esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, a combined DL network to obtain the optimal decoding rate is proposed in the RIS-assisted MIMO wireless system. The proposed model is configured with a 1-D CNN model and GRU module for acquiring the maximum prediction rate. The proposed model is trained with simulated OFDM data over the RIS-assisted channel network in the offline phase. Then, the learned model is deployed in the online phase to retrieve the original data in the UE terminal to test the efficiency of the proposed model. As a result, it is possible to extract the transmitted data and determine the BER and SER performance. The error performance of the proposed model is evaluated with the well-known Adam optimization algorithm. According to the simulation results, the proposed method has a \(10^{-5}\) BER and \(10^{-4}\) SER in 20 dB and is better compared to other benchmarking models. In addition, the proposed CNN-GRU model has the highest throughput of 2 in 20 dB SNR. Thus, it is shown that the performance of the proposed model outperforms the conventional, Demod-CNN, TCN, and LSTM models in terms of BER, SER, and throughput. The proposed system might offer a way to make the 5G communication system better. Future applications of the proposed model could include more advanced, complicated systems for RIS-assisted wireless communication systems.  
Conceptualization, M.H.R. and M.A.S.S.; methodology, M.H.R.; software, M.H.R., M.A.S.S. and M.A.A.; validation, M.H.R. and M.A.S.S.; formal analysis, M.H.R., M.A.S.S. and M.A.A.; investigation, M.H.R. and M.A.S.S.; resources, H.-K.S.; data curation, M.H.R. and M.A.S.S.; writing--original draft preparation, M.H.R. and M.A.S.S.; writing--review and editing, M.H.R., M.A.S.S., M.A.A., D.-S.K. and H.-K.S.; visualization, M.H.R., M.A.S.S. and M.A.A.; supervision, Y.-H.Y., D.-S.K. and H.-K.S.; project administration, Y.-H.Y., D.-S.K. and H.-K.S.; funding acquisition, H.-K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the ICT R&D Program of MSIT/IITP [IITP-202-2021-0-01816, A Research on Core Technology of Autonomous Twins for Metaverse] and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03038540).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
This paper aimed to evaluate the biomechanical effects of tibial bearing materials, from ultra-high-molecular-weight polyethylene (UHMWPE) to poly-ether-ether-ketone (PEEK) and carbon-fiber-reinforced poly-ether-ether-ketone (CFR-PEEK). The studies were conducted based on a validated finite element model. The geometry of the intact knee model was developed from computed tomography and magnetic resonance imaging of the left knee joint of a 37-year-old healthy male volunteer. Three different loading conditions, related to the loads applied in the experimental research, were applied to this study for model predictions and validation. The contact stress in the other compartments was under normal walking conditions. Also, stresses on five regions of the tibia bone were analyzed under normal walking conditions. The lowest contact stress between the lateral meniscus and tibial cartilage was achieved in the order of the use of CFR-PEEK, PEEK, and UHMWPE tibial bearings. Moreover, CFR-PEEK and PEEK tibial bearings indicated lower and greater stresses on cortical and trabecular bones, respectively, compared to the UHMWPE tibial bearing. These results show that CFR-PEEK can be used as a tibial bearing material as an alternative to UHMWPE, and such a change in the material may be a good method for reducing potential anteromedial pain.  
finite element analysis; CFR-PEEK; uni-compartmental knee +
Footnote †: journal: Applied Sciences
## 6 Simulation Results and Discussions
### Performance Metrics  
An extensive series of simulations has been executed to thoroughly assess the resilience and efficacy of our proposed protocol, taking into account a wide range of performance metrics. These simulations offer a comprehensive picture of network longevity and aid in determining how quickly SNs in the network use up their energy resources.  
1. _Network Lifetime:_ The number of rounds until the last SN runs out of energy and ceases to operate is what defines it. Two criteria were used to evaluate network lifespan: Half Node to Die (HND), which is a measure that calculates how many rounds pass from the IoT network's initial deployment to the point at which 50% of SNs run out of energy and stop working. The term Last Node to Die (LND) refers to the total number of rounds needed for all SNs in the network to run out of energy and eventually die. The count of living and dead SNs is used to measure it.
2. _Total Energy Consumption:_ This determines how much SNs consume energy after each round. The unit of measurement is Joules.
3. _Throughput:_ This measures the efficiency of the network by counting the total number of data packets, in bits, that are successfully delivered to the sink node when reaching LND. Expressed in bits per second (bps), throughput indicates the network's capacity to handle data traffic effectively, with higher throughput values reflecting more efficient data transmission.
4. _Average Delay:_ This metric captures the journey time each data packet endures on its way to the sink node, measured in milliseconds (ms). It represents the typical wait time per packet as it navigates the network, reflecting the efficiency of the data flow. By calculating this delay over multiple rounds and averaging the results, we gain insight into the mean time it takes for a packet to successfully reach its destination.  
Figure 9: SNs distribution for the first and second scenarios.
# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 3 Methodology
### IoT-Driven Adaptation and Quantitative Justification  
While Section 3.4 outlines the architectural design of our edge-cloud framework, this subsection emphasizes its practical role in improving the real-time stabilization task beyond standard control logic. Our system leverages cloud connectivity to create an iterative performance loop that enhances long-term responsiveness across deployments. Specifically:  
* **Model and Policy Refinement:** Detection failures (e.g., under new lighting or terrain types) trigger OTA weight or threshold adjustments. In prior deployments, this feedback reduced false positives by 17% and improved tilt recovery times by 11% (see Section 5.3).
* **Deployment-Driven Generalization:** Cross-vehicle data aggregation helped identify terrain-specific control thresholds, improving balancing consistency across test tracks.
* **Quantitative Impact:** Compared to isolated edge-only systems, our hybrid IoT-based approach yielded a 6.3% gain in overall stabilization success rate across four terrain profiles, as summarized in the further section.  
These results show that the IoT layer is not merely for auxiliary services, but plays an active role in refining detection-actuation logic and supporting real-time adaptability in dynamic environments. This supports the system's framing as an IoT-based stabilization solution.
## 3 Dark Current Measurement Based on Focal-Plane Temperature and Dark Current Correction Experiment  
### Satellite-Borne LWIR QWIP Detection System  
The LWIR QWIP detection system used in this study was designed by the team itself. As shown in Figure 3, it mainly consists of subsystems such as a detector (integrated cooler), information acquisition circuit, optical system, thermal control system, auxiliary data circuit, and information processor.
## 2 Results and Discussion
### Candidate Genes Involved in the Biosynthesis of Triterpenoid Saponins
#### 2.2.4 UGTs  
UGTs catalyse the transfer of glycosyl residues to the precursors that are decorated by P450s. The introduction of a glycosyl moiety to a triterpene increases its aqueous solubility, thus making it a triterpenoid saponin. UGTs catalyse the glucosylation of C3-hydroxyl and C28-carboxyl, which is essential to complete the tr triterpenoid saponin biosynthetic pathway in the _I. asprella_ root. Like the P450s, UGTs constitute a large and diverse gene family. Sequences belonging to the same family and subfamily exhibit amino acid sequences identity \(>\)40% and \(>\)60%, respectively [35]. In the cDNA library of _I. asprella_, 335 unigenes were found to encode UGTs. Among them, five unigenes (CL679.Contig3, Unigene5668, Unigene29448, Unigene26225 and Unigene3060) exhibited high homology to UGT73C10 and UGT73C12 in _Barbarea vulgaris_ (see Table S6), whose bioactivity is to catalyse the 3-\(O\)-glucosylation of oleanolic acid [36]; and one unigene (CL1465.Contig3) was homologous to UGT73F3 in _M. truncatula_, which catalyses the glucosylation of the C28-carboxy group of oleanane sapogenins [37]. Moreover, one unigene (Unigene82) was found to have the highest identity of 50.00% to the amino acid sequence of UGT74M1 in _Saponaria vaccaria_[38], which preferentially catalyses 28-\(O\)-glucosylation of oleanane-type sapogenins.
# Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes
## 2 Orthogonal Frequency Division Multiplexing Radio over Fiber Systems
PBRS: pseudo random binary sequence, S/P: serial-to-parallel, FFT: fast Fourier transform, P/S: parallel-to-serial, DAC: digital-to-analog converter, AWGN: additive white Gaussian noise, ADC: analog-to-digital converter, EC: error counting, and BER: bit error rate. account the latter observation and the representation of the data and pilot symbols, the following matrix may denote an OFDM symbol before the equalization stage:  
\[\overline{C}_{N\times 1}=\Delta\Phi_{N\times N}C_{N\times 1}\exp(j\phi_{0})+N_{N \times 1}^{*}\] \[\left[ \begin{array}{c}\frac{\overline{p_{1}}}{d_{2}}\\ \vdots\\ \frac{\Delta\Phi(N-1)}{\overline{p_{N}}}\end{array} \right]=\left[ \begin{array} []{ccccc}\Delta\Phi(0)&\Delta\Phi(-1)&\ldots&\Delta\Phi(2-N)&\Delta\Phi(1-N) \\ \Delta\Phi(1)&\Delta\Phi(0)&\ldots&\Delta\Phi(3-N)&\Delta\Phi(2-N)\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ \Delta\Phi(N-2)&\Delta\Phi(N-3)&\ldots&\Delta\Phi(0)&\Delta\Phi(-1)\\ \Delta\Phi(N-1)&\Delta\Phi(N-2)&\ldots&\Delta\Phi(1)&\Delta\Phi(0)\end{array}  \right]\left[ \begin{array}{c}p_{1}\\ d_{2}\\ \vdots\\ d_{N-1}\\ p_{N}\end{array} \right]\exp(j\phi_{0})+\left[ \begin{array}{c}n_{1}^{*}\\ n_{2}^{*}\\ \vdots\\ n_{N-1}^{*}\\ n_{N}^{*}\end{array} \right]. \tag{4}\]  
As seen, pilots are uniformly added in the OFDM frame, and by linear interpolation of these in the frequency domain, the distortion in data symbols caused by phase noise may effectively be reduced for time delays less than the OFDM symbol period [9]. In [20], the linear interpolation has shown acceptable performance together with a simple realization. Notice that this compensation results from the pilot-based equalization technique required for correcting the multipath channel effects in millimeter frequencies from a certain base station to the numerous mobile terminals in RoF-OFDM schemes [24], first demonstrated in [8].
## 3 Results and Discussion
Such electronic properties of (Ag-Au)@LCC NPs provide the opportunities to develop the novel catalytic systems with unusual properties. However, unlike the Ag 3d spectra, the formation of the Au-O bond could not be confirmed with certainty since, in the Au 4f\({}_{7/2,5/2}\) spectra, Au\({}_{2}\)O\({}_{3}\) peaks are shifted to an opposite side to 85.9 and 89.5 eV [50; 51]. Otherwise, the formation of Ag-C and Au-C bonds are also evidenced by XPS C 1s spectra (Figure 6) where their low-energy features are detected for (Ag-Au)@LCC structure. It should be noted that C 1s line for pure LCC is also located at the same energy region [52] of the (Ag-Au)@LCC sample. Due to relatively low concentrations of metal nanoparticles, we conclude that the main contribution to this energy range arises from carbon-carbon \(sp\)-hybridized bonds in LCC. This result is important because it indicates the stability of the obtained LCC which is crucial for their applications. We did not observe the signals referred to C-O and (C=O) bonds, generally observed at higher binding energy with respect to C-C bonds in the C 1s lineshape. Thus, we can conclude that the linear carbon chains in investigated samples are not oxidized. The observed two-peak structure of the XPS Ag 3d and Au 4f-spectra is absent in Ag-Au NPs. This confirms the conclusions about the formation of bonds between LCC and Ag/Au atoms. It is important to note that the binding energies of high-energy peaks differ from those in pure metals [53] and are close to the energies of Ag-Au alloys [54]. This leads to the conclusion that the used method of sample preparation ensures the formation of Ag-Au alloy nanoparticles.
## 4 QDAR Mechanism Overview
### The Packet Structures  
Packet structures for DATA_READY packet and INTEREST packet, which can help establish the routing track, are designed as follows. DATA_READY packet contains the type and ID of the packet, source node address (SNA), time stamp (TS), current node depth (CND) and two arrays C and RES in which two costs and residual energy of this node (RE) are saved respectively. SNA is permanent during the network lifetime of the packet. Elements of C are calculation results of two cost functions: delay-related cost and energy-related cost. Denoting the residual energy of node \(i\) as \(e_{res}^{i}\), we define the energy-related cost function \(ce\big{(}e_{res}^{i}\big{)}\) as:  
\[ce\big{(}e_{res}^{i}\big{)}=1-\frac{e_{res}^{i}}{e_{ini}^{i}} \tag{7}\]  
where \(e_{ini}^{i}\) is the initial energy of node \(i\). The less energy a node remains, the more it costs to forward packets. A node with higher cost is more reluctant to communicate. A delay-related cost function \(ct\big{(}t_{ij}\big{)}\) is also defined:  
\[ct\big{(}t_{ij}\big{)}=1-\frac{1}{t_{ij}+1} \tag{8}\]  
where \(t_{ij}\) represents the delay of sending packet between node \(i\) and node \(j\). Clearly, the longer delay, the higher cost. On receiving a DATA_READY packet, the nodes have two tasks:  
1. Calculate. The nodes extract RE and TS. With these information, the nodes can compute their energy-related costs and delay-related costs depending on the delay and residual energy.
2. Update and relay. The nodes update TS, C and RES packet fields with their own information or calculation results and relay the DATA_READY packet to neighbor nodes until the packet arrives at the sink node.  
When the DATA_READY packet reaches the sink node, the sink node holds the relevant data and begins routing decision if it is interested in the packet. After the routing decision phase, the sink node creates an INTEREST packet composed of type and packet ID, destination node address (DNA), current node depth (CND) and path direction (PD). DNA is the source node address (SNA) of DATA_READY packet. The structures of both packets and their relationship are shown in Figure 2.
## 3 Wireless Sensor Networks
### Energy Harvesting and SPWS Perpetual Operation  
For a battery powered device that employs energy harvesting with the objective of obtaining perpetual autonomous operation, there is one rule that must be satisfied, in average, within a given period of time: the amount of energy harvested must be larger than the energy consumed. Several tests were conducted to determine the conditions in which this rule is satisfied for the SPWS transmitter nodes.
## 2 Hardware of Transient Luminous Event (TLE) Observation System
### Mechanical Design  
For the CMOS image sensor, the ruggedized fixed-focal length lens LM8]CM-V by Kowa (Nagoya, Japan) is selected for optics. Considering the size of CMOS image sensor (1/2.5-inch in diagonal), this lens can achieve a FOV 35\({}^{\circ}\), which is derived from the mission requirement of the TLE observation system [35]. The focal length of the Kowa lens is 8 mm, thus an additional C-mount lens holder is used to fit the lens on the board. On the other hand, a pin-hole type lens is adopted for the MaPMT. The FOV of the pin-hole lens is purposely chosen to be same as that of the image sensor. The reason for this is discussed later in this paper. Subsequently, by taking into account the height of the MaPMT, the pin-hole is rendered at a distance of 76 mm from the bottom of the board. The aperture of the pin-hole lens is designed to be 300 \(\upmu\)m based on the calculation of the number of photons which would be projected on the sensing plane of the MaPMT, as discussed in Section 4.1.1.  
The mechanical design of the lens housing is shown in Figure 6. It is a box shaped fixture that has room to hold both the MaPMT and the optical lens. To minimize light reflection inside the lens housing, the aluminum fixture is anodized in matte black. Because the fixture is securely attached to the board, it can also protect the optical lens assembly from vibration and shocks, so that the structure robustness is assured. The entire TLE hardware including the PCB board and the lens housing occupies approximately the volume of 0.35U. For efficient use of the limited space of the 2U VisionCube, other modules such as the onboard battery and the attitude determination and control board are assembled by fully using the empty space next to the lens housing.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 2 Laser-Matter Interaction in the Femtosecond Regime
### Diagnostics of the Ion Formation in fs-Laser Ablation
The comparison of ion yields obtained in high irradiance ns- and fs-laser ablation (laser wavelength 1064 and 1023 nm, respectively) were reported by the [PERSON] group [20]. These extensive studies were conducted on 29 solid samples with 10 different matrices, including six metals and four insulators using buffer-gas-assisted LI-TOFMS. The mass spectrometric analysis led to conclusions that for most of the elements, except for the nonmetallic elements, the ionisation yields are proportional to atomic abundance in the bulk material for both ns- and fs- ablation. Nevertheless, the ion yields produced by ns-ablation undergo larger fluctuations compared to fs-ablation ion yields. The ion yield dependence \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element** & **A (u)** & **T\({}_{\text{m}}\) (K)** & **R (\%)** & **IP (eV)** & **Y\({}_{\text{0}}\) (10\({}^{\text{11}}\)ions/cm\({}^{\text{2}}\))** \\ \hline Mg & 24.30 & 923 &... & 7.65 & 5.7 \\ Al & 26.98 & 933 & 86.8 & 5.99 & 4.9 \\ Cr & 51.99 & 2180 & 56.8 & 6.77 & 3.8 \\ Fe & 55.84 & 1811 & 56.12 & 7.90 & 4.5 \\ Cu & 63.54 & 1357 & 96.3 & 7.73 & 1.9 \\ W & 183.84 & 3695 & 49.6 & 7.86 & 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical properties of the selected metals: A, atomic weight; T\({}_{\text{m}}\), melting point; R, reflectivity at 800 nm, normal incidence; IP, ionisation potential. Y\({}_{\text{0}}\) is the peak value of the ion yield registered along the direction normal to the target surface [79]. Figure 2: Copper ion emission yield (**a**) and specific ion yield (**b**) as a function of the fs-laser fluence. The two vertical lines mark the transition between different regimes. The inset in panel (a) shows the Langmuir probe (LP) data on a log-log plot. The left axis represents the LP and Faraday Cup (FC) measurements and the right axis represents the spectrally resolved intensified charged coupled device (ICCD) imaging. Reprinted with permission from [79]. on the material was observed to be less for fs-ablation due to lack of thermal diffusion, production of a dense plasma and more stable ionisation. The remaining differences in the stoichiometric ionisation were accounted for in the different ionisation potentials and ionisation cross-sections for different elements.
## 2 Popular Optimization Problems in Transportation
### The Vehicle Routing Problem  
The VRP is a well-known combinatorial optimization problem with a vast number of applications in the transportation sector [10]. Solving the VRP aims to design cargo vehicle routes with minimum transportation costs to distribute goods between depots and a set of consumers. Since the capacity of the cargo vehicles is usually taken into account, the VRP is often referred to as _capacitated_ VRP.  
In its basic version, the distribution network of the VRP consists of a single depot and a set of customers, geographically distributed around a coverage area. A set of cargo vehicles, initially available at a central depot, visits customers to meet their demands. Once all customers assigned to a vehicle have been served, the vehicle returns to the central depot. The typical goal is to minimize the cost of distribution, serving all customersand without exceeding the loading capacity of the vehicles (which may or may not be homogeneous). This distribution network can be defined as a directed graph \(G=(N,E)\), where: (i) \(N=\{0,1,\ldots,|C|\}\) is the set of vertices, with node 0 being the central depot and \(C\) being the set of customers; and (ii) \(E=\{(i,j)\mid i,j\in N,i<j\}\) is the set of edges connecting pairs of nodes. Each customer \(i\in C\) requires a demand \(d_{i}>0\), which affects the of the vehicle. The objective, in solving this problem, is to minimize the total cost of serving all customers, subject to: (i) each route starts and ends at the central depot; (ii) each customer is visited only once and by exactly one vehicle; and (iii) the total demand required by the costumers on a route does not exceed the vehicle capacity. Apart from this basic version, multiple extensions of the problem can be found in the literature, to name a few: heterogeneous fleet of vehicles [11; 12], time-windows [13; 14], multiple depots [15; 16], multiple delivery levels [17; 18] simultaneous pick-up and deliveries [19; 20], or combination of the former [21; 22; 23]. Many real-life versions of this problem combine some of the aforementioned constraints and others, making them difficult to solve [24].  
Figure 1 depicts the network topology of a basic VRP, in which a fleet of three vehicles is used to serve, from a central depot, 12 customers. The vehicles depart from the depot with loaded demand, which is delivered at each customer location. In this case, the routes are constrained by the capacity of cargo vehicles.