esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Passive Filter Design
### DC Filter  
The DC link capacitance is used on the battery side of the inverter. The DC filter helps to reduce the ripples on DC voltage. It is quantified using these Equations (9) and (10) [19,24].  
\[R_{filt}=\frac{V_{DC}^{2}}{P_{avg}} \tag{9}\]  
\[C_{DC}=\frac{1}{f_{s}\,\Delta V_{1\%}R_{filt}} \tag{10}\]  
\(V_{DC}\) is DC link voltage, \(P_{avg}\) is average power that is flowing into a converter, \(\Delta V\) (V) is the amplitude of the permitable DC link voltage ripple, set at 3%, and \(R_{filt}\) resistance can be calculated from average power and DC link voltage.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 2 Materials and Methods
### Traffic State Perception
#### 2.1.1 Traffic Perception System Based on V2X Communication  
At an urban intersection, the traffic state usually refers to the driving state of all vehicles, which has the characteristics of dynamic, periodicity, randomness, etc. With the development of V2X communication technologies, the traffic state is generally perceived by the fusion of multi-source traffic sensors, then the information can be fed back to the users of other subsystems such as traffic management system (TMS) [24]. Finally, the perceived traffic information can be used to solve traffic and ICVs problems for ITS.  
To improve the traffic efficiency, a traffic perception system based on the V2X communication technology is designed by taking advantages of low delay, high reliability, and high security of the V2X in this paper. As shown in Figure 2, intelligent roadside infrastructures mainly include LiDAR sensors, high definition (HD) cameras, intelligent roadside units (RSUs), switches, traffic signal light controllers, intelligent OBUs with V2X communication functions, a shared base station, and a series of MEC servers. In the perception database at the intersection of this system, the perception data are distributed, dynamic, heterogeneous, and spatiotemporal. Average traffic flow and mean speed capacity are chosen to be predicted and evaluative indicators of traffic state, then the comprehensive analyses are shown in the following parts.  
Figure 1: The framework of the proposed method.
## 2 Background
[30] examined the realm of wearable technologies for emotion detection, presenting SigRep, a pioneering framework that adopts contrastive representation learning to exploit the discreet and persistent data collection capabilities of wearable devices, thus fortifying the efficacy of emotion recognition frameworks. The quest for generalizability in emotion recognition systems across varied demographic and physiological spectra presents a formidable challenge within the realm of affective computing. Addressing this concern, [PERSON] et al. [31] introduced a globally generalized framework for emotion recognition that demonstrated high accuracy rates, irrespective of physiological signal discrepancies or demographic variations among subjects. This approach, which transcends subject-specific constraints, indicates a new era in the applicability of universal emotion recognition solutions. Concurrently, [PERSON] et al. [32] unveiled an ontology-based model that exhibited commendable precision in discerning emotional states through EEG signals, pinpointing the importance of certain signal attributes--specifically, the power ratio between the beta and theta waves--as pivotal for precise emotion categorization [19]. Together, these pioneering investigations represent the forefront of research in emotion detection, illuminating the profound capacity of physiological signals to foster more intuitive and empathetic human-computer interactions. In conclusion, the collective body of work reviewed herein underscores the dynamic and multifaceted nature of emotion recognition research within the realm of affective computing. From pioneering methodologies that leverage the latest in sensor and machine learning innovations to nuanced approaches that seek to transcend demographic and physiological variabilities, each study significantly contributes to our understanding and capabilities in this field. As we move forward, these insights not only pave the way for more sophisticated and universally applicable emotion recognition systems, but also hold the promise of fostering more intuitive and empathetic interactions between humans and technology. The convergence of these advances promises a future where digital systems can more accurately interpret and respond to the complex tapestry of human emotions, thus enhancing the user experience across a multitude of applications. Within this evolving landscape, the proposed work stands out for its novel approach, aiming to fill a gap that has not been addressed before and thus holds the potential to make a significant contribution to the field.
# Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
## 3 Results
### Harmonic Contributions to Total Harmonic Distortion  
In the previous subsection, a high-level picture of harmonic noise in the power system was established through an analysis of the THD level on the three investigated sites. In this subsection, the contribution from each of the individual harmonics on the THD is investigated. This will allow us to answer: (i) how many harmonics contribute (meaningfully) to the THD on different voltage levels; (ii) to what extent they are contributing; and (iii) whether there are differences in the most relevant harmonics across voltage levels. As indicated in Section 2, we consider only the month of January 2017 due to the large volume of data. Although only harmonics up to the 7 th order is shown in the figures, harmonics up to the 96 th order has been extracted and used for analysis. Figure 7 illustrates the average contribution of each individual harmonic to the THD over a time period of one month. The following can be concluded.  
1. Across all phases and voltage levels, \(\gtrsim\)98% of the contribution towards the THD are concentrated in at most the 13 th harmonic. The next largest contributions (1.9 percent) is the 29 th harmonic on \(V_{3}\) in the 66 kV site. Beyond this, all other contributions are \(\lesssim\)1%;
2. The highest individual harmonic with a total contribution of \(\gtrsim\)2 % are 11 th (22 kV), 13 th (66 kV), and 13 th (300 kV). At 22 and 300 kV, these harmonics also have a significant (\(>\)10%) contribution on at least one phase. However, at 66 kV, the largest harmonic with a significant contribution is the 7 th;
3. At 22 kV, the 7 th, 11 th, and 5 th harmonic contribute the most to THD. In order (and averaged over phases), they contribute \(\sim\)52, 37, and 11%. Across phases, the contributions to THD are balanced and remain within a few % of one another;
4. At 66 kV, the 3 rd, 7 th, and 5 th harmonics contribute the most to the THD. When averaged over all phases, they contribute \(\sim\)51, 40, and 7%, respectively. There is an imbalance in the contribution of \(V_{3}\) which contributes 40% more than \(V_{1}\) and \(V_{2}\) to the THD on the 7 th harmonic. For the 3 rd harmonic, the reverse holds;
5. At 300 kV, there are large differences (20 to 40%) between the contribution of each phase to the THD across different harmonics. For example, on \(V_{1}\), the 5 th harmonic dominates THD with a contribution 60%. On \(V_{3}\), however, the 13 th harmonic dominates with a 60% contribution. On \(V_{3}\), the 3 rd harmonic drives THD (with a contribution of \(\sim\)50%). The authors are not able to attribute this imbalance to any specific phenomena, and this may be the subject of future investigations.
## 6 Conclusions  
The performance of an InAs/InP QDot SSP MLL with a mode spacing of 28.4 GHz is investigated in detail. For the 56 filtered individual channels, the average RIN value is -133.54 dB/Hz and the phase noise is less than 1.5 MHz for each individual channel. Moreover, a pulse-to-pulse timing jitter of 2.1 fs and integrated timing jitter of 564.8 fs in the frequency range from 35 kHz to 20 MHz are obtained. It is observed that the \(1/\hat{F}^{2}\) white flicker frequency is the main contribution to the phase noise in the low frequency (<250 kHz), and shot noise dominants when frequency is >250 kHz. By employing this ultralow noise and timing jitter laser with 56 wavelength channels as optical carriers, 12.5 Terabit/s aggregate data transmission capacity is demonstrated with dual-polarization 16 QAM and base modulation rate of 28 GBd over a 100 km standard single-mode fiber. This performance is obtained without any form of feedback to control the laser linewidth. These results may lead to an important step towards small size, cost-efficient, low noise, low timing jitter, and a high number of multi-channel light sources for optical networking systems with the capacity of Terabit/s or even higher.  
Conceptualization, Y.M., Z.L. and J.L.; methodology, Y.M., G.L., K.Z., Z.L., J.L., P.J.P., C.-Y.S. and P.B.; software, G.L. and K.Z.; validation, Z.L., J.L. and P.J.P.; formal analysis, Y.M., G.L., K.Z. and C.-Y.S.; investigation, Y.M., G.L., K.Z., Z.L., J.L., P.J.P., C.-Y.S. and P.B.; resources, Z.L.; data curation, Y.M., G.L., K.Z. and C.-Y.S.; writing--original draft preparation, Y.M.; writing--review and editing, Y.M., G.L., K.Z., Z.L., J.L., P.J.P., C.-Y.S. and P.B.; visualization, Y.M., P.J.P., P.B. and K.Z.; supervision, Z.L., J.L., and P.J.P.; project administration, Z.L. and J.L.; funding acquisition, Z.L. and J.L.; All authors have read and agreed to the published version of the manuscript.  
This research project is supported by the National Research Council Canada's high throughput and secure network (HTSN) program.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: **(a)** BER evaluation over the mode wavelength range for B2B and after 100 km of SSMF transmission. All the selected channels are below the SD-FEC limit (BER = 2.0 \(\times\) 10\({}^{-2}\)) and HD-FEC limit (BER = 3.8 \(\times\) 10\({}^{-3}\)). **(b)** Constellation diagram obtained for a selected channel at 1552.180 nm for both \(X\)- and \(Y\)-polarization after B2B and 100 km of SSMF transmission. The color indicates the relative density of symbols detected in the complex plane, with red indicating a higher density and blue a lower density.
# Microbial Biosynthesis of Lactones: Gaps and Opportunities towards Sustainable Production
## 3 Pathways and Enzymes Involved in the Biosynthesis of Lactones  
The biosynthesis of lactones is a complex process still not fully understood at the genetic and biochemical level. First, it is important to underline the difference between de novo biosynthesis of lactones and biotransformation processes. From the current knowledge, de novo biosynthesis of lactones means that the producing microorganism does not use any immediate precursor of the final product, i.e., no fatty acid or hydroxy fatty acid derivatives are added to the production medium in order to obtain its conversion into a specific lactose. This means that in order to de novo biosynthesisize lactones a microorganism should be able to: (i) use a substrate (e.g., sugars) to support the growth and biosynthesis of fatty acids; (ii) perform post-modification of these fatty acids in order to generate a derivative with a hydroxy group; (iii) shorten this hydroxy fatty acid in the \(\beta\)-oxidation pathway, with the reduction of two carbons in the chain per cycle; and (iv) after specific cycles of \(\beta\)-oxidation, generate a hydroxy acid that undergoes spontaneous lactonization under acidic conditions [21; 23]. To the extent of our knowledge, only the filamentous fungi _Fusarium pae_[34], _Trichoderma viride_[35], _A. gossypii_[23] and the yeast _Sporidiobolus salmonicolor_[4] were reported to compile these traits (Table 1). Basidiomycetes, such as mushrooms (e.g., _Polyporus durus_), are known to produce several volatile compounds including \(\gamma\)- or \(\delta\)-lactones, either through biotransformation [36] or through de novo biosynthesis [37]; however, during this review we focused on ascomycetes due to their higher relevance for industrial biotechnology. An exception was given to the basidiomycete _S. salmonicolor_, since its yeast-like growth led to deeper investigation of lactone biosynthesis in the 1990s.  
Biotransformation comprises only steps (iii) and (iv), since an immediate precursor of the \(\beta\)-oxidation pathway is added to the production media [2; 14]. Recently, there have been some advances regarding the use of non-hydroxy fatty acids in biotransformation processes for lactone production that will be discussed in Section 4. A summarized scheme of the pathways involved in lactone biosynthesis and biotransformation is depicted in
## 4 Discussion
[17] reported the asymmetrical expansion of ANS moving more than the contralateral ANS by 1.1 mm, which was consistent with our results but insignificant in our study. On the other hand, an asymmetric maxillary expansion greater than a 1.0 mm difference was observed in 46.4% of the subjects in our study. [PERSON] et al. [19] reported that 30.3% of the subjects treated with tooth-bone-borne RPE showed an asymmetric nasomaxillary expansion. [PERSON] et al. [20] reported that 44.0% ofasymmetric maxillary expansion occurred after MARPE. [PERSON] et al. [16] reported that the presence of an asymmetric mid-palatal suture was the main causative factor of asymmetric maxillary expansion, and 46.9% of patients showed an asymmetric maxillary expansion of 1.0 mm or more after MARPE, which was consistent with our study. However, the reason why the maxilla expanded asymmetrically in the transverse direction after MSE has not been clearly elucidated. Therefore, asymmetric expansion should be carefully considered before maxillary expansion. On the contrary, the transverse asymmetry of the mandible (Pog, Me, and Go) was slightly alleviated after MSE in this study. [PERSON] et al. [32] reported that craniofacial asymmetry was alleviated in the long term, but this was not consistent with the findings in our study that showed improvement of the mandibular asymmetry in the short term but a regression back to the T1 value in the long term. The results from the other study might have differed from those in our study because the previous study [32] evaluated the mandibular changes after RME in growing patients, while our study looked at the mandibular changes after MSE in both growing and non-growing patients. The small sample size limited this study to evaluate the NSD and nasofacial changes according to age, gender, skeletal pattern, and asymmetry, and soft tissue was not included in the evaluation. We have attempted to compare as many variables as possible to evaluate NSD. However, we could not obtain the sample size to improve the statistical verification. Therefore, further studies might be needed with a larger sample size and soft tissue measurements. These days, MSE is widely used for several purposes, such as maxillary expansion, anchorage for a facemask, total mesialization and distalization of the dentition, and changing the three-dimensional adjacent craniofacial structures. According to the results from this study, variable positive nasofacial changes will occur after MSE. Therefore, more studies would be helpful to improve or camouflage facial deformities.
# Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform
## 5 Simulations
### Scenario Description  
The simulation scenario is depicted as follows.  
**Target**: We assume a single reentry vehicle flying in the exoatmospheric phase. The initial reference velocity, altitude, and tilt angle at the thrust cutoff point are 2500 m/s, 80 km, and 40.3070\({}^{\circ}\), respectively, which subsequently determine the total exoatmospheric flight time of approximately 370 s. At the reference time \(t=0\), the target is located at 0\({}^{\circ}\) N latitude 0\({}^{\circ}\)E longitude, pointing to the direction of East. The apogee is 0\({}^{\circ}\)N latitude 3.03485\({}^{\circ}\)E longitude, and the reentry point is 0\({}^{\circ}\) N latitude 6.0697\({}^{\circ}\) E longitude.  
**Radar**: The radar is positioned at 2\({}^{\circ}\)N latitude 4.5\({}^{\circ}\)E longitude with a scan period \(\Delta t=0.5\) s. The measurement accuracies of range, azimuth, and elevation are \(\sigma_{R}=50\) m and \(\sigma_{A}=\sigma_{E}=1\) mrad, respectively. The radar has received some apriori early warning information from the satellite; thus, the surveillance space is restricted to a limited region of \(\mathbf{D}=230\times 30\times 30\) km\({}^{3}\). The processing time interval is [150, 250] s. Thus, the accumulation frame number is \(N=200\), which accounts for a total data time of about 100 s.  
**False alarm:** The number of false alarm detections is Poisson distributed with a spatial density \(\lambda=8.69\times 10^{-5}/\text{km}^{3}\), which determines an average number of 18 false alarms of a single scan in the surveillance space \(\mathbf{D}\). Note that \(\lambda\) is essentially a function of the cell false alarm probability \(P_{\text{fa}}\) of the radar and is independent across frames.  
**Early warning information:** The true reference parameters provided by the early warning are \(A_{0}=\pi/2\), \(L_{\text{C}}=0^{\circ}\), \(B_{\text{C}}=0^{\circ}\), and \(H_{\text{C}}=80\) km. The indicated errors are assumed to be Gauss distributed with standard deviations \(\sigma_{A_{0}}=1\) mrad and \(\sigma_{L_{\text{C}}}=\sigma_{L_{\text{B}}}=0.05^{\circ}\).  
**EHT specification**: The Hough search space is restricted to \([a_{\text{L}},a_{\text{U}}]=[6600\text{ km},\ 6800\text{ km}]\), \([b_{\text{L}},b_{\text{U}}]=[1000\text{ km},\ 2000\text{ km}]\), and \([\theta_{\text{L}},\theta_{\text{U}}]=[5^{\circ},7^{\circ}]\), with a resolution \(\Delta a=2000\) m, \(\Delta b=2500\) m, and \(\Delta\theta=0.01^{\circ}\). The total quantization points are approximate \(8\times 10^{6}\). The judgment of the successful detection of a target is set as \(\left[(a-\hat{a})^{2}+(b-\hat{b})^{2}\right]^{1/2}\leq 5\times 10^{3}\) m and \(\left|\theta-\hat{\theta}\right|\leq 0.01^{\circ}\). Note that the threshold of judgment remains a design parameter in practice, adjusted/tuned mostly based on engineering experience and intuition. Actually, the optimal threshold is difficult to derive as it is related to specific radar-target geometry and related to many issues.
# Building Reliable Massive Capacity SSDs through a Flash Aware RAID-Like Protection +
## 4 Design of mSAPL
### Design Goal and Approach  
The design goal of mSAPL is to improve the reliability and performance of a massive capacity flash based SSD. mSAPL takes a design approach that considers the following three aspects.  
1. It is necessary to cope with more than one failure per stripe due to employing a large number of flash memories in a massive capacity SSD.
2. In order to minimize the overhead caused by parity management, a parity update policy should be designed in order to minimize a GC overhead in SSDs.
3. Applying the same protection strength to all data in a massive flash based storage is not suitable for both performance and reliability aspects. Different protection strengths are needed for different kinds of data type or a user specification among massive volume of data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Total capacity & 128 GB & No. of chips & 8 \\ Planes per chip & 8 & Blocks per plane & 2048 \\ Pages per block & 128 & Page size & 8 KB \\ Over-provisioning & 4\% & GC policy & Greedy \\ Write time & 800 us & Read time & 60 us \\ Erase time & 1.5 ms & Page Xfer time & 30 ns \\ Parities per stripe & 2 & Stripe size & 8 \(\times\) 4 KB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of Solid State Drives (SSD) Simulator.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Workload** & **Request** & **Write** & **Average** \\  & **Total** & **Ratio** & **Req. Size** \\ \hline Financial & 14.1 GB & 0.76 & 14 KB \\ MSN & 28.2 GB & 0.96 & 27 KB \\ Exchange & 19.2 GB & 0.67 & 17 KB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of I/O workloads.
## 2 Developed Methodology
### Typology Detector
To gain deeper insights into EfficientNet's performance, we subsequently analyzed the confusion matrix; Figure 6. This step was crucial to confirm that the high overall accuracy did not mask subpar performance on specific tools. It revealed that the network mostly struggles with the spade drill bit and similar tool heads of different sizes, e.g., the \(\mathcal{P}\)40 mm and \(\mathcal{P}\)50 mm self-feeding bits; Figure 6.  
Some of our data originate from test fabrications. This makes them very realistic; however, it also means that the network can learn the appearance of the wooden beam in a given fabrication step to classify the tool head instead of the tool head's topology. To ensure that the network uses the topology of the tool head for its decision, we computed class activation maps for the last convolutional layer using GradCAM [43]. The class activation maps (in Figure 7) show that the parts of the image containing the tool head had the largest impact on the classification result while objects in the background were ignored. Figure 5: Examples of images fed to the proposed model (on the left are sawing tools, and on the right are drill bits). Figure 6: Confusion matrix computed on the validation dataset for EfficientNet. The overall classification accuracy is 93%. Figure 7: Class activation maps computed with GradCAM show that the part of the images containing the tool heads has the largest impact on the classification (corresponding to the warmest colors). To employ additional tool heads, the model was retrained. This required deep learning knowledge and dedicated hardware in the form of a GPU. Since we cannot expect all users to have these prerequisites in terms of both hardware and digital literacy, we provided a web-based service for retraining the classifier with subsets of specific tool heads [44]. This service allows for the user to upload their own data for new tool heads and obtain custom-trained models for a specific application. Additionally, we reinforced the robustness and generalization capacity of the proposed model with synthetic data. The synthetic dataset was generated through Blender, and the designed add-on orchestrated the creation of an animation portraying tool heads in rotation while the camera moved. To elevate its authenticity, we implemented varied environmental lighting and incorporated metal brush effects into the 3D models. To further enrich the dataset, we introduced diverse adjustments in image temperatures and brightness levels; see Figure 8.  
Figure 8: (**a**) Animation frames of four drill bits present in the synthetic dataset. (**b**) Example of synthetic and augmented data generated from a drill bit. The tool head’s model and its textures are obtained from the scanning process via photogrammetry. The procedural rendering is achieved owing to a specially designed add-on for Blender.