esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Investigation of the Proposed Method's Performance on IEEE 57-Bus System  
In this section, three different objectives of the IEEE 57-bus are considered to be optimized. Tables 8-10 show the information comparisons of all compared approaches with the proposed ICOA approach for TPL, TVD, and \(L\) index optimization. For the three objective comparisons, all the compared methods in other studies have been run only by setting control variables to continuous values. Thus, the comparisons with these methods and four other implemented methods are only performed for the case of continuous variables. However, all results obtained by the proposed methods for the case of discrete variables are also reported. For the comparison with four implemented WCA, SSA, SFOA, and COA approaches, the high performance of the proposed ICOA approach can be identified clearly and exactly,  
Figure 8: Voltage of loads obtained by the compared methods for the IEEE 30-bus system. Figure 7: \(L\) index of 50 runs obtained by compared methods for the IEEE 30-bus system. since approximately all results from the proposed ICOA approach are superior to those from these methods. The proposed approach can reduce TPL to 22.376 MW, while the four methods found the best solutions with 26.0402, 25.3854, 26.6541, and 24.5358 MW. Clearly, the proposed approach can obtain less TPL than these methods by 3.6642, 3.0094, 4.2781, and 2.1598 MW. Similarly, the proposed ICOA approach continues to be more effective than these methods for TVD optimization and \(L\) index optimization, since the best TVD and the best \(L\) index are, respectively, less than these methods by 0.126, 0.335, 0.186, and 0.066 pu, and 0.02721, 0.03831, 0.03141, and 0.02401 pu. The values can result in the improvement level of the proposed ICOA approach over these methods. Namely, the improvement for TVD is 20.790%, 55.346%, 30.772%, and 10.907% and for \(L\) index is 10.81%, 15.22%, 12.47%, and 9.5%, respectively. The analysis of the best TPL, TVD, and \(L\) index can confirm the high efficiency of the proposed approach as compared to the four implemented methods. For judging the stabilization of search ability of the proposed approach, the average for 50 runs of the three optimization cases is compared among all methods. For TPL optimization, the averages of the proposed approach and these ones are 24.9062 MW, and 26.5319, 27.0306, 28.4249, and 26.8983 MW. Similarly, these values for TVD optimization are 0.7436, 0.7913, 1.1736, 0.9975, and 0.8198 pu and for \(L\) index are 0.25169, 0.2789, 0.29, 0.2831, and 0.2757 pu. It can be realized that the search process of the proposed approach is more stable than the approaches.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 4 Design of the TSR-DARDA Scheme
### The Overall Approach  
In this section, the overall TSR-DARDA approach is described. To ensure a clear statement, the system model shown in Figure 2 is vertically projected on to the plane from top to bottom, which is shown in Figure 3. As illustrated in Figure 3, the surface is partitioned into circular tiers and each tier is further divided into cells. Non-collecting nodes transmit data packet to collecting nodes in each cell. Those cells that do not interfere with each other could simultaneously aggregate data by unicast and retransmission within each cell. If the packet is lost in the transmission, it will be retransmitted until the maximum number of retransmissions. Moreover, in the surface circular tier partition, the tier width should meet the goal that the collecting nodes in the inner layer just finish data aggregation when the collecting nodes in the outer layer finish transmission to parent collecting nodes in the inner layer, thus minimizing the latency while maintaining reliability for data collection. All the data will be gradually aggregated to a sink by the distributed in-network aggregation under the assumptiondescribed in the system model that the wireless channels between two adjacent tiers are independent across links.  
Therefore, the proposed scheme consists of two data collection phases: (1) at one time slot, each non-collecting node in those cells free of interferences transmits its packet to its parent collecting node in the same cell simultaneously and (2) each collecting node receives the packet and implements aggregation. After data aggregation of the cell is finished, it transmits the aggregated packet to its parent collecting nodes in the inner tier in the same time slot with other nodes without transmission interference. The data collection is implemented simultaneously and the aggregated data is forwarded tier by tier from the outermost tier to the sink node.
## 2 Techno-Economic Analysis at 2030
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**LCoE (f/MWh)**} \\ \hline n (years) & & & & 30 & & & \\ WACC (\%) & & & & 9.00 & & & \\ CRF & & & & 0.07 & & & \\ Distance from the Coast (km) & 10 & 25 & 50 & 75 & 100 & 200 \\ Export Cable Costs (M€/MW) & 0.01 & 0.03 & 0.05 & 0.08 & 0.10 & 0.20 \\ Installation Costs (M€/MW) & 0.31 & 0.32 & 0.33 & 0.34 & 0.36 & 0.42 \\ CAPEX (M€/MW) & 2.32 & 2.34 & 2.38 & 2.42 & 2.46 & 2.62 \\ OPEEX (M€/MW) & 0.116 & 0.117 & 0.119 & 0.121 & 0.123 & 0.131 \\ \hline \multirow{4}{*}{CF} & 25 & **156** & **157** & **160** & **163** & **165** & **176** \\  & 30 & **130** & **131** & **134** & **136** & **138** & **147** \\  & 35 & **111** & **112** & **115** & **116** & **118** & **126** \\ \cline{1-1}  & 40 & **97** & **98** & **100** & **102** & **103** & **110** \\ \cline{1-1}  & 45 & **87** & **87** & **89** & **90** & **92** & **98** \\ \cline{1-1}  & 50 & **78** & **79** & **80** & **81** & **83** & **88** \\ \cline{1-1}  & 55 & **71** & **72** & **73** & **74** & **75** & **80** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LCoEs (in bold in the table) calculated as function of CAPEX, OPEX, and CF. WACC equal to 9%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**LCoE (f/MWh)**} \\ \hline n (years) & & & & 30 & & \\ WACC (\%) & & & & 7.00 & & \\ CRF & & & & 0.07 & & \\ Distance from the Coast (km) & 10 & 25 & 50 & 75 & 100 & 200 \\ Export Cable Costs (M€/MW) & 0.01 & 0.03 & 0.05 & 0.08 & 0.10 & 0.20 \\ Installation Costs (M€/MW) & 0.31 & 0.32 & 0.33 & 0.34 & 0.36 & 0.42 \\ CAPEX (M€/MW) & 2.32 & 2.34 & 2.38 & 2.42 & 2.46 & 2.62 \\ OPEEX (M€/MW) & 0.116 & 0.117 & 0.119 & 0.121 & 0.123 & 0.131 \\ \hline \multirow{4}{*}{CF} & 25 & **138** & **140** & **142** & **144** & **147** & **156** \\  & 30 & **115** & **116** & **118** & **103** & **122** & **130** \\ \cline{1-1}  & 35 & **99** & **100** & **101** & **90** & **105** & **112** \\ \cline{1-1}  & 40 & **86** & **87** & **89** & **80** & **92** & **98** \\ \cline{1-1}  & 45 & **77** & **78** & **79** & **71** & **81** & **87** \\ \cline{1-1}  & 50 & **69** & **70** & **71** & **72** & **73** & **78** \\ \cline{1-1}  & 55 & **63** & **63** & **65** & **66** & **67** & **71** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: LCoEs (in bold in the table) calculated as function of CAPEX, OPEX, and CF. WACC equal to 7%. A second comparison is done with the value calculated for the north European context, corresponding to the complementary row and columns in the table, with respect to the ones indicated for the Italian context. Roughly speaking, the lower resource and lower production of the parks in the Italian marine areas are mainly compensated by the lower costs due to possibility to install these kind of plants closer to the coast. Further considerations are reported in Section 4.
## 2 Related Work  
### Definition  
Understanding the foundational concepts behind Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and their multivariate extensions is crucial for contextualizing the advancements discussed in this study. This subsection provides a concise overview of these architectures and their relevance to financial time-series prediction.  
A Recurrent Neural Network (RNN) is a specialized deep neural network designed to process and analyse sequential or time-series data. It is trained to generate machine learning models capable of making predictions or deriving insights based on sequential input data [15]. RNNs are particularly suited for tasks where context and temporal relationships are critical.  
While traditional RNNs rely on sequential relationships from past states, Bidirectional RNNs (BRNNs) extend this capability by considering both past and future states to improve prediction accuracy. For instance, when predicting the next word in a sequence, BRNNs utilize both prior and subsequent context [15].  
Long Short-Term Memory (LSTM) networks are a type of RNN architecture introduced by [PERSON] and [PERSON] to address the challenges of long-term dependencies. LSTM networks are designed to retain important information while forgetting irrelevant details, making them particularly effective at overcoming the vanishing gradient problem that affects traditional RNNs. The vanishing gradient occurs during training when gradients diminish as they propagate backwards through time, leading to ineffective learning [16].  
Multivariate LSTM models expand upon traditional LSTM networks by incorporating multiple input variables to predict a target variable. This approach allows the model to capture complex relationships between input variables, making it well-suited for tasks like stock market prediction and weather forecasting [6]. Such models are particularly valuable in financial applications, where multiple factors influence market dynamics.
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
## 3 Modern Design
### Analog Domain Processing at Transceiver
#### 3.3.2 Analog Amplifier of Reception  
The transducer converts an acoustic wave signal to a differential voltage signal; however, the voltage signal is quite weak. For long distance transmissions, it is only a few tens of micro volts. However, in some cases, voltage can reach tens of millivolts, which exceeds the preamplifier requirements. At the same time, the programmable gain is very important for the realization of the actual project. Figure 6 shows the analog amplifier of the reception schematic.  
The OPA348 series amplifiers are single supply, low-power, CMOS op-amps in micro packaging. Featuring an extended bandwidth of 1 MHz, and a supply current of 45 \(\upmu\)A, the OPA348 series is useful for low-power applications on a single supply of 2.1 V to 5.5 V.  
A low supply current of 45 \(\upmu\)A and an input bias current of 0.5 pA make the OPA348 series a suitable candidate for our modem.  
Instead of a simple pre-amplifier, the analog reception amplifier in this modem includes band pass filtering and an adjustable gain amplification. As the application's environment demands a wideband filter, we use an analog amplifier to build a band pass filter by using a Butterworth low-pass filter and a second-order Butterworth high-pass filter, whose range is adjustable. The reason for choosing it is the flexibility to control the cut off frequency.  
The amplifier programmable control amplification is less than 40 dB if the frequency of the received signal is above 1 kHz. The signal could be amplified the second time in the AIC3106.
## 4 Experiments
### Experimental Environments
#### 4.1.1 Local Datasets  
The experimental local dataset was collected from various roadside parking strips and parking lots in two cities in China. The number of parking spaces in the dataset is detailed in Table 2. These parking spaces include parallel, perpendicular, and angled configurations. To increase the sample size, two field experiments were conducted at Site 1. Due to different planning batches and standards, the parking spaces do not have uniform sizes. The ground truth of the parking spaces was obtained through manual surveys and comparisons. Figure 14 shows the local experiment pictures of various types of parking spaces along with the manual annotation results for different parking space types.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experimental**} & \multirow{2}{*}{**Number of**} & \multicolumn{3}{c}{**Type of Parking Slots**} \\ \cline{3-5}  & & **Parking Slots** & **Parallel** & **Perpendicular** & **Angled** \\ \hline Site 1 (2 loops) & 256 & 0 & 185 & 71 \\ Site 2 & 107 & 107 & 0 & 0 \\ Site 3 & 279 & 0 & 279 & 0 \\ Site 4 & 91 & 0 & 91 & 0 \\ Total & 989 & 107 & 740 & 142 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parking space statistics.  
Figure 13: Diagram of parking space recognition and annotation. (**a**) Annotation diagram of perpendicular parking space; (**b**) Annotation diagram of angled parking space; (**c**) Annotation diagram of parallel parking space.  
The test vehicle is equipped with Lidar and IMU equipment. The Lidar beam has 16 lines, an operating frequency of 10 Hz, a horizontal angular resolution of 0.2\({}^{\circ}\), and can sense the surrounding environment within a range of 0.5 m to 150 m in 360\({}^{\circ}\). The IMU can output high-frequency posture data at 50 Hz. The size threshold of the experimental vehicle is 2 m \(\times\) 4 m. The details of device parameters for the local dataset used are shown in Table 3.  
Figure 14: Schematic diagram of parking slot types and actual arrangements in local data. (**a**) Perpendicular parking slots; (**b**) Arrangement of perpendicular parking slots; (**c**) Parallel parking slots; (**d**) Arrangement of parallel parking slots; (**e**) Angled parking slots; (**f**) Arrangement of angled parking slots.
## 5 Discussion
### Applications and Limitations
Material nonlinearities at high temperatures, such as temperature-dependent thermal conductivity and resistivity, were not included. Addressing this limitation will require future studies involving multiple conductor materials and associated experimental validation across a broader thermal range. Finally, post-failure validation was not performed in this study. Future work will include thermal imaging and microstructural inspections, such as scanning electron microscopy (SEM) and cross-sectional analysis, to confirm the location and severity of predicted thermal damage. These investigations will enhance the accuracy of the simulation model and establish a stronger correlation between predicted stress concentrations and actual failure mechanisms.
## 1 Introduction  
Nowadays, it is common for traction machines in battery-electric vehicles (BEV) and hybrid electric vehicles (HEV) to operate with a high phase current (hundreds of Amps). Therefore, hairpin windings are usually chosen for electric machines in traction application [1]. Differently from the traditional windings, hairpins are formed by rectangular bars [2], which are bent into a hairpin shape [3], then they are axially inserted inside the stator slot and terminals are finally bent and welded with the terminals of the other coils [4]. Figure 1 shows an elementary hairpin, while Figure 2 shows a scheme of the manufacturing process of the winding. The authors of [3; 4; 5] offer more details of the hairpin winding manufacturing process.  
Figure 1: Elementary hairpin.  
The rectangular cross-section area of hairpins leads to a higher slot-filling factor (\(k_{fill}\)) and a more rational end windings design, that yields a high reduction in the Joule losses when the electric machine needs to operate at high torque and low speed. The hairpin windings increase the conductor ampacity and allow an easier heat transfer from the slot to the stator lamination.  
On the other hand, even if hairpin windings guarantee a higher efficiency at high torque and low speed, their higher cross-section area reduces the benefits of a greater \(k_{fill}\) when the electric machine operates at high speeds. This effect is due to two high-frequency phenomena: the _skin effect_ and the _proximity effect_. The current density distribution becomes uneven at high operating frequencies, leading to an increase in the effective resistance and, hence, of the Joule losses.  
Nowadays, the request for high performance in BEV and HEV leads to an increase in the electric machine phase current, since the voltage range shall be limited by safety requirements for the electric vehicles. According to the phase current increasing, the design of electric machine windings shifts towards parallel paths, and, unfortunately, the skin effect and proximity effect tend to produce an uneven impedance of the parallel conductors, worsening additional Joule losses.  
The design of the parallel paths requires a proper methodology to mitigate the additional losses and the current density distribution issues. The authors of [6] propose a method denoted as transposition, while other design methods have already been discussed and proposed to overcome these challenges [7].  
This paper focuses on the reduction of the additional Joule losses through the variation in the geometric parameters of a single slot. The aim is to analyse if some slot parameters have a significant impact on additional Joule losses due to skin and proximity effects. This analysis considered both rectangular and trapezoidal slots. The analysis take into account three fundamental key performance indicators: total Joule losses, uneven current density distribution among slot conductors to avoid hot spots and uneven parallel path impedance to avoid unequal current-sharing in parallel conductors. The analysis considered a hairpin winding.
## 2 Materials and Methods
### Object Detection and Position Estimation  
At the final stage, the clusters representing idler frame candidates are verified with a binary classifier. A simple classifier was used based on the dimensions of a candidate cluster. A candidate is considered a valid idler if it passes all conditions of the cascade classifier  
1. Width of the cluster (on Y axis) exceeds the minimum width \(i_{w}\);
2. The highest point of the cluster is within \(i_{th}\) range of the idler height \(i_{h}\), or it is below \(i_{h}\) but it was collected on the top plane of the sensors' field of view;3. The lowest point of the cluster is within vertical angular resolution range from the ground, or it is above the ground but it was collected on the bottom plane of the sensors' field of view;
4. The center of the cluster is located in the vicinity of an elongated object of width above \(b_{w}\) (possibly belt), which was found on the 2D histogram in the density segmentation phase. The clusters not conforming to all these conditions are designated as non-idlers. In the results section we verify the importance of the last step, comparing the algorithm operation with and without considering the belt location.
# Following the Writer's Path to the Dynamically Coalescing Reactive Chains Design Pattern
## Appendix B.5.2 Solution: Definitions  
What do we mean by a \"dependency graph\" in this context?
* If the execution of some component X of an application can directly affect a subsequent execution of some other component Y in any way, then Y _depends on_ X. For example, X might trigger an event for which Y listens; change the value of some attribute of its state that Y accesses; directly call one of Y's mutator procedures; or create, delete, or modify Y.
* A _dependency graph_ is a directed acyclic graph formed by placing the components at the nodes and adding a directed edge from some component Y to some component X only if Y depends on X.  
Figure 4 shows a dependency graph for an application with ten components named with the upper case letters A through J and directed edges from every component to every other component on which it is directly dependent. The shaded area of the figure includes the six other components that are directly or indirectly dependent on component A. Any change in the state of component A may require changes in all other components in the shaded area. If each edge is implemented as an event, then six independent events must be processed to propagate the changes to all dependent components. This is the _update cycle_. The event handling system processes these events in a nondeterministic order, interleaved with any other pending events.  
To apply the DCRC pattern, we are primarily interested in recording the dependencies related to the implicit invocations--between components that listen for an event and those that trigger the event. Of course, being able to record other kinds of dependencies may also be helpful.
# Fast and Effective Tag Searching for Multi-Group RFID Systems
## 2 Related Work
### Traditional Tag Searching Protocols  
Traditional tag searching is to determine the intersection of wanted tag set and local tag set in a large-scale RFID system with the minimum time while satisfying the certain reliability requirements. Existing tag searching protocols can be classified into iterative protocols and segmented protocols, which are summarized as follows:Iterative tag searching protocols perform iterative filtering in a variety of ways to dynamically approximate the target tag set, which is currently the most mainstream tag searching approach. [PERSON] et al. [13] designed a novel technique called a filtering vector and proposed an iterative tag searching protocol called Iterative Tag Search Protocol (ITSP). ITSP filters non-wanted tags and non-target tags by filtering vectors and then keeps repeating the process until the searching results satisfy the reliability requirements. [PERSON] et al. [15] proposed a probabilistic tree-based tag searching approach called Tree-based Tag Search Protocol (TTS), which hashes multiple tags into each internal tree node and performs batched verification to verify target tags. [PERSON] et al. [16] developed a time-effective tag searching protocol called Compact Exclusive Validation Protocol (CEV). CEV utilizes the Ordering Indicator and KEY-Filter to filter out non-wanted tags so that the target tags are verified without interference.  
The segmented tag searching protocols implement interference tag deactivation and target tag verification, respectively. Compared with iterative tag searching protocols, segmented tag searching protocols can avoid repetitive verification of target tags. [PERSON] et al. [17] developed a solution calledcompact Approximator-Based Tag Searching Protocol (CATS). CATS first filters the non-wanted tags through a Bloom filter and then forms a virtual Bloom filter from the remaining local tags to determine the target tags. Subsequently, BFSearch+ tag searching protocol was proposed by [PERSON] et al. [14]. They designed new techniques called composite filter vector and indicator vector. The composite filter vector can complete non-wanted tag deactivation efficiently. The indicator vector is able to assign all the wanted tags to a singleton slot for verification and determine the target tags.BFSearch+ is the best time-effective traditional tag searching protocol available.