esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Development of a 3 \(\upmu\)m-Thick Laser Structure Grown on InP-SiO\({}_{2}\)/Si
### Thermal Strain Evaluation at Growth Temperature  
The growth on an InP bonded seed enables us to prevent dislocations formation, contrary to direct growth onto Si where high lattice mismatch and APD lead to defective crystal quality. However, this approach still has to face the difference of CTE between III-V and Si. During growth, the lattice of the InP bonded layer expands and follows the CTE of Si bulk material. Therefore, the lower CTE of Si as compared to InP one induces a compressive thermal strain on the III-V grown materials. A 3 \(\upmu\)m thick structure composed of a 500 nm-thick n-doped InP layer followed by an AlGaInAs MQW active region, a 2 \(\upmu\)m-thick p-doped InP cladding and 200 nm-thick p-doped InGaAs contact layer were grown simultaneously on InPSi and on InP substrates. The optical confinement is 8% in the MQW for both structures.  
Figure 1: Fabrication process for a vertical laser structure on InP-on-SOI platform with an insert acoustic image of the 2 inch InP/InGaAs/InP(substrate) bonded on a 200 mm SOI wafer.  
In this section, we present the thermal strain evaluation at growth temperature assessed by wafer curvature measurements. The MOVPE system is a CCS from Aixtron with a 6 \(\times\) 2 inch capacity, equipped with Laytec EpiCurve TT sensor measuring the reflectance signals at 405, 632 and 950 nm and the wafer bowing, which is highly sensitive to the strain induced in the growing layers. The curvature \(\frac{1}{R}\) measured simultaneously on InP and on InPoSi is shown in Figure 2. Since the MQW heterostructure was calibrated to be strain-compensated and lattice matched to InP at growth temperature, the curvature signal is flat on InP wafer during growth, contrary to the structure grown on InPoSi. The curvature slope during growth is negative, which is coherent with a compressive strain. The relative curvature value is \(-15.8\) km\({}^{-1}\). Using Stoney Equation (1), the stress \(\sigma_{film}\) was assessed. For simplification, the whole III-V stack, including the bonding layer, was associated to InP material (\(h_{film}\) = 3044 nm), on Si substrate (\(h_{substrate}\) = 525 \(\upmu\)m), with a negligible influence of the silica layer on the total strain induced on the III-V stack. Using elastic deformation Equation (2), the evaluated strain \(\varepsilon_{film}\) assessed is 400 ppm at growth temperature. This value is coherent with the one previously obtained from the relative curvature value measured on a simplified structure grown on InPoSi [36].  
\[\Delta\frac{1}{R}=\frac{\sigma_{film}*6*h_{film}*(1-\
u_{sub})}{h_{sub}^{2}*F_ {sub}} \tag{1}\]  
\[\varepsilon_{film}=\frac{\sigma_{film}*(1-\
u_{film})}{E_{film}} \tag{2}\]
## 5 Results and Discussion
### G3-PLC: Tone Map
#### 5.3.1 Standard Tone Map  
Table 9 gathers the absolute results applying the tone map combinations in Table 8 to G3-PLC. Table 10 summarizes the incremental results compared to the G3-PLC performance results without the tone map implementation published in [10]. Note that G3-PLC result tables include an additional column related to header robustness. G3-PLC improvement proposals under analysis are limited to payload data. Beyond the threshold established by the header robustness, the improvements in the payload will not be relevant. Header robustness is, therefore, an important specification for the analysis of these G3-PLC results. The implementation of the standard tone map in G3-PLC introduces 3.4 dB of average improvement. Link budget in presence of ETSI in-band noises increases 3.7 dB with the tone map whereas, under DER noises, it improves 3 dB. It is especially useful under narrowband tonal noises, as the tone map will not transmit data in those subcarriers affected by the noise. This way, for ETSI in-band tonal noises, it accumulates a 7 dB average increase of the link budget. On the contrary, the most complex situations for the tone map are disturbances with a nearly flat spectrum pattern. A 0.5 dB decrease of the link budget is observed for ETSI periodic impulse noise, \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Differential**} & \multicolumn{4}{c}{**Coherent**} \\ \hline Parameter & ROBO & DBRSK & DQPSK & DSSSK & ROBO & BPSK & QPSK & 8 PSK & Units \\ \hline SNR required for AWGN & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & dB \\ \hline Tonal noise link budget & 1.8 & 6,3 & 6.6 & 4.5 & 1.3 & 6.6 & 8.0 & 9.0 & dB \\ Tonal in-band noise link budget & 2.4 & 8,1 & 9.6 & 5.6 & 1.5 & 8.1 & 9.9 & 10.4 & dB \\ Periodic impulse noise link budget & \(-\)0.5 & 0,0 & 4.6 & 2.2 & \(-\)0.7 & \(-\)0.1 & 0.2 & \(-\)0.2 & dB \\ Random impulse noise link budget & 1.7 & 4,8 & 5.5 & 5.8 & 0.1 & 4.0 & \(-\)0.4 & 5.4 & dB \\ Intentional communicator link budget & 0.0 & 0,0 & 8,5 & 12,0 & 2.2 & 0.5 & 3.5 & 8.6 & dB \\ \hline Composite link budget (LBPRIV) & 0,6 & 2.2 & 5.0 & 4,9 & 0.6 & 2.2 & 2.3 & 4.6 & dB \\ Composite in-band link budget & 1.2 & 4,3 & 6.6 & 4,5 & 0.3 & 4.0 & 3.2 & 5.2 & dB \\ \hline der04 link budget & 0,6 & 1,2 & 5.6 & 6.8 & 0.1 & 0.7 & 1,3 & 5.7 & dB \\ der06 link budget & 0,0 & 0,9 & 5.1 & 9.3 & 0.3 & 1,2 & 1,5 & 5.5 & dB \\ der43 link budget & 0.6 & 2,6 & 6.4 & 6,4 & \(-\)0.2 & 3.8 & 3.5 & 4.6 & dB \\ der36 link budget & 0,4 & 3,1 & 6.3 & 5,9 & \(-\)0.1 & 3.8 & 2.0 & 4.9 & dB \\ der50 link budget & 2,0 & 4,9 & 6.0 & 6,6 & 0,0 & 4,3 & \(-\)0.4 & 4.7 & dB \\ der51 link budget & 1,6 & 3,8 & 4.7 & 6,1 & \(-\)0.2 & 2,7 & \(-\)0.2 & \(-\)0.1 & dB \\ \hline der average link budget & 0,9 & 2,8 & 5.7 & 6,9 & 0,0 & 2,8 & 1,3 & 4.2 & dB \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Incremental results with the standard tone map implemented in G3-PLC compared to the original implementation presented in [10].
## Appendix A Lyapunov Global Asymptotic Stability Theorem  
For a time-invariant system \(\dot{x}=f\left(x\right),t\geq 0\), where \(f\left(0\right)=0\), if there is a scalar function \(V\left(x\right)\left(V\left(0\right)=0\right)\) with a continuous first derivative, and \(V\left(x\right)\) satisfies the following conditions for all nonzero points x in the state space X, the origin equilibrium state of the system is globally asymptotically stable.  
1. \(V\left(x\right)\) is positive.
2. \(\dot{V}\left(x\right)\) is negative.
3. When \(\left\|x\right\|\rightarrow\infty,V\left(x\right)\rightarrow\infty\).
# Sliding Mode Control Algorithms for Anti-Lock Braking Systems with Performance Comparisons
## 2 The Anti-Lock Braking System Model
Besides these, two gravity force of the upper wheel and the pressing force of a shock absorber of the upper wheel force the contact between the two wheels [15]. The values of the ABS model parameters in (1) are [15]: \(c_{11}=1.586\cdot 10^{-3},c_{12}=259.334,c_{13}=-15.94\cdot 10^{-3},c_{14}=-398.507\cdot 10^{-3},c_{15}=13.217,c_{16}=-132.835,c_{21}=-464.008\cdot 10^{-6}, c_{22}=-75.869,c_{23}=-8.788\cdot 10^{-3},c_{24}=-3.632,c_{25}=-3.866\) and depend on the physical parameters of the ABS system. Additionally, the parameters that define \(\mu(\lambda),S(\lambda)\) are \(w_{4}=0.40662691102315\), \(w_{3}=0.03508217905067\), \(w_{2}=0.0000000029375\), \(w_{1}=-0.042400114504\) 54, \(a=0.00025724985785\), \(p=2.09\), \(L=0.37\) [\(m\)], \(\phi=1.145\) [\(rad\)]. For the subsequent sliding-mode control design, given that the dead-zone in the braking torque dynamics has known parameters and therefore it can be compensated by proper additive component at the control input level, an order reduction of the model (1) is performed. Therefore, it is justified to bring \(b(u)\) to a linear dependence of the form \(\overline{b}(u(t))\) on \(u(t)\). Then it is apparent that the linearized actuator dynamics holds as the transfer function \(M_{1}(s)/\overline{b}(s)=1/(\overline{t}s+1)\), with time constant \(T=1/c_{31}\). Neglecting these dynamics leads to \(M_{1}(t)=\chi u(t)\) with constant \(\chi=9\), as the simplified actuator dynamics that turns (1) into a second-order state-space model of the form  
\[\left\{\begin{array}{c}\dot{x}_{1}(t)=f_{1}(x_{1}(t),x_{2}(t))+g_{1}(x_{1}( t),x_{2}(t))u(t)\\ \dot{x}_{2}(t)=f_{2}(x_{1}(t),x_{2}(t))+g_{2}(x_{1}(t),x_{2}(t))u(t)\\ \qquad\qquad y(t)=\lambda=\frac{\overline{t}\overline{t}\overline{t}\overline {t}\overline{t}\overline{t}\overline{  
where  
\[\left\{ \begin{array}{c}g_{1}(x_{1}(t),x_{2}(t))=(c_{15}S(\lambda)+c_{16})s_{1} \cdot\chi,\\ g_{2}(x_{1}(t),x_{2}(t))=c_{25}S(\lambda)s_{1}\cdot\chi,\\ f_{1}(x_{1}(t),x_{2}(t))=S(\lambda)(c_{11}x_{1}(t)+c_{12})+c_{13}x_{1}(t)+c_{14}, \\ f_{2}(x_{1}(t),x_{2}(t))=S(\lambda)(c_{21}x_{1}(t)+c_{22})+c_{23}x_{2}(t)+c_{24}.\end{array} \right. \tag{4}\]  
In the following, the sliding mod control design is aimed and presented in two proposed variants.
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 5 Challenges and Future Perspectives  
Date palm waste valorization presents opportunities and challenges critical to address for sustainable and environmentally friendly resource management. These waste materials can be transformed into valuable products through the above valorization processes. However, unlocking the full potential of date palm waste valorization requires overcoming several key challenges while considering future perspectives for sustainable practices.  
One of the challenges in date palm waste valorization is the efficient collection and transportation of waste materials from date palm farms to processing facilities. Given the often remote and dispersed locations of date palm plantations, logistics and infrastructure for waste collection can be costly and complicated. Innovations in collection and transportation methods, such as mobile chipping and shredding units, can help mitigate this challenge by reducing transportation costs and increasing the feasibility of waste utilization.  
The variability in the composition and quality of date palm waste is another significant obstacle. Date palm waste consists of various components, including fronds, pits, coir, and leaves, each with different properties and potential applications. This heterogeneity demands tailored valorization approaches for different waste components. Developing efficient separation and sorting techniques to segregate these components and optimize their utilization is essential for maximizing the value of date palm waste.  
Future perspectives for date palm waste valorization involve exploring advanced technologies and innovations to enhance its efficiency and sustainability. One such innovation is the integration of biorefinery concepts, where multiple products and value streams are extracted from date palm waste. This approach can involve the simultaneous production of bioenergy, biofuels, bioplastics, and bioactive compounds from different waste components, leading to a more comprehensive and profitable resource utilization.  
Biotechnological advancements, including the development of genetically modified date palm varieties with enhanced biomass properties or improved bioactive compound content, hold the potential for optimizing date palm waste valorization. Genetic engineer ing can increase yields and improve properties, making date palm waste a more attractive feedstock for various applications.  
Furthermore, research into the utilization of date palm waste for producing bioenergy and biofuels, such as biogas, bioethanol, and biodiesel, is a popular and promising recent avenue. Advanced conversion technologies, such as anaerobic digestion, pyrolysis, and gasification, can efficiently convert date palm waste into renewable energy sources, reducing dependence on fossil fuels and mitigating greenhouse gas emissions.
# Influence of Stray Current on Fastening System Components in Urban Railway Tracks
## 4 Results
### Visual Inspection and Corrosion Classification on Fastening System Components
#### 4.2.4 Anchor Bolts  
The conditions of the anchor bolts were also analyzed after the completion of the test. Corrosion was observed on the parts of the anchors that were in contact with concrete. Figure 15 shows the condition of one anchor from each sample after completion of the test.  
Because the anchor bolts are built in concrete, in addition to the corrosion area and thinning of the anchor cross section, the cracks observed in the leveling layer and in the concrete base (Figure 11) are also considered for the corrosion classification. The percentage of anchor bolt diameter thinning was calculated for all samples, as listed in Table 7. The corrosion damage was classified as follows:  
* Low corrosion level: corroded surface up to 10%, small cracks in the concrete, no cross-sectional thinning of the anchor bolt
* Medium corrosion level: corroded surface from 10 to 20%, small or medium cracks in the concrete, cross-sectional thinning up to 0.5 mm, and
* High corrosion level: corroded surface of more than 20%, large or medium cracks in the concrete, cross-sectional thinning of 0.5 to 1 mm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fastening System**} & \multirow{2}{*}{**Dimension of the Steel Plate**} & \multicolumn{2}{c}{**Corcoded Area**} & \multirow{2}{*}{**Corrosion Level**} \\  & **[mm]** & **[mm\({}^{2}\)]** & **\%** & \\ \hline ZG 3/2 & 300 \(\times\) 120 \(\times\) 10 & 1018 & 2.83 & Medium \\ \hline PPE & 320 \(\times\) 120 \(\times\) 10 & 1572 & 4.09 & High \\ \hline DEPP & 320 \(\times\) 205 \(\times\) 10 & 939 & 1.43 & Low \\ \hline Zagreb 21-CTT & 400 \(\times\) 160 \(\times\) 15 & 640 & 1.00 & Low \\ \hline \hline \end{tabular}
\end{table}
Table 6: Corrosion level classification for the steel plates in all testing samples.  
Figure 14: Condition of the steel plate after testing was completed: (**a**) ZG 3/2, (**b**) PPE, (**c**) DEPP, and (**d**) Zagreb 21-CTT.
# A Review of Deep Learning Applications for Railway Safety
## 8 Discussion and Conclusions  
Our literature survey shows that artificial intelligence has been widely applied to various railway safety issues, such as railway infrastructure, trains, operations, and stations. This review details both opportunities and challenges for artificial intelligence in railway safety.  
First of all, advances in data-driven artificial technologies can improve conventional railway safety performance methods. In addition, many studies have shown the feasibility of automating or supplementing conventional railway safety inspection procedures that depend on visual analysis or domain knowledge of a human expert. The proposed model structures in the discussed studies were determined based on the input data types. An image or video is one of the most common data types in artificial intelligence applications for railway safety. Many studies for defect detection (e.g., catenary and rail surface defects) developed CNN-based deep learning models and train vibration is another popular data source for railway safety. Accelerometers can easily measure train vibrations and LSTM-based models have been used to extract unique patterns from accelerometer data.  
On the other hand, there are also challenging issues in utilizing artificial intelligence for railway safety that further studies should consider. We divided the addressed issues into two categories: (1) performance optimization and (2) generalization. First, many studies addressed the necessity of further performance improvement in artificial intelligence. For example, model accuracy needs to be improved to reach practical requirements or the model structure should be more optimized to be executed in real-time. Second, generalization of the proposed methods was issued by many studies. Some studies used simulation data in a lab setting, so in-situ validation needs to be performed for practical application.  
More details regarding research issues in artificial intelligence for railway safety, addressed by prior studies, are explained in subsequent subsections.
## 4 Experimental Evaluation  
To verify the effectiveness of the proposed Privacy-Preserving Federated Learning method for Space-Air-Ground Integrated Networks (PPFL-SAGINs), we compared it with existing methods and analyzed the experimental results. First, we introduce the experimental setup and datasets, followed by experimental verification of the proposed method across different datasets and dimensions.
# Steering Pull Model and Its Sensitivity Analysis
## 2 Tire Pulling Force  
In order to predict steering pull of a vehicle, tire pulling forces needs to be modeled according to the driving conditions and the geometrical characteristics of the vehicle. In this study, external and internal factors that affect a tire pulling force are considered and theoretical approaches are made to model these factors. For a complete steering pull model, this tire pulling force model is integrated with the steering system model in Chapter 3.  
A tire pulling force consists of three parts--pulling force by road bank angle, tire characteristics, and wheel alignment. When a vehicle drives on a banked road, a pulling force is applied to the tire. This pulling force is affected by the tire characteristics, such as PRAT, conicity residual aligning torque (CRAT), and lateral forces. Along with the tire characteristics, the pulling force is also affected by the wheel alignment characteristics of the vehicle such as camber, caster, and kingpin angles.
## 3 Problem Formulation  
Given the sequential data \(x_{t}=(x_{it})\)\(i\in\{1,\ 2,\ldots n\},\ t=1,2,\ldots T\) and their corresponding labels \(y_{t},y_{t}\in\{1,\ 2..C\}\), the \(x_{t}\) dimension is fixed when \(y_{t}\) is fixed; \(y_{t}\) repeats itself through time. The learning transfer (LT) model transfers knowledge from classifier \(S_{t1}\) to classifier \(S_{t2}\) when \(d(x_{t1})\
eq d(x_{t2})\). LT is assumed to have been called for moments \(t_{2}\), \(t_{3}\) due to dimensionality change, where \(d(x_{t1})\
eq d(x_{t2})\) and \(d(x_{t1})=d(x_{t3})\). The following steps are performed to optimize the performance of \(S_{t3}\):  
1. Use LT to transfer knowledge from \(S_{t2}\) to \(S_{t3}\).
2. Use external memory to add knowledge of \(S_{t1}\) to \(S_{t3}\).  
The first step is responsible for maintaining the knowledge accumulated from training, while the second step is responsible for restoring knowledge lost from the disappearance of old features. Figure 1 conceptually depicts the formulation problem.  
Figure 1 shows the classifier and depicts its evolution according to the type of active features in vector \(x_{t}\). The active features at moment \(t_{1}\) are 1, 2, and 3, which continue for time \(T_{1}\). However, the  
Figure 1: Evolution of classifiers over time based on feature changes.  
feature dimension changed to active features 1, 2, 3, 4, and 5 at time \(t_{2}=t_{1}+T_{1}\). This process requires classifier changes from \(S_{t1}\), where the input vector is (1, 2, and 3) to \(S_{t2}\) with input vectors (1, 2, 4, and 5). Learning transfer was applied to maintain the knowledge gained from \(S_{t1}\), which ensured the transfer of knowledge related to inputs 1 and 2. Furthermore, features 4 and 5 were new. Hence, new data taught the classifier about these new features. Feature 3 did not undergo learning transfer due to the fact that it was no longer active, and, therefore, external memory (EM) was used to preserve it. Features (1, 2, 4, and 5) were assumed active during time \(T_{2}\). Moreover, features 4 and 5 were deactivated during \(t_{3}=t_{2}+T_{2}\), whereas feature 3 was reactivated. Thus, a new classifier was rebuilt on the basis of the following steps:  
1. Perform transfer learning TL to move related knowledge to inputs 1 and 2 from \(S_{t2}\) to \(S_{t3}\).
2. Use external memory to move knowledge related to 3 from EM to \(S_{t3}\).  
Classifier \(S_{t}\) and external memory \(EM_{t}\) are Markov models; they were represented according to the state flow diagram depicted in Figure 2. where the event feature change (FC) moves the system from one state to another.
# Backdoor Attack Against Dataset Distillation in Natural Language Processing
## 3 Methodology  
### Threat Model  
**Attack Scenarios.** We define the attacker as a malicious entity providing dataset distillation services [25; 52]. Our investigation focuses on two distinct attack scenarios. In the initial scenario, the victim relies on the attacker to distill a specific dataset, such as using an external service to process locally stored data. This aligns with the general objective of dataset distillation [24; 25; 53]. The second scenario is more realistic; the victim no longer has access to their original training dataset filled with numerous samples, and instead opts for a smaller synthetic dataset provided by the attacker to lower expenses.  
**Attacker's Capabilities.** As demonstrated in these attack scenarios, we assert that the primary ability of the attacker lies in altering the dataset distillation procedure. This assumption holds true, as attackers can act as providers of such services [26; 89]. Furthermore, it is important to note that attackers may not control the data sources directly; for instance, victims might upload their datasets for processing. Additionally, attackers do not interfere with downstream model training, and only provide distilled datasets to victims.  
**Attacker's Goals.** The goal of the attacker is to embed a trigger within the distilled dataset, effectively introducing a backdoor into any downstream model trained on this data. Because this distilled set is significantly smaller than its original counterpart, it becomes essential for attackers to ensure that triggers remain subtle and undetectable by human reviewers so as not to be visually identified while still functioning effectively in subsequent tasks.  
**Attack Challenge.** The attacker lacks knowledge about or influence over how downstream models are trained. Executing backdoor attacks within the context of dataset distillation poses significant challenges. First, as indicated in our attack scenarios, these actions take place upstream, making it crucial to ensure that any distilled datasets containing embedded backdoors retain utility for downstream applications. Second, it must be ensured that triggers go unnoticed during potential human assessments. Finally, attackers need assurance that the backdoor can be seamlessly integrated into downstream models after training on the compact backdoored distilled set.
# Three-Dimensional Path-Following with Articulated 6 DoF Robot and ToF Sensors
## 1 Introduction
A laser beam was used to mark the tool orientation relative to the path on the surface to help extract it from the image obtained from the camera. Three different reinforcement learning algorithms were compared in terms of path tracking accuracy, with the Proximal Policy Optimisation reinforcement learning algorithm achieving a maximum tracking error of 4 mm and mean error of 0.2 \(\upmu\)m.  
Contrary to the majority of the publications, this paper presents a method for following a path that is physically defined in a 3D space. The path position is detected using a simple tool held by an industrial 6 DoF robot. The tool is equipped with six low-cost Time-of-Flight (ToF) sensors. The task to be performed is to move the robot arm with the tool along the path from the start to the finish point, without a collision with the path. Only the start position is known a priori, before the start of the whole process, so the path position and orientation must be detected, and proper robot arm movements must be calculated on-line, as the following of the path progresses.