esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 4 Experiments
### Influence of Hyper-Parameters (RQ3)  
Our model contained three main hyper-parameters (i.e., the drop ratio \(r_{\theta}\), the sample ratio \(\rho\), and the temperature \(\tau\)). In Figure 3, we show the performance of the model for the Foursquare dataset with different values of hyper-parameters. We have the following observations:  
* Overall, the different drop ratio \(r_{\theta}\) and sample ratio \(\rho\) had little effect on the model results, which indicates the robustness of the model.
* Too large or too small SSL temperature \(\tau\) values reduced the performance. This observation is consistent with the previous work [12]. The possible reason behind this is that, if the temperature is large, it is more difficult to distinguish negative examples. If the temperature is small, only a small number of negative cases affect the optimization.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Recall@5** & **Recall@10** & **Recall@20** & **Recall@50** & **MAP@5** & **MAP@10** & **MAP@20** & **MAP@50** \\ \hline NeuMF & 0.3540 & 0.4315 & 0.4631 & 0.5063 & 0.2339 & 0.2517 & 0.2562 & 0.2588 \\ NGCF & 0.3198 & 0.3952 & 0.4499 & 0.5177 & 0.2123 & 0.228 & 0.2349 & 0.2392 \\ DGCF & 0.3285 & 0.4046 & 0.4612 & 0.5335 & 0.2194 & 0.2353 & 0.2427 & 0.2472 \\ LightGCN & 0.3456 & 0.4221 & 0.4705 & 0.5315 & 0.2250 & 0.2417 & 0.2482 & 0.252 \\ SGL & 0.3373 & 0.4123 & 0.4674 & 0.5311 & 0.2432 & 0.2591 & 0.2664 & 0.2705 \\ NCL & 0.3466 & 0.4236 & 0.4667 & 0.5187 & 0.2222 & 0.2391 & 0.2451 & 0.2484 \\ STACP & 0.0054 & 0.0094 & 0.0203 & 0.0421 & 0.0022 & 0.0026 & 0.0034 & 0.0041 \\ LGLMF & 0.0005 & 0.0010 & 0.0030 & 0.0087 & 0.0002 & 0.0002 & 0.0004 & 0.0005 \\ GPR & 0.2984 & 0.3758 & 0.4573 & 0.5669 & 0.2066 & 0.2185 & 0.2253 & 0.2297 \\ MPGRec & **0.3930** & 0.4316 & 0.4655 & 0.5147 & **0.3170** & **0.3240** & **0.3272** & **0.3294** \\ SSTGL (Ours) & 0.3865 & **0.4388** & **0.4917** & **0.5645** & 0.3073 & 0.3146 & 0.3185 & 0.3209 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall performance of SSTGL and all baselines for Meituan dataset, where the best results for each column are shown in bold font, and the second-place results are underlined.  
Figure 2: Ablation study on Foursquare, Gowalla, and Meituan datasets.
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 3 Experimental Data and Analysis Indicators
### Analysis Indicators
#### 3.3.2 Improvement Percentage (IP)  
The improvement percentage is used to reflect the improvement of the positioning precision; that is, when the number of downlink frequencies is different, the calculated positioning precision is improved relative to the single frequency. The calculation formula is:  
\[\text{IP}=\frac{r_{i}-r_{0}}{r_{0}}\times 100\% \tag{23}\]  
where r\({}_{i}\) is the positioning precision when the frequency number is i, i = 1, 2,..., n, n is the frequency number; and r\({}_{0}\) is the positioning precision when the frequency is single.
## 5 Results
### Road Width Estimation Evaluation  
For the evaluation of the regression model that is described in Section 3.6 we use the model to estimate the road width in 10 randomly sampled frames and retrieve ground truth measurements. Then we calculate the mean absolute error of the predicted \(\hat{y}_{i}\) and actual measurements of \(y_{i}\) given by the following formula:  
\[\sum_{i=1}^{D}|\hat{y}_{i}-y_{i}| \tag{4}\]  
with \(D=10\), resulting in 0.53 m which is quite accurate taking into account the dynamic street environment.
## 7 Towards a Simplified Optical Transceiver
### SCOH System Classification  
This subsection presents a comprehensive analysis of the existing SCOH-based schemes. The schemes can be grouped by the receiver structure. Mainly, they can be classified as either being based on a single-ended PD or a balanced receiver scheme.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 5 Energy and Delay Aware Data Aggregation in RPL  
The Proposed Energy and Delay Aware Data aggregation in RPL (EDADA-RPL) has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parental node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the DODAG root or sink. The DODAG root gathers aggregated data and conducts the reconciliation process to recover the original data.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 16 Enhancement Prospects
#### 2.4.1 Grant-Free Access  
Most of the power consumption takes place during the NB-IoT UE active time, i.e., during Tx and Rx. In Release 16, the UE will be expected to transmit during RRC-Idle mode through Msg3 (RRC connection request) without access grant. A UE in RRC connected mode can transmit data without grant or with the simplified control-less grant. A further enhancement is on reducing NB-IoT signaling overhead while guaranteeing the needed quality of service. These features will reduce both power consumption and latency. In Release 16, it is also proposed to further study other signal waveforms (i.e., FDMA) that require less orthogonality with more relaxed timing advance (TA) alignment as compared to SC-FDMA.
# Augmented Reality for PCB Component Identification and Localization
###### Abstract  
This study evaluates the effectiveness of augmented reality (AR), using the Microsoft(tm) HoloLens(tm)tm 2, for identifying and localizing PCB components compared to traditional PDF-based methods. Two experiments examined the influence of user expertise, viewing angles, and component sizes on accuracy and usability. The results indicate that AR improved identification accuracy and user experience for non-experts, although it was slower than traditional methods for experienced users. Optimal performance was achieved at 90\({}^{\circ}\) viewing angles, while accuracy declined significantly at oblique angles. Medium-sized components received the highest confidence scores, suggesting favorable visibility and recognition characteristics within this group, though further evaluation with a broader component distribution is warranted. Participant feedback highlighted the system's intuitive interface and effective guidance, but also noted challenges with marker stability, visual discomfort, and ergonomic limitations. These findings suggest that AR can enhance training and reduce errors in electronics manufacturing, although refinements in marker rendering and user onboarding are necessary to support broader adoption. This research provides empirical evidence on the role of AR in supporting user-centered design and improving task performance in industrial electronics workflows.  
a +
Footnote †: 2020: _Applied sciences_  
mitigate human errors and accelerate the learning process for technicians. However, AR's utility is not limited to any one device; many techniques, including visual inspection tools and enhanced training modules, can achieve similar results [7; 8; 9].  
The testing of PCBs presents additional challenges due to human factors, such as fatigue and variable skill levels, which can lead to inconsistencies in component placement and the overlooking of defects [10]. Tools like multimeters, oscilloscopes, and in-circuit testers (ICTs) are essential for identifying defects, but their effective use requires considerable expertise, often resulting in a steep learning curve for new technicians. AR devices such as the Microsoft(tm) HoloLens(tm) 2 enable real-time guidance and digital overlays that enhance precision in electronics tasks. While the HoloLens(tm) 2 has shown benefits, its limitations also highlight the need for further refinement in AR systems to maximize their potential in industrial applications [11]. This study aims to evaluate the effectiveness of augmented reality (AR) using the Microsoft HoloLens(tm) 2 for identifying and localizing components on a printed circuit board (PCB). Through two controlled experiments, we compare AR performance with traditional methods and examine identification accuracy across varying viewing angles. This paper contributes by empirically quantifying AR's effectiveness in industrial electronics workflows using a head-mounted display, evaluating its usability across user expertise levels and varying viewing conditions. The dual-experiment approach enhances methodological robustness and practical relevance.
## 5 Frequency Hopping Techniques  
This section describes the different frequency hopping techniques taken into consideration in the analysis. Hopping techniques determine not only the subset of channels, but also the way these channels are scheduled. Frequency hopping techniques can be classified as either channel-ignorant or channel-aware [24]. With channel-ignorant frequency hopping techniques, the hopping pattern is selected regardless of the channel characteristics, whereas with channel-aware frequency hopping techniques, the hopping pattern is selected after determining the channel characteristics. Channel-aware hopping techniques can be further classified as reduced-hop-set and probabilistic-channel-usage techniques [24; 25; 26]. Algorithms based on reduced hop sets totally avoid bad channels. On the other hand, algorithms based on probabilistic channel usage use all channels, although bad channels are assigned a smaller usage probability that depends on environmental conditions. Figure 6 summarizes the frequency hopping technique types. These techniques are explained in more detail in the following subsections.  
Figure 5: Normalized channel gains from RSSI statistical properties of Figure 4.
## 3 Storage Degradation Belief Reliability Model for AEMR
### Uncertainty Representation of Parameters  
Usually, belief reliability modeling requires quantifying the uncertainty of its model parameters, so the possible uncertainty of the parameters of the following equation needs to be further discussed after obtaining Equation (11).  
\[[P_{r}(0)-c]\cdot[a^{\prime}\cdot\ln(t+1)+b)]+c-\omega\leq 0 \tag{11}\]  
There are two possible parameter uncertainty cases: aleatory and epistemic uncertainty. The essence of aleatory uncertainty is the inherent uncertainty of the object world, which can also be called random uncertainty. Cognitive uncertainty is caused by the limitation of the objective laws, due to the limited cognitive ability of humans. The parameters in Equation (11) have aleatory uncertainty and epistemic uncertainty due to the dispersion of materials, the volatility of working conditions and environment, and the incompleteness of cognition. Therefore, the parameters in Equation (11) are classified according to their uncertainties.  
From the above, \(P_{r}(0)\) and \(c\) are the randomly influenced parameters in the degradation model, which are, in essence, the random uncertainties caused by dimensional tolerances, assembly errors, etc. Therefore, the uncertainty types of \(P_{r}(0)\) and \(c\) are aleatory uncertainties. For model parameters, \(a^{\prime}\) and \(b\), the reed material and storage conditions are usually relatively fixed in the same batch of stored AEMRs. Therefore, the randomness of \(a^{\prime}\) and \(b\) is mainly due to insufficient knowledge of the objective change law of the stress relaxation mechanism, so its uncertainty type is epistemic uncertainty.  
Both of the above uncertainties can be quantitatively characterized by uncertainty distributions, which are considered to obey the normal uncertainty distribution. An uncertain variable \(\xi\) is called normal if it has a normal uncertainty distribution \[\Phi(x)=\left(1+\left(\frac{\pi(e-x)}{\sqrt{3}d}\right)\right)^{-1},\ x\in R \tag{12}\]  
denoted by \(N(e,d)\) where \(e\) and \(d\) are real numbers with \(d>0\)[6]. The normal uncertainty distribution is shown in Figure 3.  
Further, when considering the uncertainty of all parameters, Equation (10) can be expressed, according to Theorem 1 (Operator's Rule), as:  
\[\begin{array}{rl}R(t)&=M([P_{r}(0)-c]\cdot[a^{\prime}\cdot\ln(t+1)+b)]+c- \omega\leq 0)\\ &=\alpha[\{\{\Phi_{P,(0)}^{-1}(\alpha)-\[PERSON]_{c}^{-1}(\alpha)][\Phi_{a^{\prime }}^{-1}(\alpha)\cdot\ln(t+1)+\Phi_{b}^{-1}(\alpha)]+\Phi_{c}^{-1}(\alpha)-\Phi_ {\omega}^{-1}(1-\alpha)\}\\ &\mbox{where the inverse uncertainty distribution of the uncertain normal distribution is:}\\ &\Phi^{-1}(\alpha)=e+\frac{\sqrt{3}d}{\pi}\ln\frac{\alpha}{1-\alpha}\end{array} \tag{13}\]
# Approximation of the Riesz-Caputo Derivative by Cubic Splines
## Appendix A  
Let \(x_{+}^{\gamma}\) denote the fractional truncated power function defined as  
\[x_{+}^{\gamma}=\Big{(}\max(0,x)\Big{)}^{\gamma},\qquad\gamma\geq 0,\]  
which is the generalization to noninteger power of the classical truncated power function  
\[x_{+}^{n}=\Big{(}\max(0,x)\Big{)}^{n}\]  
defined for any integer \(n\geq 0\).  
The cubic B-spline \(B_{3}\) can be written as  
\[B_{3}(x)=\frac{1}{6}\Big{(}x_{+}^{3}-4(x-1)_{+}^{3}+6(x-2)_{+}^{3}-4(x-3)_{+} ^{3}+(x-4)_{+}^{3}\Big{)}.\]  
In the following, we provide the explicit expression of the functions belonging to the cubic spline basis \(\mathcal{N}\). The left-edge functions have the following expression:  
\[N_{0}(x)=\left\{\begin{array}{ll}(1-x)_{+}^{3}\,,&x\geq 0\,,\\ \\ 0\,,&x<0\,,\end{array}\right. \tag{1}\]  
\[N_{1}(x)=\left\{\begin{array}{ll}\frac{1}{4}\left(2-x\right)_{+}^{3}-2 \left(1-x\right)_{+}^{3}\,,&x\geq 0\,,\\ \\ 0\,,&x<0\,,\end{array}\right. \tag{2}\]  
\[N_{2}(x)=\left\{\begin{array}{ll}\frac{1}{6}\left(3-x\right)_{+}^{3}-\frac{ 3}{4}\left(2-x\right)_{+}^{3}+\frac{3}{2}\left(1-x\right)_{+}^{3}\,,&x\geq 0 \,,\\ \\ 0\,,&x<0\,.\end{array}\right. \tag{3}\]  
The right-edge functions can be obtained from the left-edge functions using the central symmetry property, i.e.,  
\[N_{k}(x)=N_{L+2-k}(L-x)\qquad L\leq k\leq L+2.\]  
The internal functions are suitable integer translates of the cubic cardinal B-spline \(B_{3}\). In particular, the internal functions are the integer translates with support all contained in the interval \([0,L]\), i.e.,  
\[N_{k}(x)=B_{3}(x-k+3),\qquad 3\leq k\leq L-1.\]
## 4 Experiments and Results
### Results
#### 4.2.3 Image Processing as Function Processing
To examine the overhead of serverless platform deployment in the edge and fog layers, we measured the CPU and memory utilization without the execution of function workloads. We measured this before and after the deployment of the serverless platforms. Our observations are described in Table 3. We found that in the edge layer, Lean OpenWhisk [72] does not consume as much memory as the fog layer does. It only consumes approximately 3% of the total memory in the Raspberry Pi after the deployment of all the containers and the controller, the Nginx server, CouchDB, and the Nodejs runtime environment. This is because the serverless edge layer combines a controller and revoker in one container and only loads the Nodejs runtime environment.
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 4 System Model
### User Registration  
Each user has its own identity ID. It is necessary to register the user's identity ID first, generate the user's unique identity DUID, and then store it on the blockchain. To ensure  
Figure 2: System model.  
the legitimacy of users, users must register on the Edge Authentication Server (EAS), as shown in Figure 3. The specific process is as follows:  
1. EAS generates a random number \(r_{i}\), public key \(PK_{i}=r_{i}\cdot G\), and sends the public key \(PK_{i}\) to the user.
2. The user generates a random number \(d_{i}\) through the mobile device, calculates \(R_{i}=d_{i}\cdot G\) and \(S_{i}=d_{i}\cdot PK_{i}\) sends \(R_{i}\) to EAS.
3. EAS can determine the shared key: \[S_{i}=d_{i}\cdot PK_{i}=d_{i}\cdot(r_{i}\cdot G)=r_{i}\cdot(d_{i}\cdot G)=r_{i} \cdot R_{i}\] (2)
4. The user selects an identity \(ID_{i}\), calculates \(H(ID_{i})\) with the hash function SHA256, encrypts \(H(ID_{i})\) with the shared key \(S_{i}\), gets \(Enc(S_{i},H(ID_{i}))\), and sends it to EAS.
5. EAS receives the encrypted message, decrypts \(Dec(S_{i},H(ID_{i}))\) with the shared key \(S_{i}\), and queries whether the user \(ID_{i}\) is in the member list of the legal user.
6. If it exists, a unique user ID \(UUID_{i}\) is generated for it and stored on the blockchain. If it does not exist, it indicates that the user is illegal and disconnected.  
At the registration stage, it uses a random number, a hash algorithm, and an elliptic curve integrated encryption algorithm to both encrypt and decrypt the terminal equipment and user information, and stores them in a decentralized and tamper-proof blockchain network.
## 4 New TIRA System Design
### Model-Based System Engineering  
To cope with the complexity of the future TIRA system during design, realization, and also operation, it was decided to apply methods from the field of systems engineering, particularly model-based system engineering (MBSE). The specific method and tooling used was the ARCHitecture Analysis and Design Integrated Approach (ARCADIA) [22] along with the Capella open-source modeling software.  
Figure 16 summarizes the principle of the ARCADIA method. The development process starts with the operational analysis evaluating the needs of the users focusing on the system. The view is then reversed in the functional analysis providing what the system has to accomplish for these users. Following these analyses, the architectural design is first considered in the logical architecture layer, which refines the system's functions and  
Figure 15: Block diagram illustrating the future TIRA system architecture for enhanced space observation capabilities as a research instrument and an operational radar system.  
allocates these functions to local components. Finally, the development cycle leads to the physical architecture and the respective component specifications.  
In addition to this structured development, MBSE also facilitates documentation of the requirements, here denoted as R-*, system design, and the system itself. These requirements are traceable from operational analysis to the physical architecture, thus consequences of design changes can be easily assessed.  
This approach, if consequently applied, will lead to a well-defined system architecture with consistent interfaces to obtain an extensible research system adaptable to new requirements.
## 2 Materials and Methods
### Preparation and Characterization of the Polymeric Conjugates
#### 2.3.2 Total Polyphenol Content Quantification  
The Folin-Ciocalteu reagent procedure was employed to determine the total polyphenol content of the synthesized PLG-GRAPE conjugates, as described [27], with slight modifications. Specifically, before analysis, all samples were diluted to 23% (\(v/v\)) in distilled water. Each sample (1 mL) was mixed with 1 mL of distilled water, 1 mL of 0.2 N Folin-Ciocalteu reagent and 1 mL of 7.5% (\(w/v\)) sodium carbonate solution. The mixture was then incubated in the dark at room temperature for two hours, and the absorbance was subsequently measured at 760 nm by using a UV-Visible Evolution 201 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).  
The same procedure was applied to both the initial grape pomace extract and a control sample (blank), consisting of a 20% (\(w/w\)) aqueous dextran solution, to evaluate any interference of the polymeric material in the Folin-Ciocalteu assay.  
The total polyphenol content was expressed as milligrams equivalents of a reference polyphenol, such as catechin, per gram of tested sample (mg eq CA/g), based on the equation derived from the catechin calibration curve. This one was recorded using five different catechin standard solutions of known concentrations (ranging from 1 \(\times\) 10\({}^{-3}\) to 1 \(\times\) 10\({}^{-4}\) M). The slope and intercept of the resulting regression equation were calculated using the least squares method.  
The assay was repeated in triplicate.
# New Resorbable Membrane Materials for Guided Bone Regeneration
## 4 Platelet Rich Fibrin  
Platelet rich fibrin (PRF) is a second-generation platelet concentrate consisting of fibrin membrane enriched with platelets, leukocytes, growth factors, and cytokines [40; 41]. PRF is autologous in nature, gradually resorbed over time, and healing biomaterial with potential for bone and soft tissue regeneration [41]. PRF is simpler and faster to prepare compared to platelet rich plasma (PRP), because it is only centrifuged blood without any additives such as anticoagulant, bovine thrombin, and any other gelling agent [42; 43]. It is also more suitable for manipulation or suturing [42]. In addition, it is cost effective and carries no risk of allergic reaction [43; 44]. The method of preparation of PRF is shown in Figure 1[40]. At this time, centrifugation should begin within 90 s, since the size decreases as the time from the blood draw to the start of centrifugation is delayed [45].  
PRF could easily be compressed into PRF membrane, by pressing the PRF clot between two pieces of surgical gauze [41; 44]. PRF membrane has been successful in oral mucosal defects [44; 46], periodontal bone defects [47; 41], gingival recession defects [42], and tympanic membrane perforation [48]. PRF membrane offers acceptable elasticity and may stretch to some extent to co  
Figure 1: The method of preparation of platelet rich fibrin. A blood sample is taken in 10 mL tubes without anticoagulants and immediately centrifuged at 3000 rpm for 10 min. A Fibrin clot is then obtained in the middle of the tube, between the red corpuscles and the acellular plasma.  
lacks rigidity and needs to be carefully handled to prevent tearing [41; 44]. Because PRF membrane is resorbed within two weeks or less at the implantation site, it can barely maintain sufficient space for bone regeneration [49].  
Several studies have been conducted to overcome these drawbacks. Heat-compression of the PRF membrane results in delayed degradation up to 4 weeks without sacrificing its biocompatibility by reducing the porosity and surface area, and it could easily be prepared at chair-side and applied as a barrier membrane [49]. In addition, Ankaferd Blood Stopper, a standardized herbal extract approved for external bleeding hemostasis, loaded PRF improved mechanical properties by reducing porosity similar to heat compressed PRF membrane [50]. However, advanced research is required on its biological properties.  
As an improved formulation of PRF, concentration growth factor (CGF) was introduced by Sacco in 2006 [51; 52]. It is considered as a new fibrin matrix block made by separating it from a centrifuged blood sample using a special device in a manner similar to PRF [52]. CGF is a bigger fibrin matrix with a higher concentration of growth factors because of the different centrifugation speed [52]. Clinically, CGF has showed good bone regeneration ability in maxillary sinus and alveolar bone augmentation, and it was reported that the use of CGF membrane showed bone regeneration similar to collagen membrane in GBR [53; 54].