esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision-Making in Fallback Scenarios for Autonomous Vehicles: Deep Reinforcement Learning Approach
## Appendix A  
This section introduces other validation methods based on the mile-driven and scenario-based approaches mentioned here.  
We highlighted the validation phase in the introduction, emphasizing mile-driven and scenario-based methods. Nonetheless, various methods exist for assessing the safety of AVs and their ADSs and have been elaborated in this context: (1) public road testing, (2) closed-course testing, and (3) simulation testing.  
_Public road testing_ was conducted to validate the performance of the ADS under real-world conditions and to expose it to unpredictable events. The AV was operated on public roads with an onboard safety driver for control, if necessary. This was the ultimate test for the readiness of AV for real-world deployment. Advantages: Real-world variability and unpredictability cannot be fully mimicked in simulations or closed courses. Public road testing provides the most accurate understanding of how an ADS performs under typical and unexpected conditions.  
_Closed-course testing_ is intended to test the AV sensors, software, and decision-making algorithms in controlled environments. The vehicle was tested on private tracks or facilities that mimic real-world driving conditions. These scenarios could be carefully staged with props, other vehicles, and robotic pedestrians. This approach allows real-world physical testing, while minimizing the unpredictability associated with public roads. This facilitates reproducibility and careful staging of specific scenarios.  
_Simulation testing_ is a safe, cost-effective, and scalable method for testing the decision-making algorithms of an ADS across millions of virtual miles in a wide array of scenarios. This involves creating virtual environments in which AV software is subjected to numerous scenarios, including those too dangerous to be tested in the real world. Simulation testing can cover rare-edge cases, reproduce complex scenarios, and run continuously, accelerating the validation process and ensuring broader coverage than real-world testing alone.
## 2 Materials and Methods
### Isotope Production Irradiation on a Medical Cyclotron
#### 2.6.2 Interpretation of Wavelength Shift  
Fiber optic sensors employing FBGs can be sensitive to both changes in strain and changes in temperature depending on the mechanical design of the sensor. As it was mentioned earlier, one FBG cannot inherently decouple the effects of strain and temperature from the measurement of its characteristic wavelength. For that reason, judgement must be used when analyzing changes in characteristic wavelength and attributing them to discrete experimental parameters. When a FBG is installed in a device in a strain isolated manner, it can be assumed that changes in wavelength are caused by changes in temperature. In this function, the FBG will report a change in wavelength vs. temperature with a sensitivity of \(\sim\) 0.010 nm/\({}^{\circ}\)C in temperature environments from \(-\)20 to 200 \({}^{\circ}\)C. When a FBG is installed in a device with the intention of measuring strain, it is impossible to remove the FBG's inherent temperature sensitivity. In order to conduct a strain-only measurement, temperature compensation is typically provided by using a secondary temperature measurement system. The FBG test specimens subjected to irradiation did not incorporate any temperature compensation system and therefore, wavelength shifts due to temperature were expected to be included in any observations of the total wavelength shift. Wavelength measurements conducted before and after irradiation were collected while specimens were stabilized to ambient room temperature. Fluctuations of room temperature within a range of \(\pm\)2.0 \({}^{\circ}\)C are expected. They would cause fluctuations of \(\pm\)0.020 nm observed in the FBG wavelength.
# Part Qualification Methodology for Composite Aircraft Components Using Acoustic Emission Monitoring
## 3 Results and Discussion
### Progressive Damage Characterization
Similar trends were observed in all tested spars, and the behavior was classified as progressive or significant based on the cumulative absolute energy patterns noted in Figure 4. For example, Figure 5 shows four additional spar tests to further demonstrate the identified data trends using the same 10\({}^{3}\) aJ energy increase to denote the behavior as significant. Figure 4: AE energy evolutions for the same tests shown in Figure 1. (**a**) Test 14, (**b**) Test 8, (**c**) Test 10, and (**d**) Test 4. Felicity ratio values for all spars at each loading step are given in Table 2. Most test specimens resulted in FR values below 1.0 and above 0.9. Noted exceptions are Test 8 and Test 9, both of which displayed the lowest FR values and further exhibited significant behavior during LL2. Interestingly, the spars that had the lowest FR values still resulted in near-average ultimate load. It should be also noted that some high FR spars failed at lower loads than those with lower FR values, potentially suggesting that these spars may enable damage distribution that leads to higher ultimate loads. In contrast, spars with higher FR values may potentially have less damage (which could not have been confirmed during testing as tests were not interrupted for post mortem inspection), however given that some of these spars failed at lower loads, damage in them is expected to be more localized and near the actual failure zone. It can be inferred from the parametric AE data trends presented herein that, generally, appreciable damage initiates once the design-limit-load (LL2) is reached, which further progresses until ultimate failure. In conclusion, the AE parametric analysis presented provided additional insight into damage progression and sample-to-sample variation that may otherwise be undiscernible using traditional test methodologies. For example, note the AE energy profile in Figure 4d, Test 4. This particular spars showed progressive damage, with a higher comparable FR value averaging 1.00 over all three loads. It may appear that something about this spar e.g., its design, production, or loading parameters, provided an advantage in controlling damage progression that resulted in achieving a higher ultimate load. By correlating spar-to-spar differences in damage behavior as indicated by AE data with structural testing, production or design data, additional value can be mined from such nondestructive evaluation, which may support design or process improvements.
# Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries
## 2 Materials and Methods
### Participants  
Total of 96 patients that had previously qualified for knee arthroscopy were included in the study. The study group consisted of 49 females and 47 males, with 52% of the patients treated due to dominant extremity involvement. The mean age of patients seeking medical attention in this study was 45 years. The mean time between the onset of symptoms and treatment was 44 months, and the patients with a gradual onset of symptoms but without a history of injury waited the longest for surgery, with mean time of 71 months. Over 60% of patients prior to surgical treatment had undergone some kind of conservative treatment such as physiotherapy, viscosupplementation or steroid injections. The most common form of conservative treatment was physiotherapy, which was prescribed to 58 patients prior to surgery. The effects of conservative treatment were not evaluated in this study, and the analysis was carried out only to determine whether the conservative treatment postponed the surgery itself. The clinical and baseline characteristics of the participants with summarized diagnoses based on arthroscopic findings are represented in Table 1. The study was approved by Bioethical Committee by Medical University in Lublin with the number of approval KE- 0254/262/2019.  
Due to great diversity of patients treated at the clinic, we have established the following exclusion criteria:  
* musculo-skeletal diseases that might affect clinical manifestation of symptoms;
* previous fractures of the distal femur or proximal tibia;
* metal implants that may impair the MRI evaluation;
* prior surgical treatment of the knee joint;
* tumors of the knee joint;
* incomplete physical examination due to pain experienced by the patient;
* viscosupplementation, platelet rich plasma (PRP) or steroid injections in the last three months; and
* lack of consent for storing and using medical images.
# Detecting DDoS Attacks in IoT-Based Networks Using Matrix Profile
## 3 Methodology: Matrix Profile  
Data scientists are usually interested in two things when analysing time-series data. These two essential aspects are anomalies and trends [35]. For instance, someone who works in finance may be interested in analysing time-series data and identifying anomaliesor trends in the stock market. Another example might be a business owner who wants to analyse sales data to find trends (seasonality demand) to prepare for orders by increasing their stock to meet projected increases in demand [36]. The similarity join is another way to highlight anomalies and trends in time-series data. Basically, it compares one part or snippet of the time-series data by computing the Euclidean distance between two datasets. This coding is easy when using a naive algorithm (a few for loops). However, with massive datasets, it could take months or even years to find any helpful information (anomalies or trends) [35; 36].  
**Example 1**.: _Assume that there are food vessels at a food factory. These vessels have sensors that log pressure as time-series data, for instance, one reading every minute. Each year in the log file, there are 525,600 entries._  
To perform a similarity self-join calculation on just a week's worth of this dataset (10,080 entries) using existing algorithms would require carrying out 132,880,692,960 Euclidean distances. If each calculation requires 0.0001 s using a nested loop algorithm, it will take 153.8 days to finish the process. [37].  
In 2016, a collaboration of two research groups from the University of California, Riverside, and the University of New Mexico in the US introduced a technique called matrix profile [38], which can be a helpful tool in analysing time-series data as it remarkably reduces the time and space complexity of the problem of analysing time-series data. In their first published work regarding matrix profile, the authors proposed it as a tool to find motifs, discords, and shapelets [35].  
If matrix profile is used with the example mentioned above, the matrix profile algorithm can finish the calculation of this huge dataset in just around 6.3 h using the same computer [37].  
Matrix profile is composed of a data structure and associated algorithms that help solve the dual problem of anomaly detection and motif discovery [39]. It is robust, scalable, and largely parameter-free [40]. There are two essential components in matrix profile: profile index and distance profile. The profile indices hold the index of their first nearest neighbour (the location of its most similar subset of data), whereas the distance profile is configurable, which means it can be a one-dimensional array of minimum Euclidean distances or \(Z\)-normalised Euclidean distances [36].
## 3 Electron Acceleration with Radially Polarized Laser Beams
### Direct Longitudinal Electron Acceleration with Radially Polarized Laser Beams
The other situation is when acceleration takes place between two positions where the beam spot size is minimal and infinite, respectively. This corresponds to \([z_{i},z_{f}]=[0,\pm\infty]\). For these two cases \(\Delta W=ez_{R}E_{z0}\). This defines the following theoretical limit to the energy gain (see also [14, 67, 68]):  
\[\Delta W_{\mathrm{lim}}[\mathrm{MeV}]\sim 31\left(P[\mathrm{TW}]\right)^{1/2} \tag{12}\]  
where \(P[\mathrm{TW}]\) is the laser power in terawatts. Different longitudinal acceleration scenarios were reported in the literature ; a selected list is given in Table 2. The first case is that of an electron that travels from \(z=-\infty\) to \(\infty\) at ultrarelativistic speed. Because of the Gouy phase, it drifts by \(2\pi\) in the wave and, on average, the energy is zero. This is a typical example that illustrates the Lawson-Woodward theorem [14]. To break the symmetry and allow for substantial electron energy gains, [PERSON] and [PERSON] [69], [PERSON] _et al._[14], and [PERSON] _et al._[15] suggested different avenues to effectively limit the electron interaction to \(-z_{R}\lesssim z\lesssim z_{R}\). However, the proposed schemes require optical materials close to the high field intensity regions. Such a configuration is inevitably limited by the destruction of the device and does not take full advantage of the high peak power delivered by actual ultraintense lasers. As a matter of fact, material destruction--where free electrons are released due to ionization by the intense laser field--can be considered as a part of the acceleration process. A target should thus be composed preferably of a material with deeply bound inner shells so that most electrons remain bound during the rise time of the laser pulse, but released near the peak [17; 19; 70; 71]. In that situation, electrons experience an optimal acceleration from \(z\simeq 0\) to \(\sim\infty\). In that range, the carrier phase shifts only by \(\pi\). Here, the [PERSON] theorem is not violated because the electron is initially at rest and the interaction is maintained over a semi-infinite distance. According to works by some of us, where preionized targets were considered [16; 18; 67], half of \(\Delta W_{\mathrm{lim}}\) comes from acceleration outside the Rayleigh zone, _i.e._, between \(z_{R}\) and \(\infty\). In this region, the longitudinal electric field is weaker than at focus but the Gouy phase evolves much more slowly (which considerably increases the dephasing time). The integration of Equation (11a) to get \(\Delta W_{\mathrm{lim}}\) represents ideal acceleration scenarios. Rigorous numerical simulations show that, instead, the maximum energy gain is much less unless the beam parameters are carefully optimized [67; 68; 73; 74; 75]. We will see in Section 4 that exceptional conditions are provided by ultrashort and tightly focused pulses.
# A Review on Upper Limb Rehabilitation Robots
## 4 Examples of Rehabilitation Robots
### Mit-Manus  
MIT-MANUS is a CPM robotic workstation developed by the Massachusetts Institute of Technology that is used to rehabilitate upper limbs post-stroke [36]. MIT-MANUS is an interactive workstation where the patient has to visually interact with a PC game to follow specific movements [20]. The MANUS workstation provides five degrees of freedom, two translation degrees of freedom for the elbow and the forearm, represented by flexion/extension and pronation/supination, respectively. In addition, three degrees of freedom for the wrist, represented by flexion/extension, pronation/supination and abduction/adduction [36]. A magnet safety lock is used to attach the patient's hand to the device; this technique provides an easy procedure to free the patient from the device [36]. Furthermore, the MANUS workstation provides low torques [36]. Therapy sessions, however, have to be monitored by a therapist to ensure the patient's safety [3]. The MANUS workstation is characterized by its impedance control, which provides a specific movement to the limbs [36]. The MANUS workstation records the patient's performance (specifically, the force, velocity and the position); then, the performance data are sent to the control computer to adapt the robot according to the patient's requirements [37]. The MANUS workstation has been experimentally proven to provide therapeutic effects for upper limb impairment [28].
## 1 Introduction
[48] used an SLA system to manufacture three inserts for microcantilevers with thicknesses measuring 30, 120, and 275 \(\upmu\)m. All three inserts failed within five injection moulding cycles to produce parts in PP. [PERSON] et al. [24] evaluated the insert life produced by a DLP- based inverted SLA process. These inserts exhibited an array of 100-\(\upmu\)m wide microfeatures, having different heights and aspect ratios. Following the optimisation of \(\upmu\)IM process parameters, the inserts showed a maximum lifetime of 78 moulding cycles before failing. Damage was noted in inserts with higher aspect ratio features, while those with lower aspect ratio features remained unaffected. The discussed state-of-the-art shows that the main goals in using additive manufacturing in rapid tooling for injection moulding and micro injection moulding aim to determine the number of parts produced, the mechanical strength of the inserts, and the minimum achievable size of the insert microstructures. However, it also highlights the lack of rapid prototyping of microfluidic chips produced by combining AM and \(\upmu\)IM techniques. In this context, the authors tested material jetting technology to produce polymer inserts for micro injection moulding. In this way, microfluidic devices should be prototyped with a mass production approach just at the early stages of the design phase to match the needs of changes in functionalities, producing few test devices with a considerable reduction of time and costs. The MJ technique, compared to other polymer AM processes, ensures higher dimensional accuracy, surface roughness, and high design complexity, thanks to the degradable support material [49]. The importance of insert orientation during 3D printing to produce thin PMMA plates with more uniform thickness and lower roughness has been investigated. Microstructured inserts were realised to test a typical LOC geometry with two different channel sizes (200 and 300 \(\upmu\)m) and two different cross-sectional shapes (semi-circular and trapezoidal). The inserts and samples produced were inspected to evaluate process performance, and preliminary microfluidic tests were also performed on some samples to verify the effectiveness of the microchannels.
## 2 Intelligent Processing on Image and Optical Information
### Feature Extraction and Segmentation  
The paper entitled 'Feature Extraction with Discrete Non-Separable Shearlet Transform and Its Application to Surface Inspection of Continuous Casting Slabs' by [PERSON], [PERSON], [PERSON], and [PERSON] [11] proposes a new feature extraction technique for the direction and texture information of surface defects of continuous casting slabs with complex backgrounds.  
[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [12], in their paper on 'A Novel Extraction Method for Wildlife Monitoring Images with Wireless Multimedia Sensor Networks (WMSNs)', proposed an segmentation method with a low computational complexity, which can extract the target area from the background based on the image texture and color information. This proposed method can realize more accurate extraction of wildlife monitoring images and effectively support image transmission in wireless sensor networks.
## 2 Related Works and Contribution
### Other Methods  
Apart from the methods dedicated for sensor networks, many interesting solutions have been also proposed to reduce data transmissions and extend battery lifetime of individual wireless sensor nodes, Internet-of-Things (IoT) devices, and networked control systems.  
For instance, energy savings can be achieved in wearable devices, by eliminating unnecessary data processing operations. In [35] an event-triggered machine learning approach was introduced, which enables the sensor node to perform feature extraction only when it is necessary for accurate fall detection. According to that method, an event is detected if magnitude of accelerometer readings is above a threshold. The detected event triggers feature extraction and classification procedures. It was shown that such approach improves the accuracy of falls detection. However, that study has considered only two types of human activity (falls, and non-falls).  
In [36] a method was proposed for IoT devices to enable energy-aware sampling of GPS signal and processing of location data. That method utilizes cognitive control and event recognition techniques. The proposed cognitive control algorithm reduces uncertainty about user mobility by adapting a sampling policy to current state of mobility. The adaptive location sampling is implemented only when relevant events are detected.  
An event-based networked control systems was presented in [37]. In such system the limited bandwidth of wireless network has to be effectively used for transmitting state and control information. Thus, the data transmissions among sensors, controller and actuators are performed when difference between sensor signal and a reference signal is above an event threshold.  
Other approaches to reduction of transmissions and energy consumption in wireless sensor networks are based on appropriate design of medium access control (MAC) protocols. A number of the MAC protocols for wireless body area networks have been analysed in [38]. The analysis took under consideration reliability, delay, collision and energy consumption. It should be noted that the method presented in this paper can be integrated with the MAC layer or routing layer transmission reduction for further energy savings.
## 2 System Model
### Vehicle Mobility Model  
The vehicle is traveling at a constant speed with speed \(v\). To prevent the problem of interrupted task transmission due to RSU switching, the task should be completed within the range belonging to the current RSU as much as possible. Assuming that the vehicle is within the coverage range of the RSU, the expected dwell time [21] of the vehicle in this range is:  
\[T_{m}^{stop}=\frac{R+r}{v} \tag{8}\]  
where \(R\) is the radius of communication coverage of RSUs and \(r\) is the horizontal distance between the vehicle and the RSU, denoted as:  
\[\mathtt{r}=\left\{\begin{array}{l}-v\times t_{m}+R,t_{m}<\frac{R}{v}\\ v\times t_{m}-R,t_{m}\geq\frac{R}{v}\end{array}\right. \tag{9}\]