esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operator** & \(\mathbf{N}^{\circ}\) **Satellites (Deployed)** & **Spectrum** & **Services** & **Operational** \\ \hline Starlink & 12,000 LEO (6078) & Ku-band & Broadband & Yes \\ Kuiper & 3236 LEO (2) & Ka-band & Broadband & Scheduled \\ Viasat & 4 GEO (4) & Ka-band & Broadband & Yes \\ Hughenesat & 3 GEO (2) & Ka-band & Broadband & Yes \\ Iridium & 66 LEO (66) & L-band & messaging & Yes \\ One Web & 648 LEO (648) & Ku-band & Broadband & Scheduled \\ Telesat & 198 LEO (2) & Ka-band & Broadband & Scheduled \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of actual and scheduled satellite services for IoT. of commercially available components to assess how the impairments and non-idealities of such components affect the pattern and link performances, in a framework of a digital-twin model. The paper is organized as follows: Section 2 presents the study of the irregular active lattice architecture, the optimization strategy adopted and the inter-element analysis of mutual coupling; Section 3 presents the system-level architecture of the whole device, analyzing the quality of the signal thanks to some key performance parameters: Error Vector Magnitude (EVM) and bit error rate (BER) for transmitting and receiving, respectively. Eventually, Section 4 draws conclusions and highlights future work.
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 1 Introduction  
In light of climate change, countries are increasingly looking for ways to reduce their greenhouse gas emissions. The European Union, for example, has set a goal of net carbon neutrality by 2050 in the _European Green Deal_[1]. In pursuit of this goal, big efforts are being made to increase the modal share of climate-friendly modes of transportation [2]. In an effort to reduce people's reliance on individual car ownership, various kinds of Mobility as a Service (MaaS) systems are being developed and deployed to supplement these aforementioned modes of transportation.  
Free-floating car sharing systems are part of the MaaS concept where instead of owning a car themselves, users can simply rent a vehicle whenever they need one. They can use the car for the required trip, and return it afterwards near their destination. If they want to return to the origin later, they can start a new trip or use alternative transport modes. The operators of such systems always designate some area of operation (AoO) within which rented cars may be picked up or parked and returned. Shared car ownership can reduce the burden on a city's infrastructure (such as parking spaces). As shown in a study conducted for the city of Bremen, most users of car sharing systems actually forgo private car ownership in favor of car sharing (rather than use both) after becoming used to the concept, thereby reducing the demand for parking infrastructure within the city [3].  
The success of a free-floating car sharing system critically depends on the users' satisfaction with the system's ability to fulfill their mobility demand. Specifically, whenever a user wishes to undertake a trip, a suitable car should be available near their point of departure. An empirical study using real booking data has shown that the willingness to use car sharing drops significantly as the distance a user has to walk to their rented car increases [4]. Likewise, it is important that users can actually end their rental close to their destination. The system's area of operation thus needs to be sufficiently large to cover the start and end locations of as many of its prospective users' trips as possible, and at thesame time be compact enough to be reasonably densely covered by a fleet of vehicles that the operator can actually afford to purchase and maintain.
## 3 The Proposed Methodology for Prediction of Bearing Defect Degradation Using Deep Neural Network
### Estimation of Bearing Defect Severity Using the Gaussian Window Method  
Through the observation of the abnormal symptoms at the harmonic frequencies of BPFO, BPFI, and 2 \(\times\) BSF, an optimal approach for evaluating the defect severity of bearings was applied to correctly measure the defective degree, which was computed by using the GDM-based window method around harmonics of the fault frequencies, respectively. This GDM-based method has been extensively used for HI calculation to estimate the defect severity of bearings [8] and results in detecting and diagnosing the bearing defects. Figure 3 describes the process of estimating fault severity based on the Gaussian window method. Figure 3: An overall description of estimating the bearing fault severity by the health-related indicator (HI) calculation. EPS: envelope power spectrum; GDM: Gaussian distribution model. Figure 2: An overall flow diagram of the bearing defect degradation monitoring system. DNN: deep neural network. Step 1: The recorded AE signals related to the bearing conditions were processed by envelope analysis technique to detect the characteristic defect frequency components showing the abnormal symptoms in signal. The EPS signals were first calculated using the Hilbert transform [23] and delivered by the following equations:  
\[\hat{x}(t)=\frac{1}{\pi}\int\limits_{-\infty}^{+\infty}\frac{x(\tau)}{t-\tau}d\tau \tag{1}\]  
\[z(t)=x(t)+i\hat{x}(t),\hskip 14.226378 pt\left(i=\sqrt{-1}\right) \tag{2}\]  
\[x_{env}(t)=\left|z(t)\right|=\sqrt{x(t)^{2}+\hat{x}(t)^{2}} \tag{3}\]  
where \(\hat{x}(t)\) is the Hilbert transform of the time signal \(x(t)\), z(t) is the analytical signal, and the the envelope signal is illustrated by \(x_{env}(t)\). The EPS signal was then determined by calculating the absolute value square of the fast Fourier transform (FFT) of the envelope signal, \(\left|F\{x_{env}(t)\}\right|^{2}\). Step 2: A GDM-based window was used to catch the bearing fault frequencies and other around harmonics from their envelope spectrum domain. The values of the window \(w_{Gauss}(j)\) were then determined as follows:  
\[w_{Gauss}(j)= \begin{cases}\sum\limits_{i=1}^{3}\exp\left(-\frac{1}{2}\left( \alpha\frac{(j-P_{i})}{\sqrt[3]{2}}\right)^{2}\right),\\ 0,\hskip 28.452756 ptotherwise\end{cases} \hskip 14.226378 ptP_{i}-f_{ range}\leq j\leq P_{i}+f_{range} \tag{4}\]  
where \(\alpha\) is a parameter of Gaussian distribution, \(P_{i}\) indicates the ith harmonic of the bearing defect frequencies, j is an index term of each frequency bin, and M represents the frequency bins around each defect frequency harmonic. In this study, \(f_{range}=\frac{1}{4}\)BPFO was given for the outer surface, whereas \(f_{range}=\frac{1}{2}\)BPFI or BSF were defined for the inner surface and roller. Hence, the Gaussian windows for capturing the fault frequencies had a bell shape because of the appearance of the sidebands around the defect harmonics in their spectrum analysis domain.
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 3 Results
### Canopy Aerodynamic Conductance  
In controlled-environment chambers, g\({}_{\rm A}\) is determined by an interaction between canopy architecture and air circulation in the chamber. Typically, fan speed is constant and canopy architecture remains constant over several days once the canopy is closed. In these conditions, Equation (6) permits canopy g\({}_{\rm A}\) to be calculated from the slope of a plot of H versus \(\Delta\)T\({}_{\rm IR}\). Stepwise increases in chamber CO\({}_{2}\) concentration from 400 to 1200 mol mol\({}^{-1}\) were used to simultaneously alter H and \(\Delta\)T\({}_{\rm IR}\) via physiological changes in canopy G\({}_{\rm S}\) at a constant canopy g\({}_{\rm A}\). H and \(\Delta\)T\({}_{\rm IR}\) increase simultaneously when chamber ambient CO\({}_{2}\) increases because elevated CO\({}_{2}\) reduces stomatal conductance, and the canopy is warmed due to less evaporative cooling.  
A plot of H and radiometric \(\Delta\)T\({}_{\rm IR}\) was used to calculate the g\({}_{\rm A}\) of a wheat canopy (Figure 7; 25-day-old; PPF = 1200 mol m\({}^{-2}\) s\({}^{-1}\); T\({}_{\rm air}\) = 22 \({}^{\circ}\)C; RH = 70%). The slopes of H versus \(\Delta\)T\({}_{\rm IR}\) at each CO\({}_{2}\) concentration were similar (separate regressions not shown in Figure 7), which suggests that g\({}_{\rm A}\) did not respond to changes in ambient CO\({}_{2}\).  
The variability in H (\(\sim\)\(\pm\)25 W m\({}^{-2}\)) corresponds to an uncertainty in \(\Delta\)T\({}_{\rm IR}\) of \(\sim\)\(\pm\)0.4 \({}^{\circ}\)C, which is close to the error in determining T\({}_{\rm R}\) from T\({}_{\rm canopy,IR}\). The dashed line in Figure 7 represents the plot of H versus \(\Delta\)T\({}_{\rm A}\), determined by subtracting a constant offset to \(\Delta\)T\({}_{\rm IR}\) (Equation (7)). This offset equals the value of the difference between \(\Delta\)T\({}_{\rm IR}\) and \(\Delta\)T\({}_{\rm A}\) when H is zero. In wheat, this offset was +0.75 \({}^{\circ}\)C at 1600 mol m\({}^{-2}\) s\({}^{-1}\) and was +1.0 \({}^{\circ}\)C in soybean at 750 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\).  
The g\({}_{\rm A}\) of the 25-day-old wheat canopy was 5.5 mol m\({}^{-2}\) s\({}^{-1}\) (Figure 7). The g\({}_{\rm A}\) of a 45-day-old soybean canopy was 2.5 mol m\({}^{-2}\) s\({}^{-1}\). These conductances correspond to aerodynamic resistances of 7.5 and 16.5 s m\({}^{-1}\), respectively. Soybean has a smaller g\({}_{\rm A}\) compared to wheat because soybean leaves are wider than wheat leaves and have a smaller leaf boundary layer conductance.
# Secure Cooperative Routing in Wireless Sensor Networks
## 4 Discussion
### Performance Evaluation Parameters
#### 4.1.3 Latency  
Latency is a deferral of time between the source and the destination of the physical change in the framework being analyzed, or it delineates the time length between the response and simulation. In this research to avoid energy loss.  
Figure 7 represents that latency is higher at the beginning of the algorithm as the number of nodes participating is higher, as it is an expression of how much time MIDS takes for a packet to reach its destination after being sent. When the network size is larger till the 10 th round, the latency value is almost \"5 ms\" but it suddenly drops till the 1000 th round to \"0 ms\". Though the network size does not decrease much, the MIDS algorithm decreases latency value comparatively by the efficient usage of routes.
# Label-Free Model Evaluation with Out-of-Distribution Detection
###### Abstract  
In recent years, label-free model evaluation has been developed to estimate the performance of models on unlabeled test sets. However, we find that existing methods perform poorly in environments with out-of-distribution (OOD) data. To address this issue, we propose a novel automatic model evaluation method using OOD detection to reduce the impact of OOD data on model evaluation. Specifically, we use the representation of datasets to train a neural network for accuracy prediction and employ energy-based OOD detection to exclude OOD data during testing. We conducted experiments on several benchmark datasets with varying amounts of OOD data (SVHN, ISUN, ImageNet, and LSUN) and demonstrated that our method reduces the RMSE compared to existing methods by at least 1.27%. Additionally, we tested our method on transformed datasets and datasets with a high proportion of OOD data, and the results show its robustness.  
out-of-distribution detection; model accuracy prediction; model generalization +
Footnote †: journal:  
as predicting rotation [6], tend to overestimate the performance of models on OOD data due to the information learned from the ID data. For classification-based methods [11], the inclusion of OOD data will complicate the selection of threshold values. In conclusion, it can be seen that OOD data in a deployment environment has a significant impact on the accuracy of existing methods. This phenomenon significantly affects the performance of label-free model evaluation on unlabeled test sets.  
To address this problem, our method improves the performance of the AutoEval method by utilizing OOD detection [12; 13] to identify inputs beyond the training data distribution. OOD detection can be used to identify inputs outside the training data distribution. We use OOD detection to reject OOD data in the test set and feed in the ID data for model accuracy prediction. Specifically, when computing the features of images in the dataset, we also generate a mask for the dataset using energy-based OOD detection. The mask hides data that are recognized as OOD data to avoid the impact of OOD data on AutoEval. We conducted experiments under different settings, and the results proved the effectiveness and robustness of our method.  
The main contributions of this paper include: (1) We propose the use of OOD detection to improve the performance of AutoEval in a deployment environment. We demonstrate the effectiveness of the method on datasets with OOD data. (2) We experimentally select OOD thresholds that are more suitable for predicting model accuracy and explore the relationship between OOD detection and AutoEval.
# Topology Optimization and Testing of Connecting Rod Based on Static and Dynamic Analyses
## 2 Materials and Methodology  
### Design and Material Selection  
All the IC engine components including the connecting rod were designed using Autodesk Inventor Professional 2025. Since the objective of this study was to optimize the topology of the connecting rod, all other components were set as rigid bodies during the transient structural analysis and suppressed during the TO process.  
The connecting rod was mostly designed with an I-Section for normal IC engines and an H-Section for race engines, and the mass of the connecting rod should be somewhere between 0.4 kg and 1 kg. The connecting rod ratio (\(\lambda\)) is defined by the relationship between the crank radius (\(r_{c}\)) and the length of the connecting rod from the center of the small end and the big end (\(l_{cr}\)). Typically for passenger car engines, the connecting rod ratio (\(\lambda\)) should be between 0.28 and 0.32 [15].  
\[\lambda=\frac{r_{c}}{l_{cr}} \tag{1}\]  
\[\lambda=\frac{45}{153}=0.2941 \tag{2}\]  
Here the connecting rod ratio (\(\lambda\)) was around 0.30 and it satisfied this condition [15]. The major dimensions of the original connecting rod geometry are shown in Figure 1.
## 2 The Structure and Operating Principle of CIPMRM  
The mode of the silicon waveguide needs to eventually be coupled into the plasmonic micro-ring modulator. The plasmonic micro-ring modulator consists of the silicon waveguide section, which is used for coupling, and the plasmonic waveguide section, which is used for modulating. First, the mode couples to the silicon waveguide and then tapers into the plasmonic waveguide. To decrease the footprint of CIPMRM, a coupling length of 2.53 \(\upmu\)m is chosen, and the coupling efficiency can be adjusted by changing the coupling distance. The width of the silicon waveguide is 300 nm, and its height is 340 nm. The electromagnetic wave circling the micro-ring will couple back to the waveguide to interfere with itself and to form a resonant peak at 1550 nm. Therefore, there is no optical signal output at the resonant state. As the effective refractive index of the micro-ring changes, the resonant peak shifts away from 1550 nm, and it will have the outputting optical signal. The change in the effective refractive index is realized by the plasma dispersion effect, as minority carriers flow into the plasmonic modulator as a forward voltage is applied at the PIN junction. The carrier concentration in P++ and N++ is \(5\times 10^{20}\) cm\({}^{-3}\). Figure 1 shows the structure of the plasmonic waveguide and CIPMRM. It has a 300 nm width for the ridge waveguide with a 100 nm slab height. A thin oxide layer, a 5 nm stop layer of Ta, and 200 nm Cu lying on the top of the ridge support the plasmonic mode when the transverse magnetic (TM) mode inspired. Electrodes grow on the slab 1 \(\upmu\)m from the ridge. Lumerical commercial software is used in this paper, and the complex refractive indices of Cu, SiO\({}_{2}\), and Si at 1550 nm are set as follows: Cu, 0.282 + 11.048 [17]; SiO\({}_{2}\), 1.445; and Si, 3.46. The process flow is presented in Figure 2. The MRM (shown in Figure 2 IV) can be fabricated based on the photonic silicon platform [18]. Then, the SiO\({}_{2}\) that covers the ridge waveguide can be etched and a thin SiO\({}_{2}\) layer can be deposited on it. After that, a thin Ta layer and 200 nm Cu layer are deposited. The unwanted copper is removed by CMP [17]. All of the fabrications are compatible with the zero-change CMOS process.
## 3 A Design of the Lightweight Autonomous Vehicle Self-Diagnosis (LAVS)
### The Multi-Protocol Integrated Gateway Module (MIGM)  
Since the MIGM supports the in-vehicle communication between two protocols, transfers the payloads to the In-VDM rapidly, and works in the OBD-II in software, not in hardware, it improves the speed of the self-diagnosis. Figure 4 shows the structure and functions of the MIGM, which consists of four sub-modules. The first message interface sub-module (MIS) acts as an interface between the sensors and the MSS. The second message storage sub-module (MSS) manages the message transferred from the MIS and the message converted in the MCS. The third message conversion sub-module (MCS) converts the message transferred from the MSS to a destination protocol message. The fourth WAVE message generation sub-module (WMGS) makes the vehicle condition diagnosed in the In-VDM and the payloads used for the diagnosis a WAVE message and transfers the WAVE message to RSU or the Cloud through the internet of vehicles (IoV). If the MIGM receives a sensor message from electronic control unit (ECU), the message works as follows.  
First, the ECU transmits the sensor messages to hardware devices (transceiver and controller) through FlexRay, CAN, and media oriented systems transport (MOST) Bus. Second, the MIS transfers  
Figure 4: The structure of the multi-protocol integrated gateway module (MIGM).  
Figure 3: The structure of the lightweight autonomous vehicle self-diagnosis (LAVS).  
the sensor messages to the MSS. Third, the MSS divides the received sensor messages into header information, sensor messages, and payloads. The received header information is stored in an address table (1), the sensor messages are stored in a message queue (2), and the payloads are stored in a data collection table (3). The MSS transfers the header information of the address table and the sensor messages to the MCS (4, 5), and the payloads measured in the data collection table in the same time to the In-VDM(6). Fourth, the MCS converts the sensor message transferred from the MSS to a destination protocol message and transmits the transformed message to the MSS (7) and, the process of the message reception is vice versa. Fifth, if the MSS of the MIGM receives the diagnosis result from the In-VDM, the received result is stored in the data collection table (8). The self-diagnosis data stored in it is transferred to the WMGS with the payloads used for self-diagnosis (9). The WMGS converts the received data collection table information to WAVE messages and performs IoV to transfer the WAVE messages to the neighboring RSU and Cloud (10).
## 3 Case Study
### Manufacturing of the Chip Breaker  
The chip breaker was manufactured using a nano-second laser, the ablation process was performed with a SPI-G4-HS ns ytterbium-doped fiber laser, Hedge End, Southampton, United Kingdom, emitting pulses at a wavelength of 1064 nm and using a mean power value of around 40 W. It has been reported that the ablation of PCD using ns pulses takes place via graphitization of the diamond in the PCD which is transformed into graphitic carbon [41]. The proportionality between the graphite-layer thickness, the pulse duration and the laser fluence has been proven [42]. To minimize the thickness of this unwanted graphite-layer, the pulse duration and the fluence of the laser pulses to process the PCD were as low as possible.  
Initial experimental trials were conducted over a PCD turning-insert by ablating overlapping trenches under the minimum fluence of 23.74 J/cm\({}^{2}\), pulse duration of 40 ns for scanning velocities varying from 0.6 m/s to 1.0 m/s, with overlapping distance between parallel trenches from 5 to 20 \(\upmu\)m.  
The chip breaker geometry was produced by ablating sequential layers, but it is well known that the resultant surface roughness is higher when layers are thicker. Thus, in order to minimize surface roughness, the thickness of the layers was chosen to be less than 4.0 \(\upmu\)m. The results of the preliminary experiments showed that when ablating parallel trenches under 0.8 m/s scanning speed and 10 \(\upmu\)m of overlap distance between parallel trenches, the ablated-layer thickness was among 3.6-4.0 \(\upmu\)m. As a result, the concluding chip breaker contour was divided into 20 layers and fed into the laser software to be ablated sequentially using the identified optimum parameters. Figure 9 illustrates the geometry of the chip breaker after the laser manufacturing process. The ablated geometry of the chip breaker was measured, as seen in Figure 10, using a confocal 3D optical profiler, S NEOX by SENSOFAR Metrology, Terrassa, Barcelona, Spain. The results revealed a good agreement (with 6% error) between the measured and designed width of the chip breaker. However, a higher discrepancy of 14% was detected for the produced depth of the ablated chip breaker when compared with the designated geometry.  
Figure 9: Ablated chip breaker in the polycrystalline diamond (PCD) insert.