esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 7 Future Directions
Practical experience with AI tools and platforms should be emphasized to equip students with hands-on skills. Interdisciplinary programs combining software engineering with AI specializations can produce professionals adept at navigating the complexities of AI integration. The rapid pace of AI innovation necessitates a commitment to lifelong learning among software engineering professionals. Continuous education programs, workshops, and certifications can help professionals stay abreast of the latest developments. Employers play a crucial role by supporting ongoing training initiatives, fostering a culture that values adaptability and continuous improvement. Embracing lifelong learning ensures that the workforce remains competent and competitive in an evolving technological landscape. The findings of this study raise important questions about the future of AI in software engineering and its applicability across organizations of different scales. * Applicability to Small Startups: Startups with limited resources can use AI tools to automate routine tasks, such as code generation and defect detection, reducing overhead costs. For instance, a small development team could employ GitHub Copilot to streamline coding tasks while focusing their efforts on innovation. Additionally, AI tools offer startups an opportunity to adopt agile practices more effectively, despite their constrained budgets. * Applicability to Big Companies: In contrast, larger organizations can benefit from AI's ability to optimize workflows across distributed teams. For example, large-scaleenterprises could integrate AI tools into their DevOps pipelines, ensuring continuous integration and delivery. However, big companies must also invest in employee training programs to maximize AI's potential and address ethical challenges, such as bias in AI algorithms. * Future Directions for AI in Software Engineering: As the field advances, AI has the potential to foster decentralized software teams that operate autonomously, driven by adaptive AI tools. Additionally, the development of universal benchmarks and ethical standards for AI-driven tools could facilitate cross-industry collaboration, ensuring sustainable and fair AI adoption.
## 3 Materials and Methods  
### NDVI Time Series Analysis Using PROBA-V  
PROBA-V is an ESA microsatellite from the PROBA (Project for On-Board Autonomy), where V is the abbreviation of the VEGETATION series. It was developed mainly by a Belgian consortium (Qinetiq Space, OIP, and VITO) with a small contribution notably from Luxembourg and Canada via the GSTP program of the ESA [38]. The main mission of PROBA-V is to take over from the VEGETATION instrument on board the Spot 4 and 5 satellites. It analyses a time series of low-resolution images and characterization of vegetation seasons using the \"Time series viewer\" developed by VITO available at this link [39]. For more detail about PROBA-V, the reader cordially invited to consult the paper of [40; 41; 42].
# Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
###### Abstract  
Microwave tracking, usually performed by on ground processing of the signals coming from a spacecraft, represents a crucial aspect in every deep-space mission. Various noise sources, including receiver noise, affect these signals, limiting the accuracy of the radiometric measurements obtained from the radio link. There are several methods used for spacecraft tracking, including the Delta-Differential One-Way Ranging (\(\Delta\)DOR) technique. In the past years, European Space Agency (ESA) missions relied on a narrowband \(\Delta\)DOR system for navigation in the cruise phase. To limit the adverse effect of nonlinearities in the receiving chain, an innovative wideband approach to \(\Delta\)DOR measurements has recently been proposed. This work presents the hardware implementation of a new version of the ESA X/Ka Deep Space Transponder based on the new tracking technique named Wideband \(\Delta\)DOR (W-\(\Delta\)DOR). The architecture of the new transponder guarantees backward compatibility with narrowband \(\Delta\)DOR.  
\(\Delta\)DOR; W-\(\Delta\)DOR; spacecraft tracking +
Footnote †: journal: Applied Physics and Astronomy  
_Article_  
Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
## 2 Wind Turbine Specification  
We used order a 40 W capacity wind turbine to validate the performance of the proposed controller for wind turbines in this study. The scaled wind turbine had a rotor diameter of 1.1 m and a tower height of 0.9 m. Its rotor speed at the rated condition was 678 rpm, possessing a rated generator torque of 0.04 Nm. Additional turbine specifications are presented in Table 1. The target wind turbine was originally designed and developed by researchers at the Technical University of Munich (TUM) [24]. However, modifications were made to the turbine encourage the development of new three-dimensional (3D)-printed blades, accelerometers in the nacelle, and various control algorithms for experimental validation in this study. Additionally, despite having a low-rated power of 40 W, this wind turbine was designed to have the optimal tip speed ratio, defined as the ratio of blade tip speed to wind speed required to improve the aerodynamic efficiency of the rotor to be approximately 7.3, granting an operating range similar to that of large wind turbines. These scaled wind turbines have been widely adopted as a method for validating the control performance of new control algorithms [25].
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### Event Logs Description  
By using _PM4 Py_, we give the base descriptive statistics for the real-life event logs as shown in Table 2. The table illustrates significant variations in the characteristics of the event logs. Trace uniqueness is an important analysis criterion. A high degree of trace uniqueness indicates that individual process instances can be distinctly identified based on their event sequences, thereby improving the granularity and accuracy of behavioral analysis. However, this heightened specificity also elevates the risk of re-identifying sensitive cases. Conversely, a lower degree of uniqueness mitigates privacy risks by enhancing trace similarity; however, it may also obscure significant variations in process behavior. We assert that the data collection methodology employed is well suited to ensuring a high degree of external validity in the results.  
To establish a comparative baseline for the utility of PPPM models, the event log conformance checking metrics, obtained following process discovery without the application of anonymization, are presented in the Table 3. The process model for each event log was derived using the _PM4 Py_ InductiveMiner algorithm, represented as Petri nets, with conformance checking metrics assessed through token-based replay. Figure 3 shows an example of the discovered PetriNet of the Road Traffic Fines event log which has the highest instance count and lowest trace uniqueness.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Event Log** & **\#Events** & **\#Cases** & **\#Activities** & **\#Variants** & **Trace Uniqueness** \\ \hline Sepsis & 15,214 & 1050 & 16 & 846 & 80\% \\ BPIC13 & 65,533 & 7554 & 4 & 1511 & 20\% \\ CoSeLog & 8577 & 1434 & 27 & 116 & 8\% \\ Road Traffic Fines & 561,470 & 150,370 & 11 & 231 & 0.1\% \\ \hline \end{tabular}
\end{table}
Table 2: Characteristics of the event logs used in the experiments.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 2 Literature Review
### 5G Non-Public Networks and Fixed Wireless Access Deployments
#### 2.1.2 Performance Analysis and Optimisation in 5G NPN FWA Backhaul  
Based on the efforts outlined above, there is now a limited body of work discussing practical performance measurement and optimisation in the scope of 5G NPN FWA networks. Previous work in the context of 5G link performance outlined how mmWave mesh networks perform in a range of deployment scenarios, including both urban and rural environments [19; 21]. There has also been some work that characterised the uplink and downlink performance of these networks in practice [5; 22]. However, to the best of our knowledge, there are currently no studies that have characterised the practical deployment and organisation of the network as a whole. There have, however, been some studies that discussed 5G NPN networks in this context. Recently, a number of such studies have focused on the use of 5G in an industrial environment to support Industry 4.0 [23]. There have also been good economic analyses of these deployments that provide cost models for cellular 5G NPNs and highlight the likely benefits based on Capex and Opex savings [24].  
Beyond the practical analysis above, there are also a number of studies that explore FWA backhaul networks from a theoretical perspective [25; 26; 27; 28]. In particular, [PERSON]. [25] explored the energy optimisation of mmWave backhaul networks using a mixed integer model. In addition, [PERSON]. [26] provided a comparison of 802.11 routing performance in ad hoc networks and found significant performance differences based on the use of static or mobile nodes. Moreover, the work by [PERSON] et al. [27] presented an interesting analysis of multipath routing for mmWave backhaul in order to provide robust connectivity. Finally, there is a good discussion of QoS-aware scheduling in meshed backhauls in [28]. However, in many cases, these studies draw on a wider body of previous research on Wireless Mesh Networks (WMNs) that have established much of the fundamental knowledge in this field. Thus, we next outline past research on WMNs and highlight the areas that are most relevant to the work presented in this paper.
## 3 System Design and Implementation
### Implementation  
At Bremer Institut fur Produktion und Logistik GmbH, we deployed the implemented ad hoc network. The deployed setup forms a network that comprises of Raspberry Pi (a low-cost micro-computer with an open-source operating system) nodes, which can either transmit their own packets or forward packets generated by other Raspberry Pies, hence acting as Relay Nodes (RNs). GoPiGo (General Purpose Input Output) robot cars fabricated by Dexter Industries, which comes with their motherboards and can be assembled manually, are used to provide motion to the Raspberry Pi nodes. Each GoPiGo robot car has a D-Link Nano USB Adapter Dongle that provides support for routing or forwarding the received contents. The node acting as an OBU, additionally has a GPS sensor and a keypad for user input.  
The hardware and software had to be chosen carefully to meet the necessary requirements of the task. Therefore, the following hardware was used for successful TestBed implementation:
## 3 NARX Model on the Rotor-Bearing System
### NARX Model under Different Frequency Ranges
#### 3.3.1 Under the Low-Speed Condition  
When the input signal contains the frequency of \(\omega_{low}\in[1,200]\) rad/s, the NARX model can be identified as:  
\[\begin{split}& y(k)=\sum\limits_{i=1}^{15}\beta_{i}(k)\widehat{ \theta_{i}}\\ &=1.36296y(k-1)-0.1352y(k-3)+4.5869\times 10^{-5}u(k-3)\\ &+\cdots-8.0035\times 10^{-4}u(k-9)u(k-9)\end{split} \tag{26}\]  
where the harmonic input with \(\omega=100\) rad/s is used to test the model in both the time and the frequency domain, shown in Figure 9. Also, the RMSE is \(9.0925\times 10^{-7}\) m.  
Figure 9 indicates that the NARX model identified in the low frequency range provides a more accurate prediction result than that of (25). Also, the RMSE is better than \(3.1582\times 10^{-6}\) m, which is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency Range (rad/s)** & **RMSE of MPO Method (m)** & **RMSE of OSA Method (m)** \\ \hline Low speed & \(3.1582\times 10^{-6}\) & \(2.9461\times 10^{-7}\) \\ Critical speed & \(5.2704\times 10^{-6}\) & \(4.7590\times 10^{-7}\) \\ Over critical speed & \(1.4032\times 10^{-6}\) & \(2.0754\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Root square mean error (RMSE) of MPO and OSA method in a large frequency range.  
Figure 8: Output comparison in time and frequency domain when \(\omega=500\) rad/s.
# Low Power FPGA Implementation of a Smart Building Free Space Optical Communication System
## 4 Results
### Hardware Results  
Figure 17 shows the ALTERA Cyclone IV DE2115 board used for the proposed design with the transmitting and receiving UART cables. The RS232 and USB ports were tested as two independent lines for transmission and reception, respectively, on Linux Mint OS using PUTTY.  
To test the communication system, using one FPGA kit as a transceiver, the laser beam was directed to the mirror that reflects it to the photo-detecting circuit. Figure 18 shows the hardware implementation of the system in the darkness to avoid any external lighting interference during transmission. This can further frame this design into a capsulated ASIC implementation in an opaque box to mitigate any lighting interference that could degrade the communication performance.  
Figure 16: Receiver Circuit Simulation for 2 V input.  
Figure 17: The ALTERA FPGA Kit of the proposed system.  
With the optimized modulation scheme, a data rate of approximately 230 kbps is obtained, as shown in Figure 19. The received signal is then detected and demodulated using a comparator threshold decision circuit to correctly decode the incoming bits and interpret them to their original form. Figure 20 shows the incoming characters' successful reading.  
The photo-detection circuit design has an amplifying comparative approach that determines the threshold voltage required for demodulation. For audio transmission, MATLAB was the test bench where a predefined file space was used to capture the data sent. The size of the file was monitored along with a message detecting the number of errors in the received bits for each transmission.
# A Numerical Investigation of Laser Beam Welding of Stainless Steel Sheets with a Gap
## 2 Simulation Model
### Discussion on Mesh Density
The effect of laser energy absorption being sensitive to the local incident angle can be seen when plotting the absorption of laser radiation according to the Fresnel equations, which were also discussed by [PERSON] [24]  
\[A_{S} =1-\frac{a^{2}+b^{2}-2a\cdot\cos\alpha+\cos^{2}\alpha}{a^{2}+b^{2}+2a \cdot\cos\alpha+\cos^{2}\alpha}, \tag{2}\] \[A_{P} =1-\frac{a^{2}+b^{2}-2\big{(}a\big{(}n^{2}-\kappa^{2}\big{)}+2 bn \kappa\big{)}\cos\alpha+\big{(}n^{2}+\kappa^{2}\big{)}^{2}\cos^{2}\alpha}{a^{2} +b^{2}+2(a(n^{2}-\kappa^{2})+2 bn\kappa)\cos\alpha+\big{(}n^{2}+\kappa^{2} \big{)}^{2}\cos^{2}\alpha},\] (3) \[a^{2} =\frac{1}{2}\bigg{(}\sqrt{\big{(}n^{2}-\kappa^{2}-\sin^{2}\alpha \big{)}+4n^{2}\kappa^{2}}+n^{2}-\kappa^{2}-\sin^{2}\alpha\bigg{)},\] (4) \[b^{2} =\frac{1}{2}\bigg{(}\sqrt{\big{(}n^{2}-\kappa^{2}-\sin^{2}\alpha \big{)}+4n^{2}\kappa^{2}}-\Big{(}n^{2}-\kappa^{2}-\sin^{2}\alpha\Big{)}\bigg{)},\] (5) \[A =\frac{A_{S}+A_{P}}{2}, \tag{6}\]  
as a function of the angle of incidence, as can be seen in Figure 4:0 Here, the incident angle \(\alpha\) is the angle between the laser beam direction and the surface normal; therefore, \(\alpha=90^{\circ}\) corresponds to a laser beam parallel to the surface. \(A_{S}\), \(A_{P}\) and \(A\) denote absorption of radiation of perpendicular, parallel and no polarization, respectively. It is obvious that a small deviation in incident angle, especially in the regime of high incident angles, as is typical for keyhole welding, has a significant effect on the fraction of laser power absorbed by the material. Another consequence of using smaller cells is the decrease in the energy required to reach the melting threshold inside a computational cell, since otherwise the same energy gets distributed over a larger volume. This effect also goes hand in hand with a decrease in numerical diffusion for more refined meshes. The calibration using the coarser mesh thus yielded values for the complex refractive index (\(n\) and \(\kappa\)), and a minimum threshold for material in a cell to be taken into account for the absorption of laser energy and a maximum number of reflections being considered by the ray tracing algorithm, which compensated for the effects described above. When switching to a finer mesh, these parameters therefore would require a new calibration in order to produce results that are in agreement with the experiments. This was omitted due to the associated high computational cost, as the purpose of the simulations conducted using a finer mesh was not the reproduction of experiments or the use of data to  
Figure 4:1 Absorption of laser power by 304L stainless steel at melting point (\(n=9.09\), \(\kappa=4.21\)[28]) as a function of incident angle according to Equations (2)–(6). adjust a real-world process, but the investigation of transient effects for the purpose of fundamental research.
# NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation
## 4 Experimental Analysis
### Datasets  
Robustness of the proposed modern object detector, NextDet, is evaluated on two publicly available multi-class datasets: Argoverse-HD [9] and Microsoft COCO [10]. These datasets provide annotated images and bounding boxes for object detection, tracking and forecasting for autonomous driving, everyday objects, and humans. Each dataset is split into a pre-defined train and test sets where each set contains a fixed number of images. Figure 8 provides an overview of images from each of the two datasets utilized to train and test the proposed object detector. Table 1 provides a statistical overview and comparison between these two datasets. The two datasets chosen possess images with unique features and characteristics, as seen in Table 1 and Figure 8, in order to provide an extensive evaluation of the proposed object detector.  
Figure 8: An overview of raw images taken from the two datasets used for training and testing the proposed object detector. Images (**a,b**) denote sparse and dense multi-class objects from the Argoverse-HD dataset in overcast and clear conditions respectively, and images (**c,d**) denote sparse and dense multi-class objects from the COCO dataset in grayscale and RGB color space respectively. These datasets contain multiple images containing objects of different classes, complex scenarios, and diverse lighting.
# Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO
## 2 Related Work
### Smart Grids Improvements Due to V2X Communications  
Nevertheless, the majority of the charging strategies used in the literature do not take into account communication of the EVs, which cannot be neglected nowadays due to its importance in fast charging. Therefore, real-time information must be taken into account to design new, efficient charging strategies for electric vehicles.  
The authors in ref. [16], using smart grid communication with VANET-enhanced capabilities, proposed a predictive charging strategy for EVs that addressed drivers' concerns about autonomy by reducing the average trip cost. These authors showed that this strategy performed better than a strategy that did not take into account the mobility and trip cost of the EVs. In ref. [17], the authors proposed a communication framework of an electric vehicle mobile charging application based on the publish/subscribe (P/S) mechanism and public buses. On the one hand, the results showed that the frequency with which P/S published information about the state of the vehicles determined the charging performance in terms of waiting time and number of recharged electric vehicles. On the other hand, the flexibility and mobility of buses improved the charging performance compared to when RSUs were deployed. Finally, they observed that the advantage of reservation aggregation led to a reduction in communication costs.
## 4 Methods
### Dual Feature Extraction Neural Network
#### 4.2.1 BiLSTM  
Long Short-Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN). Unlike the traditional RNN, LSTM can better solve the problems of gradient disappearance and gradient explosion when processing sequential data. The LSTM cell structure is shown in Figure 6. The LSTM contains an internal state called \"cell state\", it can control the flow and forgetting of information to better handle long sequences of data. Among them, the calculation method for each step is as shown in Equations (1)-(6).  
\[f_{t}=c\Big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]  
\[i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\overset{\sim}{Ct}=tanh(W_{c}[h_{t-1},x_{t}]+b_{c}) \tag{3}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\overset{\sim}{Ct} \tag{4}\]  
\[O_{t}=\sigma(W_{o}\times[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=O_{t}\times tanh(C_{t}) \tag{6}\]  
Based on the calculation steps mentioned above, the LSTM model selectively discards some unnecessary information and improves neuron memory, which can better solve the long-time dependency problem. However, LSTM fails to fully leverage the contextual information of power marketing data. BiLSTM is a bi-directional LSTM. The uni-directional LSTM model can only capture the information passed from front to back, while the bi-directional network can capture both forward and backward information. BiLSTM can simultaneously utilize the contextual information of the power marketing text, learn the long sequence semantic features of the text, and enhance the recognition ability of the model, which is calculated as in Equations (7)-(9).  
\[\overset{\rightarrow}{h_{t}}=LSTM(x_{t}) \tag{7}\]  
\[h_{t}=LSTM(x_{t}) \tag{8}\]  
\[h_{t}=<\overset{\rightarrow}{h_{t}},h_{t}> \tag{9}\]  
Figure 6: LSTM cell structure diagram.  
The final hidden layer state \(h_{t}\) is calculated by the above equation to capture the long-range context dependence of the power marketing data, and the output of BiLSTM is (\(h_{1}\), \(h_{2}\),..., \(h_{n}\)).
## 3 Results and Discussion
### Hardness Distribution  
The values of Vickers hardness were measured for the studied Al-4 Cu/(Al\({}_{2}\)O\({}_{3}\))\({}_{\rm x}\) AMCs. The increase in the compaction pressure resulted in a momentous increase in the hardness of the compacted discs. Furthermore, increasing the reinforcement percentage of Al\({}_{2}\)O\({}_{3}\) and decreasing the H/D values caused increases in the hardness property. It was clearly revealed that using a compaction pressure of 600 MPa resulted in an 11.48% increase in the Hv of Al-4 Cu/(Al2O3)0. Moreover, strengthening with a different wt.% of Al2O3 (2.5, 5, 7.5, and 10) caused increases of 11.29%, 9.09%, 19.12%, and 28.57%, respectively, compared to discs compacted under 500 MPa. Furthermore, increasing the Al2O3 percentage revealed increases of 1.64%, 8.2%, 11.48%, and 47.57% in the Hv values of 2.5, 5, 7.5, and 10 compared to the Al-4 Cu/(Al2O3)0 pressed under 600 MPa. A further increase in the compaction pressure up to 900 MPa led to increases of 1.47%, 5.88%, 19.12%, and 32.35% in the Hv Al-4 Cu/(2.5, 5, 7.5, 10)Al2O3 compared to discs compacted under 600 MPa. The maximum hardness was reached at 126.27 Hv, for Al-4 Cu/(Al2O3)10 with an H/D = 0.75 compacted under 900 MPa.  
The hardness of the AMCs principally depended on the matrix and the reinforcement materials content. The hardness increase was obviously accompanied by the reinforcement hard particle addition. Moreover, the increase in hardness can be attributed to the grain boundary growth that grain refinement caused [60]. Additionally, the hardness improvement can be argued to reduce the dislocation movement during plastic deformation; consequently, the interface area between the particles increases and impedes the grain's development during sintering [13].  
Figure 6: RSM optimization results of RD% for AMC.  
Figure 7: Optimal RD% by (a) GA and (b) hybrid DOE-GA.