esa-sceva/satcom-chunk-collection huggingface dataset:
# Grid Anchor Lane Detection Based on Attribute Correlation
## 4 Experimental Results
### Ablation Study
#### 4.3.1 Impact of Different Attributes  
Section 3.1 discussed the grid anchor-based lane line representation, which includes five attributes. Among these, existence and instance attributes correspond to traditional low-resolution segmentation-based methods, while the direction, offset, and slope attributes are novel to the grid anchor representation. To evaluate the impact of these added attributes on the model's performance, we constructed loss functions based on partial or all of these five attributes during training. To maintain the principle of single-variable testing, the experiments used only the intra-anchor loss function to converge the model.  
From Table 6, it can be observed that compared to segmentation-based methods, learning any single additional attribute improves the model's detection performance. When the model learns all the attributes during training, the detection accuracy is maximized. This is because the new attributes provide additional information dimensions that guide the model to better fit the dataset, expanding the range of information the model can focus on. On the other hand, the offset attribute transforms the discretized coordinates produced by semantic segmentation into continuous coordinates within the image range, thereby improving the model's expression accuracy. Therefore, the grid anchor-based lane line representation method enhances the model's ability to capture lane line features.
# Approximate LSTM Computing for Energy-Efficient Speech Recognition
## 1 Introduction
The skipping method is then applied by limiting the number of consecutive disabled cells, remarkably reducing the number of activated cells during the end-to-end speech recognition with acceptable accuracy. The computational cost is further relaxed by adopting the pseudo-skipping approach, which is basically the variation of delta-LSTM processing in [12]. In the proposed accelerator, we first realize the efficient controller architecture to support the on-demand calculation of the similarity score. The processing elements (PEs) are then carefully designed to support two types of MAC operations dynamically, i.e., the normal full-precision MAC computations and the approximate MAC processing with the reduced precision. The new data scheduling technique is proposed to allow the small-sized SRAM buffer by latching only the survived parameters to handle the sparse matrix-to-matrix multiplications. As a result, the prototype LSTM accelerator in 65 nm CMOS technology supports the low-power on-device speech recognition by achieving the energy-efficiency of 211 GOPS/W, which is only 49% of the required energy at the baseline implementation. The rest of this paper is organized as follows. Section 2 describes the background knowledge of the end-to-end speech recognition using the DeepSpeech Network [15], which is the baseline model of this work. Section 3 describes the algorithm-level innovations of the proposed method, including skipping and approximate cell operations. The proposed accelerator architecture is then detailed with the hardware-level optimizations in Section 4. Simulation and implementation results are shown and compared to the prior works in Section 5, and the concluding remarks are finally made in Section 6.
# Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB
## 3 Overview of Centralized and Decentralized Ledgers
### Overview of Centralized Ledgers  
In the case of centralized ledgers, transactions are stored in a ledger under the control of a single entity. The physical infrastructure is usually co-located in the same location and the centralized node that controls the ledger is unique. The ledger trustee must maintain the ledger, record asset transfers and receive properly verified notifications [20].  
Centralized ledgers are used as a combination of classical transactional technology (centralized recording and authorization/validation of the transactions) implemented using blockchain technology. This paradigm is close to traditional approach in accounting systems and offers an easier migration path from the perspective of enterprise users (being both familiar and regulated in sensitive industries such as finance and controlled research and development). In this instance, the control and validation of data is allowed via a single centralized node, which increases the cost and decreases the viability of the system (if the central node fails the entire system becomes non-functional because the system has a single point of failure), even if the entire system is backed-up (accounting for the downtime and the lost transaction that where in process of validation). Moreover, in this situation, the trust authority is set towards a single entity that controls the access to the validation mechanism and to the data. Even if the singular data (transaction) is immutable (owning to the cryptographical approach), the entire chain can be rebuilt if the trust agent wishes so, as no consensus and cross-validation is required [7]. A zero-trust approach is not possible in this context.  
To summarize, the main drawbacks of this approach are its decreased usability, limited trust, limited privacy, the lack of verifiable immutability and the presence of a single point of failure. These concerns can be alleviated in a decentralized approach.  
CLDs include LedgerDB, QLDB, Oracle BC Table, ProvenDB, and others. The findings of the QLDB evaluation will be presented in this paper.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 5 Multi-Operator Game for SF Assignment
### Player Utilities  
Each player, i.e., network operator, will seek to maximize the logarithmic sum of its own normalized throughput. As attested to in [13], the logarithmic utility function is intimately associated with the concept of proportional fairness. The goal is to strike a good compromise between efficiency (maximizing the log of throughput will increase the throughput) and fairness. The utility per network operator can be expressed as:  
\[U_{i}=\sum_{c=1}^{\mathcal{C}}\sum_{s=1}^{S}x_{c}^{i}\cdot\log(G_{sc}^{i}\exp (-2G_{sc})), \tag{5}\]where \(\exp(-2G_{sc})\) represents the success ratio per spreading factor (the same for any network \(i\)) and \(G_{sc}^{i}\) represents the traffic load of network \(i\). As such each network will seek to maximize its own delivered traffic, regardless of the others.
## 4 RMSSA Problem Formulation in Spatial Channel Networks
### Traffic Request Set  
Model a node-to-node traffic request set as \(T=\left\{t_{1},t_{2},\ldots,t_{|T|}\right\}\) and each traffic request \(t_{i}\in T\) can be represented as a tuple \(t_{i}=\left(s_{i},d_{i},r_{i}\right)\) defined by a source node \(s_{i}\in V\), a destination node \(d_{i}\in V\), and traffic rate \(r_{i}\in R\), where \(R\) is the traffic rate set. For a traffic request \(t_{i}\), its source node \(s_{i}\) and destination node \(d_{i}\) (\(s_{i}\
eq d_{i}\)) are randomly and uniformly generated, and the sum of all traffic rates \(\sum r_{i}\) in the same set \(T\) is a fixed value.
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## Acronyms and Abbreviations  
SNRsignal-to-noise ratio  
MMSEminimum mean-squared error  
MSEmean-squared error  
DNNdeep neural network  
PESQperceptual evaluation speech quality  
STOIshort-time objective intelligibility  
CLcomponent loss  
GLgeneralized loss  
SI-SDRscale-invariant speech distortion ratio  
T-Ftime-frequency  
DFTdiscrete Fourier transform  
SGDstochastic gradient descent  
STFTshort-time Fourier transform  
BNbatch normalization  
ELUexponential linear unit  
TMSEtime mean-squared error  
iSTFTinverse short-time Fourier transform  
NAnoise attenuation  
SAspeech attenuation  
SDRspeech distortion ratio  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **GL** & **MSE** & **Equal** \\ \hline
**Preference** & 70.0\% & 22.0\% & 8.0\% \\ \hline
**Methods** & **GL** & **TMSE** & **Equal** \\ \hline
**Preference** & 66.5\% & 22.0\% & 12.5\% \\ \hline
**Methods** & **GL** & **SI-SDR** & **Equal** \\ \hline
**Preference** & 70.5\% & 23.5\% & 6.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of subjective listening test. The numbers indicate the percentage of votes in favor of one approach. The choice “Equal” means no subjective difference.
## 6 Conclusions  
In this paper, the vibration localization characteristics of the CMS are investigated by using perturbation analysis. The parameter influence on mode localization and eigenvalue splitting is analyzed, and the influence of the frequency mistuning strength and coupling strength on the vibration reduction performance is summarized. Subsequently, a numerical model is established to validate the theoretical results. The research outcomes indicate that an increase in mistuning strength enhances mode localization and eigenvalue splitting of the CMS. Setting an appropriate mistuning strength is beneficial to broaden the resonant frequency range. An increase in coupling strength will reduce mode localization and lead to an overall elevation of the eigenvalues. When the coupling strength is large, it is not conducive for the CMS to achieve stable broadband vibration reduction performance.  
Figure 19: Acceleration response of the pipeline under complex excitation. (**a**) Point A1. (**b**) Point A2.  
Figure 18: Flow chart of the excitation test.  
Therefore, it is advisable to minimize the coupling strength between resonant units as much as possible.  
A pipeline-oriented CMS is proposed, which mainly consists of a framework, resonant units, and a mounting fixture. The framework features a ring-shaped hierarchical honeycomb structure, which can embed three different types of resonant units and has the advantages of small installation size, high integration, and rich frequency regulation characteristics. The FRF test results show that when appropriate mistuning parameters are introduced, the FRF of the pipeline steadily decreases in the broadband range. The excitation test results show that compared with the bare pipeline, the installation of the CMS reduces the acceleration amplitude of the pipeline by 34.7 dB at 82.5 Hz, 16.0 dB at 90 Hz, and 19.8 dB in the broadband vibration level across the 70 Hz to 120 Hz range. The pipeline-oriented CMS exhibits favorable low-frequency broadband vibration reduction capability.  
L.Y.: Conceptualization, data curation, visualization, writing--original draft. W.X.: Conceptualization, investigation, writing--review and editing. J.Z.: Software, writing--review and editing. S.L.: Conceptualization, methodology, writing--review and editing. L.X.: Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the Independent Research Project of the High-Level Scientific and Technological Innovation Talent Program (Number: 202417F010).  
The data that support the findings of this study are available from the
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 5 Results of the Current Hard SyDR Implementation
### Results of the Acquisition Stage
The potential effects of solutions S1 and S2 on the hardware resource budget are explored in Table 2. We provide absolute post-synthesis resource utilization estimates, in Lookup Tables (LUTs), Flip-Flops (FFs), DSP slices, and BRAMs, along with savings relative to our reference [16k, float ] implementation. ap_fixed(16,1) refers to a 16-bit fixed-point format with one integer bit and fifteen fractional bits. As mentioned above, BRAMs are the resource bottleneck. Combining solutions S1 and S2, we can achieve savings  
Figure 8: Comparison of acquisition results from different FFT implementations with and without zero-padding. Vertical dash lines represents the value found by the acquisition. of almost 80% of the BRAMs using \(\{8\}\)k, \(\texttt{ap\_fixed}\{16,1\}\) ) instead of \(\{16\}\)k, \(\texttt{float}\) ]. Vitis HLS also natively supports fixed-point formats with fewer than 16 bits, yet our architecture requires further adaptation to function properly with such. It is noteworthy that post-synthesis resource utilization numbers from HLS flows tend to be associated with rather large errors [45]. This is observed for the LUT and FF numbers in our experiments too, for which the estimates can be as much as eight times off the accurate post-implementation results. However, importantly, the BRAM estimates are not affected by these errors as they result from fully specified array declarations in the C/C++ code. Additionally, we experienced that Vitis HLS produces invalid HDL code for its FFT IP when using its array-based interface as specified in [38]. We have reported this on the Xilinx forums; see [[https://shorturl.at/ntvGM](https://shorturl.at/ntvGM)]([https://shorturl.at/ntvGM](https://shorturl.at/ntvGM)) accessed on 14 December 2023. Initial experiments following S3 with manual allocation of nearly all non-FFT-related buffers to the UltraRAM slices available on UltraScale devices indicate that at least one full channel with acquisition and tracking can be fit on the KV260's FPGA. Therefore, although the current implementation requires a large amount of resources, these can be greatly reduced, and we foresee exploring the effects of the three solutions to its optimization, including the unexplored solution S3, further in our future development.
## 1 Introduction
### ROI Concept
The authors applied this described use of software tools to a study focused on zebrafish embryos, with three main goals: (1) the distribution analysis of natural elements (based on \({}^{12}\)C, \({}^{39}\)K, and \({}^{31}\)P isotopes) and the reproducibility between ablations of different individuals; (2) finding the accumulation zones of iodine- and bromine-containing xenobiotics; and (3) analyzing the influence of the exposure time and developmental stage on the uptake and distribution of xenobiotics. Addressing all of the above-mentioned questions was possible because of the proposed tools [62]. Despite several examples described in the literature, it can be seen that the ROI concept is neither well established nor widely used. In 2020, [PERSON] et al. [60] called extracting ROIs a \"more sophisticated image processing method\". In their review, [PERSON] et al. [63] mention ROIs only in the context of SIMS, even though LA-ICP-MS is described as one of the methods to study metal-based drug distributions in different biological systems. In their review from 2014, [PERSON] et al. [64] summarize the data treatment in few steps: (1) the reconstruction of a two-dimensional map in dedicated software from the raw data; (2) the selection of ROIs (the whole sample or anatomically selected areas) and compulsorily the background; and (3) averaging the information from each ROI. The authors do not define how to delineate ROIs. Basing on the other publications from [PERSON]'s group, it can be assumed that they are extracted manually (ROIs are described as \"freely drawn\" in the software) [47; 58; 59; 65; 66]. This group also called partitioning the tissue into ROIs \"segmentation\" [67]. The same authors describe more precise alternatives to the extraction of relatively large ROIs, i.e., comparing every single pixel with magnetic resonance, or maybe simply microscopic images. However, significant deformations occur during the slicing process that may influence the exact position of microscopic fragments [67]. Great advantages of image segmentation for ROIs have been emphasized by many authors [47; 60]. The differentiation of histologically relevant fragments of a tissue is valuable for understanding the essential biochemical and biological processes [60]. Moreover, data concerning small substructures may be lost if the entire ablated area is analyzed together or, even worse, bulk analysis is applied [47]. In the most of the cited papers, ROIs were extracted manually; however, the support of automated and objective procedures is described as desirable [67]. It is emphasized that dividing the sample into ROIs is valuable, especially for group comparisons [67]. Individual ROIs should correspond to the anatomical structures and should be as homogeneous as possible. Depending on the aim of a study, divisions can be executed on a finer or a coarser scale [67]. In our study, a mathematical approach to distinguish two regions was proposed, in order to find more universal and simple method of data analysis.
## 3 Results
### Exposure Assessment  
Figure 1 displays estimated total daily intake of AFM1 after a Monte Carlo simulation for two recall methods and two scenarios. Results showed that mean values were more dependent on the recall method as opposed to the scenarios employed. Mean AFM1 exposure of the Serbian population (Figure 1, Table 5), depended on the recall number of days and scenario employed was in the range between 62-74 \(\times\) 10\({}^{-3}\) ng/kg bw/day. On the other side, maximal values (Table 5) were determined by the scenario applied and distributions span from 0.00397-0.75724 ng/kg bw/day (7 d, scenario 1) to 0.00323-7.05512 ng/kg bw/day (7 d, scenario 2). Figure 1 shows an asymmetrical distribution shape slightly tailed where the data reflected a non-normal shape with exponential decreasing: the probability of exposure (presented as frequencies of exposure) decreased as the exposure value increased. Also, EDIs reflected the asymmetrical scattering of the data (Table 5).  
[PERSON] et al. [46] assumed in 2013 that exposure to AFM1 from milk was 0.21 ng/kg bw/day. Our results are promising compared to a study using data from 2014 and 2015 performed by [PERSON] et al. [47] where exposure to AFM1 from milk in Serbia was between 0.18 and 0.20 ng/kg bw/day. When comparing exposure assessments with other countries, results from Serbia should be compared to those obtained from regions with climate conditions suitable for growth of AFs [9]. Values from this study are higher than in Italy or Spain where exposure assessments revealed a range between 0.025 and 0.328 ng/kg bw/day and a value of 0.039 ng/kg bw/day, respectively [8,48]. In parallel, our results are in line with the estimations from the French and Brazilian studies estimating 0.09 and 0.08 ng/kg bw/day [22,49].  
The result of uncertainty analysis of Monte Carlo variation (Table 5) shows that for EDI of AFMI, 95% CI of the mean value is 73.3-76.6 \(\times\) 10\({}^{-3}\) ng/kg bw/day (1-day recall, both scenarios) and 61.8-64.8 \(\times\) 10\({}^{-3}\) ng/kg bw/day (7-day recall, both scenarios).  
\begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**AFM1 Intake**} & \multicolumn{2}{c}{**AFM1 ng/kg bw/day (1-Day Recall)**} & \multicolumn{2}{c}{**AFM1 ng/kg bw/day (7-Day Recall)**} \\ \cline{2-5}  & **Scenario 1** & **Scenario 2** & **Scenario 1** & **Scenario 2** \\ \hline Mean & 0.07362 & 0.07592 & 0.06207 & 0.06420 \\ Minimum & 0.00782 & 0.00658 & 0.00397 & 0.00323 \\
1 st quartile & 0.04559 & 0.03845 & 0.04026 & 0.03445 \\
3 rd quartile & 0.08926 & 0.08255 & 0.07257 & 0.06642 \\ Maximum & 0.67381 & 6.65235 & 0.75724 & 7.05512 \\
95\% confidence & 0.07333–0.07390 & 0.07527–0.07658 & 0.06184–0.06230 & 0.06362–0.06478 \\ interval of mean & & & & \\ \hline \end{tabular}
\end{table}
Table 5: Estimated daily intake (EDI) of AFM1.
## 4 Results and Discussion
### Air Temperature Distribution and Relative Temperature Deviation
As depicted in Figure 8, the air velocity above the crops in the  
Figure 9: The average temperature above the crop in each layer of the cultivation tray at various \(P_{d}\) as a function of _ACH_: (**a**) \(P_{d}\) = 0.9; (**b**) \(P_{d}\) = 0.7; (**c**) \(P_{d}\) = 0.5; (**d**) \(P_{d}\) = 0.3; (**e**) \(P_{d}\) = 0.1.  
upper cultivation ray is higher when the \(P_{d}\) of the crop area is lower compared to when it is higher. This can be attributed to the lower resistance to flow within the room at lower \(P_{d}\), which facilitates a stronger mixing flow within the heated air. The uniformity of air temperature distribution above the crops plays a crucial role in promoting optimal crop growth. Figure 12 illustrates the variation in the overall \(RSD_{T}\) of the air temperature above the crops at all five planting densities at different \(ACH\). It can be observed that the highest \(RSD_{T}\) values occur at \(ACH\) = 20 h\({}^{-1}\) for all planting densities. This is because, in cases with lower \(ACH\) where the indoor air is primarily influenced by thermal buoyancy, there is a tendency for temperature stratification along the height. As the \(ACH\) increases, the \(RSD_{T}\) decreases for all planting density cases. This is because the inlet of low-temperature air increases, leading to a stronger mixing flow indoors (Figure 13). Consequently, the influence of thermal buoyancy on the indoor airflow gradually diminishes, resulting in a more uniform air temperature distribution above the crop. Moreover, as the \(ACH\) increased (in cases where \(ACH\) < 60 h\({}^{-1}\)), the \(RSD_{T}\) decreased significantly in all cases with \(P_{d}\), though it showed a weaker decrease in cases where \(ACH\) > 60 h\({}^{-1}\). Meanwhile, the disparity between the \(RSD_{T}\) of each \(P_{d}\) decreased with increasing \(ACH\). This can be attributed to the fact that, when the \(ACH\) exceeded 60 h\({}^{-1}\), although there is a gradual improvement in the mixed airflow within the room, it did not have a significant impact on reducing the temperature differences above the crops in each layer of the cultivation trays. Consequently, the changes in \(RSD_{T}\) at each \(P_{d}\) became insignificant. Figure 11: The temperature distribution of the airflow above the crop at each \(P_{d}\) at \(ACH\) = 60 h\({}^{-1}\) is presented in two dimensions. Figure 10: Indoor air temperature at \(P_{d}\) = 0.3. From the temperature distribution analysis in the room with different \(P_{d}\) and \(ACH\), it can be observed that increasing \(ACH\) and decreasing \(P_{d}\) appropriately can enhance the uniformity of temperature distribution. However, it is noteworthy that further increasing \(ACH\) (in the case of \(ACH\) > 60 h\({}^{-1}\)) and decreasing \(P_{d}\) does not result in a significant decrease in the \(RSD_{T}\). Additionally, it is important to note that significant thermal stratification occurs at lower \(ACH\) values, leading to higher \(RSD_{T}\) values. On the other hand, increasing the \(ACH\) results in higher energy consumption by the ventilation system.