esa-sceva/satcom-chunk-collection huggingface dataset:
# Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs
## 4 Transfer-Efficient Face Routing
### The Exchange of Location Information  
The first step of TEFR is to exchange location information between nodes. Since TEFR is a position-based routing method, it is necessary to collect the location information of the neighbors that is exchanged periodically using a beacon or aperiodically when there are topological changes. A beacon message consists of three fields:  
* _id_: the local identifier of a node used to distinguish nodes within 2-hop range,
* \(L_{x}\) and \(L_{y}\): the \(x\) and \(y\) coordinates of a node indicating its location.  
Since the message transfer node in TEFR uses the planar graph of itself and its neighbors, it can receive position information on 2-hop nodes from its neighbors. Therefore, all node _id_s should be identified within 2-hop range. Figure 3 demonstrates the exchange of location information between nodes. Since message transfer node \(u\) can receive location information on \(n_{1},n_{2},n_{3},n_{4}\), and \(n_{5}\) located at 2 hops from neighbors, \(i_{1},i_{2},i_{3},i_{4},i_{5}\), and \(i_{6}\), node _id_s need to be distinguished within 2-hop range.  
Figure 2: Face routing according to the node density: (**a**) a low density network; and (**b**) a high density network.
## 3 Results and Discussion  
### Isolation and Identification of Compound **1**  
Ground samples were extracted twice with 80% methanol. Compound **1** was isolated from the extract using column chromatography followed by purity analysis by HPLC. The compound had one peak, and the peak area of the purified compound was 99.94%. Compound **1** consisted of a white powder with a molecular ion peak at _m/z_ 276.0638, which corresponded to the molecular formula C\({}_{14}\)H\({}_{12}\)O\({}_{6}\) (Figure 3). The \({}^{1}\)H-NMR spectrum of **1** (Table 2) showed one methylene (\({}^{8}\)H 2.85, m), two methine (\({}^{8}\)H 4.39, m; and 5.452, s), _meta_-coupled (\({}^{8}\)H 5.78, \(J=2.4\) Hz; and 5.591, \(J=2.4\) Hz) aromatic, and three olefinic (\({}^{8}\)H 6.51, \(J=0.9\) Hz; \(\delta\) 5.19, br s; and \(\delta\) 5.27, br s) proton signals. The \({}^{13}\)C-NMR spectrum (Table 2) of **1** revealed the presence of one phloroglucinol-type aromatic (\({}^{8}\)C 95.9, 96.7, 99.3, 156.6, 157.8 and 157.9), one methylene (\({}^{8}\)C 25.4), two methine (\({}^{8}\)C 72.9 and 73.5), and four olefinic (\({}^{8}\)C 109.8, 116.6, 139.3, and 148.1) carbon signals. From these \({}^{1}\)H- and \({}^{13}\)C-NMR spectral data, we predicted that **1** had ring moieties that resembled the A- and C-ring structures in flavan-3-ol (catechins). The HMBC and HSQC spectra of **1** (Table 2, Figure 4) showed an olefinic carbon sequence (from C-12 to C-15) in the correlation peaks from H-13 to C-12 and C-14, and from H-15 to C-14. Additionally, the correlation peaks from H-15 to C-2 and from H-13 to C-16 in the HMBC spectrum showed the presence  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 2 Materials and Methods
### Random Forest Classifier (RFC)  
Random forest is widely used as a supervised learning algorithm in both classification and regression tasks [21]. It uses the idea of ensemble learning, where multiple decision trees are trained on different subsets of the dataset to improve model performance.  
The name 'random forest' reflects its operation as a classifier that encompasses multiple decision trees, each trained on different subsets of the given dataset. The goal is to enhance the model's overall predictive accuracy. Rather than depending on a single decision tree, random forest combines the predictions of multiple trees, with the final output determined by majority voting for classification or averaging for regression.  
Figure 2: Examples of a hyperplane in 2D and 3D space. Source: Deep AI [18].  
Classification Process in Random Forest, as illustrated in Figure 3 [22]:  
1. Construction of Decision Trees: Random forest generates multiple decision trees, each trained on a random subset of the training data and features. As shown in the image, Tree #1, Tree #2, Tree #3, and Tree #4 are constructed using different subsets of features (N_features), ensuring diversity in the model.
2. Individual Predictions: Each decision tree independently predicts the class of the input data. In the image, Tree #1 predicts Class C, Tree #2 predicts Class D, Tree #3 predicts Class B, and Tree #4 predicts Class C.
3. Majority Voting: The predictions from all trees are aggregated, and the final class is determined by majority vote. In the illustration in the image, the majority voting process results in a final prediction of Class C.
# Conflict Resolution in Mechatronic Collaborative Design Using Category Theory
## 3 State-of-the-Art
### Synopsis  
A multitude of different approaches are proposed in the literature in the field of conflict resolution. These approaches are classified into six different types. As discussed previously, the interoperability approach presents some data loss problems because of data transit between heterogeneous domain and tools. Ontological representation ensures consistency checking during the design process, but the creation of ontologies can be a time-consuming task. Moreover, some efforts in the literature are based on explicit modeling of dependencies in order to facilitate conflict checking but this solution does not deal with conflict resolution and can be also a time-consuming task. Model synchronization approaches are proposed in the literature as an efficient solution in the field of inconsistency management. However, this solution does not take into consideration the conflict resolution of fine granularity data which is in the scope of this study. The rule and pattern-based approach showed its efficiency in conflict management of fine granularity data. Whereas some studies dot not present explicit solution to manage the detected conflicts, other research works handle this problem by proposing several conflict resolution techniques. Using rules and patterns tends to be flexible since the rules can be removed or added without revising the entire consistency checking system. This can be beneficial for mechatronic system design in terms of reducing development time. Finally, parameters and constraints approaches are presented as a conflict resolution technique. The principle of this approach is the core idea of our study. As discussed beforehand, our main goal is to check and handle conflicts between parameters and constraints. Nevertheless, the proposed research works in this field do not present solutions to handle these conflicts.  
Hence, to overcome this drawback, we propose a new conflict management methodology regarding fine granularity data based on patterns and rules. By means of these rules, our proposition will be more flexible, which in turn will ease the conflict resolution task. In the aforementioned rule and pattern-based approaches, graphs are used to create patterns and query the different involved models in the design process. These graphs have shown their efficiency in making the conflict resolution process easy and simple. However, using these graphs may not take into account some insights that come from the practice, which is essential to keep the right meaning of the model [31]. Furthermore, mechatronic design encompasses several disciplines collaborating among each other, which makes the design quite complex. Therefore, in order to deal with this complexity, formal methods based on mathematical techniques are required [32]. In this context, CT, as a mathematical theory, provides a unifying framework to formalize the interconnection between stakeholders, as well as to facilitate conflict detection and resolution. Hence, the basic concepts and related works to this theory are presented in the next section.
## 4 Security Threats and Attack Methods in Blockchain-Based Supply Chain Management
### Traceability and Anti-Counterfeiting Measures
Blockchain's impact on anti-counterfeiting and traceability in wine supply chains was analyzed using Stackelberg game theory, revealing that blockchain enhances transparency and reduces counterfeiting, though its adoption is influenced by cost and privacy factors [60]. A novel blockchain-based system named Janus was introduced to enhance drug traceability and prevent counterfeit drugs from entering the supply chain. \begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline
**Ref.** & **Objective** & **Methodology** & **Outcome** \\ \hline \multirow{5}{*}{[59]} & Developing an Ethereum-based blockchain blockchain system to prevent counterfeit products & Manufacters record product details on blockchain, creating a verification mechanism using smart contracts and digital signatures & Provides a low-cost, decentralized anti-counterfeiting solution \\  & Preventing counterfeiting and enhancing traceability in wine supply chains & Blockchain-based monitoring systems analyzed using Stackelberg game theory & Blockchain enhances transparency but adoption is influenced by cost and privacy concerns \\  & Developing the blockchain-based system Janus to prevent counterfeit drugs from entering the supply chain & Clone-resistant hologram labels and a multi-quorum consensus protocol ensure secure traceability & Creates a transparent tracking system providing load balancing and fairness \\  & Enhancing traceability and security in food supply chains through a blockchain-based model & Performance evaluated using simulations on Hyperledger Fabric & Blockchain strengthens food traceability but faces scalability and data privacy constraints \\  & Improving security and traceability in tea supply chains using a Hyperledger Fabric-based system & System performance successful in Federic, ECDSA, and IPFS; performance assessed by Hyperledger Caliper & System performance successful in terms of transaction latency and volume \\ \hline \end{tabular}
\end{table} 
Table 4: Analysis of blockchain-based traceability and anti-counterfeiting studies in supply chains. Utilizing clone-resistant hologram labels and a multi-quorum consensus protocol, this system offers transparent and secure traceability, emphasizing load balancing and fairness [61]. Another blockchain-based model aimed at enhancing traceability and security in food supply chains demonstrated efficient transaction volumes and latency through simulations conducted on Hyperledger Fabric. The results indicated that blockchain could significantly strengthen food safety despite certain limitations related to scalability and data privacy [62]. Additionally, a Hyperledger Fabric-based system was developed to improve security and traceability in tea supply chains. Employing ECDSA for authentication and IPFS for data storage [64], the system's performance was positively evaluated with Hyperledger Caliper, showing satisfactory results regarding transaction latency and volume [63].
###### Abstract  
The spinal hybrid elastic (SHE) rod is a semi-rigid pedicle screw-based rod for spinal dynamic stabilization. This study investigated the biomechanical effects of different ratios of SHE rod using finite element analysis (FEA). A three-dimensional nonlinear FEA of an intact lumbar spine model (INT) was constructed. The SHE rod was composed of an inner nitinol stick (NS) and an outer polycarbonate urethane shell (PS). Four groups implanted at L3-L4 had the same outer diameter (5.5 mm) but different NS diameter/PS thickness ratios: Nt45, Nt45, Nt45, and Nt15. The resultant intervertebral range of motion (ROM), disc stress, facet joint contact force, screw stress, NS stress, and PCU stress were analyzed. The results indicated that ROM, disc stress, and facet force decreased moderately in the implanted L3-L4 levels and increased slightly in the adjacent L2-L3 levels. The NS stress and NS diameter trended towards inverse proportionality. Changing the ratio did not markedly influence screw or PS stress. The SHE rod system with elastic NS and insulated PS has a 5.5 mm diameter for universal pedicle screws. The SHE rod system provides sufficient spinal support and increases gentle adjacent segment stress. Considering the durability, the optimal NS diameter/PS thickness ratio of the SHE rod system is 3.5/2.0 mm.  
spine hybrid elastic rod; finite element analysis; polycarbonate urethane (PCU); nitinol; dynamic stabilization; rod diameter ratio +
Footnote †: journal: Accepted: 16 November 2022  
Footnote †: journal: Accepted: 19 November 2022  
Footnote †: journal: Accepted: 16 November 2022  
analysis study was conducted to investigate the precise ratio of the thicknesses of the NS and PS. We hypothesized that an optimal SHE rod ratio can provide sufficient spine support, gentle adjacent segment stress, and remarkable durability.
## 2 Results and Discussion
### Method Validation
#### 2.3.4 Selectivity and Specificity  
No interferences were observed when analyzing the blank plasma samples from six ventilated patients who did not receive discussed antibiotics. Due to the complex composition of blood and BAL samples investigated in this study, the putative influence of biological matrix on the mass-spectrometric analysis of \(\beta\)-lactams was considered and evaluated according to the protocol described in Section 3.4.4. The matrix effect for impenem and piperacillin at the same concentrations of 0.01, 0.1, 0.5 and 1 mg/L was found to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**Concentration**} & \multicolumn{3}{c}{**Within-Run [\%]**} & \multicolumn{2}{c}{**Between-Run [\%]**} \\ \cline{3-6}  & & **1 st Run** & **2 nd Run** & **3 rd Run** & \\ \hline Imipenem & 0.05 & 1.81 & 1.35 & 2.06 & 10.59 \\  & 0.15 & 3.67 & 2.77 & 9.05 & 6.91 \\  & 0.5 & 5.90 & 10.49 & 2.79 & 11.48 \\  & 1.0 & 3.38 & 2.72 & 4.54 & 6.47 \\ Piperacillin & 0.02 & 14.73 & 13.12 & 11.51 & 13.43 \\  & 0.06 & 10.43 & 9.32 & 10.38 & 10.72 \\  & 0.25 & 9.29 & 8.09 & 7.64 & 9.74 \\  & 0.5 & 3.38 & 7.81 & 2.87 & 5.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Within- and between-run precision presented as the Relative Standard Deviation [%].  
Figure 4: Calibration curves for extraction of impenem and piperacillin from human blood plasma.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**Concentration**} & \multicolumn{3}{c}{**Within-Run [\%]**} & \multicolumn{2}{c}{**Between-Run [\%]**} \\ \cline{3-6}  & & **1 st Run** & **2 nd Run** & **3 rd Run** & [\%] \\ \hline Imipenem & 0.05 & 110.54–115.53 & 109.07–112.82 & 88.52–92.87 & 88.52–115.53 \\  & 0.15 & 102.02–112.09 & 99.88–106.51 & 88.60–108.93 & 88.60–112.09 \\  & 0.5 & 85.19–98.33 & 71.47–92.74 & 94.83–102.00 & 71.47–102.00 \\  & 1.0 & 91.68–100.12 & 93.65–86.91 be <4% (99.49-96.17% of nominal value for pure standards) and <6% (98.5-94.11% of its nominal value), respectively. These results confirm that the TFME method enables a reliable quantitative LC-MS/MS analysis of target analytes that are unaffected by matrix-related factors, even in very complex samples.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 3 Methodology
### Problem Formulation  
We formally define the Zero-Shot Food Safety Hazard Detection (ZS-FSHD) problem as follows. Let \(\mathcal{X}_{s}\) denote a training set containing \(M_{s}\) food images with annotated bounding boxes belonging to \(C_{s}\) seen hazard classes. The label sets for seen and unseen hazard classes are denoted as \(\mathcal{Y}_{s}=\{1,\dots,C_{s}\}\) and \(\mathcal{Y}_{u}=\{C_{s}+1,\dots,C\}\) respectively, where \(\mathcal{Y}_{s}\cap\mathcal{Y}_{u}=\emptyset\), and \(C=C_{s}+C_{u}\) is the total number of hazard classes (with \(C_{u}\) being the number of unseen classes).  
It is important to clarify that \"zero-shot\" in this context does not refer to detecting zero incidents or the absence of hazards. Rather, it describes the ability to detect hazard classes that have zero training examples--that is, hazard types that were never seen during training. Our framework achieves this ability through a knowledge-transfer mechanism that leverages semantic relationships between seen and unseen hazard classes.  
For each class \(y\in\mathcal{Y}=\mathcal{Y}_{s}\cup\mathcal{Y}_{u}\), we have corresponding semantic vectors \(\mathbf{v}_{y}\in\mathcal{V}=\mathcal{V}_{s}\cup\mathcal{V}_{u}\), where \(\mathcal{V}_{s}\) and \(\mathcal{V}_{u}\) are the semantic vector sets for seen and unseen classes, respectively. These semantic vectors are 768-dimensional embeddings obtained from BERT [42], where detailed textual descriptions of each hazard class are processed to capture their visual and safety characteristics.  
The key insight enabling zero-shot detection is that while the detector never observes training examples of unseen hazard classes, it learns the relationships between visual features and semantic attributes through the seen classes. For instance, during training on seen hazards like \"mold on bread\", the detector learns to associate visual patterns (fuzzy texture, irregular patches) with corresponding semantic attributes. When encountering an unseen hazard such as \"bacterial colonies on fruit\", the detector recognizes it by matching the observed visual patterns to the semantic attributes of the unseen class, effectively transferring knowledge from seen to unseen domains through our Knowledge-Enhanced Feature Synthesizer.  
During inference, we are provided with a test set \(\mathcal{X}_{t}\) containing images with both seen and unseen hazard classes. The goal of ZS-FSHD is to train a detection model on \(\mathcal{X}_{s}\) with semantic vectors \(\mathcal{V}\) and detect both seen and unseen hazards in \(\mathcal{X}_{t}\). We evaluate our method in both ZSD settings, where the test images contain only unseen hazards, and Generalized Zero-Shot Detection (GZSD) settings, where the test images may contain both seen and unseen hazards.  
Figure 1: Overview of our Zero-Shot Food Safety Hazard Detection framework.
# Research on the Impact of Flexible Working Hours on Reducing Traffic Delays in the City
## 3 Results and Discussion
### Limitations of the Study
By spreading out the morning or afternoon peak hours, not only is the pressure on infrastructure reduced, but it is also easier to manage peak times in terms of public transportation. However, flexible WHs also have certain limitations and risks in terms of implementation, regulations, and potential unintended consequences [65]. From an implementation perspective, it should be noted that not all industries (e.g., manufacturing) are prepared to adopt flexible WHs. It is therefore generally true that office-based professions are more suitable for flexible WHs. However, changing the start of the workday could also be problematic in these professions. Some families, particularly in the morning, may share a commute to work and school, so the start of their work shift would likely be aligned with the start of their children's school day or their partner's work shift. Another issue could be managing people who are not present at the workplace for the entire time, and there is also a need for related technological infrastructure if employees work from home. From a regulatory perspective, it is important to ensure that social legislation (labor laws) is not violated, and therefore, each company's internal regulations must be properly set. Flexible WHs may also have negative impacts. In transportation, they may not only reduce traffic congestion but could shift it to another time. In public transportation, there may be a demand for additional services at different times, requiring the addition of more routes. Problems may also arise in the area of social interaction [66], similar to those experienced with the introduction of remote work [66], such as difficulties in separating work and private life and the risk of social isolation if team members work at very different times. Another limitation of this study is the lack of an environmental assessment. Although the Aimsun microsimulation program provides for this, its emissions model is outdated. It can be assumed that if the stop time, the number of stops, and the need for vehicle acceleration and deceleration are reduced during the simulation, this would have a positive  
Figure 20: Share of the traffic load during the peak hour (K—intersections analyzed in this case study, RS—Rimavska Sobota, SN—Spisska Nová Vès, MI—Michalovce, HE—Humenñ, BA—Batislava, PK—Pezinok). Source: processed by authors. impact on fuel consumption and the associated exhaust emissions. The environmental impacts of flexible WHs should be the subject of further studies.
###### Abstract  
The steady-state energy flow calculation (EFC) of multi-energy systems (MESs) is a fundamental foundation for MES planning and operation. However, most of the existing MES models are designed case-specifically, making them incapable of modelling diverse scenarios. Moreover, since it involves initial value setting, the convergence of the Newton-Raphson (NR) method to solve the EFC problem of MESs is often unsatisfactory. To tackle these problems, a matrix-based EFC method of MESs is proposed in this paper. The universal matrix formulations of heat and gas subnetworks are first constructed, where the injection of alternative gas sources and the effect of gas compressibility factor on the MES state are both considered. Due to the uncertainty of gas flow direction during the NR iteration process, the gas composition tracking equations are modified to avoid ill conditions. The Jacobian matrices for the constructed subnetwork models are then derived and expressed in matrix form. On this basis, the unified NR strategy is adopted to solve the constructed models. Finally, the performance of the proposed method is verified through case studies. The results demonstrate that the proposed models can accurately capture the MES operating state and achieve significant improvements in convergence and computational efficiency compared to traditional models.  
multi-energy system; matrix-based model; alternative gas injection; energy flow calculation; Jacobian matrix +
Footnote †: journal: Applied sciences  
Article  
[PERSON], [PERSON] Energy Flow Calculation Method for Multi-Energy Systems A Matrix Approach Considering Alternative Gas Injection and Dynamic Flow Direction