esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks
## 5 Conclusions  
Efficient and reliable relay-aided IoT networks are going to play a pivotal in the widespread connectivity of low-powered devices. Using the coordinated retransmission from both the source and the relay, this work has provided an efficient modulation mapping solution for a relay-aided HARQ system. The modulation diversity design has been formulated as a quadratic assignment problem which is solved with the help of iterative search approach. The simulation results demonstrate the superiority of the proposed solution over simply repeated use of Gray mapping. The proposed approach also shows significant improvement when compared to the heuristic method.  
Although the proposed solution significantly improves the performance of the network, there are many ways that it can be further improved. For instance, the impact of the proposed solution on multi-user interference-limited networks should be evaluated. Furthermore, the impact of imperfect channel information should also be explored. From the perspective of security, the effect of untrusted relays must be evaluated on the broadcast nature of wireless signals. These challenging but interesting tasks will be undertaken in future studies.  
Conceptualization: S.L.M.; methodology: I.K.; data curation: W.S.; formal analysis: A.B.F.; software: P.U.; writing--original draft: I.K.; funding acquisition: P.U.; supervision: S.L.M.; project administration: P.U.; writing--review and editing: S.B. and A.A.I.; visualization: A.A.I.; validation: S.B.; resources: A.A.I.; software: S.B. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the SUT Research and Development Fund.  
The authors would like to say thanks to the reviewers and editors for reviewing this manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
Figure 6: BER of proposed scheme for different values of \(K\).
## 3 Results and Discussion
### Characterization Analysis of PAP\({}_{\text{UAE}}\) and PAP\({}_{\text{HWE}}\)  
#### 3.3.1 FT-IR Spectroscopy Analysis  
The FT-IR spectra of PAPs obtained using different extraction methods are shown in Figure 3. Each sample displayed typical polysaccharide absorption peaks at 3600-3000 cm\({}^{-1}\), 1400-1200 cm\({}^{-1}\), and 1200-700 cm\({}^{-1}\)[22]. The strong band at 3420 cm\({}^{-1}\) was attributed to the hydroxyl stretching vibration. The absorption peak at 1636 cm\({}^{-1}\) was attributed to the asymmetric stretching of the carbonyl group (C=O) [23]. The region at 936-1241 cm\({}^{-1}\) was dominated by the stretching vibration of C-O-C and the hydroxyl group of the pyranose ring, indicating the presence of pyranose [24]. These results indicated that the ultrasonic treatment did not influence the glycosidic bond and sugar ring structures,  
Figure 2: Response surface plots (**A–C**) and contour plots (**D–F**) showing the interactive effects of the ultrasonication temperature, power, and time on the extraction yield of _Potentilla anserina_ L. polysaccharides (PAPs).  
although there were some differences in the peak intensities between the products obtained using different extraction methods.
# Faster MDNet for Visual Object Tracking
## 3 Method
### Channel Attention Module
#### 3.2.2 Adaptive Selection of Channel Interactions  
Since the channel attention module is designed for capturing local cross-channel interactions, it is necessary to determine the coverage of interactions; that is, the kernel size of fast 1D convolution. The kernel size can be determined by manual tuning but requires a large number of computational resources. In this paper, we use cross-validation to adaptively determine the kernel size. The coverage of the cross-channel interaction is proportional to the channel dimension, i.e., there may be a mapping between \(k\) and \(C\). We introduce an exponential function to represent the nonlinear mapping relationship as follows:  
\[C=2^{(\lambda\ast k-b)} \tag{7}\]  
The kernel size \(k\) can be determined by  
\[k=\varphi(C)=\left|\frac{\log_{2}C}{\lambda}+\frac{b}{\lambda}\right|_{odd} \tag{8}\]  
where \(|x|_{odd}\) denotes the nearest odd number of \(x\). We set \(\lambda\) to 2 and \(b\) to 1. It can be seen that low-dimensional channels have a smaller interaction range, while high-dimensional channels have a larger interaction range using mapping \(\varphi\).
## 3 Analysis of Test Results
### Analysis of the Distribution of Pile Axial Force on Pile Body  
Based on the data recorded by the reinforcement meter, assuming that the reinforcement and concrete of the pipe pile could deform consistently together under the load, the stress of the reinforcement can be obtained from the cross-sectional area of the reinforcement and the modulus of elasticity. The pile axial force of the test pile under each level of load is shown in Figure 6. As can be seen from Figure 6, under each level of load, the axial force decreases as the depth of the pile increases. In the early stages of loading, the axial force in the lower part of the pile is small, the end resistance is small, and the lateral resistance comes into play before the end resistance. As the load increases, the slope of the axial force transfer curve increases continuously, the axial force in the lower part of the pile increases gradually, and the end resistance begins to come into play gradually.  
The axial force diagrams from both test piles reflect that the axial force gradually decreases along the measured pile throughout the loading process, with the slope of the distribution decreasing with depth. The axial force decreases fastest in the bedrock layer, reflecting the progressive development of the pile lateral resistance along the pile body from top to bottom. This is mainly due to the relative displacement between the pile body  
Figure 5: Q-s curves and s-lgt curves of PHC\(-\)1 and PHC\(-\)2, (**a**) Load–settlement curves of PHC\(-\)1 and PHC\(-\)2; (**b**) s-lgt curves of PHC\(-\)1; (**c**) s-lgt curves of PHC\(-\)2.  
and the soil caused by the compressive deformation of the pile body, while the pile lateral resistance transfers part of the pile shaft force to the soil layer around the pile, resulting in a gradual reduction in the pile axial force. Although the distribution of axial forces along the pile body is relatively similar for PHC\(-\)1 and PHC\(-\)2 piles, overall, the maximum axial force and pile end resistance are much higher for PHC\(-\)2, thus showing that the bottom flaring pipe pile has a higher pile end-bearing capacity.
# ODIN112-AI-Assisted Emergency Services in Romania
## 3 Method
### ODIN112 Architecture
The communication was optimized for real-time processing, different modules can benefit from the entire call without having to maintain a state between requests (new call samples are added to the TCP flow dedicated for that specific call), and the modules have the possibility to return results at times independent of receiving samples. Based on our experimental evaluation, the overall experience is similar to observing automated captioning of live streaming; intermediary results are published into the operator's interface, and the automated feedback from our solution is provided to the operator without delays. The integration between the components inside the ODIN112 pilot is depicted in Figure 5. When a new call arrives, the ODIN112 Manager component notifies the backend of the user interface module to register the call. The ODIN112 Manager also starts multiplying the audio stream to the transcription, noise identification, and voice sentiment analysis services. The communication between the manager and the processing services/modules is done using bidirectional TCP streams, allowing new audio fragments to be delivered for processing at any time (achieving real-time processing) and each service module to provide new results as soon as they are available. Data-wise communication (consisting of Transcriptions, Emotion, and Sound detection results) between the manager and the ODIN112 backend application is made through Kafka queues ensuring scalable, fault-tolerant stream processing. Using such an implementation helps decouple the producers and the consumers of the system while still providing reliable processing for each provided message. Each call is handled on a Kafka topic defined as the unique ID of the call. When the manager observes the end of a call, it notifies all the other components (including the backend module), waits for the last results from each of the processing services, and then decommissions the streams for the call that just ended. Kafka offers a fault-tolerant implementation for ordered queues of messages. When compared to using another open-source implementation of a consensus algorithm (such as Raft),Kafka comes with the advantage of being highly configurable in respect of replication and fault tolerance. The text obtained from the transcript is sent to the keyword identification and text sentiment analysis services. Keyword identification considers predefined word lists of interest, whereas the text analysis service for emotion detection from text considers a pretrained Romanian BERT model [34]. All obtained results are transmitted to the decision module and recommendations will be triggered. As soon as the results start to be generated, they are sent to the presentation interface and to the archiving module for long-term storage. The archive module is backed by a Hadoop Distributed File System (HDFS) to provide scalable, highly fault-tolerant storage required for the monitorization of any critical system. These later components are not included in the scope of this paper.
## 4 Discussion
### Computational Cost  
Although online performance is more important, many studies have used offline experiments at their beginnings to show the effectiveness of their methods. Thus, we performed off-line experiments in this work at first as well; however, considering the importance of online performance, we further evaluated the computational cost of our method as an extension of this study. The computational cost was evaluated on a server with 40-thread Intel(R) Xeon(R) Silver 4316 CPU and NVIDIA Geforce RTX 3090 GPU, and a Raspberry Pi 4B with 4G of storage. The inference time of one sample was 570 \(\upmu\)s on the sever and 49 ms on the Raspberry Pi, which were all less than 200 ms (which is an acceptable time delay in real use). This indicated the proposed method had the potential to be used in the real world.  
Figure 9: Correlations of classification accuracy and two clinical amputation parameters. (**a**) is the correlation between classification accuracy and Remaining forearm percentage. (**b**) is the correlation between classification accuracy and Years since amputation.
## 3 Strain Transfer Efficiency of the Surface-Bonded FBG Sensor
### Analysis of the Mechanical Model of the Adhesive Layer  
In order to describe the viscoelastic mechanical behavior of the adhesive layer more accurately and study the sensing response characteristics of the FBG sensors under long-term fixed loads, we assume that the adhesive layer is a standard linear solid model consisting of a Kelvin model and a spring, as shown in Figure 7, where \(E_{a1}\) and \(E_{a2}\) represent the elastic modulus of the spring element and the elastic modulus of the Kelvin element, respectively. \(\eta_{2}\) represents the viscosity coefficient of the Kelvin element.  
Figure 6: Schematic diagram of the sensor structure. (**a**) The main view of the schematic diagram; (**b**) the side view of the schematic diagram.  
Figure 7: A standard linear solid model.  
According to the literature [16], we can obtain  
\[\xi_{0}=(\frac{1}{2G_{\rm a1}}\cdot\frac{(2\pi r_{\rm p}+D)r_{\rm p}E_{\rm c}h}{4D} +\frac{(r_{\rm p}+r_{\rm c})r_{\rm p}E_{\rm c}}{4G_{\rm p}})^{-\frac{1}{2}}, \tag{9}\]  
\[\xi_{\infty}=(\frac{G_{\rm a1}+G_{\rm a2}}{2G_{\rm a1}G_{\rm a2}}\cdot\frac{(2 \pi r_{\rm p}+D)r_{\rm p}E_{\rm c}h}{4D}+\frac{(r_{\rm p}+r_{\rm c})r_{\rm p}E_{ \rm c}}{4G_{\rm p}})^{-\frac{1}{2}}, \tag{10}\]  
The instantaneous and quasi-static strain transfer efficiency of the FBG sensor are, respectively:  
\[\eta_{0}=\overline{\frac{\varepsilon_{\rm c}(x,0)}{\varepsilon_{0}}}=1-\frac {2\text{tanh}(\xi_{0}L/2)}{\xi_{0}L}, \tag{11}\]  
\[\eta_{\infty}=\overline{\frac{\varepsilon_{\rm c}(x,\infty)}{\varepsilon_{0} }}=1-\frac{2\text{tanh}(\xi_{\infty}L/2)}{\xi_{\infty}L}, \tag{12}\]  
Finally, the bolt strain under the instantaneous and quasi-static states can be obtained as  
\[\varepsilon_{m}\approx\frac{\Delta\lambda_{\rm B}L_{\rm s}}{\eta\lambda_{\rm B }(1-P_{\rm c})L_{\rm f}}, \tag{13}\]  
where \(\eta\) is equal to \(\eta_{0}\) in the instantaneous state and \(\eta\) is equal to \(\eta_{\infty}\) in the quasi-static state.  
For the specific derivation process of Equations (6)-(12), please refer to the published literature [17].
## 6 Conclusions and Future Work  
This paper has presented a Home Care Monitoring System (WiSPH), which represents a potentially valuable tool to assist caretakers, family members or health care practitioners to monitor the heart rate and potentially dangerous falls of elderly patients who can still live independent, but assisted lifestyles. The proof of concept testing presented in this work permits us to conclude:  
* WiSPH implements a security mechanism (AES) that does not allow nodes from outside the network to decipher data.
* The routing algorithm, based on the IEEE 802.15.4 standard, functions efficiently in health applications as only two of the 109 DATA packets that were generated in this study were lost, representing less than a 1.8% of the packets sent.
* An efficient algorithm was developed to detect falls which correctly reported 100% of serious falls.
* The already established value for tachycardia made it possible to efficiently identify the 10 events that were programmed in this test, resulting in a 100% success rate. In contrast, the value for a low heart rate (bradycardia) could not be validated because subjects could not lower their heart rates below the 70 BPM criteria.
* The monitoring function of the network and alert interface worked well in conjunction. Of a total of 109 push notifications, caretakers 100% of the real-time alerts.  
In sum, WiSPH employs important features from various platforms/systems that have been previously compared in this work. WiSPH implements an effective security mechanism and has relatively low power consumption. Our system combines these two characteristics with an efficient fall detection algorithm and an accurate means to detect established thresholds for bradycardia and tachycardia. WiSPH also possesses the flexibility and scalability to add other sensors because the routing algorithm provides the necessary QoS. The abovementioned characteristics not only permit WiSPH to be used by geriatric patients living at home, but with a wide variety of health care scenarios where mobile monitoring of different physiological variables is necessary. In conclusion, the variety of parameters WiSPH effectively monitors and reports on favorably compares to systems that are presently available in the marketplace.  
Future work will include in-depth simulations to validate the system performance, in particular to simulate multiple nodes in different spatial areas to confirm its scalability. These simulations will provide the antecedents before performing large-scale real-world implementation inside a regional hospital. Additionally, subsequent work will include optimizing the PNR algorithm to detect different the severity of falls by means of combining enhanced accelerometer data with the accompanying heart rate changes. Another area of work in the future is to optimize the system to include greater personalization according to a patient's specific physiological variables, including amount of nutrition, age, weight, body fat, triglycerides, cholesterol _etc._ Other parameters can include stress test results to set a baseline for elderly patients that can be introduced into the system.
# Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem
## 3 Fire Scheduling Problem
### Deterministic Model for Fire Scheduling Problem  
It is assumed that there are \(n\) targets at which a single artillery unit should fire, and that the targets vary in type, size, and threat level. The threat level of a target is a certain parameter, and its value is known.  
In this case, the problem of determining the firing sequence is a single machine scheduling problem [17; 18; 19]. If all of the parameters are deterministic, the deterministic fire scheduling (DFS) model for minimizing the total enemy threat to friendly forces until all of the targets are fired at is given by  
\[minimize\ \sum_{j=1}^{n}f_{j}x_{j}\qquad(\mathrm{DFS}) \tag{1}\]  
subject to  
\[x_{j}+p_{j}+s_{j\
eq j^{\prime}}\leq x_{j^{\prime}}+M\Big{(}1-y_{j^{\prime}} \Big{)}\qquad j,j^{\prime}=1,\ 2,\ldots,\ n;\ j^{\prime}>j \tag{2}\]  
\[x_{j^{\prime}}+p_{j^{\prime}}+s_{j^{\prime}}\leq x_{j}+My_{j\
eq j^{\prime}} \qquad j,j^{\prime}=1,\ 2,\ \ldots,\ n;\ j^{\prime}>j \tag{3}\]  
\[y_{j\
eq j^{\prime}}=0\ or\ 1,\qquad j,j^{\prime}=1,\ 2,\ \ldots\,\ n \tag{4}\]  
The objective function given by Equation (1) is intended to minimize the total threat of the enemy targets to friendly forces until a single artillery unit completes firing at \(n\) targets. Here, \(f_{j}\) is the threat posed by target \(j\) to a friendly unit per unit time, and \(x_{j}\) is the time at which a single artillery unit starts firing at target \(j\). Thus, \(f_{j}x_{j}\) is the threat of target \(j\) to friendly forces until the single artillery unit fires at target \(j\). Further, once the single artillery unit has fired at target \(j\), target \(j\) no longer causes damage to friendly forces. Equations (2) and (3) determine the sequence of firing at the targets, and the second target that is fired at between any two targets is constrained to consider the preceding target destruction time and current target preparation time. In Equation (4), \(y_{j\
eq j^{\prime}}\) is a binary decision variable. If target \(j\) is fired at before target \(j^{\prime}\), \(y_{j\
eq j^{\prime}}\) is 1; otherwise, it is 0.
###### Abstract  
Improving the reliability and durability of internal combustion engines in marine vessels is a complex issue. The vibrations generated in these engines significantly affect their proper operation. One of the current research challenges is identifying effective methods to reduce, among other things, torsional vibrations generated within the crank-piston system. To mitigate these vibrations, viscous dampers are commonly used. The selection of a viscous damper for a high-power multi-cylinder engine, such as those in marine power plants, requires a thorough understanding of the thermo-hydrodynamic properties of oil films formed in the spaces between the damper housing and the inertial mass. The description of the phenomena involved is complicated by the variable positioning of the inertial mass center relative to the housing during operation. Most previous studies assume a concentric alignment between these components. The main novelty of this work lies in highlighting the combined effect of the eccentric motion of the inertial ring on both hydrodynamic resistance and thermal characteristics, which has not been fully addressed in existing studies. This article defines the oil flow resistance coefficients and develops static characteristics of the dampers. Additionally, it evaluates the impact of the size of the frontal and cylindrical surfaces of the damper on its heat dissipation capacity. The presented characteristics can be utilized to assess the performance parameters of this type of damper.  
Keywords:combustion engine; vibration damper; torsional vibrations; temperature; saturation time; oil flow resistance coefficients; friction torque +
Footnote †: journal: Applied sciences
## 5 Materials and Method
### Study Site  
Lake Chaohu, the fifth largest shallow lake in China, has four distinct seasons influenced by East Asia mansoons with multiple uses such as water supply, commercial fishery and sightseeing. Eastern Lake Chaohu is the sole drinking water source for around 4.23 million people in the city of Chaohu. To evaluate the impact of cyanobacteria and their metabolites on the safety of EDWS, we took three main drinking water intakes at EDWS as sampling sites: Intake-A (\(117^{\circ}50^{\prime}15.13^{\prime\prime}\) E, \(31^{\circ}35^{\prime}36.34^{\prime\prime}\) N), Intake-B (\(117^{\circ}50^{\prime}54.32^{\prime\prime}\) E, \(31^{\circ}35^{\prime}24.82^{\prime\prime}\) N) and Intake-C (\(117^{\circ}50^{\prime}58.40^{\prime\prime}\) E, \(31^{\circ}35^{\prime}32.30^{\prime\prime}\) N) (Figure 6a). The seasonal variations of physicochemical parameters in this drinking water source during the sampling period are summarized in Table 1.  
The full-scale DWTP associated with Intake-A was chosen to evaluate the elimination of cyanobacteria and their metabolites in treatment processes with the routine sequences of coagulation-flocculation (C/F), sedimentation, filtration and chlorination. The production rate of the DWTP was 40,000 m\({}^{3}\)/d. Water from Lake Chaohu was transported through a water pipe at a distance of 2 km. In general, the dosing of coagulant polyaluminum ferric chloride and flocculation aid dimethyl diallyl ammonium chloride were 12 mg/L and 2 mg/L, respectively. In summer and autumn, 9 mg/L of KMnO\({}_{4}\) and 10 mg/L of PAC were dosed into a reaction tank with coagulants and flocculants.