esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 2 Methodology
### Feature Processing Framework  
The framework encompasses three categories of acoustic representations, each designed to capture different aspects of the speech signal:  
1. MFB--Traditional Features: The foundation of our feature framework relies on Mel-filterbank coefficients, which provide a perceptually-aligned representation of the speech spectrum. These features serve as our baseline and capture fundamental acoustic properties essential for speaker discrimination.
2. Speaker Embeddings: Speaker representations derived from the ECAPA-TDNN [12] architecture provide specialized speaker-discriminative information. These embeddings are designed to capture speaker characteristics independently of phonetic content, complementing the temporal acoustic features.
3. Paralinguistic Features: GeMAPS and eGeMAPS [30] introduce a theoretically-motivated selection of acoustic parameters. These features encompass prosodic, spectral, and voice quality characteristics that may enhance speaker differentiation beyond traditional acoustic representations.  
Feature Combination Strategy  
Our framework implements feature combination through vector concatenation, formally expressed as:  
\[F_{combined}=F_{MFB}\oplus F_{auxiliary} \tag{1}\]  
where \(\oplus\) denotes the concatenation operation that preserves temporal alignment between feature streams. This approach enables the model to leverage complementary information while maintaining the temporal structure necessary for diarization.
## 3 Results  
### Quantitative Changes in ESS  
The results of this study indicate that there are quantitative changes in ESS when fatty streaks are present in the aortic wall. The ESS map in the presence of FS shows a distinctive corridor candidate highlighted in yellow with an oval shape. (Figure 2a) This region is not detected in the same geographical area without the presence of fatty streaks. (Figure 2b) The data for these changes are presented in Figure 2c,d, which depict the ESS values for 700 different longitudinal points of fatty streak position in both models. The graph of the aorta without fatty streaks (Figure 2d) is relatively linear in relation to the position and has an average ESS value of 3.5 Pa. However, when fatty streaks are added, there is a clear and significant change in ESS, as shown in the graph in Figure 2c, which has a double-peaked shape with the first peak at 6.8 Pa and the second peak at 6.3 Pa.  
The impact of fatty streaks (FS) on endothelial shear stress (ESS) is a nuanced aspect of atherosclerosis that has often been overlooked in the literature. While the presence of FS is typically associated with the biochemical progression of atherosclerotic disease, its mechanical implications are not as widely recognized. Our findings suggest that FS can induce significant fluctuations in ESS, particularly at lower blood velocities. These fluctuations are evidenced by an increased standard deviation (Standard Deviation (SD) = 2.41 (Pa), Mean 3.2 (Pa)) of ESS measurements, highlighting the oscillatory nature of shear stress in affected areas. Such insights compel a reevaluation of FS beyond their traditional role as mere precursors to more advanced plaques, implicating them as active mechanical participants in the early stages of atherosclerotic development. This recognition of FS as influential mechanical entities may prompt further investigation into their role in endothelial dysfunction and vascular pathology (Table 2).
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 6 Network Parameters Adjustment and Performance Enhancement
### Study of the Speed of the Mobile Sink  
We also studied the speed of the mobile sink. As the sink is carried by vehicles, such as the intelligent car, its speed is controllable. Therefore, we set the angular velocity of the mobile sink as \(\pi/20\), \(\pi/10\), and \(\pi/5\), and the radius of the mobile sink was 0.25R. The simulation results are shown in Figure 14. It can be clearly seen that the lifetime of the network was almost unchanged with the increasing speed of the mobile sink. Therefore, we draw a conclusion that the speed of the mobile sink has little influence on the lifetime of the network, and it should not be too fast or too slow.  
Figure 13: Different number of clusters.
# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 4 Experimental Results and Discussion
### Lightweight Comparative Experiment  
Under narrowband white-noise excitation ranging from 0 to 100 Hz, with 0.3 s of data for root mean square calculation, Table 1 illustrates the setup of platform simulators for vibration control using our DRSL-MPER algorithm and under the DDPG framework using fully connected (FC), CNN, GoogleNet, and LSTM methods. The first column indicates various algorithms; the second column shows the values of the vibration signal before being controlled, measured in volts (V); the third column displays the values of the vibration signal after being controlled, also measured in volts (V); the fourth column indicates the parameter quantity, measured as a number (N), representing the total number of weight parameters and bias parameters in the neural network; and the fifth column represents the degree of vibration signal reduction, measured in decibels (dB).  
Reference [26] proposed the fully connected network and DDPG framework as the primary points of comparison, detailing their parameters and vibration reduction levels. Considering the prevalent use of fully connected network methods in the current domain, we introduced methods such as convolutional neural networks (CNNs), GoogLeNet, and Long Short-Term Memory networks (LSTMs) to expand the experimental scope; relevant parameters and vibration reduction levels are listed in the table above. Under narrowband white-noise excitation ranging from 0 to 100 Hz, our DRSL-MPER method achieved a vibration reduction of up to 20.240 dB with a reduction in parameters by more than 7.5 times. A greater reduction indicates better vibration control effectiveness.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 3 Terminologies Used in WSNs
### Network Lifetime  
The main reason for the design of energy-efficient and energy-balanced routing protocols for WSNs is to extend the network lifetime, which in turn maintains the network functionality. The term \"network lifetime\" is a primary evaluation metric that is mostly used to measure the energy efficiency of a WSN. There are different definitions for the network lifetime of a WSN in the literature. Some literature defines the network lifetime as the time until the energy in any SN in the network is depleted or the time until the energy in all the SNs in the network is depleted. Whereas some authors refer to the network lifetime as the time until the energy in a defined percentage of the SNs in the network is depleted [40; 41; 42], other literature defines the network lifetime of a WSN based on the network application and formulation. However, the general idea remains the same. For the purpose of this survey, we simply define the network lifetime of WSNs as the maximum time that the network is capable of measuring a physical value or event.
## 3 Compliance Model of _n_-4R Compliant Parallel Pointing Mechanism
### Analysis of Compliance Performance of n-4R Compliant Parallel Pointing Mechanisms
Figure 5: Compliance variation with the structure parameters of the flexure hinge and the scale parameters of the mechanism: (**a**) variation of \(\mathbf{C}_{\theta}\) in terms of the parameters \(L\) and \(r\); (**b**) variation of \(\mathbf{C}_{\delta}\) in terms of the parameters \(L\) and \(r\); (**c**) variation of \(\mathbf{C}_{\theta}\) in terms of the parameters \(l\) and \(t_{0}\); (**d**) variation of \(\mathbf{C}_{\delta}\) in terms of the parameters \(l\) and \(t_{0}\); (**e**) variation of \(\mathbf{C}_{\theta}\) in terms of the parameters \(R\) and \(w\); (**f**) variation of \(\mathbf{C}_{\delta}\) in terms of the parameters \(R\) and \(w\). As shown in Figures 6 and 7, \(\mathbf{C}_{\theta}\) will not be affected when the scale coefficient \(s_{1}\) increases, and \(\mathbf{C}_{\delta}\) is directly correlated with the scale coefficient \(s_{1}\), while \(\mathbf{C}_{\theta}\) and \(\mathbf{C}_{\delta}\) decrease rapidly with the increase in the scale coefficient \(s_{2}\). Furthermore, \(\mathbf{C}_{\theta}\) is more affected by the scale factor \(s_{2}\) compared with \(\mathbf{C}_{\delta}\). The reason for this is that \(\mathbf{C}_{\theta}\) is mainly determined by the compliance of the functional direction of the flexure hinge, which is more easily affected than that of the nonfunctional direction when the flexure hinge is scaled in equal proportion. Finally, the influence of the number of mechanism branches \(n\) on the overall compliance of the \(n\)-\(4\)R compliant parallel pointing mechanism is analyzed. As shown in Figure 8, \(\mathbf{C}_{\theta}\) and \(\mathbf{C}_{\delta}\) decrease when the number of branches \(n\) increases from 3 to 7, i.e., the overall stiffness of the mechanism increases. This provides an idea for the compliance design of the mechanism without changing the dimension parameters of the mechanism and the structural parameters of the flexure hinge. Figure 8: Compliance variation in terms of the number of mechanism branches \(n\): (**a**) \(\mathbf{C}_{\theta}\); (**b**) \(\mathbf{C}_{\delta}\). Figure 6: Compliance variation in terms of the scale coefficient \(s_{1}\): (**a**) \(\mathbf{C}_{\theta}\); (**b**) \(\mathbf{C}_{\delta}\).
## 5 Performance Evaluation
### Energy Consumption
Compared to existing energy efficient methods such as methods discussed in [2], the RLS scheme is much more energy efficient as they only involve the energy spent for estimating the location and omit the big proportion of energy consumption for sending the estimated location to the sink or beacons. In a real time localization method, the estimated coordinates will attach to the packet when the localized sensor reports a phenomenon, so the sensed data which is tagged with location information should be delivered to the sink or beacons for processing to finish the localization task successfully. RLS combines and merges all steps, from detecting an event to estimating the location in the sink and can achieve lower energy consumption by decreasing the number of messages exchanged. Its centralized computational mechanism helps the RLS improve energy efficiency. As mentioned before, one of the practical solutions to reduce the energy consumption is by reducing the size of messages. As another solution, an energy efficient scheme should reduce the number of messages exchanged for localization process in order to localize an unknown node. Due to the low bandwidth, low data rate, high delay and high bit error rate the USN localization protocols are expected to avoid excessive overhead and establish localization with the least possible number of messages. In a large-scale USN, the messaging intensification of localization protocols may increase. In addition, for a mobile USN, localization should be repeated periodically which also increases the overhead. Considering all these challenges, it is essential to develop a localization scheme with low message exchanges. The number of messages sent or received by anchors and sensors are investigated for some highlighted underwater localization methods in Table 2. Based on this table, in order to exhibit the effectiveness of proposed approach, the exact number of exchange messages for localizing an unknown node is extracted and illustrated in Table 3. This table analyzes the communication cost for various localization methods. Communication cost is the number of messages sent by anchors and sensor nodes in order to localize an unknown node [7]. The reported number of messages is calculated under ideal conditions. In fact, these are the least message numbers required for localization per unknown node by various methods. For example, SLMP requires two messages when we assume that its prediction algorithm is valid. Otherwise it needs at least three messages. The highest message number for these methods can be extracted by Table 2. As it is implied from Table 3, RLS can achieve the lowest message exchange among acoustic channels compared to other methods. It even has less message number than silent schemes like D'N'R, UPS and LDB. In silent methods, unknown sensors do not transmit any message, but in RLS only one message for localizing one sensor node is sent by the sensor.
# A Compact Thevenin Model for a Rectenna and Its Application to an RF Harvester with MPPT
###### Abstract  
This paper proposes a compact Thevenin model for a rectenna. This model is then applied to design a high-efficiency radio frequency harvester with a maximum power point tracker (MPPT). The rectenna under study consists of an L-matching network and a half-wave rectifier. The derived model is simpler and more compact than those suggested so far in the literature and includes explicit expressions of the Thevenin voltage (\(V_{\mathrm{oc}}\)) and resistance and of the power efficiency related with the parameters of the rectenna. The rectenna was implemented and characterized from \(-30\) to \(-10\) dBm at 808 MHz. Experimental results agree with the proposed model, showing a linear current-voltage relationship as well as a maximum efficiency at \(V_{\mathrm{oc}}/2\), in particular 60% at \(-10\) dBm, which is a remarkable value. An MPPT was also used at the rectenna output in order to automatically work at the maximum efficiency point, with an overall efficiency near 50% at \(-10\) dBm. Further tests were performed using a nearby transmitting antenna for powering a sensor node with a power consumption of 4.2 uW.  
RF harvesting; rectenna; Thevenin model; maximum power point tracking; MPPT; L-matching network; sensor node +
Footnote †: journal: Sensors
## 3 Discussion  
The CLEAS methodology improves the stability of the enzyme and its reusability in enzymatic cycles at the cost of a decrease in its activity [37]. The increase in specific activity compared to the free enzyme may be related to the distorting/inactivating effects of both cosubstrates in the enzyme structure that could be partially solved if the immobilization produces a more stable enzyme [27; 29; 59; 60]. Moreover, it cannot be discarded that the use of ethanol and glutaraldehyde produced some change in the enzyme structure that led to a more active enzyme molecule structure [60]. That way, the immobilizations using this optimized CLEA strategy not only reduced the costs because they did not use any pre-existing support, but they also produced a gain in the specific activity of the enzyme.  
In the catalysis of peroxididases, hydrogen peroxide can have an enzyme inactivating effect, also known as \"suicide inactivation\" [46; 47]. This effect leads to the formation of a free radical of iron III porphyrin during the catalytic cycle of peroxididases. Therefore, two scenarios can be presented. The first corresponds to a reversible pathway, where compound III, being unstable, returns to its initial state. The second is an irreversible pathway, where compound III releases the heme group, leading to enzyme inactivation [45; 55]. Hence, hydrogen peroxide is necessary for the reactions, but its excess negatively affects the stability and activity of GGP and GGP-CLEAS. Therefore, our results indicate that the formation of crosslinked enzymatic aggregates of GGP confer stability to the tertiary structure of free peroxidase, possibly preventing enzymatic inactivation by heme group loss. This result suggests that the enzyme stability must be further increased (e.g., using other immobilization techniques, using systems to partition the hydrogen peroxide from the enzyme environment) to be used under the optimal conditions. The current biocatalyst requires the use of a lower hydrogen peroxide concentration (perhaps using some feedback configuration or a biocatalytic in situ hydrogen peroxide formation). That way, there is room for further improvements of the biocatalyst pursuing a higher operational stability of the biocatalyst.
# Cross-Feature Transfer Learning for Efficient Tensor Program Generation
## 2 Background and Related Work
### Hardware-Aware Autotuners
This advancement contributes significantly to the overall optimization of tensor program generation. A notable achievement of this framework is its ability to reduce the number of program candidates subjected to resource-intensive hardware measurements. This is accomplished by utilizing highly accurate cost models on a family basis, without compromising the quality of the search. Furthermore, the literature survey highlights the authors' in-depth analysis of the accuracy of these cost models regarding various subgraph attributes, including the number of operators, core operators, and operator sequences. The findings presented in this study shed light on the framework's effectiveness and its potential impact on optimizing tensor program generation, making it a valuable contribution to the field of automated program tuning. Lastly, [PERSON] et al. [43] introduce a specialized autotuner for deep learning operators that adapts to hardware, particularly tailored for tensors with dynamic shapes. They use micro-kernels akin to DietCode, optimized explicitly for dynamic shape tensors. While DietCode primarily focuses on tensor shapes, HAOTuner goes further by considering the available hardware resources. They present an algorithm for selecting hardware-friendly micro-kernels, reducing the time required for tuning. Additionally, they offer a model transfer solution that enables the rapid deployment of the cost model on diverse hardware platforms. The evaluation of HAOTuner covers six different GPU types. However, it is important to note that their proposed method is constrained by hardware architecture compatibility, as it exclusively functions with CUDA-enabled hardware. This limitation restricts its applicability for transfer learning across various hardware architectures.
## 2 System Architecture and Communication  
### System Architecture  
The integrated protection and control system developed has been installed on a 10 kV feeder in China Southern Power Grid, as shown in Figure 1. The feeder is connected to two 110 kV/10 kV substations by CB F05 and F36, respectively. The feeder is normally run in open-ring configuration, that is, the normal open point of the 10 kV feeder is at 601 of RMU2 (Ring Main Unit 2). DTU is installed at two 110 kV/10 kV substations and each 10 kV Ring Main Unit (RMU). Each DTU communicates with the adjacent DTUs for current differential protection function, also communicating with the existing central DMS (Distribution Management System) for SCADA (Supervisory Control And Data Acquisition) function and auto-switching function to restore power supplies after a power system fault is cleared.  
The adaptive current differential protection function is located in each DTU at each RMU. The protection communicates with adjacent DTU to obtain the current data from remote terminals. If the topology of the feeder changes, the DMS can detect the change and send the information to the affected DTUs. The current differential protection then changes its configuration settings according to the new topology. The DMS is responsible for modeling, real-time monitoring, and clock synchronization of the entire distribution network, and for performing topology dynamic analysis on the entire distribution network, transmitting topology change data to each DTU terminal. The DTU is  
Figure 1: Topology of the distribution network. DTU, Distribution Terminal Unit; RMU: Ring Main Unit.  
responsible for transmitting the respective sampled data and trip information to adjacent terminals, collecting the sampling data and operation information of adjacent terminals, and responding quickly to a power system fault by running its differential protection algorithm. The protection is fast and can dynamically adapt to topology change of distribution networks.
# A Comparison of Households' Energy Balance in Residential Smart Grid Pilots in the Netherlands
## 3 Results
### Annually Imported and Exported Electricity  
Figure 4 shows the boxplots of annual imported and exported electricity for all households. For this graph, PMC \(\upmu\)-CHP production is not considered. Most households in R4I have a slightly lower imported electricity than Liander Zonnedael. The average in R4I is 3.01 MWh and in Zonnedael 3.2 MWh. Given that R4I has many PV systems installed, and PV-generated electricity that is directly consumed on-site (i.e., self-consumption), is excluded from the residual load, we can conclude that electricity demand in R4I is probably somewhat above the Dutch average of 3.3 MWh, whereas that of Zonnedael is in line with the average. JEM Easy street is excluded as it has neither heat pumps nor PV.  
Figure 3: Load duration curves of the “PowerMatching City” project and “Jouw Energie Moment” project—the Meulenspie group (all households are equipped with heat pumps).  
Figure 2: Load duration curves of the “Smartgrid: Rendement voor ledereen” project, the Liander Zonnedael open access dataset, and the “Jouw Energie Moment” project (exclusively Breda-Easy Street).  
Figure 4b shows that for the two pilots including heat pumps, JEM, the import is 1 MWh above the Dutch average. These households show a large difference between electricity import and PV feed-in, which means that they are not able to supply their electricity demand with their PV systems.  
We observe large differences between the households, both in terms of imported electricity and the amount of PV energy fed into the grid. The upper quartiles show a spread of around 2 MWh. Since most households in the Zonnedael dataset are not equipped with PV systems, all households that do have PV systems are considered outliers for the PV electricity feed-in. Both in imported electricity and the PV energy fed into the grid, we observe large differences between the households, indicated by the maximum and minimum whiskers in the boxplot. This result shows that even households within these small communities are not very similar. This might imply that households with high electricity import may have much potential for energy efficiency measures, whereas households with a low PV feed-in have much potential to increase PV electricity generation. However, a more detailed case-by-case analysis at the household level is required to confirm this.