esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Data Similarity to Improve SSD Read Performance
## 4 Design
### Multi-Path Read Scheme  
#### 4.3.1 Address Mapping Management  
As data with the same content are distributed across different parallel units, such as different channels or chips, DS-FTL utilizes the modified mapping table to enable reading data from an idle channel/chip, even when other channels/chips are busy. This further enhances parallelism and improves system performance. The existing mapping table in SSDs typically stores a one-to-one mapping pair between logical and physical addresses. However, DS-FTL modifies this mapping to a one-to-many (1-to-n) relationship. To facilitate this, DS-FTL employs a two-level mapping table, as utilized in previous works [4; 29], to record the 1-to-n mapping. The two-level mapping table consists of a preliminary mapping table and a secondary mapping table. The preliminary mapping table contains entries with LPN and PPN pairs. The highest bit in the PPN is utilized to indicate whether a lookup in the secondary mapping table is required. For unique data within the SSD, the mapping table records the mapping entry as usual. However, for redundant data within the SSD, multiple LPNs point to the same Virtual Physical Page Number (VPPN), and a lookup in the secondary mapping table is performed using the VPPN to retrieve the page addresses with the same content. The garbage collection scheme works as normal; the only difference is that DS-FTL updates the 1-to-n mapping pair in the secondary mapping table if the data in the corresponding PPN are deleted.
# Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios
## 4 Simulation Environment: Implemented Scenario
The second one is deterministic, and depends on the multi-path direction \(\theta_{MP}\), and how the gain terms \(G_{n}^{(i)}(\theta)\) and \(G_{T}^{(i)}(\theta^{\prime})\), as well as the pathloss \(L^{(i)}(D)\) including \(\alpha_{(i)}\), impact on the signal recombination at the anchor reference, also in dependence of \(\chi\) and \(\alpha\). The  
Figure 4: Pictorial rendition of the multipath-impaired scenario for the DoA estimation. Figure 5: Pictorial rendition of multi-path propagation using an image-source model. proposed controlled multi-path scenario is thought to predict that deterministic part which is usually unknown by the DoA estimation algorithm. It is worthwhile to mention that in a real implementation the estimation of RSSI is based on signal-matching strategies, which mitigates the impact of multi-path on actual input data by at least partially filtering out the echo signal. Consequently, the proposed investigation is more severe than a real application in this sense. With reference to Figure 7, the \(P_{R}^{(t)}\) for \(f_{1}=2.45\,\mathrm{GHz}\) and \(f_{2}=5.2\,\mathrm{GHz}\) are reported in the form of a contour plot, in both angle and distance. This figure depicts the case when the multi-path signal power \(\chi\) is equal to 20 dB, hence the reception is almost identical to the unperturbed case. Figure 8 shows the same information, but with a very strong image target (\(\chi=1\,\mathrm{dB}\)). It is easy to notice that the multi-path signal affects deeply the power distribution. Further  
Figure 6: Pictorial rendition of the proposed controlled multi-path scenario. Figure 7: Power distribution relative to antenna #1 in the unperturbed case, at \(2.45\,\mathrm{GHz}\) (**top**) and \(5.2\,\mathrm{GHz}\) (**bottom**). more, it is interesting that the positions of the nulls is almost complementary in the two cases, so that when one frequency is strongly impaired by the disturbance, the other one is almost unscathed.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 7 Proposed Scheme
### Modem Design of Proposed Scheme  
In this section, the components used for designing the adaptation layer are shown. The basic components of underwater multi-media/multi-band communications links are shown in Figure 5. The main component of the modem hardware is the transmitter, which contains components such as a modem controller, medium switch controller, power controller, modulator, frequency band splitter, etc. The medium switch controller (MSC) controls the switch to select the type of channel, e.g., VLC, IR or Ultrasonic. Then, the modulator contains the desired information in light or acoustic form. The acoustic Tx is the acoustic transmitter used to transmit the acoustic signal through the underwater channel. The VLC Tx and IR Tx are used to transmit an optical signal through an underwater channel. At the receiver end, the detector is used to receive the acoustic or optical signal. The received signal process demodulates to get the original data in the form of bits.  
Figure 4: MAC extension and adaptation layer.  
Figure 5: Physical layer components and workflow.
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 2 Materials and Methods
### Acoustic Feature Extraction
#### 2.2.3 Mel-Frequency Cepstral Coefficients  
MFCCs have been widely used in many speech applications, as they model the human cochlear processing steps by including a mel-scale frequency mapping prior to cepstrum processing [55]. For precision beekeeping, these features have also been shown to be popular, appearing in roughly 30% of the papers reviewed in [24]. For example, the first three MFCC coefficients have been widely used for bee, queen presence, and swarming detection. To calculate MFCCs, the following five steps were taken:  
1. Apply a pre-emphasis filter to enhance high frequencies.
2. Compute the STFT of the pre-emphasized signal and its power spectrum.
3. Apply the mel filterbank, composed of triangular filters simulating cochlear processing.
4. Apply the logarithm operation.
5. Compute the discrete cosine transform (DCT) to extract the mel frequency cepstral coefficients.  
In this experiment, a Hann window with a length of 125 ms with a 50% overlap was used in step 2. A total of 12 coefficients were extracted in step 5, plus the zeroth coefficient, which was used as a measure of signal power. Figure 5 shows the 12 MFCCs, plus the zeroth coefficient, as computed over the 24 h duration of the previous audio signal. In Section 3, more details will be provided on the effects of ambient noise on the MFCCs.  
Figure 4: RMS curve and corresponding spectrogram for a hive with with 3 full boxes of bees (12 August 2021).
# A SAR-Based Index for Landscape Changes in African Savannas
## 3 Results  
### Annual Changes  
#### 3.1.1 Natural Resources  
The results gathered based on the LULC classifications were transferred into NR for a hexagon grid as described in Section 2.4. A summary of different statistics over all 1793 grid cells is given in Table 6. It shows that overall resources did not significantly decline within the study area during the investigated period. Neither their mean (NR\({}_{\text{mean}}\)) nor their median (NR\({}_{\text{median}}\)) shows significant trends. A smaller drop of NR\({}_{\text{median}}\) can be observed for 2015. This indicates that natural resources were slightly the lowest in that year or at least at the time of image acquisition. This is supported by the value of the overall sum of all Natural Resources (NR\({}_{\text{sum}}\)). However, the percentiles of 95% and 5% (NR\({}_{95\%}\) and NR\({}_{5\%}\)) indicate that this could also have been caused by a smaller proportion of outliers for the year 2015.  
A small decline in the maximum NR value (NR\({}_{\text{max}}\)) reached can be observed as it decreases from 91.9% to 88.6% over the investigated period. The minimum (NR\({}_{\text{min}}\)) was constant each year as it represents cells which are completely covered by bare rock--this being the class with the lowest attributed importance (see Table 5).  
Overall, no significant change can be observed for the investigated area. A spatially explicit description of changes based on the weighted Natural Resource Depletion is presented in Section 3.1.2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **2007** & **2008** & **2009** & **2010** & **2011** & **2015** & **2016** & **2017** \\ \hline NR\({}_{\text{max}}\) & 91.9\% & 91.5\% & 90.7\% & 90.1\% & 91.4\% & 91.7\% & 89.5\% & 88.6\% \\ NR\({}_{95\%}\) & 73.5\% & 76.2\% & 74.7\% & 74.3\% & 74.6\% & 73.9\% & 73.1\% & 73.3\% \\ NR\({}_{\text{mean}}\) & 46.9\% & 47.1\% & 47.0\% & 47.1\% & 46.9\% & 46.5\% & 46.8\% & 47.0\% \\ NR\({}_{\text{median}}\) & 45.7\% & 45.4\% & 45.9\% & 45.9\% & 45.8\% & 44.3\% & 45.6\% & 45.1\% \\ NR\({}_{5\%}\) & 24.8\% & 24.4\% & 25.0\% & 24.9\% & 24.2\% & 25.0\% & 25.4\% & 25.6\% \\ NR\({}_{\text{min}}\) & 23.1\% & 23.1\% & 23.1\% & 23.1\% & 23.1\% & 23.1\% & 23.1\% \\ NR\({}_{\text{sum}}\) & 840.8 & 844.6 & 843.5 & 843.8 & 840.7 & 833.1 & 839.9 & 842.8 \\ NRD\({}_{\text{w mean}}\) & 0.187\% & \(-\)0.03\% & 0.016\% & \(-\)0.175\% & \(-\)0.421\% & 0.379\% & 0.158\% \\ NRD\({}_{\text{w net}}\) & 3.36 & \(-\)0.65 & 0.28 & \(-\)3.14 & \(-\)7.55 & 6.79 & 2.83 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Development of Natural Resources (NR) and weighted Natural Resource Depletion (NRD\({}_{\text{w}}\)).  
Figure 5: Calculation of the variability v of an area.
## 3 Results and Discussion
### Lactones  
Among the 42 cultivars, the most-abundant lactones were \(\gamma\)-decalactone (L6), \(\delta\)-decalactone (L7), and 6-amyl-\(\alpha\)-pyrone (L5) (Figure 5). The top three cultivars with the highest amount of \(\gamma\)-decalactone (L6) were \"Majestic\", \"Southern Pearl\", and \"Fairtime\" with 502.8 ng\(\cdot\)g\({}^{-1}\), 442.9 ng\(\cdot\)g\({}^{-1}\), and 394.2 ng\(\cdot\)g\({}^{-1}\), respectively (Tables 4 and S1). The cultivar with the lowest amount of \(\gamma\)-decalactone (L6) was \"Flameprince27\" with 27.1 ng\(\cdot\)g\({}^{-1}\). \(\gamma\)-decalactone is characterized by a \"peach\" odor (Flavornet dataset) and a \"fruity\" odor and flavor (Good Scents Company dataset). The second-most-abundant lactone was \(\delta\)-decalactone (L7) (Tables 4 and S1). \"Southern Pearl\" had 190.9 ng\(\cdot\)g\({}^{-1}\), \"Fairtime\" 189.3 ng\(\cdot\)g\({}^{-1}\), and \"Majestic\" 135.2 ng\(\cdot\)g\({}^{-1}\) of \(\delta\)-decalactone (L7). \(\delta\)-decalactone is characterized by a \"coconut\" odor and flavor (Flavornet and Good Scents Company databases). The third-most-abundant lactone was 6-amyl-\(\alpha\)-pyrone (L5) with \"Fairtime\" 134 ng\(\cdot\)g\({}^{-1}\), \"Jurepince\" 84.1 ng\(\cdot\)g\({}^{-1}\), and \"Springprince\" 83.7 ng\(\cdot\)g\({}^{-1}\) having the highest amounts among all cultivars evaluated (Tables 4 and S1). Mid-season cultivars were the highest for \(\gamma\)-hexadecure, \(\gamma\)-octanolactone, \(\delta\)-decalactone, and \(\gamma\)-decalactone compounds in comparison with early- and late-season cultivars (Table 3). Lactones, especially \(\gamma\)-decalactone (L6) and \(\delta\)-decalactone (L7), have been described to be a major factor in the production of the characteristic peach-like aroma [6]. However, they do not work in isolation, but rather in combination with terpen, alcohols, and aldehydes to give peaches their characteristic aroma. This result was consistent with previous studies where \(\gamma\)-decalactone (L6) and \(\delta\)-decalactone (L7) were identified among the most-abundant lactones, while \(\gamma\)-nonalactone (L4) and \(\delta\)-octalactone (L3) were among the least-abundant lactones [8]. The relative total concentration of lactones per cultivar were higher in mid-season cultivars in comparison with early- and late-season cultivars (Figure 1).
# Experimental Investigations on Laser Ablation of Aluminum in Sub-Picosecond Regimes
## 2 Theoretical Background  
The discovery of the chirped pulse amplification (CPA) method [6] and, in consequence, ultrafast lasers, opened a new field for both fundamental and applied research. Ablation that occurs during the athermal state of the sample in the dynamics of the pulse absorption, commonly known as cold ablation, became one of the most promising applications of ultrafast lasers.  
The process occurs within a time range of tens of nanoseconds and differs significantly for long and ultrashort laser pulses. Most of the experiments are carried out with Gaussian beams and just above the ablation threshold. The ablation process involves stages such as: energy absorption, energy transfer to the lattice and material removal. After the pulse energy is deposed into the material, the excitation of electrons from the valance to the conduction band occurs, as well as the free carrier absorption. With significant beam intensity, either multiphoton or avalanche ionization appears. Nonlinear absorption is therefore a dominant mechanism in ultrashort pulse regimes [7].  
The occurrence of the complex processes during laser interactions with matter is almost simultaneous, which makes it difficult to determine the contribution of each one. Depending on the material, on a timescale from hundreds of fs to a few ps, the energy transfer from electrons to the lattice takes place. Hence, the non-equilibrium state of the electrons and the lattice are best portrayed by a two-temperature model [8]. The transfer of energy results in an accelerated thermal or non-thermal melting [9]. Since the mass transport happens in much longer timescales than the non-thermal or thermal melting, the melted parts of material are left at a hot, near solid state. Subsequently, the hydrodynamic expansion of the ablated material begins, on a timescale of a few hundred ps after the initial excitation [10].  
Despite the numerous investigations on the fundamentals of laser material removal, those in the femtosecond regime are still argued. To calculate the estimated ablation threshold in this study, several theoretical models were considered [11; 12; 13]. We found the following solution proposed by [PERSON] et al. to be the most adequate:  
\[F_{th}=\frac{3}{8}(\varepsilon_{b}+\varepsilon_{esc})\frac{\lambda\eta_{\mu}} {2\pi} \tag{1}\]  
According to Equation (1), in case of metals, the ablation threshold's \(F_{th}\) definition is based on the condition that the electron energy must reach the value equal to the sum of the atomic binding energy \(\varepsilon_{b}\) and the work function \(\varepsilon_{esc}\); \(\eta_{\mu}\) is electron density [11]. By this definition, the threshold values for the aluminum samples used in our research have been calculated for each wavelength \(\lambda\). The results are presented in Table 1.
# Effects of Wind Barrier Porosity and Inclination on Wind Speed Reduction
## 1 Introduction
[34] tested snow fences of different gradient slopes using a cold wind tunnel. The authors reported that the slope angle could substantially affect snowdrifts, but much depended on the porosity of the snow fence. These findings suggest that the effectiveness of wind barriers in providing wind protection may vary based on both porosity and inclination. However, to the best of our knowledge, no information is currently available on the simultaneous impact of wind barrier porosity and inclination on wind protection. Thus, quantitative research on this topic is required for the optimal design of windbreak systems. Our study aimed to evaluate the effectiveness of wind barriers by examining both porosity and inclination using a wind barrier made of wood. To achieve this, we conducted wind tunnel experiments with three wind barrier cases: (1) Case A (porosity of 0% with three different inclinations (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\))), (2) Case B (porosity of 25% with the same inclinations), and (3) Case C (porosity of 50% with the same inclinations). To analyze the reduction in wind speed, we measured the wind velocity on the windward and leeward sides of the wind barriers in relation to their distance. Our findings provide valuable insights for the design of windbreak systems, which can help protect saplings and improve windbreak forest management strategies.
# Analyzing the Alignment between AI Curriculum and AI Textbooks through Text Mining
## 4 Textbook Analysis Results
### Tool Utilization through Content Analysis  
Content analysis was used to structure a framework for analyzing platforms based on the foundational AI curriculum, and coding was carried out. This is because both theory and practice are crucial in AI education, and even for identical content, the depth and level of content can vary depending on the tool used. The content analysis method is designed to reflect the uniqueness of the computing field. Consequently, a content analysis framework was developed to identify the composition of the educational tools in each chapter of the textbooks. The tools from textbook chapters were extracted and clustered based on their roles to structure the framework. Coding was performed based on this foundation.  
The results of the tool analysis conducted through content analysis are presented as shown in Table 11.  
First, regarding the use of tools for data processing, including recognition, collection, analysis, and model generation, the following observations were made: In the \"2. Principles and Application of AI\" section, three textbooks (A, B, and C) utilized Quick, Draw! to facilitate the learning of image data recognition, whereas textbook F used code.org to provide hands-on experience with the supervised learning principles of AI. In \"3. The Data and Machine Learning\" section, textbooks B, G, and H used Orange3, whereas textbook C used Brightes AI to generate AI models based on various real-life data samples. Notably, some textbooks solely focused on theoretical explanations without incorporating specific tools. Each textbook used a distinct approach to explain the same concept, and only a few textbooks included practical training examples.  
Figure 4: The trained model visualized using the pyLDAvis.  
Second, regarding AI models and program development, the following perspectives were identified: In the \"2. Principles and Application of AI\" section, five textbooks used ENTRY; one, Scratch; and six, block-based programming languages. However, these textbooks did not cover program development using programming languages. In the \"3. Data and Machine Learning\" section, three textbooks used ENTRY and one used Scratch, resulting in four textbooks using block-based programming languages. In addition, three textbooks developed AI programs that processed the data using Python. Notably, in Units 2 and 3, the two textbooks provided options for both text- and block-based programming languages. However, in two or three sections, certain textbooks did not include development practices that use programming languages.  
Third, the analysis considered tools that support AI ethics. Seven out of the eight textbooks used moral machines to facilitate ethical decision making in various dilemma situations.
## 4 Experiments and Results
### Effects of LCCDNN and FCCDNN Parameters on Neuron Classification
#### 4.3.1 Accuracy Analysis of Different Mini-Batch Size  
For neural networks, a reasonable mini-batch size plays a key role in the well-trained model results [35]. Figures 11 and 12 show the performance comparison of LCCDNN and FCCDNN models with a different mini-batch size. In order to facilitate parallel computing, the mini-batch size in this paper mainly used the power of 2. At the same time, considering the speed and accuracy, this paper set the base mini-batch size to 64 and then adjusted the parameters of the model. The selected training mini-batch sizes were 32, 64, and 128, respectively. It can be seen that LCCDNNs have a higher classification accuracy of neurons than FCCDNNs.  
Figure 11: Comparison of accuracy with different mini-batch sizes by LCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.  
Figure 10: The change curve of accuracy during training by FCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.  
Figure 11 shows the variation in classification accuracy by using the LCCDNNs. The mini-batch sizes were 32, 64, and 128, while the other parameter settings remained the same. For the C. elegans dataset (Figure 11a), the accuracy of the training set was 97.25%, 98.03% and 96.14%, and the accuracy of the test set was 82.56%, 87.43%, and 74.62%, respectively. Figure 11b shows the accuracy variation in the zebrafish dataset with different mini-batch size, where the accuracy of the training set was 90.65%, 98.16%, and 83.96% and the accuracy of the test set was 71.86%, 81.16% and 75.58%. Figure 12 shows the classification results of FCCDNNs. When the mini-batch size was 32, 64, and 128, the accuracy of the test set of C. elegans was 83.08%, 83.33%, and 83.33%, and the accuracy of the test set of zebrafish was 66.98%, 72.33%, and 65.81%., respectively Therefore, when the mini-batch size was 64, the LCCDNN and FCCDNN models had the highest classification accuracy of neurons.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 7 Experiment in Laboratory Environment  
The proposed methods can localize a sound source accurately and robustly based on the simulation result. However, testing the proposed methods in practical environments remains necessary because the simulated data are generated based on a mathematical model with several assumptions, which might be difficult to achieve in a practical environment. Therefore, in this section, the proposed localization methods are tested with data recorded in a real indoor environment with the existence of reverberation and background noise.  
The experimental evaluation of the localization performance is conducted in an enclosed laboratory at the Australian National University. A GRAS KEMAR manikin Type 45 BA human-like simulator is used to collect the binaural testing data, which is performed by playing a source signal via loudspeakers fixed at different directional positions. Two Type 40 AG polarized pressure microphones are placed at the entrance of the ear canal of the dummy head. To test the robustness and practicality of the proposed method, the testing dataset is collected in a real laboratory environment, while the training data are synthesized as described in the previous chapters based on the open HRTF database (i.e., CIPIC database) [24]. Hence, the testing environment is completely unfamiliar to the localization model, and no prior knowledge about the room acoustical characteristic is learned during the training process.