esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing the Total Product Cost at the Product Design Stage
## 4 An Example of Business Analytics for New Product Performance
### Prescriptive Analytics
#### 4.3.1 Identifying Possible Variants of Reducing the Total Product Cost  
A set of possible variants is identified using a CP that specifies decision variables, their domains, and constraints, including dependencies between variables. These dependencies refer to, among others, the duration of product design, the number of prototype tests, density of materials, and the specific cost (\(V_{1}\), \(V_{14}\), \(V_{15}\), \(V_{18}\)). In the previous subsection related to the predictive analysis, the phase of product design was evaluated at 5 months, the number of prototype tests on 80 cycles, and density of materials on 290 units. The domains of the selected variables to analysis are as follows: \(D_{6}=[5,6,7],D_{7}=[80,81,\ldots,100],D_{9}=[250,251,\ldots,290]\). It is worth noting that the variables in the model are specified in a discrete manner to solve the above-described problem using variable distribution and constraint propagation embodied in constraint programming. Consequently, the domains consist of integer numbers and the simulation step is related to the increase of these numbers by one for each variable separately. The number of possible solutions depends on the number of variables, their granularity in domains, and constraints. For example, the granularity of \(V_{9}\) (the amount of material used for manufacturing a new product) is in grams. The decrease of granularity for this variable (specified in milligrams) results in a more extensive search space to find  
Figure 4: Comparison of MAPEs for estimation models.  
possible solutions. In this study, the domain of variable \(V_{9}\) has been set for the granularity, by which the constraint programming techniques can be smoothly used.  
The above-described problem was implemented in Mozart/Oz software that includes the programming paradigms, such as logic, functional, imperative, object-oriented, concurrent, constraint, and distributed programming. The advantage of a CP is related to its components related to constraint and distributed programming which are able to effectively solve many practical problems, for example, timetabling and scheduling problems, including optimization problems in production and distribution [54,55].  
The identified relationships indicate that the increase of duration of product design and number of prototype tests can, to some extent, reduce the number of defective products, and as a result, reduce the cost of production and warranty, and finally, improve the customer satisfaction and loyalty. The criterion of selecting the best variant is the total product cost (\(TC=V_{1}+V_{2}+V_{3}\)).  
Table 3 presents a few possible variants of changes in the values of variables, for which the total product cost is less than the desirable level.  
The set of possible solutions informs the decision-maker about changes that can be made during the stage of product design towards reducing the costs in production and after-sales service.
## 4 Methodology
### Characterization of End-to-End Latencies  
To characterize the age, maximum age, and reaction latencies, the semantics in [7] were considered and are defined below.  
The age latency (Last-to-First, L2F) measures the delay from the last input that is not overwritten to the first output generated with the same input.  
\[\alpha(EC)=\Sigma_{i=1}^{n-1}(\phi_{i}^{last}+\Delta_{i+1}) \tag{3}\]  
The reactive latency (First-to-First, F2F) measures the delay from the first input that can be overwritten to the first output generated with the next different input.  
\[\delta(EC)=\Sigma_{i=1}^{n-1}(\phi_{i}^{0}+\Delta_{i}) \tag{4}\]  
The maximum age latency (Last-to-Last, L2L) or maximum age measures the delay from the last input that is not overwritten to the last output, including duplicates.  
\[\alpha_{\max}=\Sigma_{i=1}^{n-1}\alpha_{i,i+1}(EC) \tag{5}\]  
Figure 10 shows how the \(L2F\) latency is measured from the first input that can be overwritten to the first output generated with the next different input. The \(F2F\) latency is measured from the first input that can be overwritten to the first output generated with the next different input. The \(L2L\) latency is measured similarly to the \(L2F\) latency, from the last input that is not overwritten to the last output, including multiple executable instances.  
Figure 9: Characterization of producer and consumer tasks. This figure illustrates the runnable of the writer (\(\tau_{W}\)) and reader (\(\tau_{R}\)), allowing management of the synchronization of communication between tasks with different periods and offsets in real-time systems.
# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 3 Problem Statement  
The role of the non-terrestrial transport network in 6G was anticipated to acquire a more significant function over a secondary or backup link (see Figure 2). However, there are challenges in determining the optimum uses of the respective transport networks based on various traffic classifications. A report [1] synthesized the coexistence of terrestrial networks and the upcoming network topology as a single 6G access network. However, the current approach leads to an apparent gap of inefficient resource management. The regular approach is to incorporate a novel path and route optimization using ML. This study developed a traffic steering mechanism to analyze the traffic and direct relevant traffic to the appropriate transport network types using ML and MILP methodology. This study also assessed the accuracy of decisions based on the ML clustering output and the transport network selection. Consequently, traffic was assigned optimally to designated transport networks in an ideal environment.  
Figure 2: The non-terrestrial transport networks for the 6G infrastructure.
## 5 Phase Compensation Technique for Effective Heat Focusing in Microwave Hyperthermia Systems  
In this paper [4], a method of electromagnetic (EM) focusing with a phase compensation technique for microwave hyperthermia systems is proposed. To concentrate on a specific target inside the breast tumor, input phases of each element of a circular antenna array are calculated for single and multiple tumor cell locations. In addition, the input phases of each antenna are calculated for single and multiple tumor cell locations depending on the cancerous cell conditions in the breast. Then, beam focusing technique using sub-array is presented, which can minimize damage to normal cells. The authors verified that the thermal treatment effects on single and multiple tumor locations using both simulated and experimental results.
## 3 Case Study
### Analysis and Decomposing the Photovoltaic Output  
In order to facilitate the observation of photovoltaic data in the two regions, this paper shows the photovoltaic output changes in the two regions in Figure 4.  
Figure 4: _Cont._  
It can be seen from Figure 4 that the volatility of the photovoltaic power station output in the two regions is very strong, and the output of Gansu photovoltaic power station is higher than that of Yunnan photovoltaic power station. In order to better observe the photovoltaic output situation in the two regions, the monthly data of two regions is counted, and the statistical results are shown in the Table 1.  
It can be seen from the maximum value of Table 1 that the maximum output of Gansu photovoltaic power station is higher than that of Yunnan photovoltaic power station. In addition, by observing the average and standard deviation of the two power stations, it can be found that the difference between the average value and the standard deviation is larger. From this, it can be seen that the fluctuation of photovoltaic power output of the two power stations is very strong.  
In the following analysis, this paper takes Yunnan photovoltaic power station as an example for specific explanation. First, the VMD is used to decompose the photovoltaic output data. The parameter setting in the decomposition process references to [34]. The decomposition results are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Region** & \multicolumn{4}{c}{**Yunnan**} & \multicolumn{4}{c}{**Gansu**} \\ \hline \multirow{2}{*}{**Month**} & \multicolumn{4}{c}{**Index (MW)**} \\ \cline{2-9}  & **Max** & **Min** & **Average** & **Standard Deviation** & **Max** & **Min** & **Average** & **Standard Deviation** \\ \hline
1 & 51.0880 & 0 & 8.5451 & 12.9116 & 74.3600 & 0 & 15.3126 & 23.8975 \\
2 & 52.1515 & 0 & 10.1411 & 14.0405 & 74.0806  
Figure 5 shows each component of photovoltaic output after VMD decomposition. By observing Figure 5, it can be seen that the amplitude fluctuations and frequency fluctuations of the Intrinsic Mode Function (IMF)1-IMF7 components are relatively intense and there is no periodicity, the frequency fluctuation of IMF8-IMF9 is less moderate than the former, but the amplitude fluctuation is much lower, and both the amplitude and frequency fluctuations of the IMF10 are the slowest.
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 2 Related Work
### Interaction Styles of Telementoring
#### 2.3.3 Gesture-Based User Interface  
A gesture-based UI mostly refers to exploiting hand gestures in telementoring systems. Commercial smartglasses such as Oculus Quest 2 and Microsoft HoloLens 2 provide hand tracking via computer vision or separate controllers. Furthermore, specialized hardware and software for data gloves and suits from Manus, BeBop sensors, Feel the Same, and Tesla can be adopted to include gesture-based UIs. Similar to voice-based UIs, gesture-based UIs can be used in hands-free and touch-less manner. On the downside, the less expressive gesture-based UIs often require additional and costly sensors, processing units, and heavy computation. In our review, several studies incorporated gesture-based UIs in their systems [22; 36; 42]. Figure 3 demonstrates typical touch-less gesture-based interaction styles.
## 3 Results
### Anisotropy of Acoustic Emission  
Acoustic emission (AE) can be used to monitor the internal damage of rocks under loading. Among the AE parameters, ringing count and AE energy are particularly significant and widely utilized. Based on the AE monitoring results from DTT and BST, the variation in accumulated AE energy and ringing count with stress level under different loading angles was compared and analyzed. The results are presented in Figure 6. It is evident that the accumulated AE energy and ringing counts of DTT and BST specimens showed notable differences across various loading angles. Except for loading angles of \(\beta\) = 60\({}^{\circ}\) and \(\beta\) = 90\({}^{\circ}\), the accumulated AE energy and ringing counts of DTT specimens were higher than those of BST specimens at all other loading angles. No clear trend was observed in the accumulated AE energy and ringing counts as the loading angle increased. For BST specimens, the maximum and minimum accumulated AE energy and ringing count occurred at \(\beta\) = 60\({}^{\circ}\) and \(\beta\) = 0\({}^{\circ}\), respectively, while for DTT specimens, they occurred at \(\beta\) = 30\({}^{\circ}\) and \(\beta\) = 0\({}^{\circ}\). During the loading process, when the stress level reached 100%, the corresponding parameters for specimens with continuously increasing accumulated AE energy and ringing counts were smaller. This is attributed to energy dissipation caused by micro-cracks in the matrix or along the bedding plane of the specimen during loading. For anisotropic rocks, a continuous increase in accumulated AE energy and ringing counts during loading typically corresponds to failure along the bedding plane.  
Figure 5: The direct and indirect tensile strength under different loading angles.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 3 QoS and UAVs Endurance in VoWiFi Networks  
The optimization problem addressed in this work provides the number and position of a set of UAVs to the deployed over a known area to provide WoWiFi service to a set of ground users at known locations. Our objective function minimizes the ratio of drones per hour required to provide this service constrained to user's coverage and user's speech quality (i.e., QoS). In this section, we study the relationship between drone position, QoS, and UAV endurance.  
Figure 1 illustrates two UAV-mounted APs providing VoWiFi to ground users (note that only the UAV-to-user communication is considered and the communication between UAVs or with the data network backhaul is taken for granted). On the leftmost part, a lower-altitude drone provides coverage to four users. Their traffic (red lines show VoIP traffic flows) is aggregated at the aerial AP. This traffic load may influence the QoS perceived by these users and the UAV's power consumption due to the transmission/reception of packets. The rightmost UAV is covering eight ground users so its AP aggregates more traffic which may have an impact on the QoS perceived by ground users. Hovering at a higher altitude and servicing more users will likely result in more energy expenditure due to both hovering and more transmission/reception. Thus, the rightmost drone is expected to be replaced earlier than the leftmost drone due to earlier battery exhaustion. This simple example illustrates how the decision variables of the optimization problem (i.e., number and position of drones) have an impact on both, the level of speech quality perceived by VoIP users and UAVs' energy expenditure. Next, we provide the analytical expressions used to model VoWiFi's QoS and UAV's energy expenditure as both aspects are central in the problem's objective function.
## 4 Materials
### Evaluation Metrics and Protocol  
Ranking of FR-IQA methods relies on measuring the correlation between predicted and ground truth quality scores of benchmark IQA databases, such as KADID-10k [20]. To be more specific, the performance of an FR-IQA metric was evaluated using three different criteria in this study: Pearson's linear correlation coefficient (PLCC), Spearman's rank order correlation coefficient (SROCC), and Kendall's rank order correlation coefficient (KROCC). In addition, a nonlinear logistic regression was applied before the calculation of the PLCC as recommended in [49], because nonlinear relationship exists between the ground truth and predicted scores:  
\[Q=\beta_{1}\bigg{(}\frac{1}{2}-\frac{1}{1+e^{\beta_{2}(Q_{p}-\beta_{3})}}\bigg{)} +\beta_{4}Q_{p}+\beta_{5}, \tag{11}\]  
where \(Q\) and \(Q_{p}\) are the fitted and predicted scores, respectively. In addition, the regression described by Equation (11) is determined by the \(\beta_{i}\) (\(i\in\{1,2,\ldots,5\}\)) parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Ref. Images** & **Dist. Images** & **Dist. Types** & **Dist. Levels** \\ \hline KADID-10k [20] & 81 & 10,125 & 25 & 5 \\ TID2013 [7] & 25 & 3000 & 24 & 5 \\ TID2008 [21] & 25 & 1700 & 17 & 4 \\ CSIQ [8] & 30 & 866 & 6 & 4–5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of benchmark databases used in this study.  
Figure 2: Empirical MOS distributions in benchmark IQA databases: (**a**) KADID-10k [20], (**b**) TID2013 [7], (**c**) TID2008 [21], and (**d**) CSIQ [8].
## 3 Results  
### Performance of OCx, CI and CalCOFI 2 Algorithms  
The performance of two global algorithms, OCx (Equation (12)) and CI (Equation (4)), alongside the CCS-specific CalCOFI 2 (Equation (5)), was assessed for the IMECOCAL region waters (Figure 2). All algorithms were applied to the reflectance data, yielding the logarithm of the mChl-\(a\) concentration. The results, plotted against F (Equation (2)), are shown in Figure 2. The CI algorithm exhibited greater dispersion in mChl-\(a\) estimates compared to OCx and CalCOFI 2. This is attributed to the CI algorithm's reliance on blue, green, and red bands (Equation (3)), whereas OCx and CalCOFI 2 utilize only the blue and green bands. This means that the CI algorithm has one more input variable than the others; therefore, there is greater dispersion on the resulting mChl-\(a\).  
As waters adopt a greener hue, F values decrease as mChl-\(a\) values increase. Upon applying the algorithms to the IMECOCAL data (Figure 2), the primary discrepancies between the OCx and CI algorithms correspond to values of mChl-\(a\) higher than or equal to 0.3 mg m\({}^{-3}\) (log\({}_{10}\)(mChl-\(a\)) \(\sim\,-0.5\)) and a ratio of blue/green reflectance of \(\mathfrak{R}\leq 2.5\) (F \(\leq 0.4\)). For these reflectance ratios, OCx yields mChl-\(a\) values surpassing those of CI. Thedifference between CI and OCx retrievals for waters with large \(\mathfrak{R}\) values is small, yielding very similar results for the area, though with slightly higher retrievals for the CI algorithm.  
The CalCOFI 2 algorithm consistently underestimated the Chlorophyll concentration across the data spectrum. This underestimation increases with the value of the blue/green reflectance ratio \(\mathfrak{R}\). Therefore, for the IMECOCAL area, OCx is the algorithm that yields the best results from a qualitative point of view. A comprehensive quantitative analysis will be presented subsequently. Based on both the qualitative and quantitative scrutiny of the algorithm retrievals, OCx is used as the basic algorithm for further comparisons, discarding the CI and CalCOFI 2 algorithms.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 6 Validation  
Satellites do not measure precipitation. Instead, they capture sensor-dependent quantities, which are further converted to precipitation rates [69; 79]. The conversion processes can introduce significantly varying errors among SPEs [145]. Consequently, the rigorous validation of SPEs becomes indispensable, as it is a prerequisite to enhance the quality of SPEs and ultimately advance their applicability across diverse domains [146; 147].
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 3 Design and Implementation
### Software Packaging  
The Clonezilla program depends on many programs, because, as described in Section 3.2, it uses the UNIX pipelines to make Partclone and the existing open source software work together. Hence, it is tedious for users to collect all of the required programs when they want to use Clonezilla. In addition, most of the system deployment is executed in the so-called bare metal way--that is, the machine may have only one hard drive without any OS or software. An extra OS is required to conduct the system deployment. Thus, the best way for system deployment is to have a live system [42] that includes all of the required programs and has the capability to boot from a machine. A live system is the complete bootable computer software, including OS, which allows for users to boot from removable media and run applications without affecting the contents of the computer. This is why Clonezilla live was designed for system deployment [10] in this research. In order to make use of the open source software, Debian live [42], which is a live system based on Debian Linux, was chosen as the underlying system because its software repository provides thousands of open source software packages. The file size of the developed Clonezilla live system is about 200 to 300 MB. In addition, a mechanism is required to predefine all of the steps required by an unattended mode in Clonezilla live. The kernel boot parameters mechanism [43] was used to predefine the steps in Clonezilla live. Additional parameters that were developed and implemented in Clonezilla live include ocs_live_prerun*, ocs_live_run, ocs_live_postrun*, and ocs_daemonon. The ocs_live_prerun* parameter provides the commands to be executed before the main program, ocs_live_run, can be executed. The ocs_live_postrun* parameter provides the commands to be executed after the main program, ocs_live_run, can be executed. Finally, ocs_daemonon predefines the services that are to be started after the system is booted. The boot parameters mentioned could be used to customize the unattended mode of a deployment system. An example of automatically configuring the network
# Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems
## 2 System Model  
### Transmitted Signal  
Generally, a transmitted UWB signal is composed of \(N_{\rm s}\) pulses and is mathematically expressed as  
\[s(t)=\sum_{j=0}^{N_{\rm s}-1}\psi(t-jT_{f}), \tag{1}\]  
where \(\psi(\cdot)\) denotes the UWB pulse, \(T_{f}\) is the frame duration and \(j\) represents the frame index (the system is assumed to be a slow time-hopping system in which the number of frames and pulses is equal). Various different pulses, \(\psi(t)\), have been recommended for use in UWB systems. These pulses are derived based on a Gaussian pulse and its derivatives, modified hermite polynomial, and Gaussian modulated sinusoidal pulses, etc., and references within them [48; 49].
# Hybrid-Attention Network for RGB-D Salient Object Detection
## 2 Proposed Method
### Feature Extraction  
We adopt two popular backbone networks for feature extraction. Specifically, Res2 Net [30] extracts RGB features, and a simplified Inception-v4-ResNet2 [29] extracts depth features. The latter can handle the relatively less information from depth maps while preventing overfitting and reducing the computation time by omitting unnecessary parameters. Therefore, we establish an asymmetric architecture for this two-steam network.  
For RGB images, the Res2 Net backbone has been used to extract multilevel features for different tasks, being widely used in semantic segmentation, key-point estimation, and salient object detection. We have conducted comprehensive experiments on many datasets and benchmarks and verified the excellent generalization ability of Res2 Net. For salient object detection, we remove all the fully connected layers of Res2 Net to ensure that the output is an image. To preserve the feature information, we delete the first max pooling layers of the network and set the stride of the convolution to 1 (instead of 2) to prevent excessive downsampling. This prevents severe information loss and failure to reconstruct object details after saliency detection. As we obtain the features at each downsampling process, Res2 Net provides four outputs: low-level features extracted by Layer1, middle-level features extracted by Layer2 and Layer3, and high-level features extracted by Layer4. In [27], \(1\times 1\) convolutions have dual purpose: most critically, they are used mainly as dimension reduction modules to remove computational bottlenecks, that would otherwise limit the size of our network. This allows for not just increasing the depth, but also the width of our networks without significant performance penalty. Then, inspired by [27], we use four \(1\times 1\) convolutions to reduce the number of channels to one-eighth of the original number, which is high and requires long computation time during both training and inference.  
For depth maps, we use a simplified Inception-v4-ResNet2. To reduce the computational complexity, we only adopt its Stem part and five Inception-ResNet-A blocks. In addition, we follow the same procedure for RGB images to ensure that the output is an image. Likewise, we delete the first max pooling layers, set the stride of the convolution to 1, and use \(3\times 3\) convolutions to construct the depth attention map.