esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Sensors Deployment  
#### 3.2.1 Deployment Scheme  
As outlined in Equation (9), modulus back-calculation requires the precise acquisition of four mechanical responses under vehicular loading. To achieve this, each section was instrumented with a tri-columnar FBG sensor array comprising 6 transverse strain sensors, 6 longitudinal strain sensors, 6 vertical strain sensors, 3 Earth pressure sensors, and 2 temperature sensors.  
The purpose of setting up the temperature sensor was to monitor the changes in the elastic modulus of the asphalt pavement at different temperatures over a long period of time. However, since the data collection of the loading in this test was carried out almost all within the same period of time (approximately 9 p.m.), the variation in pavement temperature was very small, and thus, the contribution of the temperature sensor was not significant.  
The central longitudinal sensor column was positioned directly over pavement cracks, while lateral columns monitored adjacent intact areas, establishing a comparative framework for evaluating localized modulus enhancements post-grouting. This triaxial monitoring configuration enables differential analysis of mechanical responses across treated zones, untreated regions, and reference sections. Figure 3 illustrates the sensor deployment schematic for representative sections.  
Figure 3: Schematic of sensors deployment plan. (**a**) Pavement structure layer and placement of sensors; (**b**) sensor layout plan.
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 7 Conclusions and Future Work  
The design of a many-core based accelerator for ML/DL applications requires improvements in all levels of the system (from programming interface to chip micro-architecture level). In this context, the interconnection subsystem plays a critical role, since it is required to efficiently exchange information (data packets) among a large number of PEs. In this paper, we propose a highly efficient interconnect fabric, whose architecture is based on a modular, hierarchical approach. To this end, we leveraged on the combination of ring-based interconnects to better serve local traffic exchanged among PEs placed close to each other, while global traffic (i.e., traffic exchanged among PEs that are far away from each other) is managed by a 2D-mesh interconnection. The advantages of the combination of different topologies are many-fold. Firstly, the performance is better than those shown by a flattened 2D-mesh design. Secondly, area and power consumption are greatly reduced. Finally, it better supports PXMs that are generally at the basis of ML/DL applications' domain.  
Here, we proposed integrating a reconfiguration feature into our lightweight modular design approach, allowing local and global traffic to be decoupled. A software interface is also proposed to easily exploit the advantages offered by our design. The low-level instructions executed by PEs allow controlling the network topology, its reconfiguration, and also the data packet communication. Next, a more abstracted interface is also explained: a high-level programming language is used to support a producer-consumer execution scheme, which directly maps to the features exposed by the underlying NoC architecture. Later, the preliminary evaluation confirms the advantages of our interconnection architecture.  
Future work directions comprise a more in-depth performance analysis of the proposed interconnection fabric using different traffic patterns (e.g., bit reverse, transpose) and multiple injection rates. A detailed comparison with other topologies (e.g., fat-trees, 2D-torus) will also be the part of our future research work. It will also be very interesting to study the ML/DL application behaviour running on such hybrid NoC subsystem.  
All the authors contributed to the several aspects presented in this work as follows: Conceptualization by [PERSON], [PERSON], [PERSON]; Proposed system design by [PERSON], [PERSON]; Design implementation by [PERSON]; Experimental evaluation by [PERSON], [PERSON], [PERSON]; Writing by [PERSON], [PERSON], [PERSON]; Publication fees paid by [PERSON].  
This work was partly supported by the Ministry of Education, Youth and Sports of the National Programme for Sustainability II (NPU II) under the project \"IT4 Innovations excellence in science-LQ1602\" and by the European Union's Horizon 2020 Research and Innovation programme under the project ExCAPE (grant no-671555). The authors also wish to thank the editor and the anonymous referees for the helpful and insightful comments.  
The authors declare no conflict of interest.
# Machine Learning Applications for Short Reach Optical Communication
## 3 Machine Learning for Short-Reach Optical Communication System Signal Processing  
DSP in optical communications has seen rapid and significant developments during the past couple of decades to compensate for various types of impairments, and the performance of optical communications has been substantially improved [78]. For short-reach systems, due to the widely use of receiver configurations based on the direct detection and other low-cost and small-size components, innovative signalling and DSP are critical to allow these components to reach their full potential and to meet the data rate demand in a cost-effective manner. ML-based techniques are considered to be a viable alternative to classic DSP due to their capability in solving nonlinear problems. For example, the neural network can be used to optimize the DSP function of the transmitter or the receiver [47]. In particular, in low-cost IM/DD short-reach optical fiber communication systems, where the combination of dispersion that introduces intersymbol interference (ISI) and nonlinear signal detection by a photodiode imposes severe impairments, the ML technique has been seen as a viable DSP solution to improve the system performance [1; 102], where it has been applied in different manners, mainly including linear and nonlinear equalization, dispersion compensation, auto-encoder, and nonlinear predistortion, as summarized in Table 2. In this section, we review recent related studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Applications** & **Features Type** & **ML Techniques** & **System Info.** & **Ref.** \\ \hline Nonlinear equalization & \begin{tabular}{c} DSO sampled digital \\ signal \\ \end{tabular} & FFNN (ANN) & \begin{tabular}{c} 32 GBd, PAM-8, \\ IM/DD, 4 km SSMF \\ \end{tabular} & [103] \\ \hline Linear and nonlinear equalization & \begin{tabular}{c} DSO sampled digital \\ signal \\ \end{tabular} & Compressed FFNN & \begin{tabular}{c} 112 Gbps PAM-4 \\ PAM-8 VCSEL, 100 m \\ MMF \\ \end{tabular} & [104] \\ \hline \begin{tabular}{c} Adaptive channel \\ equalization \\ \end{tabular} & Training error sequence & RNN & SUI channels BPSK & [105] \\ \hline \begin{tabular}{c} Nonlinearity \\ equalization \\ \end{tabular} & \
\begin{tabular}{c} Frequency domain \\ signal after CD \\ compensation \\ \end{tabular} & LSTM &
\begin{tabular}{c} 25 Gbaud, 16 QAM, \\ 50 km \\ \end{tabular} & [54] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Signal processing applications based on machine learning.
## 1 Introduction
### ESTCube-Luna Consortium and Paper Structure  
The E-sail experiments are designed by UT Tartu Observatory in collaboration with the Finnish Meteorological Institute (FMI). The high-voltage source and electron emitters as well as the miniature attitude thrusters are designed by Technische Universitat Dresden (TUD). RT-16 and RT-32 radio telescopes for E-sail experiment ranging as well as for high-speed communications are operated by Ventspils University of Applied Sciences. The E-sail environment characterisation with Langmuir probes is proposed by the KTH Royal Institute of Technology. The ESTCube-LuNa stretch goal of lunar escape manoeuvre is proposed by the University of Pisa. The experience of developing ESTCube and Foresail satellite series have been used to define the ESTCube lunar nanospacecraft. The ESTCube platform is being developed by UT Tartu Observatory in collaboration with the Estonian Student Satellite Foundation. The Foresail platform is being developed by Aalto University. The tether camera and cameras for Triangulated Celestial Navigation (TCN) are designed by Crystalspace in collaboration with UT Tartu Observatory. The TCN on-board computer is designed by Bitlake Technologies.  
The ESTCube-LuNa experiments are described in Section 2. The payload and experiment operations are described in Section 3. The overall results of lunar escape manoeuvre are presented in Section 4. The E-sail experiment simulations and trajectory options for ESTCube-LuNa are analysed _in detail_ by [PERSON] et al. [42], which serves as a modelling study, whose toolkit is being continuously updated. The ESTCube lunar nanospacecraft platform is described in Section 5. The conclusions and future work are presented in Section 6.
# Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
###### Abstract
It has even been claimed that we are entering a 'post-blockuster era' [18], in which the development costs of new drugs are becoming prohibitive. The ability to predict clinical trial outcomes could cut the costs of clinical trials (by allowing smaller sample sizes) and improve the chance of regulatory approval by improving efficacy and safety within a pre-selected patient subpopulation predicted to benefit from the drug. This strategic pre-selection is known as 'clinical trial enrichment.' There have not yet been any regulatory approvals of drugs using clinical trial enrichment based on ML. However, the U.S. Food and Drug Administration (FDA) has encouraged research in this area [19]. Accordingly, ML-based clinical trial enrichment could become an essential part of drug development. Prostate cancer is a leading cause of cancer deaths in men [20]. The two main first-line treatments for prostate cancer are prostatectomy and radiotherapy to the prostate. The typical second-line treatment for prostate cancer is androgen deprivation therapy (ADT), which results in dramatic reductions in androgen levels [21]. ADT can mean either surgical castration or treatment with pharmacological antiandrogens. ADT typically causes demasculinization side effects, such as reduced sexual function, genital shrinkage, and loss of muscle mass. Further, ADT may increase the risk for high blood pressure, diabetes, stroke, and heart disease [22]. Finally, cancer relapse after 2-3 years of ADT is prevalent. These risks and uncertainties underline the need to develop precision medicine approaches for prostate cancer. One common pharmacological form of ADT is bicaltumide, which is approved as a monotherapy in over 50 countries. This current study used Phase III clinical trial data on bicaltumide as a treatment for prostate cancer (drug arm only; see Materials and Methods for details). A standard evaluation of efficacy and tolerability using data from the same studies (drug arm vs. placebo) was published in 2010 [23]. This study reported that bicaltamide treatment significantly improved progression-free survival in patients with advanced prostate cancer, but not in patients with only localized disease. Early-stage prostate cancers are often detected by routine measurement of blood levels of prostate-specific antigen (PSA). PSA levels are strongly correlated with tumor diagnosis, tumor aggressiveness, and bone metastasis [24] and are a recognized disease biomarker. Change in PSA levels was critical to how we operationalized the response to bicaltamide in this study. To date, there have been no published attempts to predict individual cancer treatment outcomes based on data from clinical trials. This is an important gap in the literature. To address this gap, we processed data from three comparable Phase III clinical trials on bicaltamide for prostate cancer and used ML to predict two-year treatment outcomes, based only on measurements available at baseline.
## 6 Open Challenges, Future Prospects and Research Directions
### Standardization and Regulatory Challenges
#### 6.2.1 Current State of FSO Standardization Efforts  
The standardization of FSO systems is crucial for ensuring interoperability, reliability, and widespread adoption. This section explores the ongoing efforts to develop and implement standards for FSO technology. It examines the roles of various international bodies and industry groups in establishing guidelines and protocols that address key aspects such as performance metrics, safety regulations, and compatibility with the existing communication infrastructure.
# Characterization and Optimization of a Conical Corona Reactor for Seed Treatment of Rapeseed
## 2 Materials and Methods
### Discharge Characterization
#### 2.2.2 Optical Emission Spectroscopy  
Optical emission spectroscopy (OES) was used to analyze the excited species in the discharge produced by the conical-shaped reactor. A dual-channel spectrometer (AvaSpec-2048-2-USB2, Avantes, Apeldoorn, The Netherlands) with a spectral resolution of 0.7 nm was employed. The light emitted in the discharge was focused into a quartz optical fiber by means of a set of collimating lenses assembled in a way that the focal point was in the middle of the Petri dish (as shown in Figure 1). To avoid blocking the UV region, the emitted light was acquired through a hole (diameter of 1 mm) in the sidewall of the Petri dish. An irradiance calibration was performed prior to measurements.
## 3 Results
### Case Studies Analysis
#### 3.2.1 Area Analysis for Dedicated FSED Zones  
While analyzing the case studies for the FSEDs, several zones were identified like the core emergency areas, triage spaces, etc., for drawing comparisons to get a general idea about the ranges, within which it usually varies. A total of six FSEDs were studied and a range of surfaces in terms of percentage of functional area on the total square meters of the facility have been highlighted. The results are briefly presented below with a range of findings as in benchmarking activities such an approach could be more informative, rather than just the average of very different values.  
The Triage system plays a vital role and has its influence over the flow of the users and room placements and overall organization of functions and zones. Most of FSED case studies allocated 10% to 12% of the total surface to the triage and waiting functions, except two of them, who scored higher than 20%. The Core Emergency Area accounted for 12-23% of the total surface. Only one FSED had space for Patient Stay and accounted for about 16%. The Nurse Station space was very variable depending on the different FSED: a group of cases ranked between 8% and 16%, while two of them were up to 25%.  
The Imaging and Diagnostic area was instead considerably coherent among the different cases scoring between 9% and 11% with only one case that reached 16% of the total area. Finally, Lounge areas for Staff were between 2% and 5% with one exception, who reached 10% of the total surface. The remaining space is dedicated to distribution, technical and logistic spaces that are not considered in this analysis as it is outside the scope of the paper.  
The complete list of FSED results is reported in Figure 8 and a comparison is provided in Figure 9.
# Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks
## 2 Memristor Noise Model with FPE  
The memristors have a metal/insulator/metal (MIM) two-terminal structure with an insulator layer between the metals [17]. A memristor model in which the insulator consists of TiO\({}_{2}\) is used [30]. The TiO\({}_{2}\) memristor forms its operating mechanism by inducing oxygen vacancies under an externally applied electric field [22]. The memristive resistance of the system is expressed as follows [22; 24; 30; 31]  
\[M(\lambda)=\frac{\lambda}{L}R_{ON}+(1-\frac{\lambda}{L})R_{OFF} \tag{1}\]  
where \(L\) is the length of the memristor device and \(\lambda\) is the doped area. \(R_{ON}\) and \(R_{OFF}\) represent the resistance when the memristor is fully doped and undoped, respectively [22; 30]. Figure 1 illustrates the basic equivalent circuit of a TiO\({}_{2}\) two-terminal memristor based on dopant concentration. The region with a high dopant concentration has relatively low resistance, while the region with low dopant concentration has high resistance [30]. The memristor state (\(x=\lambda/L\leq 1\)) is determined by the voltage applied in time \(t\). Thus, the change in memristor states forms an on-off switching mechanism, which creates a hysteresis loop in the memristor dynamic system.  
The actual memristor model does not include inherent noise. Therefore, a new system is required to explain it [21]. The memristor stochastic systems are discussed by incorporating a noise term into a conventional dynamical system. It is described by the [PERSON] equation under the voltage \(V(t)=V_{0}\text{sin}(2\pi ft)\) with a frequency \(f\) given by [22; 24; 30]  
\[\frac{d\lambda}{dt}=\frac{\mu_{D}R_{ON}}{L}I(t)F\bigg{(}\frac{\lambda}{L} \bigg{)}+L\xi(t) \tag{2}\]  
where \(\mu_{D}\) is the dopant mobility, and \(F(x)=1-(2x-1)^{2}\) represents the window function. The current is expressed as \(I(t)=V(t)/M(\lambda)\) and \(\xi(t)\) is the white noise represented by [22; 23; 24]  
\[\langle\xi(t)\rangle=0,\ \big{\langle}\xi(t)\xi\big{(}t^{\prime}\big{)}\rangle =\Gamma\delta(t-t^{\prime}) \tag{3}\]  
where \(\Gamma\) is the noise intensity, affecting the stochastic I-V fluctuations [22]. Brownian motion due to randomness can be represented by the FPE that is the time-varying form of the probability density function. Therefore, the probability density function linked to Equation (2) is expressed as follows [23; 24; 32]  
\[\frac{dx}{dt}=r(x,t)+\xi(t) \tag{4}\]  
\[r(x,t)=\frac{\mu_{D}R_{ON}}{L^{2}}I(t)F(x) \tag{5}\]  
The FPE that includes noise is given by [23; 24; 32]  
\[\frac{\partial P(x,t)}{\partial t}=-\frac{\partial}{\partial x}[r(x,t)P(x,t)] -\frac{\Gamma}{2}\frac{\partial^{2}P(x,t)}{\partial x^{2}} \tag{6}\]  
where \(r(x,t)\) is the drift term and \(P(x,t)\) is the probability density function. This means that a memristor is not simply a deterministic system with a fixed output for an input but rather a complex stochastic system whose output can be varied by stochastic factors [21].
# Experimental Investigation into the Thermal Performance of Personal Cooling Mechanisms
## 3 Results  
### Mean Skin Temperature of Torso Zone  
Table 5 presents the mean skin temperature of the trunk of the manikin wearing the different vests and the result for the nude manikin (T\({}_{\text{Sk}}\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Activity Level**} & **Range of** & **Range of** & **Range of** \\  & **Metabolic** & **Metabolic** & **Metabolic** \\  & **Rate (W)** & **Rate (W/m\({}^{2}\))** & **Rate (met)** \\ \hline Resting & 100–125 & 55–69 & 1.0–1.2 \\ Low metabolic rate & 125–235 & 69–130 & 1.2–2.2 \\ Moderate metabolic rate & 235–360 & 130–200 & 2.2–3.4 \\ High metabolic rate & 360–465 & 250–258 & 3.4–4.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification of ranges of metabolic rate according to ISO 8996-21 standard and equivalences between units.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Samples Tested**} & \multicolumn{2}{c}{**Mean Skin Temperature T\({}_{\text{Sk}}\) (\({}^{\circ}\)C \(\pm\) 0.1 \({}^{\circ}\)C)**} \\ \cline{2-4}  & **1.4 met** & **2.8 met** & **4.0 met** \\ \hline Nude manikin & 35.3 & 36.0 & 37.0 \\ Vest 1 & 33.4 & 34.9 & 37.0 \\ Vest 2 & 34.3 & 35.2 & 36.5 \\ Vest 3 & 34.6 & 35.3 & 37.0 \\ Vest 4 & 34.6 & 36.0 & 37.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of mean skin temperature (T\({}_{\text{Sk}}\)).  
Figure 3 compares the manikin's mean skin temperature wearing the tested samples across different metabolic rates.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 5 Design Considerations for High-Gradient Accelerating Structures
### RF Power Flow  
The dependence of the rf breakdown rate on the rf power flow became a topic of systematic research during NLC/JLC development, when 1.8 m long X-band TW accelerating structures could not be processed to the required gradient of 65 MV/m. This gradient was much lower than the one reached previously with short SW cavities [113]. The main hypothesis at the time was that the group velocity somehow determined the limiting gradient: the lower the group velocity, the higher the gradient. The physics of this empirical dependence was not understood.  
Then, an _ablation limit_[52] was introduced as \(P_{rf}\sqrt{t_{p}}\). I noticed that numerous TW structures showed excessive damage at the ablation limit above 100 MW \(\sqrt{400\text{ ns}}\).  
This was followed by [PERSON]'s _ratio of rf power, which flows through the cell to the aperture circumference \(C\)_, \(P_{rf}t_{p}^{1/3}/C\)[72] and then [PERSON]'s _peak modified Poynting vector \(|Sc|t_{p}^{1/3}\)[56].  
Currently, both \(|Sc|\) and \(P_{rf}f/C\) are used for rf design where \(|Sc|\) should be less than 5 MW/mm\({}^{2}\) for \(t_{p}=200\text{ ns}\) and \(P_{rf}/C\) less than 3 MW/mm for the same pulse length in order to achieve an rf breakdown rate below \(10^{-6}\) per pulse per meter [109].
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 2 System Overview
### Paper Contribution  
The main contributions of the paper are as follows. Starting from the design presented in (2018), we extended the original architecture up to 1024 PEs. Performance and area/power costs are evaluated on a more extensive and complex configuration. The proposed design merges the benefits of a lightweight and scalable hybrid interconnection, based on the 2D-mesh routers and local rings, with those of the dynamic reconfiguration. In the proposed hierarchical topology, routers are in charge of moving traffic at a global (chip) level, while exploiting data locality is ensured by groups of PEs connected to each other through fast and low power bi-directional rings. Router micro-architecture has been extended to support dynamic reconfiguration (virtual tiles). To this end, groups of specific bits control the status of each link. During the configuration phase, the links can be power gated (PG). PG mode disables the links, thereby allowing the network to save power. Furthermore, PG mode allows for better use of hardware resources. Figure 2 shows an example of such resource splitting. The left side of the figure shows the physical chip infrastructure, while the right side depicts an example of virtual tiling (i.e., three applications/tasks concurrently run on the chip using reserved resources). Eventually, virtual tiles can span on multiple chips, thanks to the use of dedicated I/O units.  
Performance can be assured by using high-speed clocks in the rings, while monitoring is provided by a per-link counter, which tracks traffic statistics and whose value is exported to the software layer through a small ISE. Compared with the solution presented in [29], here we also describe a high-level software interface, which can easily be mapped on concurrent execution models offered by high-level languages. Specifically, better use of data and computation locality is achieved by exploiting data-driven PXMs. The minimalist set of additional instructions is also used to dynamically control the configuration of each router, thus allowing the software to define customised topologies and partition the chip resources. It is worth noting that this group of instructions have been inspired by other works [16; 30; 31]. Here, we provide effective implementation of the physical substrate, thus further demonstrating the benefit of mapping data-driven execution paradigm over a highly configurable massive parallel processing fabric. All of these elements contribute to designing a fully scalable and programmable (software defined) NoC architecture.