esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In transport companies with a large fleet of diesel vehicles and in military repair companies, the maintenance strategy is often based on the OCM. The speed of obtaining OCM results is often more important than their accuracy.  
Therefore, the paper presents the methodology of FTIR-PLS/PCR models for the prediction of kinematic viscosity at 100 \({}^{\circ}\)C of worn-out SAE 15W-40 motor oils, recommended for high-performance diesel engines.  
As an alternative to the standardized determination of \(\text{KV}_{100\,^{\circ}\text{C}}\) of worn-out oils by the Ubbelohde capillary viscometer according to CSN EN ISO 3104, FTIR spectrometry in conjunction with PLS regression proved to be the best. The presented results show that it is important to identify the following for the design of a robust FTIR multivariate model: deviating oil samples--outliers; the spectral range that most strongly correlates with the change in viscosity; number of PCs that include variability in spectral data associated with viscosity and test the adjustment of spectra by derivatives. The predictive power of the FTIR regression model needs to be validated by an independent set of oil samples for which the viscosity value was determined by a standardized method.  
The benefit of the proposed and verified methodology presented in the article is a relatively simple way of determining the kinematic viscosity (i.e., 'physical' quantity) from the infrared spectrum. This spectrum can also be used not only for qualitative and quantitative analysis of chemical composition, but also for the determination of other 'non-chemical' quantities; a multivariate model must be available for them, created from a sufficient amount of quality data. The paper presents a methodology for determining \(\text{KV}_{100\,^{\circ}\text{C}}\), but the process of creating a model is generally applicable to a number of other quantities (viscosity index, TBN, TAN, flash point, etc.). The possibility to obtain the values of as many quantities as there are robust FTIR-multivariate models available from a single spectrum is of particular practical importance. This approach can replace several sophisticated laboratory instruments with a single FTIR spectrometer.  
Conceptualization, M.S.; methodology, M.S.; software, M.S.; validation, M.S.; formal analysis, M.K. and I.H.; resources, M.K. and I.H.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, M.K. and O.V.; visualization, M.K. and O.V.; supervision, M.S.; funding acquisition, M.K. and I.H. All authors have read and agreed to the published version of the manuscript.  
This article was produced with the support of the project VEGA 1/0609/20: Research of the cutting tools at the dendromass processing in agricultural and forestry production.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Cost Components
### Environment Costs
#### 2.3.1 Energy Consumption and Emission Costs during the Operation Phase of Asphalt Pavement  
According to the classification of road vehicles and their emission factors, as well as the vehicle fuel consumption estimation model, the user's environmental emissions during operation can be calculated, as shown in Equation (19) [32] (Table 8). Considering the highest levels of CO, HC, NOX, and CO\({}_{2}\) in vehicle exhaust emissions, only CO, HC, NOX, and CO\({}_{2}\) are used to represent typical environmental emission components.  
\[\rm{EE_{j}=\sum_{i}\left(\sum_{m}FC_{i,m}\times EF_{j,i,m}\right)} \tag{19}\]  
where  
EEj--The emission amount of environmental emissions, j is CO, HC, NOX, or CO\({}_{2}\)  
FCi,m--The consumption of m-type fuel for i-type vehicles is calculated according to Equation (2).  
Based on the environmental emissions and unit price of emissions, the environmental emission cost during the operation period can be calculated, as shown in Equation (20).  
\[\mathrm{EC_{j}}=\mathrm{EE_{j}}\times\mathrm{P_{E,j}} \tag{20}\]  
where  
\(\mathrm{P_{E,j}}\)--Cost of unit environmental emission i (yuan/ton)  
Based on vehicle fuel consumption, fuel calorific value, and energy consumption unit price, the energy consumption cost of users during operation can be calculated according to Equation (21):  
\[\mathrm{NC_{oper}}=\mathrm{Q}\times\mathrm{FC}/100\times\mathrm{R}\times 1 \times\mathrm{P_{N}} \tag{21}\]  
\(\mathrm{NC_{oper}}\)--Vehicle energy consumption cost of the road section (yuan)  
R--the calorific value of the fuel; the calorific value of gasoline is 31.2 MJ/L, while the calorific value of diesel is 35.8 MJ/L  
\(\mathrm{P_{N}}\)--Unit price of energy consumption (yuan/MJ))
## 5 Discussion  
Figure 14 shows examples of nodules detected by our proposed CAD system. Not only a typical nodule but also a juxtapleural nodule and an irregular nodule can be detected.  
In the initial detection process, many false positives are generated due to the noises caused by the failure of the lung region extraction in the apex regions. Figure 15(a), (b) and (c) show an example of a CT slice including an apex region, the result of the lung region extraction, and the result of the initial detection, respectively. In Figure 15(b), several brighter spot regions remain as noises which cause the false positives in Figure 15(c).  
The proposed filters cannot correctly recognize the nodules of irregular shapes. The LIDC dataset of the agreement levels 3 and 4 contain large nodules which tend to have irregular shapes. Figure 16(a) shows an example of a large nodule, (b) is the result of the moment-of-inertia filter and (c) is the result of the center displacement filter. In Figure 16(b), the center voxel of the VOI has high value, but it is mistakenly suppressed in Figure 16(c).  
The performance of the k-means clustering strongly depends on the numbers of clusters as shown in Figure 12. In this paper, the numbers of clusters are determined experimentally.  
Figure 12: FROC curves of individual FP numbers for training in FP reduction.  
Figure 13: FROC curves of individual cluster numbers for training in FP reduction.  
In the experiment of the FP reduction, we also employed one-step k-means clustering. The numbers of the FPs per scan at the TP ratio \(84.6\)% are \(97\) / \(153\) / \(103\) / \(95\), respectively. The best performance is \(95\) FPs at the TP ratio of \(84.6\)%. It implies that the two-step k-means clustering is more promising than the one-step k-means clustering.  
In the experiment, we used the \(50\) CT scans that are selected from the LIDC dataset. Although the dataset contains more CT scans, we can apply the proposed method only to this subset. The main reason is that the method needs a lot of calculation time for the FP reduction process where the high-dimensional feature vectors are classified. In the future, we should apply the method to larger dataset by reducing the calculation time of the proposed method.  
The FP reduction of the proposed method is not sufficient yet. The proposed method can be used as preprocessing for our previous methods[21, 22] which can detect nodules more accurately. For further  
Figure 16: An example of a false negative. (**a**) An example of a large nodule. (**b**) The result of the moment-of-inertia filter. (**c**) The result of the center displacement filter.  
Figure 14: Extracted nodules. (**a**) example 1; (**b**) example 2; (**b**) example 3.  
Figure 15: The failure of the lung region extraction generates false positives. (**a**) Original slice section. (**b**) Extracted lung region. (**c**) Result of the initial detection.  
accuracy improvement, we should build a hybrid detection system by combining the proposed method with these methods in the future.
## 5 Conclusions
Ongoing work on scaling the platform, both technically and organizationally, will accommodate a growing user base, a wider range of industries, and more extensive use cases. Future research and development may broaden the platform's scope to include additional sectors such as manufacturing, healthcare, finance, and sustainability-focused fields. Enabling the possibility for a user to join a decentralized autonomous organization and to be able to influence its development through a transparent voting system will trigger an active and engaged community. Incorporating advanced analytics and structured decision-making processes will help participants identify trends, forecast opportunities, and make more informed choices. SPARK-IT expands access to expert networks and protects intellectual assets. It promotes balanced participation across regions and communities. This supports innovation-led growth and benefits society. In closing, SPARK-IT's approach to resolving ecosystem challenges not only improves and sparks connections between innovators and a larger entrepreneurial arena but also creates a basis for new activities, spin-offs, and cross-sector partnerships. The work presented here provides a solid foundation for refining, scaling, and enriching the platform, paving the way for its ongoing role in encouraging global innovation and societal advancements. **Author Contributions:** Conceptualization, A.A. ([PERSON]), D.-E.B. and C.N.B. ; methodology, A.A. ([PERSON]), D.-E.B., C.N.B. and G.-A.S.; software, A.A. ([PERSON]), D.-E.B., C.N.B., G.-A.S. and S.-D.P. ; validation, A.A. ([PERSON]), D.-E.B., C.N.B., G.-A.S., S.-D.P., A.A. ([PERSON]) and C.M. ; formal analysis, D.-E.B. and C.N.B. ; investigation, A.A. ([PERSON]), D.-E.B., C.N.B., G.-A.S., S.-D.P., A.A. ([PERSON]) and C.M. ; resources, A.A. ([PERSON]), D.-E.B. and C.N.B. ; data curation, A.A. ([PERSON]), D.-E.B., C.N.B. and G.-A.S.; writing--original draft preparation, A.A. ([PERSON]), D.-E.B., C.N.B., G.-A.S., S.-D.P., A.A. ([PERSON]) and C.M. ; writing--review and editing, A.A. ([PERSON]), D.-E.B. and C.N.B. ; visualization, A.A. ([PERSON]), D.-E.B. and C.N.B. ; supervision, A.A. ([PERSON]); project administration, A.A. ([PERSON]); funding acquisition, A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** The SPARK-IT project described in this paper has received funding from the Next Generation Internet Initiative (NGI) within the framework of the \"NGI TrustChain project\", which was funded by the European Union's Horizon Europe research and innovation programme under grant agreement No. 101093274. **Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 7 Experimental Results
### Trajectory Measurement  
The data position of USV is defined in the form of longitude (latitude) and latitude (longitude). The plot of longitude and latitude versus time is depicted in Figure 16. Longitude indicates the position in the west or east of the Prime Meridian line while latitude is the position in the south or north of the Equator [30]. The unit of longitude and latitude in this research is the Decimal Degree (DD). Figure 16 depicts a plot of measured longitude and latitude vs. time. The position data is obtained at a rate of 1.33 bytes/second when the USV is sailing along at 31.62 m within 64 s. The longitude and latitude were acquired using GPS. The measured data were sent from the USV to the GCS on the computer using wireless transceiver. While the latitude and longitude commands were generated and sent from GCS on PC to the USV.  
The comparison of positions between the commanded and measured trajectory of USV towards the time is depicted in Figure 17. The error calculation found that the Root Mean Square Error (RMSE) was about 0.99 m, and the average of Cross Track Error (CTE) was around 0.86 m. The RMSE is not considerably different from that of the USV developed by [PERSON] et al. in which experimental testing for a zigzag path revealed it to be 0.54 m [19]. While from the prior study by [PERSON], USV equipped High-Precision RTK Receiver and speed of 2.5 km or 1.3 m/s were acquired at 0.919 m for the average of CTE. It concludes that The effect of RTK Receiver for zigzag path-following is not considerably high compared to the performance of USV; this is in line with the conclusion that has been revealed [17].
# Design and Analysis of Systematic Batched Network Codes
## 5 Inner Code for Systematic Batches
### Detailed Inner Code Formulation
* When using SIC for systematic batches, if the network links have no packet loss, the destination node receives all the message packets without decoding. If each link has an erasure probability \(e>0\) independently, the number of received message packets at the destination node drops exponentially rapidly with \(L\) increasing. In other words, for both RLIC and SIC, the destination node cannot benefit from the systematic outer code. We are motivated to study the recoding \(\Phi^{(u)}\) such that a non-source node \(u\) can receive more message packets from a systematic batch even when there are packet losses.
# Multi-LEO Satellite Stereo Winds
## 4 Discussion
### Applications
#### 4.2.1 Consistency in Polar Day-Night Cloudiness  
The Arctic climate is warming at an alarming rate, nearly four times faster than the rest of the world since 1979 [61]. Polar clouds play a crucial role in the Earth's climate system, including the Arctic, because of their major influence on surface radiation and subsequent cryospheric/atmospheric thermodynamics. During the summer months, bright white clouds can reflect a large amount of solar radiation back into space and reduce direct solar heating of the polar surfaces. In the winter, polar nighttime clouds can trap heat near the surface and keep the polar region warmer than without them. In a comparative study of MODIS and CALIOP cloud-mask products, [PERSON] et al., 2010 [62], found that the Arctic cloud cover from the passive IR radiometric method tends to bias low in the presence of Sea Ice Concentration (SIC). Both data sets show that the cloudiness tends to decrease with increased SIC, but MODIS cloudiness decreases at a larger rate due to the systematic low bias. The MODIS cloudiness bias is more pronounced in the nighttime than the daytime measurements, likely because of lacking the visible band in the cloud detection. Climate models struggle to reproduce the observed Arctic cloud amount and differ significantly between each other, showing larger differences in wintertime than summertime cloudiness [63]. On the other hand, differences also exist between cloud amount from satellite observations and reanalysis data [64]. Thus, reducing uncertainties of the satellite observations in the polar region is critically needed, as a cloud mask is fundamental to retrieving other cloud properties in subsequent data processing. The multi-LEO stereo cloud mask developed in this study from the IR band, which classifies clouds as a feature above the terrain height from the stereo technique, can be used as a new independent data set for polar cloud detection. In addition to its swath coverage, the independence of visible channels and radiometric calibration allows the multi-LEO stereo data to produce consistent cloud observations between day and night for studying diurnal and annual variations. Stereo methods have been successfully used to discriminate clouds from polar surface backgrounds using ATSR-2 [65]. Here, we exploit the MODIS-SLSTR stereo-wind product to derive a probabilistic cloud mask that classifies whether a tracking template has clouds or is almost all clear using the likelihood ratio between the alternative hypotheses: the template is cloudy or the template is clear. The ground-point clustering algorithm introduced in Section 4.1.1 provides an identification of retrievals over clear-sky terrain. We use
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 2 Background
### Previous Work
#### 2.3.4 Post-Quantum Setting  
Regarding the viability of carrying out this attack against post-quantum cryptographic schemes, there are already several works showing the feasibility of this attack against some post-quantum cryptographic schemes implementations. The first paper that evaluated a post-quantum cryptographic scheme in this setting was that of [PERSON] et al. [36]. They looked into this attack against NTRU and their work reviewed two existing NTRU implementations, the ntru-crypto implementation and the tbuktu Java implementation based on Bouncy Castle. For each in-memory format found in these implementations, they introduced dedicated key-recovery algorithms and tested them in the bit-flipping model. According to the results reported in [36], their key-recovery algorithm was able to find the private key, when \(\alpha\) is fixed and small and \(\beta\) ranges from 1% up to 9%, for one of the private key in-memory representations. This paper was followed upon by that of [PERSON] [11], which extended previous results and introduced a general key-recoverystrategy via key enumeration. Additionally, they applied their general strategy to BLISS and their results are comparable to the results reported for the NTRU case. Another recent paper by [PERSON] [37] adapted and applied this general key-recovery strategy to the signature scheme LUOV, exploiting the fact that LUOV's private key is a 256 bit string, and showed the versatility of this general key-recovery strategy and promising results. Moreover, [PERSON] et al. [38] investigated cold boot attacks on post-quantum cryptographic schemes based on the ring--and module--variants of the Learning with Errors (LWE) problem. Their work centered on Kyber key encapsulation mechanism (KEM) and New Hope KEM, where two encodings were considered to store LWE keys (polynomials): coefficient-based in-memory format and number-theoretic-transform (NTT)-based in-memory format. Finally, they presented dedicated key recovery algorithms to tackle both cases in the bit-flipping model.
## 3 Materials and Methods
### Framework of Monitoring Data Analysis  
Geotechnical and hydrological monitoring data, simultaneously acquired from June 2015 to December 2017, were analyzed to explore the patterns of movement of the Fontane  
Figure 7: Stratigraphic logs showing the installation depth of automatic inclinometric probes within FON1 (**a**), FON2 (**b**), and FON 3 (**c**) tubes. (Legend: (1) man-made fill, (2) organic soil, (3) clayey gravel, (4) clayey sand, (5) bedrock, (6) monitored section).  
di San Bernardo landslide and to investigate both the factors and mechanisms regulating its kinematics. The investigation has been developed following different phases. Rainfall and piezometric trends have first been taken into consideration to examine both the hydrological and pore water pressure regime throughout the monitoring period. In this regard, rainfall levels over different time spans (i.e., yearly, monthly, and daily) have been determined, while sub-daily pore water pressure data have been averaged into daily values and their fluctuations have been examined through the definition of minimum, maximum, and mean values. Moreover, pore water pressure change descriptors, namely daily variation and deviation from daily mean, have been examined. Subsequently, the relationships between rainfall and pore water pressure have been considered to investigate the hydrological response of the landslide.  
In order to identify the patterns of movement, the displacement time-series acquired by automatic inclinometric probes were used. Hourly displacement readings were averaged over 24 h to obtain daily mean values. When uniform trends in movement from the time series were recognized, the least-squares linear fitting method was applied to daily cumulative displacements. The slope of the best fitting line was deemed as indicative of the mean displacement rate, while the standard error of the estimate was defined as a measure of the precision of the assumed linear model. The obtained mobility rates were considered statistically significant if the displacement gradient was larger than the standard error of the gradient at the 95% confidence level.  
To explore in detail the relationships between rainfall, pore water pressure, and subsurface displacement, monitoring data acquired by sensors installed in FON2 and P3 verticals were used. These two sensors were positioned close to each other (see Figure 3) and at the same failure depth, which was detected in advance through traditional inclinometer measurements, and were therefore considered to be adequately representative of the characterization of deep displacement mechanisms.
## 2 Materials and Methods
### Related Work on Clinical Named Entity Recognition
combined Word2 vec embedding and an LSTM-trained character-level embedding as features of a word, which performed much better than only word embeddings [41]. [PERSON] et al. combined a Word2 vec embedding, and a CNN-trained character-level embedding as features of a word, which performed better for some indicators [18]. Along with the development of contextual word embeddings, studies [20; 21] showed that contextual embeddings achieved better performance than previous work [7; 14; 19]. Just like Word2 vec embedding, a domain-specific pretrained contextual embedding model performs better in the domain-specific NER task. [PERSON] et al. compared general pretrained ELMo and clinical pretrained ELMo, and found that clinical ELMo performed much better than general ELMo. They achieved 88.60% F1 score, 89.34% precision score, and 87.87% recall score on the 2010 IB2/VA dataset (the version with 170 training notes) [24]. [PERSON] et al. compared Word2 vec, GloVe, fastText, ELMo, BERT-base, BERT-large, and Bio-BERT, and found that BERT-large achieved the best performance [42]. In general, systems with biLSTM-CRF architecture and contextual word embedding set the new state-of-the-art record in many CNER datasets at present [24; 42]. However, methods that combine character-level embedding and attention mechanisms, such as in References [41] and [19], with contextual word embeddings are yet to be tested. Moreover, among the existing studies [7; 14; 19; 40; 41; 42], the systems all had a relatively low recall, which indicates that those systems were not sensitive enough to unknown entities. [PERSON] et al. proposed a multitask system for NER in social media in order to reduce noise, and their system achieved the highest F1 score and decent precision score compared to other systems [26]. [PERSON] et al. 's work indicates that we can try to introduce the multitask mechanism in CNER tasks to make the system more sensitive to emerging clinical concepts. In our work, we design a multitask attention-based biLSTM-CRF model (Att-biLSTM-CRF) to test the effect of the multitask mechanism in the CNER task. Compared with rule-based systems and machine learning-based systems, our work is based on deep learning, whereby we do not rely on human-designed rules and manually engineered features, which significantly improves our system's robustness and usability. Compared to prior work based on deep learning, we combine the biLSTM-CRF architecture, clinical pretrained context embedding, attention mechanism, and multitask mechanism in order to achieve better performance than prior work. Specifically, we test whether the multitask mechanism can improve the system's recall.