esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Two Self-Fitting User Interfaces for Bimodal CI-Recipients
## 3 Procedures
### Self-Fitting Procedure  
Depending on the group, the participants either started with the EQ UI or the 2D-Surface UI. In both cases, while different sounds were presented via a loudspeaker (soft and middle-loud speech in quiet, loud speech in noise, pop and classical music), the participants had the task of adjusting their CI and HA with regards to volume and timbre. For the condition of speech in quiet, a text spoken by a female speaker was played once with a normal voice (\(65\ dB_{a}\)) and once with a quieter voice (\(50\ dB_{a}\)). Loud speech in noise was realized as a recorded conversation with background babble talk, comparable to a cocktail-party situation. For the pop music conditions, the song: \"The Alan Parsons Project--Limelight\" was used, and for classical music the piece: \"[PERSON]--Brandenburgische Konzerte Nr. 1 In FMaj, BWV 1046\". The sound samples were presented until the participant completed the self-fitting. The sound samples were started and stopped by the supervisor. During the adjustment, the participants were placed in front of the loudspeaker at a one meter distance. Besides the laboratory-situations, a self-fitting was also conducted in two natural sound scenarios. The first location was the entrance hall of the German Hearing Center (GHC) at the Medical School Hannover. This is a large room with strong reverberation and a radio running quietly in the background. The second location was directly beside a busy street in front of the GHC. This location was outdoors and therefore occasionally faced weather-related challenges, such as wind. In both situations, the supervisor had a conversation with the participant during the self-fitting to give them a idea of their changes. At the end of each adjustment, the participants were asked how satisfied they were with the adjustment on a scale from (1) very satisfied, to (5), very dissatisfied. This was used to evaluate the spontaneous acceptance of the new settings. To check the reproducibility of the settings made with the two UIs, a re-test was conducted at the end of the study session. For this purpose, the participants repeated the self-fitting, but only for the loud speech in noise and the pop-music sound samples.  
Figure 4: **Study procedure:** The flow of the study appointments, from top to bottom.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 4 Developed E-SSA-Based NOMA System Elements
### The HyperCube Platform (MBI)
One of the main advantages of the adopted software approach is the simplicity of the porting of the software on different hardware platforms and the ability to easily profit from the advances made by new multipurpose GPUs continuously improved to serve larger markets (e.g., artificial intelligence (AI), 3D gaming, blockchain processing, video encoding and other computationally intensive applications). This design approach ensures that the latest state-of-the-art COTS equipment can be quickly adopted for the commercial demodulator. At the same time, the SDR-based design makes very low-cost E-SSA testbed PC-based implementations available. Such compact testbed including few user terminals, faithful traffic emulation, a gateway demodulator and an ancillary monitor and control software has been used to perform early live satellite demonstrations of S-MIM and F-SIM IoT solutions, to assess their performance in a laboratory environment and to assist terminal manufacturers. The SDR gateway comes with a high degree of scalability. In fact, an additional node(s) can be added in a cascade configuration using 10 GBE links as the system throughput requirement grows. Thanks to this, the entry-level solution is very light, compact and cost-effective as additional demodulation nodes can be added later on. The random access protocol used by F-SIM is based on the innovative iterative detection process detailed in Section 3. This type of signal processing requires a significant computational burden at the gateway. The following tasks are independently performed for every received packet by each demodulation node present within the demodulation chain: preamble detection, channel estimation, demodulation and decoding of the control and data channels, regeneration of a baseband replica of the received packet and, finally, cancellation from the sliding window memory of the decoded packets using the regenerated replicas. The number of packets that can be demodulated and cancelled from the sliding window memory depends on the HW resources of each node. Once a node has performed the maximum number of SIC it is able to perform, the window sample memory (where the demodulated packets have been cancelled) is passed, for further processing, to the following demodulation node, etc.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 2 Related Works
### Load Balancing Routing Assignment Problem  
When routing in large-scale network systems, the load balancing problem needs to be fully considered to avoid partial network congestion. To solve the load balancing routing problem, some existing clustering protocols for wireless sensor networks (WSNs) have appreciable reference value. The LEACH (low-energy adaptive clustering hierarchy) algorithm [37] was the first proposed hierarchical routing algorithm, whose core idea is to divide the network nodes into clusters and randomly select nodes in turn to be the cluster head nodes. The other nodes forward their collected data to the cluster head node and, eventually, the cluster head node consolidates the data and forward them to the sink node. [PERSON] and [PERSON] [38] proposed a HEED clustering approach. Its major difference from the basic LEACH protocol is that HEED uses a multi-hop method to communicate with the sink node, while LEACH uses a single-hop method. Inspired by these two important clustering protocols, [PERSON] et al. [39] proposed SEECH, [PERSON] et al. [40] proposed FLEAC, and [PERSON] et al. [41] proposed MOFCA, forming a rich variety of clustering routing methods for WSNs that have a better performance.  
There are researchers continuously trying to apply ant colony optimization (ACO) algorithms to solving the load balancing routing assignment problem. ACO is an intelligent optimization algorithm that optimizes practical problems by imitating the foraging behavior of ants in nature, which was first proposed by Italian scholar [PERSON] in the 1990s [42]. [PERSON] et al. [43] proposed an enhanced hybrid ant colony optimization routing protocol (EHACORP) to improve the efficiency of the routing process using the shortest path. [PERSON] et al. [44] explored two artificial intelligence optimization techniques, including ACO and PSO, for load balancing in SDN. [PERSON] and [PERSON] [45] proposed a modified evolutionary computing-driven dynamic load balancing model, named intrinsically modified ant colony system (IMACS), for mega-cloud infrastructures. The above algorithms have different degrees of optimization for the route assignment problem of load balancing. However, to the best of our knowledge, there is still a lack of well-performing load balancing route assignment methods for TSN multi-priority scheduling characteristics.
## 2 Materials and Methods
### Data Source
#### 2.2.2 Remote Sensing Data  
The global carbon fluxes dataset (GCFD) is based on field carbon flux data, meteorological data, and remote sensing data. It utilizes convolutional neural network models (CNNs), artificial neural network models (ANNs), and random forest (RF) models to generate a global carbon flux dataset that includes net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco). The dataset has been validated and analyzed, revealing that NEE has lower accuracy compared to GPP and Reco in terms of temporal variability. However, the GCFD dataset fills in the gaps caused by the uneven distribution of global flux sites and the shortage of data in certain regions. The dataset has a spatial resolution of 1 km at three time steps per month from January 1999 to June 2020. The GCFD can be a useful reference for various meteorological and ecological analyses and modelling, especially when high resolution carbon flux maps are required [33] (Table 1). As per the GCFD data, the time scale is from April to September 2018, with a step of every 10 days, totaling 18 remote sensing images. We downloaded the GCFD dataset through the National Tibetan Plateau Data Center ([[https://dx.doi.org/10.11888/Terre.tpdc.300009](https://dx.doi.org/10.11888/Terre.tpdc.300009)]([https://dx.doi.org/10.11888/Terre.tpdc.300009](https://dx.doi.org/10.11888/Terre.tpdc.300009)), (accessed on 1 June 2023)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Parameters** & **Detailed Information** \\ \hline Variable & NEE, GPP, Reco \\ Time coverage & 1990–2020 \\ Spatial resolution & 1 km \\ Coverage area & 180\({}^{\circ}\)E–180\({}^{\circ}\)W, 80\({}^{\circ}\)N–60\({}^{\circ}\)S \\ Data storage format & NetCDF4 \\ Data units & 0.01 g cm\({}^{-2}\) d\({}^{-1}\) \\ Missing values & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information about the GCFD dataset.  
Figure 3: (**a**) Ulastai station flux tower, (**b**) radiation observation system, (**c**) eddy covariance system; (**d**) 2D anemometer, and (**e**) photo of the underlying grassland, taken on 8 May 2023.
## 3 Experimental Results
### Experimental Setup  
The experiments were performed on the 200 test samples from the tongue dataset (refer to Section 3.1 and Table 2). All experiments were conducted on a PC with a 3.40 GHz Intel Core i7-2600 CPU and 12 GB of RAM. The network architecture is built on a tensorflow 1.13 platform and using a NVIDIA GEFORCE GTX 1070 Ti graphics card. For the parameters used in training, we set the learning rate \(\alpha=10^{-4}\), batch size \(b=20\), number of epoch \(N=100\), and applied the Adam optimizer [30]. In the morphological layer, we set the open operation filter size \(\rho=20\), the close operation filter size \(\sigma=5\), the filter size in morphological reconstruction \(r=3\), and the tolerance \(t=0.001\).
## 2 Method
### Experiment Setup  
#### 2.2.1 Apparatus and Materials  
In this study, a video-watching method was utilized to simulate street-crossing tasks. The visual stimuli were displayed on an LG 34 WR50 QK curved LED monitor (LG Display Co., Ltd., Seoul, Republic of Korea), featuring a 34-inch screen and a resolution of 3440 \(\times\) 1440 pixels. This method, due to its safety and simplicity, has been widely adopted to study the impact of eHMI on pedestrians' street-crossing decisions and their perception and understanding of eHMIs, e.g., [28,31,35,36].  
Traffic scenario. The street-crossing scenario, developed using Unity (version 2023.2.3), represented a typical secondary urban road, characterized by an open two-lane layout without traffic lights or zebra crossings. In such unregulated environments, eHMIs play a critical role by providing clear visual cues that help pedestrians make safe and confident crossing decisions [37].  
As depicted in Figure 1, each traffic lane measured 3.5 m in width, and the total length of the visible roadway extended 100 m. The simulated environment featured clear weather and standard daylight conditions, replicating a typical daytime pedestrian crossing scenario. The video stimulus portrayed the traffic scene from a first-person pedestrian viewpoint, positioned at the curb and oriented perpendicular to the road. To ensure uniformity across participants, the camera angle remained static throughout the simulation, eliminating the possibility for viewers to alter the visual perspective.  
Vehicle platoon. Traffic flowed from left to right in the near lane at a constant speed, without acceleration or deceleration, while the far lane remained empty. To enhance realism,  
Figure 1: Traffic scene from the perspective of a pedestrian.  
all vehicles were sedans, and their gap size and speed were randomized. The initial vehicle appeared 100 m from the pedestrian at the left edge of the screen, traveling at a constant speed of 36 km/h. Subsequent vehicles moved at either 30 km/h or 36 km/h, with each speed equally represented. Speeds below 40 km/h were selected because pedestrians tend to underestimate higher velocities [38], and the two similar speeds reflected realistic variations under comparable road conditions. Gap size, defined as the TTA of approaching vehicles, ranged from 2 to 6 s in 0.5 s increments (i.e., 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6 s). The simulation ensured that, after the rear of a preceding vehicle passed the participant, the next vehicle arrived at a position corresponding to one of these nine gap sizes, randomized in equal proportions. A total of 72 trials (2 speeds \(\times\) 9 gap sizes \(\times\) 4 repetitions) were presented in a fully randomized order, preceded by one fixed-speed vehicle, resulting in 73 vehicles in total.
# Torque Range, a New Parameter to Evaluate New and Used Instrument Safety
## 4 Discussion
Indeed, despite also the external cutting surface of the instruments could present microscopic deformation after multiple intracanal instrumentation, these can be detected using a high microscope. Instead, the internal deformation caused by torsional stresses, lead to internal crack formations that cannot be detected. The presence of these microscopic deformations could reduce the torsional resistance of a NTR files. Moreover, the AF-H wire, not only could allow the instrument to better withstand to torsional stress but could also explain the percentage of torsional resistance reduction (27.6%), if compared to the M-Two (34.10%). The operative torque of brand-new files, shows a more efficient (i.e., less operative torque) dynamic shaping of the M-Two. Despite the multiple factors that influence the operative torque are not completely understood, this result can be explained by the greater cutting efficiency of the instrument. The results of operative torque test for used instruments (group B) showed that the S-One 20.06 (Table 2) developed less operative torque (\(0.47\pm 0.01\) Ncm) compared to the M-Two. This result is due to the difference in the effect of instrumentation on the two different files. Indeed, while used M-Two increased in a significant way the values of operative torque compared to news (+194.2%), the shaping seems to less affect the performance of S-One. No statistically relevant difference was found between operative torque for new and used S-One. The increase of operative torque could be explained by the reduction of cutting ability of an NTR instruments after repetitive usage. In current literature many studies described the role of blunted blade during intracanal instrumentation [20]. The lack of sharpness increases operative time during RCT and reduce the cutting efficiency of an instrument. Moreover, the instrument would necessarily develop higher values of torque to progress inside the canal, cut dentin and remove debris. The two parameters alone, despite describing the behavior of the NTR instrument in both static and dynamic condition, do not relate the in vivo shaping and the in vitro resistance conditions. Therefore, torque range was introduced as a parameter to understand the performance of an instrument regarding torsional stresses. It was developed to relate the mean values of operative torque test to the values registered during the classic torsional resistance test [6; 21]. As a result, an instrument that exhibits a wide range is safer than an instrument that exhibits a shorter or even negative range (Figure 3). Figure 3: Torque range evaluation for M-Two 20.06 and S-One 20.06. The S-One exhibited a wider range compared to the M-Two. Knowing the maximum deformation limit of the alloy in a determined portion of the instrument (torsional resistance) and the working condition, i.e., amplitude of pecking and apical pressure, during root canal instrumentation (operative torque), it could be possible to suggest a safety interval in which the instrument should act [22].
# Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
## 4 Experimental Results and Simulation Analysis
### 1 A Constant Current Discharge Condition  
The constant current discharge condition is a more basic working condition. When the driver cruises at a constant speed, the electric vehicle will drive the car forward in a state of constant torque output. According to the proportional relationship between torque and current, it can be determined that the output current at this time is an almost constant current output. The vehicle does not enter the energy recovery state, and there will be no feedback current generated by braking to charge the battery. Since the current variation of constant current discharge is minimal, it can be used as the primary operating condition for verifying SoC prediction algorithms.  
Under the constant discharge condition, the SoC initial value of the three estimation methods is set to 1. According to the discharge data, the initial value of the voltage when SoC is set to 1 is set to 4.193 V. The SoC estimation results and estimation error under the 1 A constant discharge condition are shown in Figures 5 and 6.  
It can be seen from Figure 5 that all three algorithms take a certain amount of time to converge based on the estimation results from the set initial value to the near true value after running. However, the convergence speed of LM-IEKF and IEKF is significantly faster. When SoC is between 0.9 and 0.3, the LM-IEKF algorithm shows a stronger following ability and the highest estimation accuracy. Additionally, the fluctuation range is small compared with the true value. When SoC drops below 0.2, the prediction accuracy of the three algorithms decreases. This is because the battery voltage changes drastically at a low SoC. The interval of the HPPC experiment should be narrowed here to obtain more detailed model characteristic parameters.  
Figure 5: SoC prediction results for the constant current 1A discharge condition.
## 4 Branch-and-Price-and-Cut Algorithm
### Label Setting Algorithm
#### 4.4.2 Extension Functions  
The process commences with the initialization of \(L_{N_{0}}=\{N_{0},Y_{N_{0}}=0,AC_{N_{0}}=\rho_{0}\), \(RC_{N_{0}}=\rho_{0},V_{N_{0}}=(1,0,0,...,0)\}\) associated with the source node \(N_{0}\). As the algorithm progresses, it extends label \(L_{i}=\{i,Y_{i},AC_{i},RC_{i},V_{i}\}\) to generate successive feasible labels in the form of \(L_{j}=\{j,Y_{j},AC_{j},RC_{j},V_{j}\}\). This extension is achieved by adding an extra arc \((i,j)\) to the partial path only under the condition that this extension meets the following criteria:  
\[Y_{i}+T_{i}+Y_{ij}\leq U_{j} \tag{38}\]  
\[V_{i,j}=0 \tag{39}\]  
Condition (38) guarantees that label \(L_{j}\) complies with the time window constraint, while condition (39) prohibits extensions to nodes that have already been visited. If the extension toward node \(j\) abides by these constraints, the extension is accepte+d. Afterwards, the components in \(L_{j}\) can be updated using the following set of extension functions (40)-(43):  
\[Y_{j}=max\{Y_{i}+T_{i}+T_{ij},L_{j}\}, \tag{40}\]  
\[AC_{j}=AC_{i}+C_{ij}+C_{j}, \tag{41}\]  
\[RC_{j}=\left\{\begin{array}{ll}RC_{i}+C_{ij}+C_{j}-\beta_{j},&j\in N_{IF} \cup N_{OF}\cup N_{OE}\\ RC_{i}+C_{ij}+C_{j}-\gamma_{j}-\sum_{\in\{t|t\geq Y_{j},t\in T_{critical}\}}( \kappa_{t}+\lambda_{t}),&j\in N_{IF}^{DP}\\ RC_{i}+C_{ij}+C_{j}-\gamma_{j}+\sum_{\in\{t|t\geq Y_{j},t\in T_{critical}\}}( \kappa_{t}+\lambda_{t}),&j\in N_{IF}^{P}\cup N_{E}^{P}\\ RC_{i}+C_{ij}-\delta,&j=N_{end}\right. \tag{42}\]  
\[V_{j,m}=\left\{\begin{array}{ll}V_{i,m}+1,&m=j\\ V_{i,m},&m\
eq j\end{array}\right. \tag{43}\]
# Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas +
###### Abstract  
While there have been important advances within wireless communication technology, the provision of communication support during disaster relief activities remains an open issue. The literature in disaster research reports several major restrictions to conducting first response activities in urban areas, given the limitations of telephone networks and radio systems to provide digital communication in the field. In search-and-rescue operations, the communication requirements are increased, since the first responders need to rely on real-time and reliable communication to perform their activities and coordinate their efforts with other teams. Therefore, these limitations open the door to improvisation during disaster relief efforts. In this paper, we argue that flying ad-hoc networks can provide the communication support needed in these scenarios, and propose a new solution towards that goal. The proposal involves the use of flying witness units, implemented using drones, that act as communication gateways between first responders working at different locations of the affected area. The proposal is named the Flying Real-Time Network, and its feasibility to provide communication in a disaster scenario is shown by presenting both a real-time schedulability analysis of message delivery, as well as simulations of the communication support in a physical scenario inspired by a real incident. The obtained results were highly positive and consistent, therefore this proposal represents a step forward towards the solution of this open issue.  
Flying Real-Time Network; flying witness units; UAVs; real-time schedulability; communication support; disaster management +
Footnote †: 24 April 2018; Accepted: 18 May 2018; Published: 22 May 2018