esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 2 CDRs and Development
### Retrieval and TCDR  
Retrieval algorithms developed for Environmental Data Record (EDR) production from SDR are normally used to produce TCDR from FCDR through reprocessing. The algorithms are usually updated through intensive validation to meet certain maturity criteria, such as those defined in Bates and Privette [20]. TCDRs are generally produced for Essential Climate Variables (ECVs) for land, ocean, and atmosphere, as defined by the Global Climate Observing System (GCOS) [2,3]. The specifications given by GCOS for ECV data products are designed to provide information to characterize the state of the global climate system and enable long-term monitoring, which are considered most valuable to climate researches as a long-term dataset.  
The retrieval algorithms developed for the current and future satellite observations are inevitably more sophisticated than that of legacy instruments, thus, seamlessly stitching the past, current, and future satellite observations together to produce long-term TCDRs is a challenge. Bias correction used for generating satellite TCDR products by using _in-situ_ measurements or another climate-quality satellite observation [21, 22, 23] may help to effectively homogenize TCDRs produced from the algorithms developed differently for the past, current, and future instruments. Unified enterprise algorithms [24,25] applicable to multiple sensors with different designs can be also a feasible solution.
# Hybrid Adaptive Bias OFDM-Based IM/DD Visible Light Communication System
###### Abstract  
Conventional optical orthogonal frequency division multiplexing (OFDM) schemes, such as adaptively biased optical OFDM (ABO-OFDM) and hybrid asymmetrically clipped optical OFDM (HACO-OFDM), are unable to tap all the resources of the subcarriers and only achieve relatively high power efficiency. In this paper, a hybrid adaptive bias optical OFDM (HABO-OFDM) scheme for visible light communication (VLC) is proposed to improve spectral efficiency and power efficiency. In the proposed HABO-OFDM scheme, different optical OFDM components are combined for transmission at the same time, and the adaptive bias is designed to ensure the non-negativity, as well as obtaining significantly high power efficiency. Meanwhile, the implementation complexity of the HABO-OFDM receiver is notably lower than the conventional superimposed optical OFDM schemes. Simulation results show that the proposed HABO-OFDM scheme outperforms ABO-OFDM and HACO-OFDM in terms of both peak-to-average-power ratio (PAPR) and power efficiency. The PAPR performance of HABO-OFDM is about 3.2 dB lower than that of HACO-OFDM and 1.7 dB lower than that of ABO-OFDM. Moreover, we can see that the \(E_{h(dec)}/N_{0}\) required for HABO-OFDM to reach the BER target is lower than the other two schemes at the Bit rate/Normalized bandwidth range of 3.5 to 8.75, which means that the power efficiency of HABO-OFDM is higher in this range.  
orthogonal frequency division multiplexing (OFDM); visible light communication (VLC); power efficiency; peak-to-average-power ratio (PAPR)
## 2 Models of Emotion in Pictures  
The inherent problem of emotion representation can be described in the simplest terms by the polarity and intensity of generated emotions [1; 2]. Regarding emotion elicitation, some stimuli will provoke intense reactions, while others will produce no apparent feedback [10]. Moreover, dissimilar individuals or well-defined homogeneous groups will show characteristic responses depending on the stimulation semantics and context [10; 11].  
As mentioned earlier, in contemporary affective multimedia databases, information about emotions is described using at least one of the two models of affect: the discrete model and the dimensional model [6; 7; 8]. These two models can effectively describe emotions in digital systems but are not mutually exclusive or incompatible. While most repositories use only one model, usually only the dimensional, some include annotations in both emotion models or offer the discrete model as an add-on option. The availability of annotations under both models is useful because it allows for a better characterization of multimedia affect content.  
Affective multimedia databases have many practical applications. In addition to the study of human emotion mechanisms, the generation, and appraisal of emotions, they are employed in the study of the mutual influence of perception, memory, attention, emotion, and reasoning [12]. From a practical standpoint, they are successfully used in the detection and treatment of stress-related mental disorders such as anxiety, various phobias, and post-traumatic stress disorder (PTSD), as well as in the selection and training of personnel for occupations regularly involving high levels of stress such as in the military (for instance, [13; 14; 15]). In these applied systems, the emotion-provoking stimuli are typically delivered to participants using head-mounted displays (HMDs) or fully immersive VR [16; 17; 18]. In addition, multimedia stimuli have many different uses in different disciplines, such as cognitive science, psychology, and neuroscience, as well as in human-computer interaction and other interdisciplinary studies (for example, [19; 20; 21]).  
Many affective multimedia databases currently exist. Some have general semantics, while others include content suitable for domain use, such as pictures of food, pictures in military settings, pictures with emotions rated by children only, etc. However, the Nencki Affective Picture System (NAPS) [22], the International Affective Picture System (IAPS) [23], the Geneva affective picture database (GAPED) [24], the Open Affective Standardized Image Set (OASIS) [25], and The DIgust-RelaTed-Images (DIRTI) database [26] stand out as the most often used and the largest general picture repositories, respectively. A concise overview of these specific types of databases is provided in the related literature [4; 5].
# Light Transmission in Fog: The Influence of Wavelength on the Extinction Coefficient
## Theory and Definitions: Light and Fog  
### Fog Definition  
According to the definition given by the World Meteorological Organization (WMO) [10], fog is the suspension in the atmosphere of microscopic water droplets that reduce MOR on the ground to less than one kilometer.  
MOR is the distance through fog for which the luminous flux of a collimated light beam is reduced to 5% of its original value.  
According to this definition, MOR (denoted as \(V\) and expressed in meters) is related to the extinction coefficient (\(k\)) [10]:  
\[V=-\frac{ln(0.05)}{k}\simeq\frac{3}{k} \tag{1}\]  
where \(k\) denotes the extinction coefficient in the visible band, more precisely defined in Section 2.3. In the general case, the extinction coefficient \(k\) depends on the wavelength. In the visible domain, \(k(\lambda)\) is considered as constant by the WMO [10]. This is not the case for infrared wavelengths [2].  
The lower the MOR, the denser the fog and the higher the extinction coefficient.
## 6 Experimental Evaluation
### Numerical Results
#### 6.2.2 Problem Variant #2: Offset and Headways Optimization
The objective function values achieved by exact and EA for the studied problem instances in BSP variant #2 are reported in Table 7. The real timetable is reported as baseline for the comparison, and columns 'EA vs. exact', 'EA vs. real', and 'exact vs. real' summarize the comparison, as in Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _Scenario_ & _EA_ & _Exact_ & _Real_ & _EA vs. Exact_ & _EA vs. Real_ & _Exact vs. Real_ \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 7: Summary of results: exact and EA for BSP variant #2. 63.4% (on average) over the real timetable, and the exact method improved 66.2% (on average) over the real timetable. The average improvements of exact and EA over the baseline real solution, grouped by tolerance and instance size for BSP variant #2, are reported in Table 8. Regarding tolerance levels, EA improved up to 150.95%, and the exact method improved up to 158.13% over the real timetable. The best improvements were computed for \(\lambda=30\), which pose the tighter bounds for user waiting times. In less restrictive scenarios, improvements over the real timetable were over 25.23% for both methods. Regarding scenario dimension, both methods computed robust solutions that improve between 58.50% (EA, instances with 110 transfer zones) and 70.25% (exact, instances with 30 transfer zones) over the real solution. The reported improvements on the objective function values grouped by tolerance and problem dimension demonstrate that for all BSP instances, the improvements over the real timetable increase as user tolerance decreases. Similar to problem variant #1, the optimization algorithms properly scale with the complexity of the problem, computing better solutions for tighter instances. The proposed EA computed the same solution as the exact method in 33 problem instances (10 with 30 transfer zones, 18 with 70 transfer zones, and 5 with 110 transfer zones). Furthermore, in another 14 instances, the difference was below 1%, and in two problem instances, the EA computed a better solution than the exact method.
## 4 Experiments and Results
### Ablation Experiment and Result
In the high-density distribution scene of shade houses and warehouses (Figure 11f), where shade houses and similar objects such as warehouses are closely distributed, other models tend to confuse the background and target areas. In contrast, ShadeNet's self-attention mechanism and spatial-channel attention mechanism are better at balancing the target's detailed features with contextual information, significantly reducing the misclassification rate. Finally, in the handling of shade houses and complex shadow scenes (Figure 11g), ShadeNet demonstrates strong robustness in dealing with shadow variations, accurately distinguishing between shade houses and the background, and reducing the impact of shadow regions on the segmentation results. Mask2 Former (ResNet) is more susceptible to shadow interference, leading to misclassifications. Mask2 Former (Swin Transformer) shows improvements in handling shadows, but still experiences misclassifications in regions with strong shadows. In summary, ShadeNet demonstrates strong robustness and accuracy in multiple complex scenarios, particularly in scenes with shade houses and complex backgrounds, similar objects, and shadow variations, significantly improving segmentation accuracy. In contrast, traditional Mask2 Former (ResNet) and Mask2 Former (Swin Transformer) exhibit certain limitations in handling some complex backgrounds and finer details.
## 4 Experimental Comparison and Analysis
### Combined Adaptability Consideration for \(Par\) and \(F\)  
In order to consider the effects of parameters \(PAR\) and \(F\), the same four functions are used to analyze the performance difference on the aHSDE with different parameter settings. The statistical results in 30 runs are shown in Table 1 and Tables S1-S3. The data in the Tables S1-S3 represent the statistical results of multiple runs from different \(PAR\) and \(F\) combinations.  
Table 1 shows that function f1 gets the best result when the parameter pair (\(PAR\), \(F\)) is (0.9, 0.4). Table S1 shows that function f10 gets the best result when the parameter pair (\(PAR\), \(F\)) is (0.1, 0.6). Table S2 shows that function f21 gets the best result when the parameter pair (\(PAR\), \(F\)) is (0.9, 0.3). Table S3 shows that function f28 gets the best result when the parameter pair (\(PAR\), \(F\)) is (0.7, 0.6). At the same time, it is easy to see that the performance of algorithm varies greatly with different parameter pairs. For example, the result of algorithm varies from \(1.35\times 10^{7}\) with (\(PAR\), \(F\)) being (0.8, 0.9) to \(3.53\times 10^{3}\) with (\(PAR\), \(F\)) being (0.9, 0.4) for Function 1. The result of the algorithm varies from \(8.66\times 10^{5}\) with (\(PAR\), \(F\)) being (0.1, 0.3) to \(8.89\times 10\) with (\(PAR\), \(F\)) being (0.9, 0.3) for Function 2. This tells us that different functions have different sensitivities to the parameter \(PAR\) or \(F\).  
Figure 4: How the scaling factor \(F\) changes f1, f10, f21, f28.  
Observed from the comparison analysis of different parameter pairs on (_PAR_, _F_), the performance of the algorithm is sensitive to the parameter pair (_PAR_, _F_) to different problems. Simultaneously, it demonstrates that a certain parameter adaption scheme is necessary for problem solving. The algorithm aHSDE can obtain the best parameter pair of (_PAR_, _F_) and converge the best solution with the adaptive strategy. This scheme can reduce the dependence on the parameter for the algorithm.  
Thus, it can be said that Table 1 and Tables S1-S3 fully demonstrate the effects of the adaptive strategy. In conclusion, the aHSDE is highly successful with the tuned parameter settings of _PAR_ and \(F\) through the learning period and the weighted Lehmer mean method.
## 3 Circuit Architecture for Multi-MFC PMS
### Multi-MFC PMS Algorithm  
A microcontroller's 10-bit Analog to Digital Converter (ADC) determines the interleaved power extraction time per MFC. As seen in Figure 5, the algorithm defines two thresholds for the power extraction \(V_{th-High}\) and \(V_{th-Low}\) as follows:  
\(V_{th-Low}\) determines when to stop the EH operation and is predefined at 50% of the initial measured \(V_{MFC}\). When \(V_{th-Low}\) is reached, the MFC is removed from the PMS until the voltage threshold high, \(V_{th-High}\), is reached. The recovery time is defined as the time it takes for the MFC to go from \(V_{th-Low}\) to \(V_{th-High}\). \(V_{th-Low}\) and \(V_{th-High}\) are determined after selecting the voltage thresholds that yield the most optimum power extraction and recovery time for energy harvesting, without over-depleting the MFC from a series of threshold testing of an adaptive algorithm.  
The power extraction algorithm presented in Figure 6a is described below:  
1. The Power Ranking Algorithm subroutine (described in Section 3.5) is executed. As a return variable, a look-up table is filled out with the individual MFCs' power rankings
2. The look-up table is accessed to interleave the MFC power extraction process
3. The DC-DC boost converter is enabled to start EH
4. \(V_{MFC}\) is compared to the low threshold voltage \(V_{th-Low}\)
5. When \(V_{th-Low}\) voltage is reached the DC-DC converter is disabled
6. If more MFCs in the array are waiting to be harvested, then the program jumps to instruction #2. Otherwise, the system stops its EH mode and waits until the recovery time of the highest ranked MFC is reached
7. After the recovery time is completed, the algorithm restarts.  
The proposed interleaved energy extraction allows the step-up DC-DC converter to dynamically find the MPP of each individual MFC, ensuring a maximum power output per MFC while at the same time, the idle time for the remaining MFCs is used as an MFC's recovery time that directly affects the MFC's health status.  
Figure 5: Key algorithm reference points of MFC power extraction waveform.  
Another important feature of this approach is that if there is a failure at one or more MFCs, the system automatically adapts to it by assigning a rank of zero to that specific MFC, which allows the system to neglect the damaged cell.
## 2 Audio Features and Pre-Trained Neural Networks
### Audio Signal Features
#### 2.1.3 Spectro-Temporal Features  
Spectro-temporal features stem from the fusion of temporal and spectral features. Although not widely explored in the field of multi-channel audio classification, several works have devised algorithms that integrate the use of both temporal and spectral features for acoustic event detection [26; 27]. [PERSON], et al. proposed the use of a non-negative matrix factorization algorithm in order to detect a set of patches containing relevant spectral and temporal information that best describes the data [27]. The results achieved in their experiment suggest that their features provide more robustness in noisy environments as opposed to MFCCs as sole features. [PERSON], et al. [26], on the other hand, devises a spectro-temporal feature extraction algorithm through two-dimensional Gabor functions for robust classification.  
Nevertheless, these algorithms were tested solely on acoustic events as opposed to acoustic scenes. Similarly, the applicability of these algorithms to multi-channel audio scenes remains controversial; aside from not being widely utilized, comparison against other top performing feature combinations for the same application were not apparent.  
However, one of the most notable works in the field of spectro-temporal features is scalogram features, which are computed through the continuous wavelet transform (CWT) [28]. Such methods consider both the time and frequency components of a signal. The time components represent the motion of the signal, and the frequency components symbolize the pixel positions in an image [28]. Taking a computer vision approach, the velocity vectors are first calculated through multi-scale wavelets, which are localized in time [29]. The CWT of a continuous signal is defined by Equation (3) [29].  
\[CWT_{c}(s,t)=\int_{-\infty}^{\infty}y_{avg}(u)\frac{1}{\sqrt{s}}\psi^{*}\bigg{(} \frac{u-t}{s}\bigg{)}du \tag{3}\]  
where \(\psi^{*}\) refers to the complex conjugate of the mother wavelet, \(t\) refers to the time domain, \(u\) signifies the signal segment, and s refers to the scale, which is a function of the frequency [29].  
Separation of the audio channels is then performed via the low-dimensional models that reverberated from the firmness of the harmonic template models [28]. Such a process is beneficial for multi-channel audio classification due to its ability to separate mixed audio sources, which allows a thorough analysis for individual audio channels.  
The scalogram is a visual representation of the absolute value of the CWT coefficients, represented by Equation (4) [30]:  
\[E(s,t)=\left|CWT_{c}(s,t)\right|^{2} \tag{4}\]  
Nonetheless, despite its advantages, computation of CWT coefficients are often extensive and are subject to high computational time duration [31]. Wavelets are computed through comparing and inverting the DFT of the signal against the DFT of the wavelet,which can be computationally expensive. Thus, integration of other techniques in order to reduce this complexity must also be examined.
# Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems
###### Abstract  
With a target to maximize the throughput, a fast link rate adaptation algorithm for IEEE 802.11a/b/g/n/ac is proposed, which is basically preamble based and can adaptively compensate for the discrepancy between transmitter and receiver radio frequency performances by exploiting the acknowledgment signal. The target system is a \(1\times 1\) wireless local area network chip with no null data packet or sounding. The algorithm can be supplemented by automatic rate fallback at the initial phase to further expedite rate adaptation. The target system receives wireless channel coefficients and previous packet information, translates them to amended signal-to-noise ratios, and then, via the mean mutual information, selects the modulation and coding scheme with the maximum throughput. Extensive simulation and wireless tests are carried out to demonstrate the validity of the proposed adaptive preamble-based link adaptation in comparison with both the popular automatic rate fallback and ideal link adaptation. The throughput gain of the proposed link adaptation over automatic rate fallback is demonstrated over various packet transmission intervals and Doppler frequencies. The throughput gain of the proposed algorithm over ARF is 46% (15%) for a 1-tap (3-tap) channel over 10 m-250 m (16 m-160 m) normalized Doppler frequencies. Assuming a 3-tap channel and 30 m-50 m normalized Doppler frequencies, the throughput of the proposed algorithm is about 31 Mbps, nearly the same as that of ideal link adaptation, whereas the throughput of ARF is about 24 Mbps, leading to a 30% throughput gain of the proposed algorithm over ARF. The firmware is implemented in C and on Xilinx Zynq 7020 (Xilinx, San Jose, CA, USA) for wireless tests.  
ACK; firmware; link adaptation; mean mutual information; preamble; rate adaptation; WLAN
## 2 Materials and Methods
### Characterization Protocol
#### 2.3.2 ATR-FTIR Measurement  
The instrument Perkin Elmer 100 FT-IR spectrometer (ATR-FTIR) was used to determine the functional groups of the surface of electrospun nanofibres (PET and WPET) before and after modification (PET-SiAMPy and WPET-SiAMPy). The samples were individually placed on the diamond crystal of the ATR holder then fixed for the perfect contact of the sample with the crystal. The FTIR instrument wavelength range was set to 4000 cm\({}^{-1}\) to 650 cm\({}^{-1}\) against transmittance with 2.0 resolution at 64 scanning times. For every analysis, the baseline of the FTIR was corrected using the background spectrum of the blank scan. The FTIR diamond crystal and ATR holder were cleaned with ethanol before every analysis to prevent the cross-contamination of the samples.
###### Abstract  
The ecosystem service value (ESV) gradient-evolution pattern of a river basin's land and water-intertwined zones has a variety of ecosystem service values, such as biodiversity conservation, water conservation, water purification, etc. The study of the ecosystem service value (ESV) gradient-evolution pattern of a river basin's land and water-intertwined zones will provide a scientific basis for the construction and protection of the ecological security pattern of the river basins. In this study, we combined the unit area equivalent factor method and geographically weighted regression (GWR) model to classify and analyze the gradient change pattern of ESV upstream, downstream, and along the river of the Guangdong mainstream section of the Xijiang River in China, and the conclusions are as follows: (1) The corresponding ESV share of each land use type was in the following order: water bodies > broad-leaved forest > artificial wetland > scrub > paddy field > coiferous forest > natural wetland > grassland. The level of each type of ESV does not depend entirely on the size of the area but is determined by the ecosystem service functions it can provide and the level of ESV per unit area; (2) the relationship between land use types along both sides of the river in the Guangdong section of the Xijiang River Basin shows a tendency to shift from water ecosystems to terrestrial ecosystems, and the ESV gradually decreases with the increase in distance from the water. (3) The upstream to the downstream area showed a trend of changing from terrestrial ecosystems to aquatic ecosystems, such as broad-leaved forests, scrollbands, water bodies, artificial wetlands, etc., and the mean land ESV showed a general trend of undulating change and decline with the reduction in the distance from the downstream area. (4) Natural factors, such as the topography and geomorphology of the basin and the socio-economic factors of power consumption, influence the spatial distribution characteristics of the ESV in the region; among them, socio-economic factors, such as total power consumption, industrial exhaust gas emissions, industrial wastewater emissions, etc., in the economically developed areas of the Xijiang River Basin are the determinants of the changes in ESV, which are generated by human living and production activities, and these indirectly affect the magnitude of the ESV by influencing the factors of temperature and gas.  
land-water interface; ecosystem services; GWR model; gradient; land use +
Footnote †: journal: Applied Sciences