esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 4 Network Flow Programming Models
### Special Classes of NFPs
#### 4.2.7 Minimum Cost Network Flow Problems  
The transportation, assignment, transshipment, shortest path and maximum flow problems are all special cases of the minimum-cost network flow problem (MCNFP) as shown in Figure 3. Any MCNFP can be solved by a generalization of the transportation simplex called the _network simplex_. To define an MCNFP, let:  
* \(x_{ij}\): represents the number of units of flow sent from node \(i\) to node \(j\) through arc \((i,j)\).
* \(b_{i}\): represents the net supply (outflow-inflow) at node \(i\).
* \(c_{ij}\): be cost of transporting 1 unit of flow from node \(i\) to node \(j\) via arc \((i,j)\).
* \(L_{ij}\): be the lower bound on flow through arc \((i,j)\), if there is not lower bound \(L_{ij}=0\).
* \(U_{ij}\): be the upper bound on flow through arc \((i,j)\), if there is not lower bound \(U_{ij}=\infty\).  
Then the MCNFP may be written as:  
\[min\ \ \sum_{all\ arcs}c_{ij}x_{ij}\] (5a) s.t. \[\sum_{j}x_{ij}-\sum_{k}x_{ki}=b_{i}\ \ \forall\ i \tag{5b}\] \[L_{ij}\leq x_{ij}\leq U_{ij}\ \ \forall\ arcs \tag{5c}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Minimum Edge Weight Data Structure** & **Time Complexity** \\ \hline adjacency matrix, searching & \(O\left(|V|^{2}\right)\) \\ \hline binary heap and adjacency list & \(O\left(|E|\,|log\left(V\right)|\right)\) \\ \hline Fibonacci heap and adjacency list & \(O\left(|E|+|\,|log\left(V\right)|\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time complexity for [PERSON]’s algorithm using different implementations.  
Constraints (5b) stipulate that the net flow out of node \(i\) must be equal to \(b_{i}\). Constraints (5b) are referred to as the flow balance equations for the network. Constraints (5c) ensure that the flow through the arc satisfies the arc capacity restrictions. This formulation can be used to model all of the special cases of the MCNFP explained earlier.  
Since this is a linear program, it can be solved using the simplex method. The _Network Simplex Method_ is a special implementation of the simplex method which makes use of the network structure to significantly streamline the computational effort. For a good reference explaining how this algorithm works and other variants of it like _dual network simplex_, _Two-Phase Network Simplex Method_ and _One-Phase Primal-Dual Network Simplex Method_ the we suggest the lecture slide set in [35] for the reader. It is worth noting that if all the parameters of the NFP problem in Equation (2) are integer, then the number of pivots taken by the network simplex would be _pseudopolynomial_ in the network size.
## 3 Results
### Evaluation of the Developed Empirical Failure Pressure Assessment Method  
Based on Figure 7, it was observed that the predicted failure pressures obtained using the developed empirical equation are comparable to that of FEM. The maximum and minimum percentage differences observed are 0.98% and \(-\)9.92%, respectively. A positive percentage difference indicates overestimation. However, this overestimation is less than 1.00%; hence, it is negligible. In addition, only 1.70% of the 1353 datasets resulted in overestimation. The standard deviation of the results is 2.27, and the confidence level of the percentage error of predictions that are less than 9.92% is 99.9%, as the errors fall within four standard deviations of the mean.  
Since data on burst tests of corroded pipes subjected to combined loadings for interacting defects are limited, FEM was utilised to further verify the new prediction approach using a set of arbitrary data for API 5L X52, X65, and X80 material. Table 10  
Figure 7: Probability distribution of the percentage error obtained using the new empirical failure pressure assessment method and FEM based on the parameters of the ANN training data.  
summarises the parametric details, failure pressure predictions using FEM, and the developed equation, as well as the percentage difference between the methods. Based on Table 10, the Root Mean Square Error (RMSE) of the failure pressure predictions is 0.05. This indicates a good correlation among the predictions obtained using FEM and the developed equations. The minimum and maximum percentage differences observed are \(-\)9.42% and \(-\)0.01%, respectively.  
The predicted failure pressures fall within the 99.9% confidence level for true ultimate tensile strength values of 612 MPa, 629 MPa, and 718 MPa, normalised defect spacings of 0.00 to 3.00, normalised effective defect lengths of 0.00 to 2.95, normalised effective defect depths of 0.00 to 0.80, and normalised axial compressive stress of 0.00 to 0.60. The equations resulted in minimal overestimations, while underestimated values were  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\sigma_{\text{UITS}}^{*}\) & **(  
not overly conservative (within an error of 0.98%). Using these equations, a correlation between defect geometries and the failure pressure of API 5L X52, X65, and X80 with circumferentially aligned interacting corrosion defects subjected to internal pressure and axial compressive stress was established. This contributes to overcoming the limitations of the conventional assessment codes as the conventional codes do not cater for pipes with interacting defects subjected to combined loading. Furthermore, the developed ANN model in this study can be easily retrained using data for different materials to increase its robustness, and the equations can be revised accordingly, which can be useful for future research.
## 2 Dielectric Resonator Antenna
### Dual- or Multiple-Feed CP DRA  
More than one feed antenna requires a power divider and a polarizer to excite CP radiation. The feed points need to be orthogonal to each other with a phase difference of 90\({}^{\circ}\)[55]. This type of feed generates TM\({}_{10}\) and TM\({}_{01}\) modes with an equal amplitude and are 90\({}^{\circ}\) out of phase. Each mode radiates individually and combines to yield CP. By using this technique, the AR bandwidth is improved. It can be as wide as the impedance bandwidth depending on the bandwidth properties of the phase shifter used, as this type of antenna is not dependent on the mode perturbing element. However, it suffers from a complex design, large size, and a high manufacturing cost compared to single-feed CP techniques. In [28], a bowtie dielectric resonator is produced by two rotationally symmetric triangular ceramic elements. A vertical flat metal strip is attached to one of the triangular DRs and connected to the 50 \(\Omega\) microstrip line by a probe through the ground plane. On the other hand, the second triangular DR is excited by cutting an L-shaped slot inside the ground plane, as shown in Figure 11. The lower CP band is formed by the slot mode and fundamental DR modes and the upper CP band is mainly formed by the slot mode and high-order dielectric resonator modes. The impedance bandwidth, AR bandwidth, and gain have been enhanced by using this sequentially rotated technique. The results indicate that the proposed antenna has wide overlapping working bands of 27.1% for the LHCP at the lower band and 12.8% for the RHCP at the upper band. Hence, this antenna is suitable for Wi-Fi applications, mobile-satellite services, radio navigation systems, and radiolocation equipment. However, this antenna requires high-precision fabricating technology and a low-loss feeding structure to enable it to work in a high frequency band. A 2 \(\times\) 2 sequentially rotated rectangular DRA array is proposed for 5.8 GHz applications in [56]. The dielectric resonators are coupled to the eye-shaped slots in order to generate circular polarization. Then, the array is fed by two interlaced ports and each port excites two radiating elements for the sake of size miniaturization, as shown in Figure 12. The proposed antenna has good gain and good port isolation with reduced size. However, this antenna exhibits a narrow bandwidth at ports 1 and 2 at a range of 5.769 to 5.913 GHz and 5.788 to 5.922 GHz, respectively. Some modification on the dielectric resonator geom
## 3 Kilowatt-Class Picosecond Thin-Disk Lasers
#### Improvements of Thin Disk Laser Operations at HiLASE  
Within the HiLASE thin disk laser development, several processes that improve laser operation have been systematically studied. First, we demonstrated that the pulsed pumping can improve the operation of the 1-kHz regenerative amplifier [33]. Further, we showed the advantages of zero-phonon line pumping, not only with respect to a lower thin disk temperature, a lower thin disk temperature difference between the lasing and non-lasing conditions [31], but also a lower amount of thin disk deformations [34; 35; 36]. The details will be described later in the text.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 4 Performance of fs-LIMS Instruments in the Element and Isotope Analysis
### Dynamic Range, Detection Sensitivity, and Limit of Detection  
The dynamic range within a single spectrum is defined as the ratio in the signal abundance of the largest and smallest useful mass peaks. The current LIMS systems report dynamic range based on summing (histogramming) multiple spectra together, which improves ion statistics, reduces background contamination and improves the measurement of smallest peaks. For the largest mass peaks, the acquisition system is important. Typically, ADC acquisition cards with an 8-bit or 10-bit vertical resolution at an 8 GSamples/s sampling rate are applied in the LIMS systems [132]. Without memory overflow, modern data acquisition firmware of the PCIe card supports a maximum summation of 64,000 spectra, which corresponds to a maximum vertical resolution of 24 bit and a maximal dynamic range of 7 decades per channel [132]. The firmware with extended memory offers a larger vertical resolution of up to 32 bit equivalent to a dynamic range of up to 9 orders of magnitude per channel. Practical limitations depend on the application. In the case of a miniature LMS, the summation of some tens of thousands of mass spectra shows a dynamic range of about 5-6 orders of magnitude by systematically averaging out the random noise. In the specific cases where a high dynamic range is required, the anode in the LMS detector system which consists of four separate active concentric segments can be used for a simultaneous readout of the signal [136]. Large laboratory systems typically have 6-7 decades of dynamic range [24; 129].  
Femtosecond LIMS can deliver sensitive measurement of chemical composition down to trace levels below ppm element abundance. Such measurements are of interest to many applications with a focus on either elements or isotopes. fs-LMS is designed to routinely achieve a detection limit at the ppm level and in some cases tens of ppb levels [132]. LI-O-TOFMS demonstrated limits of detection (LODs) in the 10\({}^{-7}\)-10\({}^{-8}\) mol/g range, corresponding to the sub-ppm level (for Li and Na, the lowest LODs were 0.2 and 0.1 \(\upmu\)g/g which is about 30 ppb and 10 ppb atomic fraction for these elements, respectively). The measurements were conducted using the correlations between the known element concentrations and the measured signal intensities on artificial samples containing various elements at known concentrations. A dynamic range of 10\({}^{6}\) in the measurements of various high purity samples was achieved [129]. In another study from the same group, the element analysis of residues of mixed salt solutions were conducted [17]. The studies show that the instrument is capable of conducting trace analysis at the ppm level. For a single laser shot and an average ablated sample thickness of about 1 nm, the absolute limit of detection was determined \(10^{-15}\) g (1 fg) for most metal elements.
## 4 Experimental Simulation
### Performance Analysis of OFDM Signal Demodulation
#### 4.2.2 Demodulation Effect of OFDM Signals in Different Stochastic Resonance Systems  
After verifying the superiority of the demodulation performance of the proposed SUUBSR system compared to the FUBSR system, the following discussion will focus on the impact of the damping coefficient on the demodulation performance of the system. The analysis method adopted is to fix the noise intensity and system parameters while only varying the value of the damping coefficient. By observing the deviation degree of the constellation points at the system output end compared to the initial 16 points, the influence of the damping coefficient and noise intensity on the demodulation effect of OFDM signals can be analyzed. The simulation parameters are set as follows: the number of subcarriers is 64; the number of OFDM signal symbols is 20, taking 16 QAM modulation as an example; and the fixed system parameters are \(a=0.2306\), \(v=0.4398\), and \(r=3.5615\).  
Let D = 100, and the damping coefficient values are set to 0.5, 0.6, 0.7, and 1 sequentially. The demodulation constellation diagrams of OFDM signals corresponding to each damping coefficient value are plotted in Figure 14. As can be seen from Figure 14, when the damping coefficient value exceeds the critical damping value, with the increase in the damping coefficient, the data points of the OFDM signal in the constellation diagram tend to cluster around the initial 16 points first and then gradually deviate. The demodulation effect of the damping coefficient above the critical damping is significantly better than that of the damping coefficient below the critical damping. However, the value of the damping coefficient is not necessarily better as it increases, but rather has a certain range. Only when the optimal damping coefficient value is achieved will the anti-noise performance of the SUUBSR system and the demodulation effect of the OFDM signal be improved.
# Digital 3D Technologies for Humanities Research and Education: An Overview
## 7 3D Modelling Technologies
### Hybrid Methods  
Traditional algorithmic-only approaches, as in photogrammetry, employ algorithms to complete a specific operation, e.g., to detect, describe, and match geometric features in images [405]. In contrast, machine learning employs algorithmic approaches to train a statistical model in a supervised or unsupervised way via training data--e.g., to detect features [451]. Current evolvements in computer vision are closely coupled to the massive renaissance in machine learning [452] with the use of convolutional neural networks (CNNs) (c.f. [453]) since 2012. Machine learning approaches are currently heavily researched and used for image and 3D point cloud analytics in cultural heritage (as recent overview [83]), but also increasingly for 3D modelling tasks.  
**3D/4D model generation via machine learning:** In 3D/4D reconstruction of cultural heritage, machine learning-based technologies are primarily used for specific tasks within the modelling process: to preselect imagery [454; 455], in semantic segmentation to classify parts of images [456; 457; 458], and to recognize specific objects (e.g., [458; 459; 460; 461]). Another strand--bypassing the modelling stage--is generating visualizations directly from imagery [459; 462; 463]. Machine learning-based technologies currently require large-scale training data (e.g., [83; 458; 459; 460]) only capable of recognizing well-documented and visually distinctive landmark buildings [464]. Machine learning fails to deal with less distinctive architecture, such as houses of similar style. Regarding transparency, most machine learning approaches are applied within black box settings in a non-standardized way (c.f. [83; 465]). Specifically generative adversarial networks (GAN) as a combination of proposal and assessment components of machine learning are frequently employed in 3D modelling. Application scenarios are single photo digitization [466], completion of incomplete 3D digitized models [467; 468], or photo-based reconstructions [469].
# Spectral Shaping of a Superluminescent Diode for Terahertz Cross-Correlation Spectroscopy
## 3 Results and Discussion
### Analysis and Comparison of Continuous Spectra
for different optical powers is shown in Figure 5. In Figure 5a, the dynamic range over the incident optical power is depicted. The DR slowly saturates for increasing optical power. This can be attributed to a saturation of free carriers inside the active material of the antennas. The impact of the optical power on the slope m is depicted in Figure 5b. The slope m shows a 0.5 dB/THz jump from 15 mW towards 20 mW optical power. For higher optical powers, no notable changes for m are observable. This indicates that the gain in bandwidth for higher optical power is only due to the increased DR. A look at the bandwidth as a function of optical power (Figure 5c) shows a notable improvement up to 25 mW. However, since the gain of the EDFA is insufficient to amplify all spectra to 25 mW or 30 mW, we decided to make the following measurements only up to 20 mW optical power per antenna with an optical bandwidth of 2 THz. The saturation can be explained as follows. High optical power leads to a roll-over behavior of the terahertz output power, which can be explained by a screening effect. The intense optical power generates a high density of positive and negative charge carriers, which then attenuate the external electric field [17]. This leads to a saturation of the dynamic range and the bandwidth.
## 5 Implementation: Deep-learning in IoMT Edge Devices
### Algorithms and Methods to Reduce Computation and Power Consumption in Embedded Devices  
There are various algorithms and methods at different levels (such as DNN modeling, computer architecture, and compiler) to improve the execution of DNNs. A popular approach is quantization [93], which reduces the precision of number representation, e.g., from 32-bit to 4-bit representation. Quantization relaxes the storage requirement and could lower the number of computations, which usually come at the cost of lower accuracy. Although quantization is an efficient approach with regard to storage, the underlying hardware should be able to skip the unnecessary zero computations as a result of reduced number representation. Thereby, quantization alone cannot be considered as an effective approach to reduce the inference time or power consumption in embedded devices.  
Another efficient approach is pruning, which is mainly based on removing weights that are smaller than a threshold value. Pruning can also be applied to filters by removing those which are the least important based on the defined criteria [88]. Like quantization, pruning also usually leads to lower accuracy, while retraining helps to retain the accuracy to some extent. Weight pruning also requires hardware support, and without such support, the underlying hardware cannot skip the zero computation and thus reduce the power consumption or inference time. Filter pruning, on the other hand, skips the zero computation related to the removed filters entirely. Thereby, it would be an efficient approach to be employed in embedded devices to reduce power as well as inference time.  
Dynamic batch sizing is another approach to control the power and throughput at run-time [94]. In this approach, instead of using a fixed batch size, depending on the power budget and the required throughput, batch size can be dynamically adjusted. This approach allows saving power in IoMT devices whenever necessary.
###### Abstract  
This retrospective study aims to further investigate the effects of rapid maxillary expansion (RME) treatment on respiratory function in growing patients with contracted upper airways, by assessing nasal resistance using active anterior rhinomanometry (AAR). Methods: At T0 (pre-RME), 122 orthodontic patients aged 5 to 12 years, exhibiting oral breathing and/or snoring along with maxillary contraction, underwent AAR with a mask using the ATMOS Rhino 31 rhinomanometer. Additionally, the SRDB or PSQ-SRDB Questionnaire was utilized to validate the orthodontic history of obstructive sleep apnea syndrome (OSAS). If AAR results at T0 were clinically significant, they were repeated at T1, corresponding to a period of three months after screw fixation, to evaluate changes in nasal resistance values between pre- and post-treatment. Results: The study group comprised 42 oral respiratory patients with a mean age of 7.71 years, suspected of pediatric OSAS. Although the questionnaires did not yield statistically significant results, the therapy induced an overall reduction in airflow resistance values from both nostrils (\(-\)24.63% in the right nostril; \(-\)26.65% in the left nostril). Conclusions: This study demonstrates the beneficial effects of orthodontic treatment with RME on nasal function in terms of airflow resistance in cases of maxillary contraction.  
sleep apnea syndrome; nasal airway resistance; rapid maxillary expansion
## 4 Encoding Technique
### Decoding Process  
We can decode the encoded representation of CFG because any nonterminal \(z\) in \(L\) indicates the position of the internal node corresponding to \(z\). We describe the process in Algorithm 4. Scanning the compressed text (combined \(F\) and \(L\)) from the left to right, we can simulate the post-order traversal of the partial parse tree and restore the dictionary \(\mathcal{D}\). To do this, we use a stack \(stk\) with two basic operations as follows:  
* \(push(stk,x)\): add symbol \(x\) into the top of the stack \(stk\).
* \(pop(stk)\): return the top of the stack \(stk\) and remove it.  
```
1:input a grammar size \(g\), an alphabet size \(\sigma\);
2:create an empty stack \(stk\);
3:\(i:=1\); \(j:=1\); \(k:=1\);
4:while\(i\leq 2g+1\)do
5:input a parenthesis \(F[i]\);
6:if\(F[i]=\)'(' then
7:input a symbol \(L[j]\);
8:output a string derived from \(L[j]\) using \(\mathcal{D}\);
9:\(push(stk,L[j])\); \(j:=j+1\);
10:else
11:\(y_{2}:=pop(stk)\); \(y_{1}:=pop(stk)\);
12:\(z:=k+\sigma\);
13:\(\mathcal{D}:=\{z\to y_{1}y_{2}\}\cup\mathcal{D}\); /* update \(\mathcal{D}\) */
14:\(push(stk,z)\); \(k:=k+1\);
15:endif
16:\(i:=i+1\);
17:endwhile
```  
**Algorithm 4** Decode.  
When we decode \(L[j]\) in the line 8, the required production rules are certainly contained in the current dictionary \(D\) by the characteristics of the partial parse tree. Thus, the algorithm can correctly output the original string by decoding the sequence \(L[1,g+1]\). The decoding time is bounded by \(O(n)\) to output the original string, and the space is \(O(g)\) to store the dictionary \(\mathcal{D}\).