esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Linking PAM and FloX Measurements  
Figure 9 shows a series of PAM measurements, taken on DOY 264 and DOY 271 during the lettuce experiment. DOY 264 was a sunnier day, with a more expressed water limitation. Two PAM parameters that were put special emphasis on were steady-state fluorescence Fs/F0, linked to the SIF, and the PSII effective quantum yield Y(II), linked to the PRI. The PAM data showed large variability, expressed by their standard deviation. This large variability is linked to changes in photosynthetic activity at the leaf level, due differences in illumination conditions, for example. During both DOY 264 and DOY 271, PRI and Y(II) followed a similar pattern, with their minimal values around between 11 h and 14 h. Linking Fs/F0 values to \(\epsilon_{760}\) values is harder due to the large standard deviation of the Fs/F0 measurements. Both the Fs/F0 and \(\epsilon_{760}\) values did not change significantly during either DOY 264 or DOY 271. The FloX measurements of \(\epsilon_{760}\) between the stress box and the reference box did not differ significantly for DOY 264 and DOY 271. The PRI was significantly higher in the reference box on DOY 264, indicating higher stress conditions in the stressed box compared to the reference box.  
Figure 8: Daily correlation coefficient of \(\epsilon_{760}\), SIFBY and FCVI to PAR for the mustard and lettuce experiments during their adult phases.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 4 State-of-the-Art FANET
### Standards for FANETs
In the following, we further elaborate on the physical layer advancements of FANETs, highlighting their significance in the network performance.
## 7 Discussion  
Comparing results of the work at hand with findings of the presented literature in the introduction (see Section 1), e.g., [PERSON] et al. (2018), [PERSON] et al. (2019) or [PERSON] et al. (2020), shows some limitations of this resesearch in terms of comparability. All of the papers' analysed scenarios take place in the year 2030. Hence, in the transition process of decarbonising energy systems, the scenarios analysed in the cited literature which include inertia constraints and modelling synchronous inertia are behind the scenarios analysed in this research. In this work, future German power system scenarios of the years 2040 and even 2050 are researched. The total RES penetration in the work of [PERSON] et al. is 30% ([PERSON] et al., 2019). The overall inverter-connected RES penetration in the work of [PERSON] et al. is 37% for Germany (2018). Non-synchronous penetration shares in the work at hand range from 77% up to 85%. This marks a significant increase compared to the discussed literature.  
Still, system cost results presented in this work are comparable. [PERSON] et al. conclude with a system cost increase of up to 37% ([PERSON] et al., 2018). [PERSON] et al. conclude with system cost increases ranging from 43.1 to 58.1% based on the analysed scenario. System cost increases in this work range from 1% in the RE100 scenario to 14% in the NT scenario and 22% in the DE scenario. It has to be noted that the lower results compared to [PERSON] et al. and [PERSON] et al. are of the 1 Hz/s, 2 s parameter combination and the resulting low demand for synchronous and overall system inertia. System cost increases for the 1 Hz/s, 30 s parameter combination, as indicated by Figure 7, are far higher due to the needed investment in storage units providing inertia. Such investments were not necessary in the discussed literature.  
As already explained in the results section, in some scenarios it is more cost efficient to dispatch fossil-fuel-fired power plants capable of providing an inertial response in the event of a power imbalance than investing in newly added storage units. Increasing the price for carbon dioxide emissions (e.g., by reducing the number of tradeable CO\({}_{2}\) certificates) is a market-based solution to decarbonise power systems while sufficient power system inertia is maintained. This approach increases system costs by 3.7 percent points at highest with respect to the 13% system cost increase in the NT scenario. Hence, pricing CO\({}_{2}\) emissions is a reasonable approach to decarbonise power systems while maintaining sufficient power system inertia. Another potential solution to overcome this limitation is another constraint which restricts overall carbon dioxide emissions and thus results in investment and dispatch of additional carbon dioxide free storage units providing inertia.
## 1 Introduction
Unlike LEACH, the probability that two nodes within each other's transmission range becoming CHs is small which means that the CHs are well distributed. The Energy-Efficient Unequal Clustering (EEUC) [6] partitions the network into clusters of unequal sizes where the clusters closer to the Sink have smaller sizes than those farther away from the Sink. Thus, CHs closer to the Sink, can save some energy for the data relaying. Unlike others protocols such as LEACH and EEUC uses an energy-aware multi-hop routing protocol for inter-cluster communication, however, the setup phase in EEUC has a lot of overhead and as a result it consumes more energy in the setup phase when compared to LEACH. In stable state phase, EEUC saves energy by using an inter-cluster multi-hop data routing mechanism. Energy-Efficient Level-based and Time-based Clustering (EELTC) [7] is a hierarchical clustering algorithm with multi-hop communication that establishes unequal clusters with very low controlling overhead. In this protocol the network is divided into radial regions using a heuristic formula. The Sink calculates upper bound and lower bound of each level and it then broadcasts the results across the network via a 'hello' message. All sensors determine their level by receiving this message from the Sink. Based on its level and energy each node sets a time to start advertising itself in the network to form clusters. The algorithm shows good energy efficiency and even load distribution across the whole network. A modified version called EELTC-M is proposed in [8]. This modification builds upon the previously proposed algorithm EELTC; the lengths of levels are modeled as an optimization problem based on the energy saved for each cluster. This energy is the difference of energy used by cluster head when using single-hop _versus_ multi-hop communication model. In this manner, the cluster in the next level expands its size to cover some extra nodes instead. A comparative simulation was performed and EELTC-M showed to have a longer network lifetime compared to both the previous version and the EELUC protocol. The remainder of the article is organized as follows. We discuss the original star-based communication architecture and sensor networks as well as the original energy model in Section 2. After that we provide a detailed investigation of current requirements for a real WSN Deployment. In Section 4 we propose a new energy efficient, robust and scalable architecture for WSN that satisfies the real requirements studied in previous section. Section 5 explains the simulation setup and presents the simulation results. In Section 6 we present the practical application and the experimental results of the proposed architecture. We then conclude our article in Section 7.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 3 Overall Architecture
### SKF Framework  
The SKF framework consists of three main components: the Semantic Feature Enhancement (SE) module, the Semantic-Guided Color Histogram (SCH) loss, and the Semantic-Guided Adversarial (SA) loss.  
First, the Semantic Feature Enhancement (SE) module is introduced into the enhancement network. This module integrates semantic-level features with the enhancement network's features, leveraging semantic information to guide image enhancement. By fusing semantic information with image features, the enhancement network can better preserve and enhance the semantic details of the image, resulting in more semantically aware enhancement effects.  
After passing through the enhancement network, the image is constrained by the Semantic-Guided Color Histogram (SCH) loss, which ensures that the enhanced image maintains color consistency with the original image while taking semantic information into account. This ensures that the colors in the enhanced image appear more natural and realistic.  
In addition to the color histogram loss, the framework employs the Semantic-Guided Adversarial (SA) loss. By using adversarial training, this loss enhances the robustness and stability of the enhancement network while ensuring that the enhanced images appear more visually realistic and natural.
## 4 Results and Discussion
### Interpretation of Achievements
In terms of ground construction conditions, WD1 is arranged in a nearly north-south orientation, almost parallel to the terrain contour line, and the terrain is relatively flat. WD2 is arranged in a nearly east-west orientation, in which the terrain between WD2-1 and WD2-5 is relatively flat. WD2-6 and WD2-8 are arranged along the hillside, almost perpendicular to the terrain contour line, and the terrain elevation difference is large. From the detection results of the above two survey lines, it can be seen that in the vertical direction the resolution of the linear array in flat terrain was significantly higher than that in undulating terrain.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 5 Discussion
### Bias and Uncertainty  
There has been much discussion and argument about the underfitting and overfitting of ML when applied to datasets with many noise sources and uncertainties, especially in high spectral passive remote sensing imagery containing noise coming from sensors or diffuse radiation. Overfitting means the model learned from noise while underfitting means the model has not fully learned the real data patterns. Unfortunately, the noise in remotely sensed data is unavoidable and sometimes cumulative in time series prediction. Many methods and calibration models have been developed for both sensor error correction and atmospheric calibration. All the remote sensing products have gone through quality control and maturity assessment. TROPOMI NRT products are processed based on the 3-dimensional global chemistry transport model and the configuration might also result in uncertainties in different locations. During operation, ESA usually upgrades their workflow occasionally and causes systematic changes in the tropospheric NO\({}_{2}\) columns under various conditions, such as cloud-free or seriously polluted areas with small fractions. The changes will have non-trivial impacts on the ML learning process and may lead to non-usable biased estimation. It is recommended that after every major upgrade, the ML model should be re-trained on the new training data including the observations after the upgrade. The single-time-frame models, such as random forest and SVM usually do not need additional adjustments during use as the errors do not accumulate. Time series models, such as the LSTM model, do need to be adjusted or aligned after a period running to offset the accumulated biases and to bring the model status back to the correct track.  
An additional cause of uncertainty is that ML models are fundamentally algorithms composed of a set of rules which involve random number generation and optimization to determine model parameters. Therefore, ML models developed on the same dataset are almost always different. The uncertainty of ML applications is a combination of uncertainties from two sources: data and knowledge.
# Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers
## 1 Introduction
* We develop a learnable WD module that leverages WD's frequency learning capability to decompose signals into higher-frequency components, facilitating more effective analysis of distorted signals. * We perform extensive simulations on the open-source dataset \"OpenDPD\", demonstrating substantial improvements over state-of-the-art WDP-based methods, including a 4.59 dB reduction in the NMSE, ACPR gains of 3.37/4.82 dBc, and a 5.14 dB improvement in the EVM.
## 3 Results  
### Empirical Algorithm  
The number of final parameters and the prediction accuracy of each stepwise regression model (Table 3 experiment number NO.1-6) are shown in Supplementary Materials Table S1. The results showed (Figure 2) that the R-squared of the models increased with the number of parameters added to the model formula. However, after the number of parameters reached a certain level, increasing the parameters of the model no longer significantly improved the R-squared value of the model, which implied that convergence was achieved. By comparing the R-squared of the models with the simultaneous window training dataset (Figure 2a,b: lines with the same color), it was clear that the group of spectral variables using the bands and band ratio combination had a higher R-squared, that is, the spectral variables using the bands and band ratio combination can better explain the relationship between remote-sensing reflectance and lake SD. Moreover, as shown in Figure 2, the model with the 1-day time window training dataset performed better than the model with a longer time window training dataset, and the R-squared value decreased as the time window of the training set increased. Table 4 shows the optimal solutions of the two groups of models with different spectral variables, and the R-squared of spectral variable group 2 was significantly higher than that of spectral variable group 1. Figure 2: The relationship between the number of parameters in the stepwise regression model and the prediction accuracy of the model. (**a**) The results of the stepwise regression model using a single band or band combination spectral variables with different training datasets. (**b**) The results of the stepwise regression model using band combination and band ratio combination spectral variables with different training datasets. The experiment number corresponds to Table 3. The blue line corresponds to the 1-day time window dataset, the orange line corresponds to the 3-day time window dataset, and the green line corresponds to the 7-day time window dataset. Moreover, to identify whether the model trained with a shorter or longer time window data can produce accurate lake SD using spectral data beyond its training data, a 1-day trained model was used for the 7-day time window spectral data, and a 7-day trained model was used for the 1-day time window spectral data. The results (Table 5) illustrate that although the 1-day model prediction accuracy for the 7-day time window dataset was lower than the prediction accuracy for the 1-day time window dataset, it was still at a high level; that is, the model still had suitable prediction accuracy for data outside the training set. Using the model trained from the 7-day time window training dataset to predict the 1-day time window data, the resulting R-squared was higher than itself; therefore, it was feasible to train the model using the 7-day time window data in case of insufficient data.
# Two-Way Linear Probing Revisited
## 2 Background and History
### Other Initiatives  
Open addressing methods that rely on the rearrangement of keys were under investigation for many years, see, e.g., [20; 46; 47; 48; 49; 50]. [PERSON] and [PERSON] [51] studied a scheme called cuckoo hashing that exploits the lcfs replacement policy. It uses two hash tables of size \(n>(1+\epsilon)m\), for some constant \(\epsilon>0\), and two independent hash functions chosen from an \(O(\log n)\)-universal class--one function only for each table. Each key is hashed initially by the first function to a cell in the first table. If the cell is full, then the new key is inserted there anyway, and the old key is kicked out to the second table to be hashed by the second function. The same rule is applied in the second table. Keys are moved back and forth until a key moves to an empty location or a limit has been reached. If the limit is reached, new independent hash functions are chosen, and the tables are rehashed. The worst-case search time is at most two, and the amortized expected insertion time, nonetheless, is constant. However, this scheme utilizes less than 50% of the allocated memory, has a worst-case insertion time of \(O(\log n)\), w.h.p., and depends on a wealthy source of provably good independent hash functions for the rehashing process. For further details see [52; 53; 54; 55].  
The space efficiency of cuckoo hashing is significantly improved when the hash table is divided into blocks of fixed size \(b\geq 1\) and more hash functions are used to choose \(k\geq 2\) blocks for each key where each is inserted into a cell in one of its chosen blocks usingthe cuckoo random walk insertion method [56; 57; 58; 59; 60; 61]. For example, it is known [57; 58] that 89.7% space utilization can be achieved when \(k=2\) and the hash table is partitioned into non-overlapping blocks of size \(b=2\). On the other hand, when the blocks are allowed to overlap, the space utilization improves to 96.5% [57; 61]. The worst-case insertion time of this generalized cuckoo hashing scheme, however, is proven [59; 62] to be polylogarithmic, w.h.p.  
Many real-time static and dynamic perfect hashing schemes achieving constant worst-case search time, and linear (in the table size) construction time and space were designed in [63; 64; 65; 66; 67; 68; 69; 70]. All of these schemes, which are based, more or less, on the idea of multilevel hashing, employ more than a constant number of perfect hash functions chosen from an efficient universal class. Some of them even use \(O(n)\) functions.
## 2 Literature Review
### Asset Management Methodologies and Models Applied to Warranty Management
#### 2.2.3. RCM and CBM: Focus on Asset Criticality in Warranty Management  
Reliability-centered maintenance (RCM) and condition-based maintenance (CBM) are advanced maintenance methodologies that focus on the criticality of assets, allowing for more timely and effective interventions. Both provide great value to warranty management, as they allow warranty policies to be adjusted based on operational needs and the specific status of each asset, thus achieving more precise and tailored management [22].  
RCM is geared towards identifying the critical functions of each asset and analyzing possible failure modes, which facilitates the prioritization of maintenance resources on those assets whose criticality is most important. This approach optimizes performance and reduces the frequency of claims, ensuring that key assets operate without unnecessary interruptions. The application of RCM in warranty management offers a preventive structure that reduces maintenance costs and improves customer satisfaction by avoiding failures in assets essential to the business.  
CBM, on the other hand, enables maintenance interventions to be carried out only when data indicate a real need, based on the current state of the asset. This approach is supported by continuous monitoring using sensors and real-time data analysis, which detect wear patterns or unusual conditions before critical failures occur. In the context of warranty management, CBM adapts coverage for critical assets according to their operational status, thus optimizing resource allocation and personalizing warranty service. In this way, CBM not only improves operational efficiency, but also reduces the downtime and costs associated with corrective maintenance. Condition-based maintenance (CBM) focuses primarily on determining the optimal time to perform maintenance, based on the specific condition of the equipment. This approach is precise and data-driven. On the other hand, RCM is oriented towards selecting the most appropriate maintenance strategy.  
Both methodologies, by prioritizing critical assets, can be said to ensure efficient and timely intervention that optimizes the asset life cycle and improves customer experience. The use of RCM and CBM facilitates adaptive and cost-effective warranty management, which responds to the real needs of assets and allows companies to reduce costs and downtime, thus promoting a competitive advantage in the market.
## 2 Methods
### Outcome Measures
#### 2.3.1 Structural Repair  
Repair tissue structure, defined as the quantity (i.e., degree of defect filling (%)) and quality (T2 relaxation time) of new cartilage tissue, was assessed by three-dimensional (3D) quantitative MRI conducted at an imaging core lab (Qmetrics Technologies, Rochester, NY, USA) using, validated segmentation and 3D reconstruction techniques [17; 18] which have been applied in Phase 3 clinical trials of cartilage repair and multiple osteoarthritis studies [19; 20; 21]. Standardized MRI scans were acquired for each subject upon enrolment into the extension study with a Siemens 3T MAGNETOR(r) Verio, A Tim+Dot System and dedicated Tx-Rx CP extremity coil at a single, prequalified and trained MRI clinic. Technician training and scanner magnet qualification were conducted prior to trial activities. A uniformity and linearity phantom scan was performed for quality control. Imaging phantoms within the field of view served as internal controls.  
The imaging protocol was designed to provide very high spatial resolution with high in-plane resolution and thin slices for all sequences. The study images were acquired using a fat-suppressed 3D spoiled gradient-echo (FS SPGR) sequence in sagittal and axial planes (slice thickness: 2 mm), and a 2D dual-echo fat-suppressed fast spin-echo (FS dual echo) sequence in the sagittal plane (slice thickness 1.2 mm). For morphological analyses of cartilage, cartilage defects, bone and bone defects, sagittal and axial FS SPGR series were used. Sagittal FS dual-echo series were used for T2 analyses. All scans were sent to an imaging core lab for centralized scan quality review, storage, and quantitative analysis. The sagittal 3D FS SPGR series and the sagittal FS dual echo series were segmented for bone and cartilage using a semi-automated atlas-based segmentation method [17]. The segmentations were reviewed and edited, if needed, by a musculoskeletal radiologist with expertise in cartilage repair. Original cartilage and bone defect boundaries for each subject were manually established on FS SPGR and FS dual echo segmentations using edited segmentation of MRI scans (sagittal dual echo) obtained 10 days post-operatively in the original interventional ChondroMimetic study.  
The segmented 3D volume of new repair tissue at follow-up was then used to quantitatively determine the degree of defect filling (%) and the mean T2 relaxation time of cartilage repair tissue. Filling was calculated as a ratio of new repair tissue (bone and cartilage) to the original osteochondral defect volume. In cases where multiple donor sites in the same knee were treated, confluence of defects was observed in the trabecular bone resulting in a single defect volume and a single fill ratio. A radiologist-selected region of interest (ROI) of cartilage on an untouched area of the trochlea in the same knee was segmented and analyzed for each subject as a native T2 control.