
\begin{table}
\begin{tabular}{l l c c c c c} \hline
**Fotation Techniques** & **REEs** & **REE Concentration [M]** & **pH** & **Surfactants** & **Frobers** & **Eff. \({}^{*}\) [\%]** & **References** \\ \hline \multirow{8}{*}{Ion flotation} & Ce & 1 [mg/L] & – & – & – & – & [132] \\  & La, Ce, Gd, and Yb & (0.01-5.4) \(\times\) 10\({}^{4}\) & 3 & NaDDS & – & 57-61 & [140] \\  & Nd & 7 \(\times\) 10\({}^{4}\) & 3 & NaDDS & – & 85-97 & [133; 134] \\  & Ce and Y & 1 \(\times\) 10\({}^{3}\) & 6 & NaDDS & – & – & [138] \\  & Y and Yb & 1 \(\times\) 10\({}^{3}\) & – & NaDDS & – & – & [135] \\  & Yb & 1 \(\times\) 10\({}^{3}\) & – & NaDDS & – & – & [137] \\  & La and Y & 1 \(\times\) 10\({}^{3}\) & – & NaDDS & – & – & [136] \\  & La and Ho & 1 \(\times\) 10\({}^{3}\) & – & NaDDS & – & – & [143] \\  & Sm & 1 \(\times\) 10\({}^{3}\) & – & NaDDS & – & – & [139] \\  & La, Ce, Gd, and Yb & (0.01-5.4) \(\times\) 10\({}^{4}\) & 9 & Monorhamolipid & – & 90-95 & [140] \\  & La & (0.3-1.6) \(\times\) 10\({}^{5}\) & – & Monorhamolipid & EtOH & – & [131] \\  & Eu and Tb & 2.5 \(\times\) 10\({}^{5}\) & – & Cetylpyridinium & EtOH & – & [142] \\  & Nd & 10\({}^{2}\)–10\({}^{4}\) & – & chloride & & & \\ \hline \multirow{8}{*}{Solvent sublimation} & Ce and Y & 1 \(\times\) 10\({}^{-3}\) & 7-8 & NaDDS & Isooctyl & 99 & [138] \\  & Ce & 1 \(\times\) 10\({}^{3}\) & 8 & NaDDS & Isooctyl & & \\  & Tb & 1 \(\times\) 10\({}^{3}\) & 7 & NaDDS & 2-octanol & 99 & [145] \\  & Sm & 1 \(\times\) 10\({}^{3}\) & 8 & NaDDS & 2-octanol & 98 & [139] \\ \hline \end{tabular} Note: NaDDS = sodium dodecyl sulfate; AKYPO\({}^{\text{\tiny{B}}}\) RO 90 VG = trade name of nonoxyethylene oleylether carboxylic acid; EtOH = ethanol. \({}^{*}\) = separation efficiency.  
\end{table}
Table 4: A summary of recent studies on ion flotation and solvent sublation for REE ion recovery.  
sublation. Several authors have obtained 99% separation efficiency using this technique at pH 7-8 [138; 139; 144; 145].
# From Brown-Field to Future Industrial Networks, a Case Study
## 3 Case Study: Iggesund Paperboard  
The Iggesund paperboard is a typical process automation factory, and the automation network can be considered as an example of production networks in manufacturing. Reliability of the production network is ensured by hot-standby redundancy consisting of two networks: primary and secondary. The communication backbone for two paperboard machines comprises three marshaling rooms and a number of switches located across the factory. The communication between different systems is provided by the configuration of several virtual LANs (VLANs). This case study covers a part of the operational network consisting of 5 control systems, with 43 stations connected to the server network and 32 process controllers on various VLANs.  
Figure 1 illustrates the network topology at the paperboard machine where the data were collected. In this study, the Client-Server Network traffic with connectivity to the Internet is considered IT traffic, and the traffic from the control systems is the OT traffic.  
Figure 1: Network topology at one of the paperboard machines at Iggesund based on the documentation [18].