esa-sceva/satcom-chunk-collection huggingface dataset:
# Approximate LSTM Computing for Energy-Efficient Speech Recognition
###### Abstract  
This paper presents an approximate computing method of long short-term memory (LSTM) operations for energy-efficient end-to-end speech recognition. We newly introduce the concept of similarity score, which can measure how much the inputs of two adjacent LSTM cells are similar to each other. Then, we disable the highly-similar LSTM operations and directly transfer the prior results for reducing the computational costs of speech recognition. The pseudo-LSTM operation is additionally defined for providing the approximate computation with reduced processing resolution, which can further relax the processing overheads without degrading the accuracy. In order to verify the proposed idea, in addition, we design an approximate LSTM accelerator in 65 nm CMOS process. The proposed accelerator newly utilizes a number of approximate processing elements (PEs) to support the proposed skipped-LSTM and pseudo-LSTM operations without degrading the energy efficiency. Moreover, sparsity-aware scheduling is introduced by introducing the small-sized on-chip SRAM buffer. As a result, the proposed work provides an energy-efficient but still accurate speech recognition system, which consumes 2.19 times less energy than the baseline architecture.  
Keywords:approximate computing; LSTM architecture; speech recognition; VLSI design +
Footnote †: journal: Electronics
# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 5 Experiments and Evaluation
### Experimental Results
#### 5.2.3 CDSTAEP Model and Baseline Model
Based on the original data set, which contains 301 abnormal host nodes, the overall prediction time of the CDSTAEP model is compared with the baseline method, as shown in Table 7. Since the model training is offline and the training time is related to computing power, the time consumption in the abnormal reasoning stage is more important to evaluate the model. As can be seen from the comparison result, because the model CDSTAEP is more complicated, the time efficiency of the reasoning stage is not the best. Compared with the model classification effect, this is acceptable. Overall, the prediction effect of the CDSTAEP model is better than that of LSTM-ATT and LSTM, and regardless of the training ratio, it can achieve better results and is more robust. The LSTM model only considers the time dependence between events but does not consider the intra-domain and cross-domain dependencies between events, resulting in a slightly poorer prediction effect. The LSTM-ATT model considers the attention of essential features based on LSTM but still does not consider the relationship between events. Therefore, the prediction effect is slightly better than LSTM and slightly worse than CDSTAEP. The above results show that the CDSTAEP model can stably improve the performance of the selected classifiers and prove the embedded representation of complex network events in the mixed-curvature space while introducing dependencies such as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Time(s)**} & \multicolumn{4}{c}{**ACC**} \\ \cline{3-5}  & & **Training Ratio** & **Training Ratio** & **Training Ratio** & **Training Ratio** \\ \hline / & / & **20** & **40** & **60** & **80** \\ CDSTAEP & 61 & 0.4826 & 0.5354 & 0.7132 & 0.7983 \\ LSTM-ATT & 67 & 0.4368 & 0.5065 & 0.6773 & 0.7435 \\ LSTM & 55 & 0.4123 & 0.4695 & 0.6072 & 0.6834 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Time efficiency and ACC for different classifiers on the dataset. Figure 9: Comparison of prediction effects of different models: the horizontal axis is the training ratio, and the vertical axis is the ACC score. time, inter-domain (between hosts), and between events, which can effectively improve the accuracy of abnormal event prediction.
## 1 Introduction  
The coast is a very dynamic and varied environment, constantly changing in response to complex interrelations between landforms and processes operating across a wide range of spatial and temporal scales. Understanding shoreline dynamics, for integration into process-based modelling, for example, and being able to predict future evolutions in the context of relative sea-level rise, requires an accurate characterization of the geometric processes acting at the sediment-water interface [1, 2, 3, 4, 5]. Sediment reworking on the beach and nearshore due to waves and currents traduces by the formation of topographic complexity (herewith generally called roughness), such as sedimentary bedforms. Thesetopo-morphological features are the imprint of the processes that shaped them, and, as such, measuring bedforms can provide meaningful information on hydrodynamic forces [6; 7; 8; 9; 10]. Besides, bedforms are roughness elements that modify the flow and dissipate energy [11; 12]. They are, thus, interesting markers of the sediment bed propensity towards resistance to entrainment and dynamic equilibrium.  
Different types of bedforms generally coexist in the coastal environment (e.g., Refs. [13; 14; 15]), whose dimensions and shapes depend on sediment availability, sediment type (e.g., size distribution and shape) and driving mechanisms (e.g., waves, currents, wind). Examples of small-scale bedforms are wave and current sand ripples, or gravel-bed clusters, with dimensions or length scales generally not exceeding a few tens of centimeters. Larger-scale bedforms include varied bar and trough morphologies (e.g., ridge and runnels) in the surf and swash zones [16], other rhythmic patterns most often found on the upper beach, such as beach cusps [17; 18], as well as aeolian sand dunes. In between those, there are a wide range of topographic variations that take part in the continuum of roughness scales and contribute to the overall structural complexity of the seabed, backshore and dune system.
###### Abstract  
This article investigates how to exploit the high-frequency mmWave for 5G-advanced and beyond, which requires new filters for the wide bandpass and its multi-sub-band. Based on the substrate-integrated waveguide (SIW), spoof surface plasmon polariton (SSPP), and metastructures, like complementary split-ring resonators (CSRRs), the development of a wide bandpass filter and a multi-sub-band filter is proposed, along with an experimental realization to verify the model. The upper and lower cutoff frequencies of the wide bandpass are controlled through an SIW-SSPP structure, whereas the corresponding wide bandpass and its multi-sub-band filters are designed through incorporating new metastructures. The frequency range of 24.25-29.5 GHz, which covers the n257, n258, and n261 bands for 5G applications, was selected for verification. The basic SIW-SSPP wide bandpass structure of 24.25-29.5 GHz was designed first. Then, by incorporating an Archimedean spiral configuration, the insertion loss within the passband was reduced from 1 dB to 0.5 dB, while the insertion loss in the high-frequency stopband was enhanced from 40 dB to 70 dB. Finally, CSRRs were integrated to effectively suppress undesired frequency components within the bandpass, thereby achieving multi-sub-band filters with low insertion losses with a triple-sub-band filter of 0.5 dB, 0.7 dB, and 0.8 dB in turn. The experimental results showed strong agreement with the design scheme, thereby confirming the rationality of the design.  
wideband and multi-band filter; SIW; SSPP; meta structure; 5G
## 4 Results and Discussion
### Simulation Results in the Non-Rotating Reference Frame  
This section analyzes the internal variables of the IPC in the non-rotating reference system for the simulations performed in the previous section. Figure 8 shows the temporal response of these variables for simulation case 2. These variables are the tilt and yaw moments (\(\text{M}_{\text{t}}\) and \(\text{M}_{\text{y}}\)) obtained by the MBC transform, the tilt and yaw pitch signals (\(\beta_{\text{t}}\) and \(\beta_{\text{y}}\)), and the evolution of the adaptive gains \(\text{k}_{\text{t}}\) and \(\text{k}_{\text{y}}\) of the controllers and the adaptive azimuth offset \(\psi_{\text{o}}\). As in Figure 7, for clarity, only three IPC schemes and only 250 s of simulation are shown. Table 5 shows the mean values and STDs of the moments \(\text{M}_{\text{t}}\) and \(\text{M}_{\text{y}}\) for the three simulation cases.  
Figure 8: Time responses of the IPC variables in the non-rotating reference frame: tilt moment (\(\text{M}_{\text{t}}\)), yaw moment (\(\text{M}_{\text{y}}\)), tilt pitch signal (\(\beta_{\text{t}}\)), yaw pitch signal (\(\beta_{\text{y}}\)), controller adaptive gains (\(\text{k}_{\text{t}}\) and \(\text{k}_{\text{y}}\)), and adaptive azimuth offset (\(\psi_{\text{o}}\)) for simulation case 2 from 250 s to 500 s. The units of \(\text{k}_{\text{t}}\) and \(\text{k}_{\text{y}}\) gains are in deg/(MNm) for IPC\({}_{-}\)P2\(\psi\).  
The CPC without IPC showed an average component of the moments \(\rm M_{t}\) and \(\rm M_{y}\) far away from zero, which negatively impacted the DEL value of the OoP blade moments. IPC schemes significantly reduced the mean of these moments by approximately 99%, especially the IPC with integral controllers. The IPC schemes that reduced these mean values the most were those with integral controllers plus the azimuth offset (\(\rm IPC\_I1\psi\) and \(\rm IPC\_I2\psi\)), as shown in Table 5. The IPCs with proportional controllers required much higher activity of the pitch signals in the non-rotating frame (\(\rm\beta_{t}\) and \(\rm\beta_{y}\)) to reduce these moments and still showed higher mean values of these moments. The IPC adaptive gains \(\rm k_{t}\) and \(\rm k_{y}\) agree with the calculations in the previous section. The IPC schemes with proportional controllers showed higher gains than the IPC schemes with integral controllers, and their azimuth offset values were much lower than those of the IPC schemes with integral action. Among the IPC schemes with integral controllers, those with the azimuth offset had higher controller gains.
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
## 3 Results and Discussion
### Simulation Results for Computational Human Models
**Adult male SAR 10g values for selected tissues and whole head are given in Figure 10. It can be concluded from the figure that there is no SAR 10g above limits at 900 MHz while some of the SAR 10g values are above the determined ICNIRP limit at 1800 MHz. The peak SAR 10g is 5.15 W/kg, the highest among all SAR 10g values. Fat tissue exhibited the highest SAR value at 1800 MHz with a value of 4.80 W/kg. The maximum of the ratios between SAR 10g values of head and brain tissue is 4.44. **  
**Figure 10.** SAR 10g values of head and selected tissues of adult male, (**a**) 900 MHz and (**b**) 1800 MHz. **Figure 11 shows the box graphs for all SAR 10g results for 900 MHz, and 1800 MHz. In the figure, the red line represents the median value, while the green diamond represents the mean value. The mean SAR values of brain, fat, muscle and head tissues at 900 MHz are 0.248, 0.453, 0.368, and 0.509 W/kg, respectively, with standard deviations of 0.268, 0.466, 0.393, and 0.533 W/kg, respectively. At 1800 MHz, the mean SAR values of the tissues are0.588, 1.828, 1.171, and 1.971 W/kg, respectively, with standard deviations of 0.441, 1.4620, 0.956, and 1.603 W/kg. When evaluating the simulation results as a whole, it is observed that at 900 MHz, no SAR value exceeds the basic restriction for general public exposure. However, at 1800 MHz, the limit is exceeded only for the whole head in children at the 3 rd power level (1 W). Moreover, at the 4 th (1.25 W) and 5 th (1.5 W) power levels, the SAR values for fat, muscle, and head tissues also exceed the limit. In both female and male, the limit is sur-passed in the fat and head tissues after the 3 rd power level. Additionally, in males, the muscle SAR value at the 5 th power level also exceeds the limit.
# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 2 Methodology
### Evaluation Metric  
In our study, the model specifically targets single end data input. For a fault in a three-terminal transmission line, it produces three predicted fault types, one from each terminal. An event's classification is considered accurate only if all terminals are accurately classified. If any terminal prediction is incorrect, the event prediction is marked as unsuccessful, denoted as pred = 0 in (1).  
\[pred=\begin{cases}1,&if\prod_{i=1}^{N_{terminal}}\hat{y_{i}}=y\\ 0,&Otherwise\end{cases} \tag{1}\]  
where \(N_{terminal}\) is the number of terminals, \(\hat{y_{i}}\) is the predicted fault type for terminal \(i\), and \(y_{i}\) is the actual fault type of the event.  
Subsequently, accuracy for the study is defined as the percentage of correctly predicted events among all events, calculated as (2):  
\[Acc=\frac{\sum_{j=1}^{N_{event}}pred_{j}}{N_{event}}\times 100\% \tag{2}\]  
where \(N_{event}\) is the total number of events, and \(pred_{j}\) is the prediction for event \(j\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters/Method** & **Method I** & **Method II** & **Method III** \\ \hline Epoch & 44 & 28 & 28 \\ Batch Size & 1 & 128 & 128 \\ Learning Rate & \(1\times 10^{-3}\) (use warmup step and learning rate scheduler) \\ Loss function & & Cross-Entropy Loss \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model parameter settings in each method.  
Figure 9: Learning rate using warmup step and a learning rate scheduler.
# Collision Detection Method Using Self Interference Cancelation for Random Access Multiuser MIMO
## 4 Interference Power Characteristics in an Actual Indoor Environment  
We carried out indoor measurement using IEEE802.11n/ac-based OFDM signals, in order to verify how serious the problem of interference is for the DT, when considering the collision detection at the AP. Figure 8 and Table 2 show the measurement environment and parameters, respectively. In this measurement, the interfering power from IT to AP and DTs is verified by assuming packet collision in an actual indoor environment. Hence, in this measurement, IT is a transmit antenna, and AP and DT are receive antennas. The OFDM signal has been measured in an actual indoor environment. The received power was analyzed by the measured OFDM signal. The proposed method is employed by the measured received signals in Section 2.  
The size of the room is 6.2 \(\times\) 7.5 m and AP and DTs are located in a corridor (outside the room). The center frequency and transmit power are 2.55 GHz and 21 dBm, respectively. The other basic measurement parameters are the same as that for the IEEE802.11 ac standard signal format. The short preamble signals are obtained by sliding correlation at each measurement point. In order to avoid specific characteristics in the measurement, the transmitter is moved with an interval of 0.5 wavelengths by using a position controller, and 17 measurement points are obtained for each transmit location.  
Figure 8: Measurement environment.  
Figure 7: Estimation error versus SIR.  
Figure 9 shows the interference to noise power ratio (INR) versus the distance from IT to AP at the AP position. The INR is obtained by averaging 17 measurement points. The broken line indicates the threshold value, which is obtained by the estimation error with 1 dB in Figure 7, because the INR is 15 dB when SIR and SNR are 30 and 45 dB, respectively. When the INR is greater than the threshold value, the proposed method can detect the interference while cancelling the self-interference between AP antennas. Moreover, Figure 10 shows that the INR versus distance from IT to AP is identical in this measurement. As can be seen in Figure 10, the interference can be detected at a distance of 16 m from the AP. Since the tendency of INR among AP and DTs are similar, the AP instead of DTs can successfully estimate the interfering power from IT when considering a room with small size.  
On the other hand, it is verified from Figure 10 that DT receives the interference but AP cannot receive interference
## 4 Discussion
### Influencing Factors of Plant Diversity in the Yellow River Delta  
#### 4.2.1 Effects of Soil Properties on Plant Diversity  
The distribution of plant communities influences the spatial distribution of soil physicochemical properties, while variations in these properties are crucial for species geographic distribution and biodiversity [49]. Consistent with classical concepts, the salt and moisture content in the soil played a significant role in species composition. Variations in soil salinity and moisture can directly affect changes in plant community diversity, subsequently influencing the multifunctionality of the ecosystem [50; 51]. Notably, the soil salinity is an important factor affecting plant diversity in the modern Yellow River Delta. This study found the significant negative correlation between soil electrical conductivity (SEC) and plant diversity, which aligned with most research findings [31; 52]. As the SEC decreased and soil conditions improved, a greater variety of herbaceous plant species began to grow and proliferate. Field investigations revealed that areas with high SEC (DS1) primarily consisted of halophytic plant communities, such as _Suada salsa_, characterized by simple community structures that resulted in low plant diversity. By contrast, areas with a lower SEC (DS5) supported communities of _Phragmites australis_, _Miscanthus sacchariflorus_, with other plants coexisting, which often exhibited higher levels of plant diversity. The SECaffected not only community structure, species composition, and succession processes, but significantly influenced species abundance, richness, and evenness. Specifically, as the SEC increased, plant species richness significantly decreased, and both plant diversity and evenness declined markedly. This indicated that the dominance of certain species within the community became more pronounced, while the abundance and distribution of other species were severely restricted. This study found a positive correlation trend between soil pH (SPH) and species diversity. However, the correlation was weak and did not reach significance. Previous studies have also reported inconsistent conclusions regarding the relationship between SPH and species diversity in the Yellow River Delta, which may be related to the habitat conditions of the established sampling plots. Some plots, being close to the Yellow River, were subject to freshwater flushing, which reduced the alkalinity of the surface soil, resulting in this phenomenon. The relevant research found no significant correlation between vegetation distribution and SPH in alkaline soils [53]. In this study area, the soil was alkaline, and its distribution did not correlate with salt-tolerant vegetation, which partially corroborated the conclusions.
## 3 Rationale of the Doppler Centroid Anomaly (DCA) Method  
Section 3.1 provides readers with a concise overview of the diverse techniques employed to estimate the Doppler Centroid, illustrating the different accuracies achieved through each method. Then, Section 3.2 presents the basic rationale of the DCA technique and its performances.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Wavelength (Center Frequency) & 0.03106658 & m \\ Band type & X & \\ Instrument mass (single sensor) & 85 & kg \\ Platform velocity (single sensor) & 7377.33 & m/s \\ Pulse Repetition Frequency (PRF) & 2000 to 10,000 & Hz \\ Chirp bandwidth & 37.6 to 299 & MHz \\ Polarization modes & VV & \\ Antenna size & 3.2 \(\times\) 0.4 & m \\ Incidence Angle range & 15-35\({}^{\circ}\) & degree \\ Acquisition modes & STRIP, SPOT, SCAN, SLEA, DWELL & \\ \hline \hline \end{tabular}
\end{table}
Table 2: ICEYE parameters.  
Figure 3: The area of interest of ICEYE SAR acquisition is located on the coastal area of in Peru (South America), to the north of Lima.
# Wind Field Characteristics of Complex Terrain Based on Experimental and Numerical Investigation
## 5 Wind Tunnel Test Results
### Wind Attack Angle  
The wind attack angle is a wind parameter that describes the degree of airflow fluctuation and represents the magnitude of the vertical wind speed relative to horizontal wind speed. Generally speaking, the value of the wind attack angle on flat terrain is often small, while the wind attack angle often becomes complex due to the irregular change in air flow in hillsides and valleys. Figure 12 shows the variation in the wind attack angle of each measuring point with the height under different inflows. The wind attack angle is mainly in the range of \(-10^{\circ}\)-\(10^{\circ}\), and the value of the wind attack angle tends to decrease with the increase in height.  
As shown in Figure 13a, the values of the wind attack angle at each height of P2 are positive. The reason is because the airflow is climbing at \(40^{\circ}\) and the direction of the wind speed is upward. Other measuring points (P3, P4 and P5) are located in the leeward area where the air flow often jumps or subducts downward, resulting in negative wind attack angles. The test results at \(90^{\circ}\) are similar to those at \(40^{\circ}\), but there are some differences at the same time. The values of the wind attack angle at P2 are reduced to within \(5^{\circ}\), and the wind attack angle of P5 is increased to \(-10^{\circ}\). The main reason is because compared with \(40^{\circ}\), the upward slope at P2 becomes smaller and the downward slope at P5 increases when the inflow enters from \(90^{\circ}\). The wind attack angles of other measuring points are also negative. Different from the first two inflows, the wind attack angle of each measuring point changes greatly under the \(270^{\circ}\) inflow. Due to the conversion of the windward and leeward area where the measuring point is located, the positive and negative wind attack angles change. Most of the wind attack angles of P2 are negative, and the wind attack angles of other measuring points are mainly positive.  
Figure 13: Profiles of wind attack angle at measurement locations. **(a)**\(40^{\circ}\) **(b)**\(90^{\circ}\) **(c)**\(270^{\circ}\).  
Therefore, the positive and negative wind angle of attack is mainly controlled by the incoming wind direction, and the local terrain affects the value of the attack.