esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Energy Routing Approach for Energy Internet
## 3 Energy Routing Approach  
Due to the structure and characteristics of EI, some features must be considered in the routing approach:  
* Energy routing is demand-dominated and the source of energy is not specified;
* The lost energy cannot be regenerated and may cause an overflow that could lead to the destruction of devices/lines, or even the crash of the whole energy system;
* The power transmission loss is not only related to the length of the path but also to the transmitted and pre-existing power. Because of this fact, the ERs should not store the routing paths but the energy information of the whole system;
* The dynamic routing algorithm must achieve the supply-demand balance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ER** & **Interface Capacity (kw)** & **Conversion Efficiency (_eff_)** \\ \hline \(R_{1}\) & 20 & 1 \\ \(R_{2}\) & 15 & 0.98 \\ \(R_{3}\) & 25 & 1 \\ \(R_{4}\) & 24 & 1 \\ \(R_{5}\) & 22 & 0.98 \\ \(R_{6}\) & 20 & 0.97 \\ \(R_{7}\) & 25 & 0.98 \\ \(R_{8}\) & 30 & 0.97 \\ \(R_{9}\) & 30 & 1 \\ \(R_{10}\) & 20 & 0.97 \\ \(R_{11}\) & 27 & 1 \\ \(R_{12}\) & 25 & 0.98 \\ \(R_{13}\) & 30 & 1 \\ \(R_{14}\) & 19 & 0.98 \\ \(R_{15}\) & 18 & 1 \\ \(R_{16}\) & 18 & 0.97 \\ \(R_{17}\) & 20 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy information of ERs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Power Line** & **Capacity \(\mathbf{P_{Line}^{C}}\) (kw)** & **Resistance (\(\mathbf{\zeta}\))** & **Voltage (V)** \\ \hline \(L_{1-3}\) & 30 & 0.6 & 400 \\ \(L_{1-9}\) & 45 & 0.45 & 400 \\ \(L_{1-14}\) & 40 & 0.21 & 400 \\ \(L_{1-17}\) & 30 & 0.24 & 400 \\ \(L_{2-3}\) & 20 & 0.64 &  
Taking into account these features, we considered the energy routing problem as an optimization problem, where the objective function is to minimize the power transmission losses and cost. To solve this issue, we proposed:  
* A centralized peer-to-peer energy trading architecture;
* A subscriber matching mechanism for both mono and multi-source consumer cases. This mechanism assigns for each consumer the optimal producer in terms of minimization of both cost and power loss;
* In the case of multi-source consumer (heavy load), we proposed an energy particle swarm optimization algorithm, to determine the amount of power for a set of producers to achieve minimum power transmission loss and cost;
* An IACO-based energy routing protocol that ensures a non-congestion efficient-energy transmission.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 6 Proof of Concept
### Simulated Scenario
#### 6.1.2 Results and Discussion
The difference between the optimal configuration parameters values for the different values of bandwidth available in the system is due to the effect of the RLC layer on the TCP layer operation. In the TCP, the loss or delays of the ACKs cause the throughput to fall. Due to the operation of the congestion control algorithm of the TCP, the throughput fall will be more abrupt when the bandwidth available in the system is greater. As shown above, in RLC AM, the sending of STATUS PDUs has priority over the sending of AMD PDUs. The ACKs of the TCP protocol are sent as AMD PDUs at the RLC level. Therefore, the delivery of STATUS PDUs will have priority over the delivery of TCP ACKs. In this way, the greater the bandwidth available in the system, the more sensitive it will be to the loss of throughput due to the excess of sending STATUS PDU. The smaller the values of the t-StatusProhibit and t-PollRetransmit timers, the greater the frequency of sending STATUS PDUs, which is why the optimal values of these timers are higher for a higher bandwidth. Figure 5: Simulated scenario—MOS optimization. The maximum E2E throughput achieved in the optimization process is 1.45 Mbps in the 1.4 MHz bandwidth case and 8.64 Mbps in the 3 MHz bandwidth case. The difference between these values is due to the amount of radio resources that it is possible to assign to the user in each scenario. When the bandwidth available in the system is higher, the user will have more radio resources to transmit and receive data, thus obtaining a higher E2E throughput. Figure 8: Simulated scenario—E2E throughput optimization. Figure 6: Simulated scenario—t-StatusProhibit adjustment. Figure 7: Simulated scenario—t-PollRetrasmit adjustment.
## 1 Introduction
Meanwhile, stationary medical devices or devices created in laboratories are hardly wearable or are available only for a very small group of potential users. Therefore, this research investigates the possibility of using commercial-off-the-shelf wrist pulse oximeters for continuous cuffless blood pressure measurement, since they are certified, quite cheap, and, most importantly, accessible to everyone (see Figure 1). Figure 1: The CMS50 FW wearable oxygen saturation (SpO2) and pulse rate device with wireless communication for real-time vital-sign monitoring. If a pulse oximeter is capable of PPG signal transmission over Bluetooth or Wi-Fi, then it is possible to send PPG values to a mobile phone or another device for further SBP estimation and SBP value recording. This allows us to implement a 24/7 system for SBP monitoring. Other authors usually obtain PPG signal values using a 125-1000 Hz sampling rate and 8-12-bit resolution analog digital converter (ADC) [10; 13; 15; 19; 20; 21; 22; 23; 24; 25]. However, energy consumption is the key feature of wearable devices, and a lower sampling rate and resolution allow us to significantly reduce ADC energy consumption, as discussed in Reference [28]. Moreover, the energy used for the data transmission is lower as well, because of the lower number of bytes per sample and the samples per second rate. This paper proposes a novel approach for the estimation of SBP, using a much lower PPG sampling rate and resolution than other authors and analyzing not all of the PPG pulse wave, but its rising edge only, thereby reducing the number of calculations (and energy consumption) to obtain a result. Experimental evaluation is done using a CMS50 FW wrist pulse oximeter with a 60 Hz sampling frequency and 8-bit resolution, which is widely available, quite cheap and is capable of transmitting PPG values over Bluetooth.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 2 Basic Theory and Solution Method  
The fatigue analysis approach of random vibration can be divided into time-domain and frequency-domain approaches. In the time-domain approach, uses a technique called rainflow cycle counting to decompose a variable amplitude time signal of stress into fatigue cycles, and then calculate the fatigue life combined with the stress-life (S-N) curve of the material. A large amount of storage space is used for the processing and calculation time-domain signals in the finite element analysis. This is not convenient for engineering applications.  
The frequency-domain approach is established on the power spectral density (PSD) of stress. It is easy to calculate and has strong engineering applicability. The variance, amplitude probability distribution and peak frequency of random stress signal can be obtained by PSD function, and then the cumulative damage can be calculated. Therefore, in this study, the frequency-domain method was used.
## 1 Introduction
In addition, most of the studies focus on conventional absorption-based CT, while there is currently a scarcity of studies on applying DL to low-dose PCCT, and the development of related techniques is still in great demand. In this paper, we propose an end-to-end DL framework for PCCT with sparse-view projections. Different from these mentioned methods, the CNNs of dual domains are trained together, allowing network parameters of both CNNs to be updated simultaneously for further removal of artifacts. Therefore, the network in this framework consists of an enhanced network in the projection sinogram domain to restore the projection structure, an enhanced network in the image domain to reduce artifacts in reconstruction and a phase contrast Radon inversion layer (PCRIL) to connect them. PCRIL can achieve PCCT reconstruction, and it allows for backpropagation of the gradients from the image domain to the projection sinogram domain, which enables CNNs in dual domains to be trained simultaneously. In addition, the differential forward projections of the images reconstructed with sparse-view projections are used as input of the network, and the reconstructed images with complete-view projections are used as the targets. Once trained, the network is fixed and can reduce artifacts in the reconstructed images. The experiments with the simulated datasets and experimental datasets are performed to validate the effect of this framework. The results show that the proposed framework can output high-quality reconstructed images with incomplete PCCT projections.
# Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks
## 2 Establishment of Dataset
### Input Parameters for Models
#### 2.1.1 P-Wave Velocity  
Interpreting the relationship between P-wave velocity and TC requires considering the parameters that influence P-wave velocity. Among the most-cited variables that affect P-wave velocity, mineral composition, lithology, porosity, and confining stress are cited as the main factors. [PERSON] and [PERSON] [56] studied the relationship between TC and the P-wave velocity for different rock types. They found that TC and P-wave velocity had a positive and robust correlation coefficient. The mineral composition was the most effective parameter influencing their relationship. Figure 1 shows the relationship between TC and P-wave and the mineral composition of rocks. There was no information found about the mineral composition of samples in the current study. As a result, the curve fitted to TC versus P-wave velocity did not include this factor (Figure 2).The effect of lithology on seismic wave velocity in rocks was well-established by [PERSON] [57]. According to him, a higher seismic velocity can indicate higher quartz content, which can result in higher UCS. In addition, more significant confining stress can close microcracks in samples and improve P-wave transmission. As porosity decreases, P-wave and TC values increase. Table 3 also demonstrates this phenomenon by showing that P-wave velocity has an inverse relationship with porosity. As a result, P-wave velocity can inherently represent the most influential parameter for TC as it has a direct relationship with TC.  
Furthermore, according to [PERSON] [58], the type of pore fluid influences P-waves in porous rocks. In our study, the samples were completely dry. However, this method can be used in future studies to evaluate TC in saturated conditions.  
\begin{table}
\begin{tabular}{c|c|c|c|c} \hline  & **P-Wave** & **Porosity** & **Density** & **UCS** \\ \hline Polynomial (1 st order) & 0.8249 & \(-\)0.1717 & 0.3517 & 0.5463 \\ Polynomial (2 nd order) & 0.8597 & \(-\)0.2841 & 0.5058 & 0.5928 \\ Power (one term) & 0.8587 & \(-\)0.5912 & 0.5113 & 0.5621 \\ Power (two term) & 0.8602 & \(-\)0.614 & 0.5113 & 0.5942 \\ \hline \end{tabular}
\end{table}
Table 2: Correlation coefficients of the simple regression models between the TC and the independent variables.  
\begin{table}
\begin{tabular}{c|c|c|c|c} \hline  & **P-Wave** & **Porosity** & **Density** & **UCS** \\ \hline P-wave & 1 & \(-\)0.2441 & \(+\)0.3876 & \(+\)0.4911 \\ Porosity & 1 & \(-\)0.8094 & \(-\)0.2578 \\ Density & & 1 & \(+\)0.4295 \\ UCS & & & 1 \\ \hline \end{tabular}
\end{table}
Table 3: Correlation coefficients of the relationships between the independent variables.
## 3 Results  
### Validation of ANN Model  
To construct a suitable ANN model for predicting the environmental changes in the YR estuary, data collected through field investigations were used for ANN learning. To implement an optimal model, learning frequency was increased during training, and at \(\geq\)3000 epochs (error 8.94%), the error decreased to \(\leq\)10%. It was thus determined that the ANN prediction would be valid after \(\geq\)3000 learning epochs; nonetheless, our ANN model was subjected to 20,000 learning epochs, obtaining an error of 4.16%.  
For the size-fractionated phytoplankton biomass, the predicted values obtained from the ANN model and actual measured values were compared (Figure 4). The results showed that the distributions of measured and predicted values were mostly similar across all sizes and sites. The predicted values of total chl \(a\) were slightly lower than the peaks measured at site A; otherwise, the accuracy was high across all periods and sites. The predicted net chl \(a\) was slightly lower than the peaks measured at Sites A and B. The predicted nano chl \(a\) showed a slight difference compared with the peaks measured at Site A. Otherwise, accuracy was high.  
The statistical validation of the ANN model indicated high accuracy: SSE 0.0001-0.0003 and RMSE 0.0119-0.0173. In addition, across all size classes of phytoplankton biomass, R\({}^{2}\) was \(\geq\)0.9952 (Table 2).
# An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks
## 3 System Model
### Network Model  
As shown in Figure 2, a wireless body area network consists of a sink and multiple sensor nodes. The sink node is composed of smart phones, personal computers or other smart electronic devices. Sensor nodes are all kinds of biosensors, which are powered by micro-batteries. Each node is responsible for collecting one or more kinds of physiological data. At the same time, the collected data is transmitted to the sink node by wireless transmission, and then sent to the medical data center by the sink for comprehensive processing. The data transmission uses one or more hops, and the maximum hops should not exceed three hops.  
In practical applications of WBAN, the data generated by nodes are mainly divided into two categories: emergency data and periodic data. Emergency data refers to abnormal data exceeding the normal threshold and query data from users' active requests, which are generated randomly and have  
Figure 2: Logical topology of wireless body area network.  
small business. At the same time, the transmission of emergency data requires high real-time reliability. Therefore, emergency data is designed as P1 priority data that needs to be processed first. Periodic data refers to continuous or discontinuous periodic data generated by nodes. This kind of data has a large amount of traffic and is transmitted periodically to sink nodes. Compared with emergency data, the periodic data has low real-time requirements. Therefore, the periodic data is designed as P2 priority data.  
Model assumptions:  
1. All sensor nodes are distributed in the corresponding position of the human body according to their different functions. After placement, all sensor nodes have their own ID and their positions remain unchanged.
2. The initial energy of each node is equal, and all nodes have data fusion function. At the same time, the transmitting power can be dynamically adjusted, and the maximum wireless transmission distance is R.
3. When the node is sleeping, the wireless module is closed and the low-energy detection module is still working.
4. According to the actual situation, the energy of the sink node is not considered or assumed to be infinite, and it has strong information-processing ability.
## 2 Materials and Methods
### Study Selection  
Two reviewers ([PERSON] and [PERSON]) were in charge of selecting/excluding publications based on a linear review of names, abstracts, and full texts (in cases of doubt). Based on the qualifying criterion, the remaining articles were read in their entirety before making the final decision (Table 2). This whole method was carried out separately, with the assistance of a third researcher ([PERSON]) in the cases of questions or disputes.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Inclusion Criteria** \\ \hline In vitro experiments conducted on extracted human teeth; \\ Root canals prepared with a file with conventional NiTi metallurgy (ProTaper Universal and Mtwo systems), hollow-file (Self-Adjusting File system), M-wire metallurgy (WaveOne, ProTaper Next and Reciproc systems), and R-phase metallurgy (Twisted File Adaptive and K3 XF systems) using a standard technique according to the product manual; \\ Inclusion of at least two of the four comparison groups: files with conventional NiTi metallurgy (ProTaper Universal and Mtwo systems), hollow-file (Self-Adjusting File system), M-wire metallurgy (WaveOne, ProTaper Next and Reciproc systems), and R-phase metallurgy (TFA and K3 XF systems); \\ Prepared teeth sectioned horizontally at 3, 6, and 9 mm from the apex, with the slices examined and numbered under a stereomicroscope or a scanning electron microscope (SEM); \\ Dentinal cracks are measured in slices instead of the number of teeth; \\ Studies published in English. \\ \hline
**Exclusion Criteria** \\ \hline In vivo studies or studies not of human teeth; \\ Root canals prepared with instruments other than files with conventional NiTi metallurgy (ProTaper Universal and Mtwo systems), hollow-file (Self-Adjusting File system) and M-wire metallurgy (WaveOne, ProTaper Next and Reciproc systems); \\ Evaluation of the crack initiation at levels other than 3, 6, and 9 mm from the apex; \\ Studies without a control group; \\ Evaluation of dentinal cracks after the root canal filling procedure; \\ Retreatment; \\ Unavailable data. \\ \hline \hline \end{tabular} Note: If any lines, microcracks, or fractures were detected in the dentinal slice, it was defined as a “slice with cracks”. A dentinal slice devoid of any craze lines, microcracks, or fractures on the external surface of the root or the internal surface of the root canal wall was defined as a “slice without cracks”.  
\end{table}
Table 2: Inclusion and exclusion criteria adopted.
###### Abstract  
Ultrasound-guided regional anaesthesia with real-time visualization of anatomical structures and needle trajectory has become the standard method for accurately achieving nerve block procedures. Nevertheless, ultrasound is particularly limited in accurately detecting the needle tip in tissues with complex echogenicity. Fat-filled circumneural fascial tissue provides a barrier to local anaesthetic diffusion. Injection during gentle needle nerve contact is more likely to spread under the circumneurium (halo sign). On the other hand, excessive force may cause hematoma or activate the piezo ion channels and intraneural calcium release. Therefore, it is vital to understand the mechanics of needle-tissue interaction for optimizing the procedural outcomes and patients' safety. We hypothesised that continuous fluid infusion would reduce the needle force applied on the nerve compared to that of bolus injection. Thus, the primary objective of this study was to compare the forces associated with the bolus injection and continuous infusion techniques on the sciatic nerves of fresh lamp legs ex vivo. A needle combining pressure and force was inserted into six legs of lambs ex vivo using a motor stage at a constant velocity and imaged with a linear transducer. Saline injections were block randomised to bolus injection or infusion in the muscle upon gently touching and indenting of the epineurium at nine sites on six sciatic nerves at three angles (30\({}^{\circ}\), 45\({}^{\circ}\) and 60\({}^{\circ}\)) in each location. The bolus was delivered over 6 s and infused for over 60 s. The result showed less force was generated during the infusion technique when gently touching the epineurium than that of the bolus technique, with \(p\) = 0.004, with significant differences observed at a 60\({}^{\circ}\) angle (0.49 N, \(p\) = 0.001). The injection pressure was also lower when light epineurium touches were applied (9.6 kPa, \(p\) = 0.02) and at 60\({}^{\circ}\) (8.9 kPa). The time to peak pressure varied across the insertion angles (\(p\) < 0.001), with the shortest time at 60\({}^{\circ}\) (6.53 s). This study explores future applications by emphasizing the significance of understanding needle-tissue interaction mechanics. This understanding is crucial for optimizing the procedural outcomes and enhancing patients' safety in ultrasound-guided regional anaesthesia administration. Specifically, continuous infusion demonstrated a notable reduction in needle force compared to that of the bolus injection, especially during gentle epineurium contact.  
force; pressure; regional anaesthesia; ultrasound +
Footnote †: journal: Applied Sciences
## 4 Evaluation and Results' Discussion
### Performance Analysis
#### 4.2.1 Experiment 1  
The main difference between our protocol and [28] is the encryption algorithm. The Paillier encryption algorithm was used in [28], while we adopted the ElGamal encryption algorithm. Firstly, with the same security parameters, the ElGamal encryption algorithm produces two ciphertexts, while the Paillier encryption algorithm produces only one ciphertext. However, the ciphertexts produced by the Paillier encryption algorithm are twice as long as those produced by the ElGamal encryption algorithm. Therefore, both algorithm have the same communication complexity.  
Secondly, we conducted experiments with these two algorithms using the NTL [41] library. We set the value of the encrypted data to \(10^{7}\) and chose the security parameter of \(k=1024\) bit for public key encryption. The size of the dataset was increased from \(10^{2}\) to \(10^{5}\), and the encryption process used the same random value. The experimental results are presented in Table 2, which demonstrate that the ElGamal encryption algorithm is more efficient than the Paillier encryption algorithm. The former encrypts approximately twice as fast as the latter, and the former decrypts about four-times as fast as the latter.
## 3 Results
### PESI-MS Spectra  
The representative PESI-MS spectra of malignant and benign nodule extractions were illustrated in Figure 2. The FNAB samples exhibit a cluster of peaks at _m/z_ 200-400, and several minor peaks from _m/z_ 400 to _m/z_ 1000 are also observed. The ion intensity is group-dependent. For instance, the intensity of _m/z_ 471 and _m/z_ 491 is over \(10^{6}\) in malignant nodules but is lower in benign ones.
Combining the relationship between medial-lateral ground reaction forces and chronic running injuries, arch stiffness has been reported as a criterion to assess injury susceptibility [14]. It has been widely recognized that more flexible arches tend to splay during the stance phase, and shift load from the midfoot to forefoot and rearfoot concurrently along the longitudinal axis of the foot [14]. However, other studies have noted that flexible arches exhibited a smaller and larger proportion of total plantar impulse in the forefoot and rearfoot than stiff arches, respectively, in both walking and running [3,14]. The unidirectional impulse transmission on the plantar longitudinal axis could be related to the fact that in the asymmetric and irregular triangular truss model formed by the plantar fascia and the arch bones, the shorter proximal side attached to the calcaneal tuberosities would suffer more impulses during arch compression [15,16]. Although previous studies have attempted to elucidate the biomechanical characteristic differences with different arch heights [6,10,11,17,18], few studies have investigated lower extremity kinetics and kinematics based on different arch stiffnesses while performing gait stopping tasks. Based on the research interest in morphology-related arch function, the purpose of this study was to analyze the biomechanical characteristic differences including lower limb joint angles and plantar pressure between stiff arches and flexible arches during gait termination. We hypothesized that the lower limb joint motions, especially for the ankle and metatarsophalangeal (MTP), would vary significantly between planned and unplanned gait termination (PGT and UGT). We also hypothesized that participants would exhibit substantial asymmetrical lower-limb biomechanical characteristics due to the stiffness of the foot arch.