esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Test Procedures  
### Experiment Equipment  
A hollow cylinder apparatus (HCA) manufactured by GDS Instruments is adopted in this study (Figure 2). The capability of GDS HCA to accurately measure and control such small stress differences has been discussed in detail and fully confirmed [22; 23]. The HCA can cyclically and independently control the vertical/torque load (\(W\)/M\({}_{\text{T}}\)) and outer/inner cell pressure \(\text{p}_{\text{o}}\)/\(\text{p}_{\text{i}}\) (Figure 3a). Inner wall radial displacement \(u_{\text{i}}\) and outer wall radial displacement \(u_{\text{o}}\) are calculated from the volume change in the inner and outer pressure, respectively, axial displacement \(\text{w}\) and rotation angle \(\theta\) are measured by an axial displacement sensor and a rotation angle displacement sensor, respectively. The application of \(W\), \(\text{p}_{\text{o}}\), and \(\text{p}_{\text{i}}\) on the specimen contributed to developing the stress components of vertical (axial) stress \(\sigma_{\text{z}}\), and \(\text{M}_{\text{T}}\) contributed to developing the stress components of torsional shear stress \(\tau\); whereas, simultaneous application of loads \(\text{p}_{\text{o}}\) and \(\text{p}_{\text{i}}\) was used to control the stress components of radial stress \(\sigma_{\tau}\) and circumferential or tangential stress \(\sigma_{\theta}\) in the hollow cylindrical specimen element. The stress components of \(\sigma_{\text{z}}\), \(\sigma_{\tau}\), \(\sigma_{\theta}\), and \(\tau_{\text{z}\theta}\) in an element of a hollow cylinder specimen are illustrated in Figure 3b. By adjusting these stresses, the major principal stress \(\sigma_{1}\), intermediate principal stress \(\sigma_{2}\), and minor principal stress \(\sigma_{3}\) can be independently controlled (Figure 3c). Furthermore, the corresponding vertical strain \(\varepsilon_{\text{z}}\), radial strain \(\varepsilon_{\text{r}}\), circumferential strain \(\varepsilon_{\theta}\), and shear strain \(\gamma_{\text{z}\theta}\) can be determined by the application of the formulas, assuming that the outer and inner surfaces of specimens retain their correct cylinder shape during torsion [24], compressive stresses and contractive strains are positive in accordance with the geomechanics sign convention.
## 3 Results
### GHG Emissions and Nutrient Leach-Out Reduction Potential  
A comparison of GHG emissions and nutrient leach-out reduction potential for biogas and compost production from various livestock groups is shown in Figure 14. About 21,402 and 45,267 kilotons CO2 eq/year of GHG emissions could be reduced from biogas and compost, respectively. The combined manure nutrient leaching reduction potential was 3122 (214.94 kg ha\({}^{-1}\)) and 748 (52.15 kg ha\({}^{-1}\)) from biogas and compost, respec  
Figure 12: Spatial distribution of electricity potential from biogas in Bangladesh in 2024: (**a**) district-wise potential; (**b**) sub-district-wise potential; (**c**) existing electricity coverage.  
tively. The district-wise net emissions reduction potential for biogas production ranges from 73.52-779.65 kilotons CO2 eq/year, while it ranges from 152.94 to 1635.78 kilotons CO2 eq/year for compost production. Similarly, the nutrient leach-out reduction potential ranged from 26 to 430, and 6 to 112 kgha\({}^{-1}\)year\({}^{-1}\) for biogas and compost production, respectively, at the district level in Bangladesh.  
Figure 14: Comparison of environmental impacts between biogas and compost production: (**a**) GHG emissions reduction potential; (**b**) nutrient leach-out reduction potential.  
Figure 13: Synthetic fertilizer replacement by compost (%) in Bangladesh in 2024: (**a**) urea replaceable; (**b**) P2O5 replaceable; (**c**) K2O replaceable.  
Overall, the GHG emissions reduction potential by producing manure bioenergy was lower than that for manure compost due to the lower amount of fossil fuels needed for composting than for anaerobic digestion. However, the manure nutrients leaching reduction potential was much higher for biogas production than for composting because of a smaller amount of biogas digestate produced than compost generated for agricultural production.
## 4 Machine Learning Methods
### Neural Network Architecture  
#### 4.3.1 Model A: BiLSTM Neural Network  
The topology of Model A is depicted in Figure 6.  
Figure 6: The network architecture of Model A. Input data goes to a bidirectional LSTM, where the hidden states of forward LSTM and backward LSTM get concatenated, and the resulting vector goes to a fully connected (“dense”) layer with size 3 and SoftMax activation function. The output \(p_{1}\), \(p_{2}\) and \(p_{3}\) are the probabilities for the word to belong to the classes B, I and O, i.e., to have a B, I or O tag.  
Its inputs are various combinations of features described in Section 4.2, and additionally, word encoding obtained with a characters-convolution-based neural network CharCNN [56] (see Figure 7).  
At the input of CharCNN, each word is represented as a fixed-length character sequence. The number of characters is a hyperparameter, which in this study has been chosen empirically with the value of 52. If the word has fewer characters than this number, the remaining characters are filled with the \(\lessdot\)PADDING\(\gtrdot\) symbol. Character vocabulary is formed from the training dataset, and also includes special characters \(\lessdot\)PADDING\(\gtrdot\) and \(\lessdot\)UNKNOWN\(\gtrdot\), the latter allowing for a possible future occurrence of characters not present in the training set. For coding each character of the word, an embedding layer [57] is used, which replaces every character from the vocabulary with a real vector of size 30. The values of these vectors are initialized randomly from the uniform distribution in the range of [\(-0.5\); \(0.5\)], and then trained. After encoding by the embedding layer, the matrix of encoded characters representing a word is processed by a convolution layer [58] (with 30 filters and a kernel size of 3) and global maxpooling function that provides a maximization function of all the values for each filter [59].  
At the output of the model, we put either a fully connected layer [19] or conditional random fields (CRF) [60], which output the probabilities for a token to have a B, I or O tag for the corresponding entity (for instance, B-ADR, I-ADR or O-ADR).
# Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
## 1 Introduction
The scenario where two landers are deployed on the surface of Mars is particularly interesting since differential range accuracies of the order of 1-10 mm can be attained. SBI measurements to  
Figure 1: \(\Delta\)DOR measurement principle and geometry. a network of landers on the moon would provide differential range at least an order of magnitude more precise, thus providing useful geophysical information on the lunar interior. This level of accuracy can be obtained by means of two-way radio links in X or Ka band enabled by suitably designed digital transponders and spread spectrum radio links, as described in this paper. Spread spectrum signals not only allow a reduction of the differential group delays in \(\Delta\)DOR and SBI measurements, but also enable simultaneous communications to multiple spacecraft from a single antenna, a scenario that is already occurring at Mars. The spacecraft group-delay measures involve differencing the measured phase of two or more discrete tones transmitted by the spacecraft. The measured phase difference between a pair of tones, divided by their frequency separation, is the primary group-delay observable. This is in contrast to the several-MHz bandpass used to measure the quasar group delays, therefore a systematic error is induced in the \(\Delta\)DOR measure, as outlined in Figure 2.  
The blue curve shows the phase dispersion as a function of frequency, while the green line shows the average linear behavior. At the spacecraft tone frequency (arrows), the measured phase differs between the narrowband spacecraft measurement (green) and the wideband quasar measurement (red), resulting in systematic phase error. To minimize the systematic error, the on-board synthesized \(\Delta\)DOR signal should resemble the quasar one as much as possible [9]. Thanks to the differential nature of the measurement, the difference between the spacecraft and quasar delays produces an observable that minimizes calibration errors due to instrument dispersion, especially the ones due to phase ripples in the acquisition filter, thus providing enhanced performances [10]. The recent advancements in the field of on-board microelectronics, e.g., Digital Signal Processing cores for space applications, are enabling factors to develop the newest \(\Delta\)DOR paradigm based on broadband observables: Wideband \(\Delta\)DOR.
## 3 Test Results and Analysis  
The collision tests between the 8 mm and 10 mm steel balls and glass were respectively performed 37 and 35 times. The test results for the 8 mm and 10 mm steel balls are shown in Tables 2 and 3 (in ascending order for velocity), and representative experiment images are presented in Figures 4 and 5. In this analysis, \(C_{max}\) and \(C_{min}\) represent the maximum and minimum diameters of the cone crack, with \(C_{avg}\) being their average. The ARC (aspect ratio of cone crack, \(C_{max}/C_{min}\)) indicates how close the cone crack is to a perfect circle. CAE and CIE are values obtained by dividing \(C_{max}\) and \(C_{min}\) by the kinetic energy of the steel ball, respectively, with \(CE_{avg}\) as their average. Similarly, \(P_{max}\) and \(P_{min}\) denote the maximum  
Figure 3: Experimental apparatus.  
Figure 2: Cone crack and perforated hole of damage.  
and minimum diameters of the perforated hole, with \(P_{avg}\) as their average, and the ARP (aspect ratio of perforated hole, \(P_{max}/P_{min}\)) represents the circularity of the perforated hole. PAE and PIE are calculated by dividing \(P_{max}\) and \(P_{min}\) by the kinetic energy of the steel ball, with \(PE_{avg}\) as their average.  
Damage limit velocity: The experimental results for the 8 mm steel ball collisions with glass revealed consistent damage occurrence at impact velocities of 38.69 m/s (Test No. 13 in Table 2) and above. For impact velocities of 38.31 m/s or less (Test No. 1 to 12 in Table 2), damage occurred with a probability of approximately 40%. No damage was observed in Test No. 1 and 2, with impact velocities of 34.31 and 34.37 m/s, respectively. These findings suggest that the minimum velocity required for an 8 mm steel ball to potentially damage 5 mm low-e glass is approximately 35 m/s. The velocity range of 35 to 39 m/s is considered the probabilistic damage limit velocity range for the 8 mm steel ball. Given that damage occurred in all experimental cases with collision velocities of 40 m/s or higher, 40 m/s is considered as the damage limit velocity.  
For the 10 mm steel ball collisions, damage was observed in all cases at impact velocities of 21.05 m/s (Test No. 56 in Table 3) and above. In contrast, for impact velocities of 19.9 m/s or less (Test No. 38 to 55 in Table 3), damage occurred with a probability of  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Velo-City**} & \multirow{2}{*}{**KE**} & \multicolumn{8}{c}{**Cone Crack**} & \multicolumn{8}{c}{**Perforated Hole**} \\ \cline{3-14}  & & & & & & & & & & & & & & & & & & \\ \cline{3-14}  & & & & & & & & & & & & & &
# Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images
## 3 Materials and Methods
### Alternative II: Deep Learning Combined with Fuzzy Logic
#### 3.2.2 Analysis  
To perform the drowsiness detection, a Mamdani fuzzy inference system [34] is designed for which we have to specify inputs, outputs, and rules.  
Inputs  
Each input has to be represented by a variable, which must have one or more fuzzy sets that define the possible values that the variable can take (for example, variable \"number  
Figure 8: Architecture of the model used for yawning detection.  
Figure 7: (**left**) Face detected by DLIB’s face detection algorithm. (**right**) Landmarks detected by DLIB’s landmark detection algorithm.  
of blinks\", which would receive an integer, could have fuzzy sets \"low\", \"normal\" and \"high\"). Each fuzzy set is defined by a membership function \(\mu A(x)\), which, for an input \(x\), assigns a degree of membership to fuzzy set \(A\). This membership value is a number between 0 and 1, where 0 means that it is completely false and that value \(x\) belongs to fuzzy set \(A\), and 1 means that is completely true.  
In this work, two types of membership functions are used: triangular (tri) and trapezoidal (trap) membership functions. These membership functions are typically used in fuzzy logic because they are able to represent easily and accurately the evolution of the values on most fuzzy sets. In this case, the use of one type of membership function or another is tailored to each variable, and it is related to the range of values associated with each fuzzy set.  
Both functions are defined by a group of points that are linearly connected, but, while triangular functions are defined by three points (with membership values 0, 1, and 0), at the trapezoidal function, there are four points (with membership values 0, 1, 1, and 0).  
Figure 9 shows an example of a variable where three fuzzy sets are defined: two of them (_low_ and _normal_) use triangular functions, and the other one (_high_) uses a trapezoidal function.  
Table 1 shows the variables used as inputs of the FIS along their respective fuzzy sets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Variable Name**} & \multicolumn{2}{c}{**Fuzzy Sets**} \\ \cline{2-3}  & _Name_ & _Membership Function_ \\ \hline No. of blinks (_blinks_) & low & \(tri(0,0,10)\) \\ normal & \(tri(0,10,20)\) \\ high & \(trap(10,20,50,50)\) \\ \hline Average duration of blinks, in seconds (_avg\_blink_) & low & \(trap(0,0,0.10,0.30)\) \\ normal & \(trap(0.10,0.30,0.40,0.60)\) \\ high & \(trap(0.40,0.60,30,30)\) \\ \hline Number of microsleeps (_microsleeps_) & low & \(tri(0,0,2)\) \\ high & \(trap(1,3,10,10)\) \\ \hline Number of yawns (_yawns_) & low & \(tri(0,0,2)\) \\ high & \(trap(0,2,10,10)\) \\ \hline Seconds spent yawning (_avg\_yawn_) & low & \(tri(0,0,10)\) \\ high & \(tri(0,10,30,30)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inputs FIS.  
Figure 9: Fuzzy sets and membership functions of variable “blinks”.
# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 4 Experimentation and Analysis
### Ablation Study
This proves that the improved algorithm proposed in this paper offers obvious improvement over the original YOLOv8 algorithm in detecting targets in remote sensing images with different characteristics.
## 4 Self-Healing Approaches
### Self-Healing Models and Frameworks
Consequently, [PERSON] and [PERSON] [26] proposed a novel sectionalised self-healing approach in decentralised systems to resolve electrical energy distribution problems. The objective set by the authors is to guarantee power supply by balancing loads in the subsection of the decentralised system by either adjusting the power outputs of the distribution network or through the implementation of load shedding. A suitable self-healing network deployment case study is a platform by [20] called fault location isolation and supply restoration (FLISR), deployed and tested on a network simulator. The test result indicates that the solution reduces the cost of commissioning on grid networks, and the platform has been deployed in grid networks in several countries, such as the Netherlands, France, Vietnam, and Cuba. A method for an automatic prediction model for systems failure, recovery, and self-healing in virtual machine (VM) networks using intelligent hypervisors was presented by [9]. The self-healing functionality implementation on the transmission network of a smart grid is achieved through optimal voltage control with a genetic algorithm, unified power flow controller (UPFC), and islanding process. The distribution network approach involves the design based on the propulsion system, ant colony algorithm, multi-stakeholder control system (MACS) for the intelligent distribution network, fault location, isolation, and service restoration (FLISR). The self-healing functionality is achieved using a predictive model that recovers failed VM instances in a physical machine through a self-healing algorithm that utilises the VM's memory snapshots. The self-healing algorithms include decision tree, Gaussian normal basis (GNB), and support vector machine (SVM). A database's self-healing functionality, extracting helpful information that identifies the type and subtype of events from the database's events report (text fields) data, was presented by [38]. The extracted information was analysed using Python version 3.6.3, Natural Language Toolkit (NLTK), Scikit-learn, Regular Expression (RE), and Pandas modules. Several self-healing methods have been proposed, such as those described in [16] and relating to a smart grid with corresponding characteristics, which has the elements and is representative of all self-healing known features. The smart grid self-healing systems, to be considered adequate, must be able to meet the following criteria:  
1. Quick detection of system faults;
2. Redistribution of network resources to protect the system;
3. Reconfiguration of the system to maintain service, irrespective of the situation;
4. Minimal interruption of service during reconfiguration or self-healing period.
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 6 The Arrowhead Framework  
The Arrowhead framework [94; 97] is designed to cover interoperability and integration issues for the IIoT world. It supports the collaboration of newly built and legacy CPS architectures based on the principles of SOA by applying the SoS approach. It realizes the local cloud concept empowered by inter-cloud communication capabilities. Each stakeholder has its local cloud(s), as shown in Figure 7, which can act as an SoS.  
Their systems implement either intra- or inter-cloud information sharing as well as security and other policies. This type of communication carries several risks that need to be addressed. The Arrowhead framework is practically designed and continually developed to manage cybersecurity risks, amongst others. The risk management of Arrowhead aligns with the Risk IT framework combined with COBIT 5 and ISO 27001 recommendations. Considering [90], threats, vulnerabilities, and risks are continuously identified and managed in accordance with the Risk IT lifecycle, providing CIA (Confidentiality, Availability, and Integrity) to the clouds and their services [98].
# Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks
## 2 Problem Definition and Our Approach Overview  
### Problem Definition  
For single-shot structured light imaging systems using a color stripe pattern, the accurate detection of feature points, which are points on the boundary of two adjacent color stripes in a deformed pattern image, is extremely important because it directly affects the quality of the reconstructed point cloud. In practice, this faces many challenges from the complex surface structure of the captured object and ambient light noise. This problem becomes even more serious when high-frequency color stripe patterns are used to increase the density of the point cloud. The two popular methods for solving this problem are edge detection methods based on gradients and color classification [10]. The first method is false detection risk caused by the appearance of irregular edges due to the surface structure of the object [37]. Thus, it is difficult to apply to the images of a high-frequency color stripe pattern [38]. Meanwhile, the second method is easily affected by the colored textures and inter-reflectivity of the object's surface [39].  
Figure 1 illustrates the influence of object surface characteristics on the accuracy of edge detection. Figure 1 shows an image of a plastic box whose surface has a lot of colored textures, such as printed shapes and text. In Figure 1, the green lines indicate the edges in the ground-truth image, while the red lines indicate the edges detected by the edge-detection method based on color classification. It is worth noting that there are several false detections in the edge image. Traditional edge detection methods cannot distinguish the inherent colored textures of the object surface from the color structured light patterns projected on the object. To overcome this, a method based on deep learning needs to be developed.  
Figure 1: Illustration of the influence of object surface characteristics on the accuracy of the edge detection; (**a**) an image of a colored textured plastic box; (**b**) false detections in an edge image.
# Quality Behaviour of Turnouts: Comparison, Problem Specification and Recommendation of Measures
## 1 Introduction
There are various ways to quantify the quality of the subsoil, for example by means of ground penetration radar data [15]. For the open track, methods have been developed in recent years that are based on measurement car data [16]. A transfer of this method to turnouts is conceivable but still requires further consideration. The ballast bed provides a stable bedding for the sleepers on the one hand and sufficient elasticity for necessary vertical sheeper movements on the other hand. However, this is only possible if the ballast grain meets requirements. Worn ballast typically consists of an excessively high fine-grain content leading among others to a loss of elasticity and thus to consequential problems within the system [17]. In case of severe abrasion, the ballast must be replaced or cleaned [17]. Evaluation methods for the ballast condition of the open track [16], and for turnouts [18], are available in Austria. A systematic problem of worn ballast are increased forces for sleepers and rail fastening systems. Sleepers, together with the rail fastening system, need to secure the rail position and to distribute loads towards the ballast bed [19]. A poor sleeper condition means that these tasks can no longer be fulfilled. A distinction must be made between concrete sleepers and wooden sleepers. While service life of turnouts with concrete sleepers is mainly limited by the condition of the ballast, for turnouts with wooden sleepers the sleeper itself is likely to be the critical element. It is possible to replace single wooden sleepers or to restore the fastening function, but these measures are expensive. Evaluation tools based on track gauge measurement exist for the sleeper condition of the open track [20]. A transfer to turnouts is not trivial for various reasons and is currently the subject of our research. While condition monitoring of metal parts is important for the open track, it is essential for turnouts due to the special features of turnouts--intricately shaped, partly moving metal parts. Crossings and switch devices are important elements because they are safety-critical and highly abrasive [21]. Due to their geometry, frogs in their widespread rigid design are causers of dynamic loads. Insulated rail joints and welding joints also contribute significantly to vehicle excitation. These dynamic forces lead to increased wear of other components and should therefore be replaced before significant damage occurs at the system level [22; 23; 24; 25]. In recent years, we developed various tools for the component description. The aim of this work is to test developed tools on real turnouts within the OBB network. Threequestions are to be answered. (1) Can we use these tools to compare quality behaviour of turnouts? (2) If quality behaviour of a turnout is poor, is it possible to define causes for this behaviour? (3) Can we provide recommendation measures to sustainably improve the quality behaviour of a specific turnout?