esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 1 Introduction
Additionally, the contact condition and surface quality are changed. Ultimately, this would impact the friction loss. Present-day research on tolerances is the factor causing kinematic errors and backlash [29; 30]. Research has revealed that tolerances [31], friction lubrication conditions [16; 26; 32], etc., significantly impact performance. Further, the assembly clearance significantly changes the space domain of the rough surface and lubricating fluid. As [PERSON] [33] pointed out in Manufacturing Technology, the various lubrication states between the relative moving parts are determined based on the value of the film thickness ratio. [PERSON] developed a mesh efficiency model based on mixed elastohydrodynamic lubrication. The authors analyzed the effects of contact force, relative velocity, film thickness, friction coefficient, and the modifications on engagement efficiency [34]. Assembly tolerances are usually ignored in such studies. Therefore, a more precise analysis of transmission efficiency is performed considering the effects of tolerances and lubrication region. In this paper, the dynamics model and the frictional power loss model of the cycloid-pinion system are first obtained. The variation pattern of the number of meshing teeth in the cycloid-pinion system under the influence of the assembly clearance is investigated. After the maximum meshing clearance has been determined during the design process, the assembly clearance is back-extrapolated. Furthermore, the tolerance allocation for the assembly dimensional chain is carried out according to the extreme value method. Finally, the assembly tolerance chain of the system is analyzed. The optimized structure is also proposed, and the transmission efficiency of both are compared. Through experiments, the meshing characteristics of the two are analyzed. The addition of the pin tooth sleeve makes the whole system run more smoothly. The workflow of this study is illustrated in Figure 1.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 3 Devices for QKD Systems
### Quantum Repeaters  
Quantum repeaters are devices conceived to extend entanglement over space, despite the fundamental limitations of the no-cloning theorem. Their purpose is to cause base-level entanglement over a physical link and coupling entangled links along an end-to-end path. There are two alternative methods to create quantum repeaters: the solid state approach, based on a static buffer memory; and the all-optical approach, without such a static buffer memory. In the following, we focus on solid-state devices. There is a range of proposals based on different physical systems relying on ensembles of atoms, intended to collectively interact with a photon in order to store the quantum information encoded by the photon itself. The purpose of employing an ensemble of atoms instead of a single atom is to increase the cross section (i.e., the probability that the photon interacts with the device). Furthermore, an ensemble of atoms can, in principle, store more than one photon at a time, which is beneficial for increasing the communication rate. There are several elements that are under consideration for creating the mentioned ensemble, among which rare earth embedded in solid state crystals are the most promising. Such elements have the drawback of extreme cryogenic cooling at around 1.5 K, but some of them, such as ytterbium (yttrium orthosilicate, YSO), can offer both operation at the 980 nm wavelength and 1 ms of coherence time, with the advantage of being compatible with telecom systems and having robustness to perturbations of the environment [114, 115]. Erbium supports 1550 nm wavelengths but has the disadvantage of having higher sensitivity to such perturbations. Europium shows a coherence time of about 6 h at 2 K [116].  
Alternatively, III-V alloy quantum dots have been used to demonstrate entanglement between a single quantum dot spin qubit and a flying (i.e., propagating) photonic qubit [117]. One should note that in all-photonic quantum repeaters, the quantum information is instead encoded and protected in multi-photon entangled states, so they operate, in principle, at room temperature. This approach and the related protocol require only two coupled quantum emitters, as described in [118; 119; 120].
## 5 Conclusions  
The study demonstrated that plant extracts exhibit differential antimicrobial activity. _C. sinensis_ and _R.idae_s showed the highest efficacy, particularly against _P. aeruginosa_ and _E. faecalis_, while _Q. robur, B. pendula, T. cordata, and _S. alba_ exhibited lower effects. No antimicrobial activity was observed for _Coffea_ L., _R. canina, T. officinala, Q. suber, V. officinalis, and P. sylvestris_. GC-MS analysis identified various bioactive compounds in the most effective extracts, including epicatechin, shikimic acid, quinic acid, gallic acid, and caffeine, which are known for their antimicrobial properties. The presence of these compounds may explain the observed inhibitory effects and supports further investigation into their mechanisms of action. From an economic perspective, the tested plant materials are locally available and low-cost, which makes them promising candidates for sustainable and accessible antimicrobial applications in water treatment systems. In conclusion, this study highlights the potential of plant-derived extracts as natural antimicrobial agents against strains isolated from wash water in the agri-food industry. In a broader context, this study investigated the potential of natural plant extracts for application in non-invasive water treatment approaches relevant to the agri-food sector. The research was exploratory in nature and represents a preliminary phase toward the development of a practical and scalable solution. The broader project constitutes a research continuum that will ultimately lead to the design and evaluation of a pilot-scale filtration bed.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15095199/s1](https://www.mdpi.com/article/10.3390/app15095199/s1)]([https://www.mdpi.com/article/10.3390/app15095199/s1](https://www.mdpi.com/article/10.3390/app15095199/s1)).  
Conceptualization, P.K. and B.B.-B.; methodology, P.K., B.B.-B. and M.S.; validation, P.K.; formal analysis, P.K. and M.S.; investigation, P.K.; resources P.K and B.B-B.; data curation, P.K.; writing--original draft preparation, P.K.; writing--review and editing, P.K. and B.B-B.; visualization, P.K. and M.S.; supervision, B.B.-B.; project administration, B.B-B.; funding acquisition, P.K. and B.B-B. All authors have read and agreed to the published version of the manuscript.  
This study was supported by funds from the Ministry of Science and Higher Education of the Republic of Poland, No: DWD/5/0207/2021. The APC was funded by Bydgoszcz University of Science and Technology, as part of the \"Dzialania Naukowe Mlodych\" program, grant number: DNM/2/24.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All relevant data supporting the findings of this article are included within the manuscript. Raw data, as well as any additional materials, are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Metal Carbides for Biomass Valorization
## 2 Carbide Synthesis and Characterization
### Temperature-Programmed Reduction (TPR)  
Solid-state reactions are the most direct method used to prepare metal carbides. [PERSON] and coworkers developed a method to prepare metal carbides using temperature-programmed reduction (TPR) [31]. This methodology consists of treating a Mo precursor under continuous H\({}_{2}\)/hydrocarbon flow of differing compositions and different temperatures. This technique has been largely utilized to produce unsupported and supported molybdenum carbides [32; 33]. [PERSON] and coworkers studied the sequential transformation of the crystal structure of the oxide precursor from molybdic acid to Mo\({}_{4}\)O\({}_{11}\) to MoO\({}_{2}\) to Mo to Mo\({}_{2}\)C, by X-ray diffraction (XRD) [34]. These transformations involve the sequential loss of lattice oxygen as H\({}_{2}\)O and lattice carburization via CH\({}_{4}\).  
[PERSON] et al. prepared Mo\({}_{2}\)C carbide with varying sizes and shapes [35]. Well-oriented \(\beta\)-Mo\({}_{2}\)C nanoparticles with a fibril morphology were formed when carbon nanotubes (CNTs) were used as the source of carbon at 700 \({}^{\circ}\)C for 6 h under an inert environment. Well-defined \(\beta\)-Mo\({}_{2}\)C 1D nanostructures were formed after carburization of the nano-sized \(\alpha\)-MoO\({}_{3}\) precursor at 650 \({}^{\circ}\)C under CH\({}_{4}\)/H\({}_{2}\) atmosphere.  
This technique was also employed to prepare WC\({}_{x}\) starting from tungsten oxide precursor under similar H\({}_{2}\)/hydrocarbon flow, but at higher temperatures [36; 37].
## 4 Experimental Results and Discussion
### Experimental Results
(**c**) Desired displacement versus actual displacement of the DSMC. (**d**) Desired displacement versus actual displacement of the DTSMC. In order to further demonstrate the effectiveness of the proposed DTSMC, a hybrid frequency input signal in range of 0.1 to 0.5 Hz is used to be the desired displacement, the tracking results of the proposed DTSMC are shown in Fig. 14.  
Figure 14: The tracking results under 0.5 Hz input signal. (**a**) Displacement output. (**b**) Tracking error. (**c**) Desired displacement versus actual displacement of the DSMC. (**d**) Desired displacement versus actual displacement of the DTSMC. Figure 13: The tracking results under 0.4 Hz input signal. (**a**) Displacement output. (**b**) Tracking error. (**c**) Desired displacement versus actual displacement of the DSMC. (**d**) Desired displacement versus actual displacement of the DTSMC. two controllers are shown in Figure 15. Table 6 gives the computational results of two indices. It can be seen that the RMSE and MAE of the proposed DTSMC is 0.0500 mm and 0.0242 mm, respectively. In contrast, the RMSE and MAE of the DSMC is 0.1536 mm and 0.0990 mm, respectively. As compared with the DSMC, the proposed DTSMC considerably reduces the RMSE and MAE by 67% and 76%. From Figure 15c,d, one can see that the hysteresis compensation of the proposed DTSMC is more obvious than that of the DSMC. Therefore, the proposed DTSMC has better performance than the DSMC in following a hybrid frequency input signal.
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 2 Materials and Methods
### Metaheuristic System of Classifications
#### 2.6.2 Navigation Scope Strategy  
This dimension aims to point out the differences in a fundamental aspect of metaheuristics' navigation system by analyzing its _meta-strategy_ to cover the search space. Essentially, the methods in this perspective can be distributed into two groups of navigation scope: (1) local and (2) global searchers [6; 34; 64].  
**Remark 2**.: _The term \"navigation scope strategy\" is defined in this study because it was considered the most suitable term to organize the definition for (1) local and (2) global searchers._  
The (1) local searchers are specialized in seeking the nearest optimum solution until achieving the peak of the region (e.g., Hill Climbing). On the other hand, the (2) global searchers attempt to examine other areas of the search space, even if it involves moving to worse solutions during the search (e.g., Genetic Algorithms).
## 3 Results and Discussion
### Ion-Exchange
#### 3.2.3 Effect of Reaction Time  
The influence of reaction time was investigated to see that the longer the reaction time, fewer was the adsorption efficiency of the resin. Figure 8 shows that the adsorption efficiencies were similar at 2 min to 16 min of reaction time. However, after 16 min, the values got lower drastically. The reason was that some Li\({}^{+}\) was adsorbed by resin and caused the precipitation of Ca\({}^{2+}\) and Na\({}^{+}\). In order to minimize the adsorption of Li\({}^{+}\), 4 min was chosen to be our reaction time.  
After getting the optimum parameters of the Dowex G26, the lithium bicarbonate solutions were purified, and the data are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & **Li\({}^{+}\)** & **Ca\({}^{2+}\)** & **Na\({}^{+}\)** & **SO\({}_{4}\)\({}^{2-}\)** \\ \hline Content (\%) & 99.475\% & 0.015\% & 0.5\% & 0.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The composition of lithium bicarbonate solutions.  
Figure 6: Freundlich isothermal adsorption curve.  
Figure 7: Adsorption efficiency on Ca\({}^{2+}\) and Na\({}^{+}\) with pH value.
# Joint Deployment of Sensors and Chargers in Wireless
## 3 Model and Problem Statement  
### Network Model  
As shown in Figure 1, there are a known number of Pols (denoted by \(O=\{o_{1},o_{2},...,o_{N}\}\)) randomly distributed in a two-dimensional plane \(\Phi\). Our aim is to deploy the minimal number of sensors (denoted by \(S=\{s_{1},s_{2},...,s_{M}\}\)) to cover all Pols, and then to deploy minimum chargers (denoted by \(C=\{c_{1},c_{2},...,c_{K}\}\)) for charging all sensors. We assume that each sensor has a nearby receiver to collect the monitored information, thus the specific deployment of receivers is not involved in this work. Table 2 lists the parameters used in problem formulation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{\
\begin{tabular}{c} **Deployment** \\ **Objective** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Coverage** \\ **Objective** \\ \end{tabular} } & \multicolumn{4}{c}{**Optimization Objective**} & \multirow{2}{*}{**Approaches**} \\ \cline{3-3} \cline{5-7}  & & & **CU** & & **DC** & & **CR** & **ECC** \\ \hline
[27] & SC & SS & \(\surd\) & & & & GH \\
[24] & SC & SS & \(\surd\) & \(\surd\) & & & GH \\
[28] & SC & SS & \(\surd\) & & & & GH \\
[29] & SC & SS & \(\surd\) & & & & GH \\
[25] & SC & SS & & \(\surd\) & & & GH \\
[30] & SC & SS & & \(\surd\) & & & GH \\
[31] & SC & SS & \(\surd\) & & & & IFA \\
[32] & SC & SS & \(\surd\) & & & & ICS \\
[26] & SC & SS & & \(\surd\) & & & GA \\
[33] & SC & SS & & & \(\surd\) & \(\surd\) & GH \& PSO \\ Our & SS \& SC \& PoIs & & \(\surd\) & & & GH \& PSO \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative work comparison. Static Sensor (SS), Static Charger (SC), Charging Utility (CU), Deployment Cost (DC), Coverage Rate (CR), Energy Consumption Cost (ECC).  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Parameters** & **Description** \\ \hline \(\Phi\) & A two-dimensional plane used to define the problem \\ \(T\) & Sensor’s life cycle \\ \(J\) & The number of time slots in a life cycle of the sensor \\ \(\tau\) & The wireless charging efficiency \\ \(d_{th}\) & The maximum charging distance of the charger \\ \(P_{max}\) & The maximum charging power of the sensor \\ \(\tau_{max}\) & The maximum operating time slot of the sensor \\ \(P_{c}\) & The average power consumption of the sensor \\ \(c_{th}\) & A continuous variable. The sensing probability the sensor perceives the PoIs \\ \(P_{s}\) & A continuous variable. The maximum transmitting power of the charger \\ \hline \hline \end{tabular}
\end{table}
Table 2: The symbols and descriptions used in the problem formulation.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 3 Technological Background
### IEEE 802.15.5 Low Rate
#### 3.2.2 Asynchronous Energy Saving (ASES)  
In the ASES mode, nodes are configured with the same _wakeup interval_ but may have different _active durations_. Also, there is no synchronization among the _active durations_ of network nodes. In order to enable communications, one or some of the nodes, depending on the case, have to delay their _active durations_. For instance, unicast communication requires that every potential receiver periodically sends a broadcast frame advertising its _active duration_. When any other node wants to transmit, upon receiving these broadcast frames, it sends its pending data during the _active duration_ of the receiver using CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) for medium access. For further details about the operation of the ASES mode, the following references give more insight [28; 30].  
The ASES mode is intended for networks with low data sampling and data transmission rate requirements and it supports node mobility. However, it is not appropriate for video transmission because it is not able to provide an adequate QoS for more intensive data flows.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 6 Test Results  
### Algorithm Complexity  
In this section, we compare the complexity of the algorithm for the WCD format (see [12]) and the one here proposed. In order to do so, we compute the number of instructions per byte (I/B) for both algorithms. The result is that the former algorithm needs, on average, 74 I/B, whereas the latter only needs 46 I/B (see Figure 8). The main reason is that the old format required a very tailored algorithm, but thanks to the optimized definition of the KMALL format, processing is now much easier. This comparison is fair because we only take into account water column amplitude samples, which are quantized as int_8 in both formats. In this comparison, the percentage of MWC datagrams is over 99% in all cases; hence, using the file ratio is unbiased. For the same reason, the combined files from Fugro have not been included.  
It is also worth pointing out the correlation between the compression ratio and the number of instructions per byte. In general, we expect that files with a lower ratio need more instructions, as more bytes need to be written. In addition, for flat distributions, the FAPEC core needs to check more conditions for the different entropy levels. In Figure 8, we show this trend both in the algorithm from [12] and the one we propose. Note that the number of instructions per byte in the best case of [12] is still higher than the number of instructions in the worst case of our implementation. Although it seems that WCD has, in general, better ratios than KMWCD, the behavior we observe is scene-specific, as we will see in the following section when comparing FAPEC to other compressors.  
Note that in this section, we have just compared the complexity of the water column algorithms. For the sake of completeness, we have also evaluated the MRZ algorithm (see Section 4.3), and it only needs 35 I/B, on average.
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 4 Experimental Results
### Functional Validation  
The system's functionality was confirmed by programming specific channels of the AFE to acquire internal test signals and noise. Channels 1, and 4 to 8 were configured to acquire a 2 Hz square wave test signal, while channel 2 captures signals from the Vastus Lateralis muscle, and channel 3 captures signals from the Rectus Femoris muscle. Results demonstrated accurate transmission of the expected square wave signal, correct differentiation between channels, adherence to the desired data acquisition rate of 8 kSPS, and no sample loss during UART transmission.  
Figure 3 shows the reception on channels 1 to 8 for functional validation. Channel 2 captures signals from the Vastus Lateralis muscle, while channel 3 captures signals from the Rectus Femoris muscle. The remaining channels represent the internal test signal.
## 4 Experiment and Results
### Experimental Environment Setting  
To prove the effectiveness of our strategy in different environments, different experimental verifications were carried out on the GPU and CPU. Under the CPU, the TensorFlow framework was deployed to build a model of YOLOv4 with the backbone network of CSPDarkerNet53 [57]. At the same time, based on the GPU, the PyTorch framework was used to build a model for the same training and testing. Specific parameter settings may vary depending on the environment. For example, the normalization method was selected according to the specific task and environment configuration. Here we chose two methods for experiments: batch normalization under the TensorFlow framework and local context normalization under the PyTorch framework. See Table 1 for the specific training parameters of the network.
## 3 Model and Scheme
### Scheme Implementation
#### 3.2.3 Characteristic Analysis  
The CCES scheme is characterized by high robustness, including eavesdropping resistance, loss tolerance, and robust synchronization. Now, we give its theoretical analysis.  
Loss tolerance means \(P_{r}\) could still demodulate the fragmentary end spreading sequences to obtain \(M\) with high accuracy. When being converted into \(e\) end spreading sequences, \(m^{\prime}_{k}\) obtains loss tolerance at the expense of becoming redundant or occupying more resources. However, the left number of pieces is required; otherwise, \(m^{\prime}_{k}\) can not be demodulated correctly with too few end spreading sequences. The maximum number of loss packets is known as the loss tolerance degree, denoted by \(\tau\). Obviously, the greater end spreading degree has greater loss tolerance performance, but it is hard to give a fixed \(\tau\) value for different \(e\) due to the various \(PRS\) and \(M\), which allows us to adjust \(\tau\) according to the actual situation.  
Figure 5: The partial results of \(DstIP_{k}\) sequences. (**a**) Result of the \(DstIP_{1}\) sequence. (**b**) Result of \(DstIP_{5}\) sequence.  
The usage of framing flags \(SrcPort\) guarantees that there is little association among the end spreading sequences of \(m^{\prime}_{k}\) and no chain reaction even if partial end spreading sequences were damaged.  
Eavesdropping Resistance  
We denote \(M\) by \(e\cdot t\) end spreading sequences which have no apparent regularity observed on the surface; hence, it is hard for eavesdroppers to conjecture the rest of end spreading sequences if they were partially monitored. Considering the worst case that the eavesdropper pilfers all the end spreading sequences, we show the following analysis of their success probability \(Q\) to obtain the original message \(M\).  
Since there is no difference in modulation and demodulation among subgroups \(m^{\prime}_{k}\) of \(M^{\prime}\), we regard \(Q\) of \(m^{\prime}_{k}\) as the \(Q\) of \(M^{\prime}\) or \(M\). Assuming that the eavesdropper obtains \(\alpha\) packets from the network, including \(\beta=e\cdot t\) legal packets, it first needs to determine at least \((e-\tau)\) legal end spreading sequences via hyper-geometric distribution. Accordingly, the probability \(P\) that it successfully obtains at least \((e-\tau)\) legal pieces in the space \(\alpha\) in \(\eta\) draws can be given by:  
\[P=\left\{\begin{array}[]  
to initialize the receiving event, which guarantees the optimum reception of end spreading sequences and makes the process of constructing more robust against network attacks.