esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulations and Results
As \(h\) continues to increase, the sensitivity will decrease as well as the FOM. The separation of the two symmetrical rectangular baffles \(H\) and the heights of the two baffles \(h\) are pivotal to the waveguide with two rectangle baffles. According to the simulation results, the successive wide-band mode has a significant effect on the line shape of Fano resonance, but not on the wavelength of the dip point. To further investigate the effects of the coupling gap between the CCDRR and the waveguide on the propagation properties, \(g\) was increased from 10 nm to 30 nm while the other geometric arguments were fixed at \(R_{1}\) = 240 nm, \(R_{2}\) = 130 nm, \(H\) = 540 nm, and \(h\) = 150 nm. The transmission performances of the structure with different coupling gaps between the CCDRR and the waveguide for \(g\) = 10, 15, 20, 25, and 30 nm can be seen in Figure 8a. With increasing \(g\), the dip wavelength of the Fano resonance shows a blue-shift, the FWHM tends to narrow and the transmittance of the dip position of Fano resonance tends to move higher. The fact that the coupling intensity weakens as the coupling gap increases can account for this phenomenon. Additionally, the sensitivity of the system decreased with the increase of \(g\), as shown in Figure 8b. Thus, the optimal performance parameters can be obtained when the sensitivity is 2260 nm/RIU and the FOM is 56.5. Figure 6: (**a**) Transmission spectra of the inner ring of CCDRR for different external radii \(R_{2}\); (**b**) shifts of the dip point in Fano resonance (\(\Delta\lambda\)) with a changed refractive-index (\(\Delta n\)). Figure 8: (**a**) Transmission spectra for various coupling gaps \(g\) between the CCDRR and waveguide; (**b**) shifts of the dip point in Fano resonance (\(\Delta\lambda\)) with a changed refractive-index (\(\Delta n\)). Figure 7: (**a**) Transmission spectra for the different separations \(H\) of the twin symmetrical rectangular baffles; (**b**) varying figure of merit (FOM) with increasing separation between the two rectangular baffles; (**c**) transmission spectra for the various heights of the two symmetrical rectangular baffles; and (**d**) varying FOM with increasing height of the two rectangular baffles.
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
## 3 Problem Solution
### Optimal Digital Pre-Encoder Design
Therefore, for any feasible solution to the above optimization problem, we can obtain an MRT-form solution with respect to it. In this case, the digital precoding vector can be written as \(\mathbf{w}^{*}=\sqrt{P_{x}}\frac{\mathbf{h}^{H}}{|\bar{\mathbf{h}}|}\), where \(P_{x}\) is an unknown variable representing the actual transmission power at the base station. By substituting \(\mathbf{w}^{*}\) into the signal-to-noise ratio expression, we can express the signal-to-noise ratio as  
\[\gamma= \frac{|\mathbf{h}^{H}\mathbf{w}|^{2}}{\sigma^{2}}\] \[= \frac{\mathbf{h}^{H}\mathbf{w}^{*}\mathbf{w}^{*}{}^{H}\mathbf{h}} {\sigma^{2}}\] \[= \frac{\mathbf{h}^{H}\sqrt{P_{x}}\frac{\mathbf{h}}{|\bar{\mathbf{ h}}|}\dotplus\sqrt{P_{x}}\frac{\mathbf{h}^{H}}{|\bar{\mathbf{h}}|}\mathbf{h}}{ \sigma^{2}}\] \[= \frac{P_{x}\|\mathbf{h}\|_{2}^{2}}{\sigma^{2}}. \tag{14}\]  
The original optimization problem (8) can be written as an optimization problem related to the unknown variable \(P_{x}\) as follows  
\[\max_{P_{x}} R \tag{15}\] \[\text{such that} P_{x}\leq P_{\text{tot}}. \tag{16}\]  
Problem (15) can be solved by an iterative optimization algorithm, whose main idea is to find a solution through continuous iteration that satisfies the constraint and maximizes the objective function. If the convergence condition of optimization is satisfied, the obtained solution can be regarded as the result of the original problem. The iteration process of our algorithm is shown in Algorithm 1. ```
Input: Initialize the transmit power that satisfies \(P_{x}^{0}\leq P_{\text{tot}}\), convergence threshold \(\epsilon\), and maximum iteration number \(L_{k}\). Step1: Set the iteration coefficient \(k=0\). Step2: Calculate \(P_{x}^{k+1}\) according to (15). Step3: \(P_{x}^{k}=P_{x}^{k+1}\). Step4: \(k\gets k+1\). Step5: The result converges or the maximum iteration number \(L_{k}\) is reached. Step6: Substitute the optimal value \(P_{x}^{k}\) into Equation (13) to obtain \(\mathbf{w}^{k}\). Step7: The optimal all-digital precoding vector \(\mathbf{w}=\mathbf{w}^{k}\). ```  
**Algorithm 1** Digital Precoder Design (DPD) Algorithm
###### Abstract  
In clinical decision support systems (CDSSs), where accurate classification of drug-drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance.
## 3 Results and Discussion  
### Repeatability and Dependence on Initial Particle Mass  
Figure 2 displays H\({}_{2}\)O concentration, gas temperature and K(g) concentration at HAP 4 mm above softwood particles as a function of conversion time. Figure 2a shows three repetitions for an initial particle mass of 500 \(\pm\) 10 mg, whereas Figure 2b presents the results for four different initial particle masses (500, 600, 800 and 1000 mg). Since the H\({}_{2}\)O concentration during char combustion was below the detection limit of the TDLAS sensor (<0.1%), H\({}_{2}\)O and temperature could only be reliably assessed in the devolatilization stage (Dev.), while the K(g) time series includes the char conversion. The gas temperature above the particle during char burning was close to the SPR set temperature (1123 K).  
The time series displayed in Figure 2a suggest that the pellet measurements performed with the current experimental setup are repeatable. Consistent results (shapes, absolute values, devolatilization time) were obtained for three softwood particles with the same initial mass for all parameters. A similarly high repeatability was also found for the other fuel types and at different heights above the pellets. Here, repeatability refers solely to the method and the experimental setup, not the biomass particles themselves, as the uniformity of the pelletized biomass is out of the scope of the current work. Local variations in fuel composition could be the reason for the small differences between the repeated measurement in Figure 2a, e.g., during char conversion. However, overall, the pellets used in this study seemed to have been uniform.  
Figure 2: **(a)** Time series of H\({}_{2}\)O, gas temperature and K(g) at HAP 4 mm above softwood particles with initial mass of 500 mg (three replicates). The devolatilization (Dev.) phase (0–50 s) and the char combustion phase (50–250 s) are indicated by arrows. **(b)** Time series of H\({}_{2}\)O, temperature and K(g) at HAP 4 mm above softwood particles with initial masses of 500, 600, 800 and 1000 mg. H\({}_{2}\)O and temperature could be measured only during devolatilization, while the K(g) time series includes the char conversion.  
From the time series for different initial particle masses (Figure 2b) it can be observed that the average gas temperatures were comparable for all masses, but that the released H\({}_{2}\)O increased with mass, probably due to the larger absolute moisture content of the particles with larger mass. Another reason for the increase in H\({}_{2}\)O concentration could be that the larger pellets led to a larger flame/plume size, which was not accounted for in the spectra evaluation as the absorption path length was fixed to 2 cm. Moreover, the figure clearly shows that the devolatilization and overall conversion times increase with initial particle mass, as expected. However, interestingly, the K(g) concentrations are largely independent of particle mass, which points to conversion conditions and chemical composition being the main factors governing K release, not primarily the absolute K content.
## 3 Pseudolite-Based Positioning System for Legacy GNSS Receivers
### System Configuration
#### 3.1.3 Data Server  
The data server plays two main roles in this system. One is to provide the pseudolites and the user terminal with necessary information. It gives the information about the simulation scenario to the pseudolites so that each pseudolite can generate a simulated GPS signal. Also, it provides the user terminal with the information about simulated GPS satellites and pseudolites. The delivered information include the positions of pseudolites and simulated GPS satellites which are required to the post-calculation module. It also includes the information about the assignment of the pseudolites and corresponding GPS satellites. Using this information, the user terminal distinguish the pseudolites using PRNs of corresponding GPS signals. The data link between the data server and the user terminal can be WiFi or any other possible means.  
The other role of the data server is generating clock control for the synchronization of the pseudolites. However, this paper will not describe the synchronization of the pseudolites in detail, because the synchronization can be achieved by using an existing method. This paper follows the method using a reference station receiver to synchronize the clocks of pseudolites [10]. Figure 3 shows the control block diagram of the pseudolite clock synchronization. According to the paper, the relative clock error of the pseudolites can be calculated by differencing the carrier phase measurements of the master pseudolite and a slave pseudolite, and subtracting the known distance values. Then the properly designed loop filter F(s) estimates the control value of each pseudolite clock. The output of the DDS  
Figure 3: Control Block Diagram of Pseudolite Clock Synchronization [10].  
(Direct Digital Synthesizing) is then controlled to eliminate the clock error \(\alpha\) of the TCXO of the slave pseudolite. The experiment results showed cm-level accuracy in CDGPS positioning based on the clock synchronized pseudolites.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 2 Related Works
To directly address the three color channels of an image, color 2 DPCA-based algorithms were proposed. [PERSON] et al. [46] used a row vector to represent the color channel and proposed the color-2 DPCA (C-2 DPCA). [PERSON] et al. [47] used a quaternion with zero real part to represent the color channel and proposed the 2D quaternion PCA (2D-QPCA). [PERSON] and [PERSON] [48] proposed a novel quaternion ridge regression model for 2D-QPCA and proposed the QRR-2D-QPCA. [PERSON] et al. [49] replaced square \(F\)-norm with \(F\)-norm in 2D-QPCA and proposed the \(F\)-2D-QPCA. [PERSON] et al. [50] replaced \(\ell_{2}\)-norm with \(\ell_{p}\)-norm in 2D-QPCA and proposed the generalized 2D-QPCA (G-2D-QPCA). [PERSON] et al. [51] introduced \(\ell_{p}\)-norm to 2D-QPCA and proposed the relaxed 2 DPCA (R-2 DPCA). Besides those mentioned above, [PERSON] et al. [52] replaced the \(F\)-norm with nuclear-norm in traditional 2 DPCA and generalized 2 DPCA [22], proposing the nuclear-norm-based 2 DPCA (N-2 DPCA) and the nuclear-norm-based bilateral 2 DPCA (NB-2 DPCA). [PERSON] et al. [53] proposed a computationally efficient Riemannian proximal stochastic gradient descent algorithm (RPSGD) to solve sparse 2 DPCA on the Stiefel manifold. [PERSON] et al. [54] introduced the human learning mechanism to 2 DPCA and proposed the self-paced 2 DPCA(SP2 DPCA). [PERSON] et al. [55] applied the Lanczos algorithm to speed up traditional 2 DPCA. Further, 2 DPCA was employed to design novel neural networks such as 2 DPCA-Net [56] and L1-2 DPCA-Net [57]. In summary, recent advances of 2 DPCA mainly focus on partitioning images into subregions to extract more structural information, introducing robustness and sparsity by \(\ell_{1}\)-norm or \(\ell_{p}\)-norm, introducing rotational invariance by \(\ell_{2,1}\)-norm or \(F\)-norm, processing color images directly under the quaternion framework, designing deep learning models, etc., as shown in Table 1. Note that some algorithms in this table incorporate multiple ideas but we still classify each of them into a single category for simplicity. For example, \(\ell_{2,p}\)-SB-2 DPCA [45] incorporates the arbitrary power of \(\ell_{2,1}\)-norm and the sequential bilateral idea from RC2 DPCA [23].
## 3 Results and Discussion
### Phenolic Compounds
#### 3.4.2. Phenolic Profile  
The separation of the phenolic compounds and 5-HMF of the samples under study, at 280 nm, is shown in Figure 3.  
According to our results, the same compounds were obtained in all considered samples. We were able to identify six different classes of phenolics, in a total of 15 compounds: hydroxybenzoic acids (gallic acid and protocatechic acid), hydroxycinamic acids (chlorogenic acid and coumaric acid), flavanols (procyanidins and catechins), anthocyanin (cyanidin-3-glucoside), flavonols (rutin and quercetins) and dihydro  
Figure 3: UV-vis chromatograms and identification of the obtained peaks of the samples under study. The predominant compound in all samples is chlorogenic acid (OD85\({}^{\circ}\) > fresh > OD65\({}^{\circ}\) > CC > Solar > CF), constituting, with coumaric acid, the most abundant group, the hydroxycinamic acids. Similar results were obtained by [PERSON] et al. [55] and [PERSON] et al. [21]. In fresh apple, the total phenolic content was 7.24 g/kg d.m., consisting of 49.45% hydroxycinamic acids, 23.62% flavanols, 11.74% dihydrochalcones, 8.98% flavonoids, 3.31% anthocyanidins and 2.90% hydroxybenzoic acids. Then oven-dried samples, at 65 \({}^{\circ}\)C and 85 \({}^{\circ}\)C, had higher phenolic content, with 6.30 and 6.32 g/kg d.m., respectively. For OD6 \({}^{\circ}\)C samples, hydroxycinamic acids (49.84%) were followed by flavanols (22.38%), dihydrohalcones (10.32%), hydroxybenzoic acids (8.25%), flavonoids (4.92%) and anthocyanins (4.29%). In OD85 \({}^{\circ}\)C samples, there was a slight variation compared to OD65 \({}^{\circ}\)C samples, with hydroxybenzoic acids (15.35%), dihydrochalcones (11.39%) and flavanols (6.49%). For solar-dried samples, the total phenolic content was 4.33 g/kg d.m., consisting of 47.81% hydroxycinamic acids, 25.64% dihydrochalcones, 16.17%flavanols, 4.85%flavonols, 3.93% anthocyanidins and 1.62% hydroxybenzoic acids. In the case of commercial samples, CC registered 4.38 g/kg d.m. and CF registered 3.93 g/kg d.m. The order of almost all compounds was the same: hydroxycinnamic acids (48.40% for CC and 35.88% for CF), hydroxybenzoic acids (24.20% for CC and 25.95% for CF), dihydrohalcones (12.33% for CC and 15.01% for CF) and flavonoids (10.73% for CC and 11.45% for CF). The difference was in flavanols (3.65% for CC and 6.11% for CF) and anthocyanidins (0.68% for CC and 5.60% for CF). Our results are in accordance with other authors [16; 56; 57; 58].
## 3 Research Methodology  
As mentioned in the previous research literature, VR can operate and practice in virtual space to provide the conditions and environments that cannot be achieved in general actual environments, such as preventions of explosion and aircraft fire, operation of replacing expensive instruments, and using VR to achieve the purpose of practice and training. An aircraft maintenance virtual reality (AMVR) system [15] was designed with three different scenarios: walk-around inspection, fuel system observation, and horizontal stabilizer maintenance for the Dornier 228. The aviation AMVR system is developed with VR technology, which has the training effect to improve the accuracy of maintenance and can also record the number of operation errors, which is key for guiding the operator to prevent mistakes. From these statistical data, the maintenance procedures can be improved, and the manual operation errors can be reduced. In addition, it also can improve flight safety and the designs of components and products. To design the AMVR system, the creation process of [PERSON] and [PERSON] [16] has been referred to. They have provided a simple and easy-to-understand workflow and detailed descriptions of the components needed to build VR applications, thus helping new VR developers to easily access and reference when developing VR applications.  
The research follows the steps shown in Figure 2.  
To generate scenarios and assign interaction functions, multiple software, programming languages, game engines, and 3D modeling tools are required (Table 1). Unity 3D version 2019.3.0f6, C#, and CATIA V5 are used to develop for VR maintenance training system in the current research. VR devices are indispensable for VR applications. To immerse in the virtualized space, users need a headgear with VR glasses. We used HTC VIVE Pro McLaren Limited Edition of HTC [17] and Oculus Quest 2 [18].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Game Engine** & **Programming Language** & **3D Modeling Software** \\ \hline Unity [19], Unreal Engine, CryEngine, id Tech, Source, GameMaker Studio, & C++, C#, Java/Java Script, Python, Swift, \(\dots\) & Blender, CATIA, SolidWorks, Autodesk Maya, Autodesk 3 Ds Max, SketchUp, \(\dots\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Commonly used software for VR systems.  
Figure 2: Workflow of the VR system.
# 6G Mobile Networks: Key Technologies, Directions, and Advances
## 3 6G Market Overview and Current Status
### Developmental Progression from 1G to 6G
The main objective of eMBB will be energy efficiency. Mobile communication networks should be able to considerably benefit from this envisioned service class in terms of handover, interference, and massive data transmission/processing. The underlying privacy and security considerations must also be taken into account. Massive-eMBB would demand a high link density to acquire tactile impressions and transform them into digital information. Massive eMBB will thus be a hot topic in the 6G future, enabling vast low-latency communication among personnel, sensors, and actuators to improve large-scale IIoT operations and capabilities. 6G also includes other services like uHSLLC, UHDD, uHRS, feUMBB, eURLLC, etc. 6G offers a latency of 1 microsecond. This is 1000 times faster than the throughput of one millisecond. 6G will employ even shorter wavelengths in the Terahertz (THz) band, which spans 100 GHz to 3 THz, than 5G, which uses mmWave in the microwave frequency range [13]. Figure 2 shows the Evolution of Mobile generations and is depicted in a detailed way as per Table 3. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Generations**} & **Access** & **Switching** & **Error** & & & & & \\  & **Techniques** & **Techniques** & **CoreMechanism** & **Data Rate** & **Frequency Band** & **Bandwidth** & **Application** & **Description** \\ \hline \multirow{2}{*}{1G} & \multirow{2}{*}{FDMA} & Circuit & \multirow{2}{*}{NA} & \multirow{2}{*}{2.4 kbps} & \multirow{2}{*}{800 MHz} & \multirow{2}{*}{30 KHz} & \multirow{2}{*}{Voice} & Let us to have a \\  & & & Switching & & & & & & conversation. \\ \hline \multirow{2}{*}{2G} & TDMA, & Circuit & \multirow{2}{*}{NA} & \multirow{2}{*}{10 kbps} & \multirow{2}{*}{200 kHz} & \multirow{2}{*}{Voice and Data} & Allow us to send \\  & & CDMA & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline \multirow{3}{*}{3G} & CDMA, & Circuit and & \multirow{3}{*}{Turbo Codes} & \multirow{3}{*}{384 kbps to 5 Mbps} & \multirow{3}{*}{5 Mbps} & \multirow{3}{*}{5 MHz} & Allow us to \\  & & & & & & & & \\ \cline{1-1}  & WCDMA & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of Mobile Technology.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 4 Problem Formulation and Analysis
### Computing Load Cost  
In the LEO-ECN, LEO satellites cannot effectively cover users most of the time, resulting in idling and waste of resources. In addition, due to the uneven distribution of ground terminals and user tasks, unloading tasks from ground terminals to directly connected satellites is easy to cause local overload of computing resources on satellites. From the perspective of system, tasks can be balanced to those idle satellites as much as possible so as to improve the efficiency and resource utilization of the whole low-orbit constellation edge cloud. We sum up the mean square error of the computing power demand assigned to each satellite as the cost of load balancing in a low-orbit constellation edge cloud network. The cost is related to the level of load balancing. Specifically, the more balanced the load, the smaller the cost. The system load balancing cost can be expressed as  
\[\sum_{m\in\mathcal{M}}(\sum_{n\in\mathcal{N}}C_{n}x_{n,m}-\sum_{n\in\mathcal{ N}}C_{n}/M)^{2} \tag{15}\]
# Bindings and RESTlets: A Novel Set of CoAP-Based Application Enablers to Build IoT Applications
## 6 Implementation and Evaluation
### Impact of Noise  
Under normal working conditions, sensor and actuator nodes suffer from interference from other sources. This might create loss of packets requiring retransmissions in case of CONfirmable transmissions which, in turn, leads to increased latency. NON-confirmable transactions also suffer from increased latency since every lost packet leads to processing to be delayed until the 5 s interval is reached. To study the impact of lossy networks on latency, we run tests by setting the Transmission/Reception (TX/RX) loss from 0% (no loss), 5% and 10% losses. The topology we used is given in Figure 24. As the figure shows, there are three data generating nodes that send packets every five seconds without any time gap between the data generations. We selected three nodes to avoid the impact of having too many or too few data nodes which might skew the result to either side. Too little data generating nodes may influence the result in favor of NO-RESTlet case while too many nodes favor the other. The test is done both for CONfirmable and NON-Confirmable transactions.  
Figure 23: Impact of Packet Arrival Time Gap on Latency.  
It is not a surprise that the results of the experiments in Figure 25 show higher overall latency for the NO-RESTlet case in both CON and NON communications. However, it is quite interesting to see that the difference between the NO-RESTlet and the RESTlet cases gets higher at higher TX/RX loss ratios. This indicates that, our solution is relatively more robust under lossy conditions for both CON and NON transactions.