esa-sceva/satcom-chunk-collection huggingface dataset:
# Seventy-Five Years since the Point-Contact Transistor: Germanium Revisited
## 3 From Ge to Si and Back  
Although in the first breakthrough the point-contact transistor and subsequent devices were based on Ge, it was Si that soon dominated the technology. This was mainly due to the requirement of a native oxide on top of the semiconducting material. In particular, germanium dioxide (GeO\({}_{2}\)) was inappropriate as it is unstable during the processing required (water soluble) for the formation of microelectronic devices. Conversely, silicon dioxide (SiO\({}_{2}\)), the native oxide of Si, is a material with excellent material properties and dielectric properties that was appropriate for scaling and device (metal-oxide semiconductor field-effect transistor (MOSFET) etc.) formation for decades.  
During the reign of Si, the reports on the material's properties and the defect processes of Ge were very limited (refer to [11; 12] and the references therein). The community during this period was focused on Si and the effort to aggressively scale it to follow [PERSON]'s law. This was interrupted by the advent of high-\(k\) dielectric materials that in essence removed the requirement of a native oxide layer (such as SiO\({}_{2}\) in Si), and therefore high mobility materials, mainly Ge, that were plagued by inappropriate native oxides were once more considered [13; 14; 15; 16; 17; 18; 19; 20; 21]. The aim of the community is currently to be able to create Ge-based microelectronic devices and, in particular, \(n\)- and \(p\)-channel MOSFETs for higher mobility complementary metal-oxide semiconductors (CMOS). At this point of the evolution of microelectronic devices we have reached devices with characteristic dimensions of only a few nanometers and, therefore, effects at the atomic level are more important than ever. In particular, it is absolutely necessary to control the intrinsic defects in the lattice, the placement and the transport properties of dopants. For example, even the migration and out-diffusion of a few atoms in the doped region will have significant implications in its conductivity! For numerous years the defect processes in Si have been determined; however, there have been studies in the defect processes and the applicability of Ge in microelectronic devices mainly after 2000 [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75], when the possibility of Ge-based devices was once more considered.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 6 Multi-Operator Game for Channel Selection  
We additionally use non-cooperative game theory for the purpose of channel selection. In this game, the SF assignment is constant, and the channel selection varies.  
```
1:Requires: Maximum tolerance \(\epsilon\gets 10^{-5}\)
2:Input: Initial SF assignment limits
3:Initialize:\(t_{b}=0\)
4:Do
5:\(t_{b}\leftarrow\)\(t_{b}\)+1
6:For i=1,...,\(\mathcal{N}\)
7: Solve the problem in (6) for \(i\)
8: Update \(p_{\mathrm{s}}^{i}\)
9: Compute \(\delta^{i}=||p_{\mathrm{s}}^{i}(t_{b})-p_{\mathrm{s}}^{i}(t_{b}-1)||\)
10:End For
11:While\(\exists\)\(i\) such that \(\delta^{i}\geq\epsilon\)
```  
**Algorithm 1** Best Response Algorithm for SF Selection
# A Hybrid Watermarking Approach for DICOM Images Security
## 3 Proposed Method
### Separation of the Medical Image  
The proposed method is based on the separation of the medical image into two regions: the anatomical region and the black background region. The separation can be done automatically by using an algorithm [4] or manually by using a rectangle to form the ROI [15; 19]. In our method, we have used an automatic separation based on a threshold. The threshold was determined starting from the theoretical value 0 corresponding to the black pixel and iteratively increasing it by 1 until reaching a value making it possible to obtain a good separation between the anatomical region (ROI) and the black background region (RONI) for the images of the medical images database. A gray value greater than the threshold belongs to the anatomical object, and a gray value below or equal to the threshold belongs to the black background region of the image. Figure 2 shows an example of the anatomical region and the black background region separation.  
Figure 1: The framework of the proposed watermarking method.  
Figure 2: (**a**) the original image, (**b**) the anatomical region I\({}_{\text{Antomical}}\), (**c**) the black background region I\({}_{\text{Blackbackground}}\) and (**d**) original image with separation between I\({}_{\text{Antomical}}\) and I\({}_{\text{Blackbackground}}\)
## 4 Aeroacoustic Results
### Engine Noise Source Term
Results for the generated sound power levels (SWL) at the BPF and its higher harmonics of tonal RSI noise are shown in Figure 9a. Due to a cut-off design for RSI noise of the fan stage, the SWL for the BPF itself is zero. For the second to fifth BPF, both configurations show a decreasing trend of SWL for higher harmonics. For the second BPF, the Clean configuration dominates the SWL. For the third to fifth BPF, the BLI configuration is dominating the SWL. The reason for this alternation of SWL results from the alternation of the rotor wakes as shown in Figure 9b. Note that the left hand side contour plots of the mean velocity show the flow region close to the mixing plane for the BLI configuration and the right hand side contour plots show the same region for the Clean configuration. For the BLI configuration, the shape of the wakes is sharp, whereas a strong tip vortex region is visible for the Clean configuration. Due to the altered wakes, the second harmonic is more strongly excited for the Clean configuration compared to the BLI configuration. This does not hold for the third to fifth harmonic and may be explained by the larger wake structures responsible for the noise excitation. However, as the second harmonic is dominating the overall sound power level (OAPWL) of all BPF harmonic tones, this trend is secondary. The flow detachment in case of the Clean configuration results from the fact that the fan stage was designed to account for the inflow distortion [31]. Thus, the assumption of a Clean configuration results in an off-design operating condition showing a flow detachment at the rotor blades. Figure 8: Illustration and definition of used inflow distortion. (**a**) Exemplary shape of the inflow distortion and Clean configuration. (**b**) Used total pressure distortion. Figure 9: Results for generated tonal RSI noise and investigation of rotor wakes. (**a**) Sound power level (SWL) of tonal RSI noise with and w/o inflow distortion. (**b**) Investigation of rotor wakes close to the mixing plane with and w/o inflow distortion. Contour plots show the mean velocity. Finally, Figure 10 shows a result of the reconstruction of the real part of the sound pressure field at the engines inlet and outlet plane for the 2 nd BPF. The modal structure of the sound pressure field for RSI noise, constituting by distinct modes (Tyler-Sofrin modes), is clearly visible.
## 4 TSN for Industrial Communications
### TSN over Wireless
#### 4.1.1 TSN over WiFi
IEEE 802.11 be also introduces \"restricted target wake time (rTWT)\" service periods (SPs) scheduled by APs in a BSS for handling time-sensitive traffic, and each station is expected to stop its transmission before the rTWT SP. Multi-AP coordinated operation is another critical feature in 802.11 be for efficiently allocating channel resources in overlapping BSSs to handle time-sensitive traffic. Ultra-high reliability is another major challenge for wireless TSN networks due to the wireless nature of the medium. Reliability is mainly handled using redundancy mechanisms at the intra-frame and inter-frame levels. In 802.11 ax, the most reliable modulation and coding scheme (MCS) is MCS = 0 with BPSK (redundancy of 1/2). Another scheme introduced in 802.11 ax for better reliability is dual-carrier modulation, where redundant data transmission occurs over different subcarriers. Inter-frame redundancy is achieved using ACK-based retransmissions or by sending extra copies of the same frame through different paths/resources without waiting for an ACK. Multi-link operation in WiFi 7 can enhance reliability through redundant transmissions over multiple links to a single AP or several APs. The multi-AP coordination feature helps make use of redundant links across non-collocated APs.
# Method and Test Course for the Evaluation of Industrial Exoskeletons
## 4 Results
### Suitability of the Test Course
#### 4.1.1. Representativity  
* Due to the diverse mix of the exemplary tasks in terms of dynamics, granularity, and handedness, the test course is capable of mapping different application scenarios. Despite the aggregation of vast industrial application scenarios to nine characteristic tasks, several industrial application scenarios are evaluable due to the possibility of minor adjustments and parameter variations within the activities.
* Characteristic activities from industrial operations are replicable and transformable to the adaptable framework of the modular test course, respectively. Thus, the test course can lay a foundation to evaluate the fundamental suitability and effectiveness of using exoskeleton types.
* The derived results in the test course for certain exoskeletons are similar and in line with insights, observations, and feedback perceived during previous field tests with the same exoskeletons. Thus, the practical applicability and comparability of the test course for laboratory trials can be approved.
* Not all exoskeletons passing the test course met the requirements to the same extent. Depending on the morphology and properties of the respective exoskeleton, each system shows the operational requirements and performances in different working contexts to a varying degree. Accordingly, the test course does not always lead to the same result but indicates different results and supportive effects for exoskeletons.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 4 Analysis of the Performance  
This section evaluates the performance of our algorithm. It mainly proves the network performance (load distribution, network lifetime, energy consumption, end-to-end delay, and stability of the route) using the former algorithm under different network sizes or with different caches in nodes.
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 6 Simulation and Results Analysis
### Node Localization Time Analysis  
Table 5 presents the average time required to locate a single unknown node for the original DV-Hop, GWODV-Hop, and the proposed algorithm in non-uniform network topologies. The original DV-Hop algorithm benefits from its simple implementation, giving it an advantage in localization time. Both GWODV-Hop and the proposed algorithm use GWO to determine the unknown node's position, with the convergence speed of GWO affecting the localization time. The processing time required by the GWODV-Hop algorithm is 223.6% more than that of the original DV-Hop algorithm. The increase in computational overhead is mainly because, during the multilateration stage, the GWO algorithm is adopted instead of the least squares method to determine the positions of unknown nodes. This results in additional calculations of the fitness function and updates of the population in the GWO algorithm. The proposed algorithm, compared to GWODV-Hop, filters anchor nodes using BGWO prior to localization, thus resulting in the processing time of the proposed algorithm being approximately 48.7% longer than that of the GWODV-Hop algorithm. As a result, the localization time of the proposed algorithm and GWODV-Hop is increased to a certain extent compared to the original DV-Hop. Although the localization time of the proposed algorithm is extended, the enhanced localization accuracy demonstrates significant application potential for the proposed method in industrial monitoring scenarios with stringent precision requirements. The time data presented in Table 5 were obtained under the stopping criterion of reaching the maximum iteration count. For IoT devices with real-time constraints, an early stopping criterion can be established. The iterative process may be terminated prematurely when the change in the fitness function value over multiple consecutive iterations falls below a predefined threshold, indicating that the proposed algorithm has converged. However, in terms of hardware complexity, the proposed algorithm does not require significant modification to the existing IoT node hardware. Nevertheless, it does demand slightly more memory to store the intermediate data generated during the BGWO-based anchor node selection process. Compared with the overall memory capacity of modern IoT nodes, this additional memory requirement is relatively small. Despite this, the proposed algorithm consistently shows superior localization accuracy and stability across various simulation scenarios with non-uniform network topologies.  
Figure 8: Stability of each localization algorithm under non-uniform network topologies. (**a**) C-type network; (**b**) O-type network; (**c**) W-type network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Running Times (s)** \\ \hline Original DV-Hop & 8.46 \\ GWODV-Hop & 27.38 \\ Proposed algorithm & 40.71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average localization time for a single unknown node (unit: ms).
## 3 Proposed Digital Shadow Model  
The physical system of the proposed framework consists of machines and sensing devices. The properties of, and adjustments to, the machines need to be obtained from diverse sources, ranging from the type of final products, cycle time of each machine, flow rates, product temperature, type of bottles, etc. Sensing techniques can be used to improve these attributes such as flow, temperature, piece counters and weight sensors, etc. The sensors used for the monitoring and data acquisition for the production floor are: (i) Hall Effect RPM sensors (Revolutions Per Minute), in order to calculate the rate of change in the transportation of products between the production equipment, (ii) ultra-sonic sensors for controlling the filling process, counting the number of bottles/cans transported through the conveyors, and ensuring that no cracks are present (applicable only in glass bottle processing lines). Regarding those lines processing cartons and cans, piezoelectric load cells are used in order to weigh the final product. Finally, since PI involves the processing of foods, the preparation of the intermediate products involves thermal processing. Consequently, infrared sensors are chosen in order to monitor the temperature of the intermediate product and to avoid any contact with the sensing device. For secondary systems, such as water boilers, additional sensors are required for monitoring the flow of resources (i.e., liquid fuel, cold water), as well as for monitoring the quality and quantity of the steam produced. Regarding the flow of the liquid resources, ultrasonic flow sensors are used. At this point it has to be stressed that steam is used in the food processing stages, and thus is required to comply with strict regulations. Concretely, there are certain aspects of steam which need to be controlled, such as moisture content, temperature (for the determination of the superheat of the steam), pressure (determination of the steam saturation), and flow rate. Therefore, content conductivity sensors are used for the steam, thermocouples for the superheat, pressure sensors for the steam's saturation, and differential pressure flow meters for measuring the steam flow. In order to ensure high reliability, security and support for complex data types, the OPC UA (Open Platform Communications Unified Architecture) Machine-to-Machine (M2M) communication protocol is implemented. In order to take advantage of the increased capabilities of modern sensing and controller systems, a Wireless Sensor Network (WSN) following a star topology has been created. By doing so, the creation of an Edge Computing layer is facilitated. In Edge Computing environments, OPC
## 3 Results and Discussion
### Volatile Compounds
#### 3.4.6 Ethyl Esters  
Esters are key contributors to wine aroma, providing pleasant fruity and floral odours. Ethyl esters are formed through the condensation reaction between ethanol and activated fatty acids catalysed by alcohol acetyl transferases. Major linear medium-chain ethyl esters are formed in the FAS complex simultaneously to their fatty acid analogues, while short branched-chain ethyl esters are the product of the esterification of fatty acids formed in the Ehrlich pathway. The synthesis of ethyl esters is regulated by a series of enzymes coded by the corresponding genes expressed in a yeast-species-dependent manner. However, their formation depends more on the concentrations of substrates than on the activity of enzymes involved [1; 48; 50]. The balance between ester synthesis and hydrolysis by yeast enzymes is crucial. While yeasts produce esters, they also produce esterases that are responsible for their hydrolysis. Higher concentrations of major linear medium-chain ethyl esters, such as ethyl butyrate, hexanoate, and octanoate, were found in control SCE compared to SPA wines. This effect was observed in a specific harvest year or in both harvest years, depending on the
## 3 Materials and Methods
### Classification Using only RSI
#### 3.2.3 Accuracy Evaluation  
In this research, the effectiveness of a model was assessed through a range of metrics, including recall, precision, F1 score, intersection over union (IoU), overall accuracy (OA), and the kappa coefficient. However, it was determined that the kappa coefficient and OA were insufficient for accurately measuring model performance in contexts such as image classification and slum mapping [25], primarily due to inherent limitations. Consequently, the investigation prioritized the evaluation of model outcomes through recall, precision,  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{1}{c}{**Value**} \\ \hline Number of epochs & 100 \\ \hline Batch Size & 128 \\ \hline Validation Spilt & 0.2 \\ \hline Optimizer & Adam \\  & Learning Rate: \(1\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network configuration parameters for training the FCN-DK model.  
F1 score, and IoU, with accompanying equations provided to illustrate the calculation of these metrics. Furthermore, the accuracy was analyzed for each category of slum identified in the revised slum reference data. The research particularly highlighted the significance of the F1 score and IoU, as the F1 score effectively integrates both precision and recall, while IoU serves as a widely recognized accuracy metric in the realm of computer vision challenges [26].  
\[\text{P}=\frac{TP}{TP+FP} \tag{1}\]  
\[\text{R}=\frac{TP}{TP+FN} \tag{2}\]  
\[\text{F1 Score}=2\times\frac{P\times R}{P+R} \tag{3}\]  
Note: P: precision, R: recall, _TP_: true positive, _FP_: false positive, and _FN_: false negative.  
Integrating spectral indices with deep learning can improve classification accuracy. The effectiveness of the models can be evaluated using metrics like precision, recall, F1 score, and intersection over union (IoU). High precision indicates a strong ability to correctly identify positive classifications, while high recall shows the model's capacity to detect relevant instances. An acceptable F1 score (above 0.7) and IoU (above 0.5) further validate the model's performance in accurately classifying slum areas. This combination enhances classification accuracy and aids in urban planning and policy development for slum improvement.
## 5 Architecture of the Proposed System
### Classification  
The proposed solution is based on two different classification units, the support vector machines and the k-nn method. The k method of nearest neighbors (k-nn), despite its simplicity, provides surprisingly good results in some applications. Its undoubted advantage is the naturally solved issue of discriminating against many classes. It only uses the distances between objects, understood as their similarity in terms of specific features. The number \(k\) is a hyperparameter, and like other hyperparameters in machine learning, there is no rule or formula for determining its value. It is best to determine it experimentally, by evaluating the prediction performance for different values of \(k\).  
SVM is one of the most widely used ML algorithms and is based on statistical learning theory. The key strength of an SVM classifier lies in its ability to identify an optimized decision boundary representing the largest separation (maximum margin) between classes. The creation of the optimal hyperplane is influenced by only a small subset of training samples, known as support vectors (SVs), which are the pivotal data structure in an SVM. This means that the training samples that are not relevant to the SVs can be removed without affecting the construction of the SVM's decision function, that is, the optimal hyperplane. The choice of an appropriate kernel function can significantly impact the performance of SVMs. We decided to use a support vector machine because this method is known to prevent overfitting.
## Results and Discussion
### Multispectral Analysis  
New multispectral PLSR models were established using the particular wavelengths identified by RC based on R, A, and KM spectra, respectively (Table 2). Compared with the preferred models built with full spectra (\(\text{R}_{\text{P}}\)\({}^{2}\) = 0.89-0.92, RMSEP = 0.48-0.64, and RPD = 4.64-6.18), the models showed an overall performance of \(\text{R}_{\text{P}}\)\({}^{2}\) = 0.84-0.85, RMSEP = 0.93-0.99, and RPD = 3.02-3.20. Although the number of wavelengths was essentially diminished from 285 to 9 or 10, the still satisfactory results shown in Table 2 indicate that the selected wavelengths contained enough information and the method for the wavelength selection using RC was efficient. In addition, it was also observed that the wavelengths selected from A spectra still performed the best among the three spectral units. The performance  
Figure 4: Determination of informative wavelengths by regression coefficients (RC) of (**a**) R-PLSR, (**b**) A-PLSR, and (**c**) KM-PLSR models.  
of the highest R\({}^{2}\) and RPD as well as the lowest RMSE performed by the model developed using A spectra was in accordance with the results in the full-wavelength models performance comparison. Therefore, the RC-A-PLSR model was chosen to be subsequently applied in image visualization of the adulteration.