esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 4 Connectivity Properties of Ship MANET
### Analysis of Motion Stability between Nodes  
For ships with various motion situations encountered by own ship, we will not consider its ship mathematical model or movement model, and use another method to measure its stability in the network. According to the above, we know that the signal is attenuating during the propagation process, and the way of attenuation is not linearly decreasing with distance, but the signal strength is attenuating. We discretize the received signal strength value of the receiver, and use \(X=P_{x}-P_{threshold}\) to represent the variation of the received signal at the receiver. For the convenience of research, we normalize \(X\) through the max-min normalization method, so \(X^{\prime}\) is expressed as Formula (10):  
\[X^{\prime}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{10}\]  
For such a discrete random variable, its variance is calculated as follows:  
\[D\big{(}X^{\prime}\big{)}=E\Big{(}\big{(}X^{\prime}-E\big{(}X^{\prime}\big{)} \big{)}^{2}\Big{)}=E\Big{(}{X^{\prime}}^{2}\Big{)}-E^{2}\big{(}X^{\prime}\big{)} \tag{11}\]  
and  
\[E\Big{(}{X^{\prime}}^{2}\Big{)}-E^{2}\big{(}X^{\prime}\big{)}=\left(\sum_{k} \frac{{X^{\prime}}^{2}_{k}}{n}\right)-\left(\sum_{k}\frac{{X^{\prime}}_{k}}{n }\right)^{2} \tag{12}\]  
Through the above formula, the variation of the received signal power of the two nodes can be calculated. We import another variable \(S(X^{\prime})\):  
\[S\big{(}X^{\prime}\big{)}=\frac{1}{D(X^{\prime})} \tag{13}\]  
Through a series of analyses of \(S(X^{\prime})\), the stability of the node motion can be judged. If \(D(X^{\prime})\) becomes larger, \(S(X^{\prime})\) becomes smaller, indicating that the stability between nodes becomes worse, otherwise \(D(X^{\prime})\) becomes smaller and \(S(X^{\prime})\) becomes larger, indicating that the stability between nodes becomes better. The quality of node stability reflects whether a node as a network member will cause drastic changes in network topology, affect the normal execution of routing protocols, whether it is a qualified cluster head, and so on.
# Design of HIFU Treatment Plans Using Thermodynamic Equilibrium Algorithm
## 2 Materials and Methods
### HIFU Treatment Plan  
To properly cover a vast target region with HIFU ablation, a number of sonications are required. The potential solution \(I\) during the therapy, which is the HIFU transducer location, follows a certain course in the tissue. The HIFU therapy is not a continuous process, but the ultrasound is intermittently focused on a specific location in the tissue during each sonication. The total number of sonications for a specific tumor is restricted to \(N\), which is often in the low tens. Sonication duration \(t_{on}\) and cooling interval \(t_{off}\) determine the amount of energy supplied in a single sonication. One sonication is defined as a quintuple \(S_{i}\), which is a function of spatial coordinates of beam focus, sonication and cooling intervals \(t_{on}\) and \(t_{off}\):  
\[I=(S_{1},S_{2},\dots,S_{N}),\text{ }where\text{ }S_{i}=(x_{i},y_{i},t_{on,i},t_{ off,i}) \tag{23}\]  
Determining the best location and sonication times for the specified number of ablations in order to thoroughly treat the intended target volume while protecting the surrounding organs is how the treatment planning problem is described in this case. The four parameters which formed \(S_{i}\) in Equation (23) are the optimization parameters, and they are converted to \(T\) and \(\forall_{i}\) based on Equation (1).
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 6 Conclusions  
In this paper, an RIS-assisted multi-antenna AmBC system model is investigated, and an efficient RIS-assisted AmBC signal detection method based on deep reinforcement learning, named RIS-TD3, is developed. Particularly, an RIS-assisted multi-antenna AmBC signal model is presented, which enables the information transmission and energy collection simultaneously. Furthermore, a Twin Delayed Deep Deterministic (TD3) signal detection method is built for the AmBC system. Compared with several state-of-the-art comparison methods, the presented RIS-TD3 method achieves better performance. Because of the powerful ability of deep reinforcement learning, more essential recessive features are well explored.  
There are some important advantages about RIS-TD3: (1) RIS-TD3 has good stability by delay updating and smooth regularization of the target policy network, so as to obtain the optimal solution of the nonconvex optimization problem, lower bit error rate is obtained. (2) The effect of a hyperparameter on the performance of the algorithm is explored, and the hyperparameter is chosen appropriately to further improve detection performance. (3) RIS-TD3 does not need to estimate channel information in advance. (4) RIS-TD3 has lower bit error rate and more stable convergence performance than the reference method under the condition of lack of channel information. (5) The problems of incomplete parameter acquisition and serious direct link interference in traditional AmBC signal detection methods are avoided.  
To further improve the signal detection performance, we will consider introducing hierarchical deep multi-task joint learning to learn more discriminative features efficiently. For model speed acceleration, a series of ingenious model compression technologies will be tried to combine into the deep reinforcement learning framework. Moreover, the effects of delicate adaptive self-training and practical unsupervised training schemes will be explored.  
**Author Contributions:** Formal analysis, M.G.; Project administration, H.Z.; Software, B.X.; Supervision, K.C.; Writing--original draft, F.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Key R&D Program of China under Grant 2018 YFE0100500, in part by the National Natural Science Foundation of China under Grant 61871387, 62106277, and 61871471, Scientific Research Plan of National University of Defense Technology (ZK21-39, and ZK21-44), Information and Communication Business Cost Project of JSCMC (ZLKT-XT-2147), and the Natural Science Basic Research Program of Shaanxi under Grant 2019 JM-019.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Focused Ultrasound in the Rehabilitation Setting:
## 4 Discussion and Future Directions
### Neurological and Psychiatric Disabilities
Compared to other noninvasive neuromodulation techniques, such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS), low-intensity focused ultrasound (LIFUS) has shown a similar or lower incidence of side effects like headaches and drowsiness. Importantly, no seizures have been reported with LIFUS, unlike rTMS (0.3%) and deep transcranial magnetic stimulation (dTMS, 2.4%) [32]. FUS appears to exert an inhibitory effect on the motor cortex, improving reaction times in visual stimulus-response tasks compared to control conditions. This suggests a behavioral advantage through neuronal inhibition, potentially enhancing motor speed and accuracy. Such effects could benefit the rehabilitation of patients with motor deficits caused by stroke or traumatic brain injuries. Selective inhibition of specific cortical circuits may also help restore the balance between excitation and inhibition in movement disorders such as dystonia or PD. This inhibition might be mediated by mechanical effects on astrocytes or mechanosensitive ion channels rather than direct action on pyramidal neurons [31]. Positive results have also been observed in tasks requiring inhibitory control, such as reducing errors in anti-saccade tasks, where participants must look in the opposite direction of a visual stimulus. These findings suggest potential applications for FUS in rehabilitating patients with motor or cognitive control deficits caused by stroke, traumatic brain injuries, or neuropsychiatric conditions like PD [34]. Furthermore, theta-burst transcranial ultrasound stimulation (tb-TUS) applied to the left primary motor cortex (M1) has been shown to enhance excitability in the stimulated M1 while reducing excitability in the contralateral M1, thereby modulating interhemispheric inhibition (IHI). This is particularly relevant for stroke patients, where an imbalance between the two M1 regions--characterized by hyperactivation of the unaffected M1--can hinder motor recovery. tbTUS may help restore functional hemispheric balance, offering potential benefits for conditions such as Parkinson's disease, dystonia, and other motor pathologies involving disrupted cortical connections [35]. Lastly, [PERSON] et al. 's phase I trial provided preliminary evidence that LIFUS is safe and feasible for stroke patients at intensities up to 8 W/cm\({}^{2}\). High-intensity stimulation seems to enhance motor learning and may improve corticospinal excitability. Phase II trials are needed to further investigate efficacy and optimize stimulation parameters for clinical applications [23].
# Improving Very High Plastic Clays with the Combined Effect of Sand, Line, and Polypropylene Fibers
## 4 Results
### Effect of Fibers on Compaction Characteristics  
Figures 7 and 8 show the variation of compaction characteristics for raw clay, lime-stabilized soil (unreinforced clay-sand mixture), and lime-stabilized-fiber-reinforced clay mixes with different fiber contents. As indicated, the MDD (Maximum Dry Density) of lime-treated-clay-sand mix decreases as the fiber content increases, which shows a proportionality between fiber content and MDD (all samples were compacted at identical compaction energies following the ASTM D698 [22] standard. For example, when adding 1.2% of fibers to the control mix, the MDD decreases by 7% in comparison to the unreinforced clay. The decrease in MDD for the reinforced samples is partially attributed to the difference in unit weight between fibers and soil. It is also attributed to the difficulty to achieve the same MDD in a reinforced (and thus, stronger) soil with the same compaction energy. However, the gain in strength provided by fiber addition compensates the reduction in density; MDD could be increased to a desired and feasible level by increasing the compaction energy.  
Figure 5: Correlation between UCS and Plasticity index.  
Figure 6: Determining lime demand.  
It can also be observed that the MDD of the unreinforced clay-sand mix is slightly higher than the respective one of the raw clay. The replacement of a percentage of clay with sand modifies the soil structure and, as a result, the compactibility is improved, creating a denser medium.  
Another observation is that the PP fibers did not induce a significant effect on the OMC due to their hydrophobic nature, whereas a substantial increase in OMC was observed due to the presence of lime in the mix. The latter consumes an additional quantity of water for humidification and chemical reactions.  
Figure 8: Optimum moisture content for different mixes.  
Figure 7: Maximum dry density (MDD) for different mixes.
## 4 Numerical Example
### Data Augmentation  
The robustness and accuracy of the deep neural network would be better if more training patterns were available for each bolt looseness case [45]. In the FE simulation, only one group of received signals was obtained for each bolt looseness case. Thus, data augmentation techniques, including time masking and frequency masking, were applied to increase the number of training patterns. Time masking was carried out on the training patterns by assigning the signal information from the four wave paths to 0 within a randomly selected time interval [\(t_{\text{s}}\), \(t_{\text{f}}\)]. The maximum duration between \(t_{\text{s}}\) and \(t_{\text{f}}\) was 35 us, and it was ensured that [\(t_{\text{s}}\), \(t_{\text{f}}\)] did not overlap with the directly incoming and end-reflected wave packets. Figure 13b shows the training pattern following the application of time masking, with the signal components within the square being masked. For frequency masking, one frequency component between 40 kHz and 120 kHz in the time-frequency spectrum from each wave propagation path was removed, excluding the range of 70 kHz to 90 kHz, which carries important information related to bolt looseness. The training pattern after frequency masking is shown in Figure 13c. The two types of data augmentation were each conducted 5 times on every training pattern, thereby increasing the total number of training patterns to 5346 (\(486\times 11\)).  
Figure 13: Data augmentation on the training pattern. (**a**) Original pattern; (**b**) after time masking; (**c**) after frequency masking.
## 2 Methodology
### Methodology for the Assessment of Data Quality
#### 2.1.2 Description of the System under Investigation  
The investigation was conducted on a testbed (see Figure 1) capable of generating data meant to support the analysis of the dynamic behaviour of a water distribution system undergoing multi-component degradation. This hydraulic system comprises critical components, including a main supply tank, an external gear pump, and an induction motor responsible for driving the pump. The rotational speed of both the motor and the pump is regulated by a variable speed drive (VSD). The system also features a solenoid shut-off valve (SHV) and five direct proportional valves (DPV1 to DPV5) added to the system to support, in a controlled manner, the degradation phenomena affecting five distinct components. Data collection is facilitated by pressure transmitters (P1, P2, P3, P4, and P5), turbine flow meters (f1 and f2), and a laser sensor to measure the pump's speed.  
System components are connected with PVC tubing, and a finger valve is used for tank isolation when needed. In the context of fault simulation, specific control valves were manipulated to emulate fault conditions. For instance, DPV1 represented a clogged suction filter, fully open at 0% fault severity, whereas DPV2 simulated pump discharge side leakage and was fully closed at 0% fault severity. The SHV solenoid valve remained open, and DPV3, emulating a blocked or degraded shut-off valve, was fully open at 0% fault severity. DPV4 represented a clogged nozzle, also fully open at 0% fault severity, whereas DPV5, simulating downstream pipe leakage, was fully closed with 0% fault severity. For clarity, Table 1 summarises the default operational states as well as the fault emulation mechanism of the system's control valves and also provides their corresponding fault codes.
## 2 Methodology  
This paper presents a novel vibration data-based hybrid ML and model updating method to identify the damage location and quantity of the loosening of bolts in a steel frame structure. Figure 1 describes the working procedure of the method. It has been found that machine learning techniques require a large number of data sets for each of the damaged and undamaged cases. For a civil engineering structure, obtaining so much data is quite a difficult task. Moreover, the model updating techniques produce inaccurate results in the presence of a large number of variables. Hence the problem was divided into two parts--(1) localization and (2) quantification. The machine learning algorithm was used to approximately localize the loosening of bolts. Considering the fixity factors at the approximate locations as variables, the model updating operation was carried out to identify the exact location of the loosening of the bolts and quantity. By employing the machine learning algorithm, the number of variables can be drastically reduced.
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 4 Assessment of the Adaptive Fault Tolerance Mechanism
### Analysis of System Overhead
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Full Model**} & \multicolumn{6}{c}{**Mem\_Ctrl**} & \multicolumn{2}{c}{**RAM**} \\ \cline{2-11}  & \multicolumn{2}{c}{**Hardware**} & \multicolumn{2}{c}{**Max. Speed**} & \multicolumn{2}{c}{**Power Consumption (W)**} & \multicolumn{2}{c}{**Hardware**} & \multicolumn{2}{c}{**Hardware**} \\ \cline{2-11}  & **LUTs** & **FFs** & \(\mathbf{f_{max}}\) (MHz) & \(\mathbf{T_{min}}\) (ns)** & **Dynamic** & **Static** & **Total** & **LUTs** & **FFs** & **LUTs** & **FFs** \\ \hline Plasma & 1852 & 478 & 100 & 10 & 1.600 & 0.145 & 1.745 & 189 & 103 & 281 & 0 \\ Plasma\_SCED & 2054 & 410 & 100 & 10 & 1.573 & 0.140 & 1.713 & 344 & 35 & 341 & 0 \\ Plasma\_ADAPTIVE (static) & 10,180 & 616 & 66.67 & 15 & 1.573 & 0.141 & 1.714 & 8985 & 241 & 341 & 0 \\ Plasma\_ADAPTIVE (dynamic) & 2770 & 617 & 83.33 & 12 & 1.574 & 0.141 & 1.715 & - & - & 341 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Synthesis of Plasma cores in the reconfigurable FPGA. Finally, the dynamic version of the Plasma_ADAPTIVE core (see Figure 6) introduces much less hardware overhead (about 1.35 \(\times\) LUTs and 1.5 \(\times\) FFs compared to the Plasma_SECDED core) than the static version. In addition, the power overhead is small (a few milliwatts), and the maximum operating frequency does not reduce dramatically. In this row, we can miss the presence of information related to the \"Mem_ctrl\" component. It is due to the fact that this information is not available in the synthesis report because this module uses internally \"Reconfiguration blocks\" (those related to the reconfigurable modules: \"ECC Encoder\", \"ECC Decoder\" and \"Error Manager\", all included in the \"Adaptive Memory Controller\" component). For this reason, the synthesis report cannot include specific information on this component. Regarding the reconfiguration latency of this version, assuming the same values of T and W used previously, and applying in Equation (9) the values for the XC7Z020 FPGA (\(\text{t}_{\text{PCAP\_init}}=0.0005\) s, \(\text{PCAP}_{\text{read\_rate}}=120\) MBytes/s), the timing of an SD card used as \"Configuration ROM\" (\(\text{ROM}_{\text{read\_rate}}=19.63\) MBytes/s), and given that the size of all bitstream files is 172 KBytes, we get an overall reconfiguration time \(\text{t}_{\text{reconfiguration}}(\text{dynamic})=31.472\) ms (milliseconds). Obviously, this value is no good for a hard real-time system, but searching for the more suitable FPGA for such a purpose is out of the scope of this paper. Anyway, we consider that the overhead introduced by the adaptive fault-tolerant mechanism proposed is affordable. Even the non-reconfigurable approach could be considered if the speed is not a limit. On the other hand, the outcomes obtained are referred to only one FPGA. Probably, other devices could allow getting much better performance in the operating frequency.
## 2 Materials and Methods
### Data Synthesis and Statistical Analysis  
The meta-analysis and the statistical analysis were conducted using the Review Manager software (RevMan 5.2; Cochrane Collaboration, Oxford, UK). A random-effects model was applied to determine the effect of endurance training prescription guided by HRV on physiological or performance variables. The effects of training on these outcomes between HRV-g and PT groups were expressed as mean differences (MD) or standard mean differences (SMD) and their 95% confidence intervals (CI). The inverse of variance model was used for the analysis. The heterogeneity between the studies was evaluated through the I2 statistic and between-study variance, using the tau-square (\(\tau^{2}\)) [25]. The I2 values between 30 and 60% were considered as moderate levels of heterogeneity. Additionally, a value of \(\tau^{2}\) more than one suggests the presence of substantial statistical heterogeneity. The publication bias was evaluated through an asymmetry test as estimated from a funnel plot. A \(p\) value of less than 0.05 was considered to be statistically significant.
## 2 HFRC Preparation and Characterization
### Static Test for Characterization  
With the foregoing concrete samples reparation procedure, the quasi-static tests, including uniaxial compression (UC) and 3-point bending, were performed to investigate the effect of fiber reinforcement on the compressive and flexural strength. Since only fine gradation of quartz sand were used as aggregate, we prepare the UC and 3 PBT specimens with similar sizes adopted in [40; 41]. In this section, the experimental program is explained in detail. Then, experimental results will be reported and discussed based on the average values of tests for 3 specimens. To ensure quasi-static condition, a loading rate of 0.5 mm/min for the load cell of MTS machine was used herein to conduct both UC test and 3 PBT.  
Specimens of 40 mm \(\times\) 40 mm \(\times\) 40 mm were cast for quasi-static compressive strength testing. Three samples of each mix were tested to determine the uniaxial compressive strength. Abrasive paper was used to smooth the surface of the specimens. The non-casting surfaces of the cube specimen were used as bottom and top surfaces of the compression test to ensure complete contact with the plates of the universal testing machine.  
To analyze the fiber effect on the flexural strength of FRC, 3-point bending tests were conducted herein with specimens of different fiber mixes. The dimensions of the tested beams are 40 mm (width \(b\)) \(\times\) 40 (depth \(d\)) mm in cross-section, and 160 mm in total length where the span \(l\) is determined as 120 mm. The beams were supported by a fixed rolling base which provides vertical constraints. The peak load value was then recorded for further study. The nominal flexural strength can expressed as \(f_{f}=3 Fpl/(2 bd^{2})\)[36; 42], where \(F_{P}\) is peak load, \(l\) is span length, \(d\) denotes the beam depth and \(b\) is the beam width.  
The UC and 3 PBT results were plotted in Figures 2 and 3. For 2% hybrid fiber reinforcement, both the 7-d \(f_{c}\) and 28-d \(f_{c}\) increase as steel fiber content increases. Although PP and PVA fibers are not expected to increase the compressive strength [43], it is observed that polyvinyl alcohol fiber could better improve the compressive strength than polypropylene fiber when the SF content remains constant; meanwhile, the flexural strength can be better improved by PP-fiber incorporation. The post-test beam specimens are shown in Figure 4 where the fibers on the broken surfaces can be clearly observed.  
Figure 2: Fiber reinforcement effect on compressive and flexure strength.  
The PVA-Steel and PP-Steel hybrid fiber-reinforced concrete materials are characterized with 0.03 mm smaller-diameter polymer-fiber and 0.22 mm larger-diameter steel fiber. The small-size PP or PVA fibers bridges micro-cracks and therefore controls their coalescence, meanwhile the larger steel fiber tends to arrest the propagation of macro-cracks. Moreover, the steel fiber, which is stronger and stiffer, provides the first crack strength and ultimate strength. It explains why the S4 mix with the more steel fiber content exhibits the highest compressive and flexural strength.