esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 SnAgCu-Based Solders
### Mechanical Properties  
During operation, solder joints are subjected to the thermal-mechanical stress because of the temperature change and mismatch in the CTEs (coefficients of thermal expansion) of different materials. Therefore, high enough strength is required for the joints due to the accumulation of residual stress during operation caused by CETs mismatch [29]. The addition of minor Al nanoparticles into the SnAgCu solder could obviously refine Cu\({}_{6}\)Sn\({}_{3}\), Ag\({}_{3}\)Sn IMCs, and eutectic microstructure, thus enhancing the mechanical properties of the solder. However, the mechanical properties of the solder would decrease once the content of Al nanoparticles exceeded 0.1 wt.%, as shown in Figure 3[30]. [PERSON] [31] found the fracture of some SnO.3 Ag0.7 Cu as flowed solder joints presented brittle characteristic and some microcracks occurred. Owing to the growth of IMCs during reflow, a large stress was accumulated inside or around the IMC, which would result in local stress concentration and strength reduction in the tensile test. Since Mn nanoparticles addition could reduce the IMC thickness and reduce the accumulation of stress during reflow, the UTS of Mn-containing solder joints was improved.  
Figure 3: The mechanical properties of SAC105-xAl solder joints: (**a**) Tensile force; (**b**) shear force [30].  
Figure 2: Microstructure of the solders: (**a**) SAC105; (**b**) SAC105-nano Fe\({}_{2}\)O\({}_{3}\) (20 nm); (**c**) SAC105-nano Fe\({}_{2}\)O\({}_{3}\) (50 nm); (**d**) SAC105-nano Fe\({}_{2}\)O\({}_{3}\) (200 nm) [28].  
Compared with the original solder, the strength and hardness of the SnAgCu-nano TiO\({}_{2}\) composite solder were significantly improved because TiO\({}_{2}\) particles could pin the movement of dislocations and grain boundaries. However, it is worth noting that the plasticity of the composite solder decreased to a certain extent and many holes were found at the Ag\({}_{3}\)Sn grain boundaries [32; 33]. The mechanical properties of SnAgCu-nano La\({}_{2}\)O\({}_{3}\) composite solder were greatly improved because the addition of La\({}_{2}\)O\({}_{3}\) nanoparticles refined the microstructure and increased dislocation density by secondary phase strengthening [23].  
Ag-GNSs nanoparticles were added into SnAgCu solder and the research result shows that compared with GNSs nanoparticles, the strengthening effect of Ag-GNSs nanoparticles is more obvious because Ag can react with Sn matrix to form Ag3 Sn, which makes the distribution of Ag-GNSs in the matrix more uniform and stable [34].
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
## 3 Shareable D-DSME Extension  
In the DSME multi-superframe, each node is assigned one or multiple GTSs for the transmission of messages belonging to one flow. In Figure 2 the multi-superframe contains two superframes (\(T_{\text{MSF}}=2T_{\text{SF}}\)). There are 15 periodic real-time flows, 8 of them \(1\ldots 8\)] with period \(P_{f[1\ldots 8]}=4T_{\text{SF}}>T_{\text{MSF}}\) and 7 of them \(\{9\ldots 15\}\) with period \(P_{f[9\ldots 15]}=2T_{\text{SF}}=T_{\text{MSF}}\). In the plain DSME schedule, 15 GTSs are assigned for these flows within one multi-superframe. The slots assigned to the flows \(\{1\ldots 8\}\) are not always used, as \(P_{f[1\ldots 8]}\) is larger than the multi-superframe duration \(T_{\text{MSF}}\).  
The _Shareable D-DSME_ extension introduces the _Shareable GTS (shGTS)_, which can be shared among multiple periodic flows, i.e., the ones with a period \(P_{f}>T_{\text{MSF}}\), transmitted by the same or  
Figure 2: Scheduling example using DSME and D-DSME.  
different nodes. Thanks to the _shGTS_, _Shareable D-DSME_ can schedule the same flows as DSME using a lower number of GTSs. This features results in more free CFP slots that can be used either to enable the CAP in each superframe (as shown in the example in Figure 2) or to accommodate new periodic flows.  
In the D-DSME schedule shown in Figure 2, the _shGTSs_ with index \(\{9\ldots 12\}\) are assigned to the flows \(\{1\ldots 4\}\) in the MSF 1 and to the flows \(\{5\ldots 8\}\) in the MSF 2. This way, the flows 5-8 can be transmitted in the same GTSs assigned to flows 1-4, but with an offset (i.e., a shift) of \(T_{\text{MSF}}\). Instead, the GTSs \(\{13\ldots 15\}\) and \(\{25\ldots 28\}\) are assigned in every MSF to the flows \(\{9\ldots 11\}\) and \(\{12\ldots 15\}\), respectively. No sharing is possible for these flows, as \(P_{\{9\ldots 15\}}=T_{\text{MSF}}\).  
The D-DSME does not restrict the applicability of DSME to centralized architectures, as it also support mesh topologies. The two following subsections describe the DSME and the D-DSME allocation procedures, respectively.
# Design and Simulation of Electromagnetic Linear Actuators for Jet Dispensers
## 5 Results and Discussion
### Minimum Excitation Current  
As described above, the thrust force produced by the actuators varies in accordance with the magnitude of the excitation current and the position of the moveable parts. For practical implementations, the stroke length is fixed (e.g., 0.5 mm), and the thrust force is therefore dominated by the excitation current. The experimental results presented in Section 3 have shown that for the jet dispenser considered in the present study, the thrust force generated by the actuators must be at least 13.3 N when a return spring is not used and 29.3 N when a spring is used. For the SA, the thrust force, \(F_{SA}\), can act in only one direction. Hence, a return spring must be used to return the plunger to its original position after each dispensing operation. However, for the MCA and MMA models, the thrust force (\(F_{MCA}\) and \(F_{MMA}\)) can work in either direction and hence a return spring may or may not be fitted. Figure 14 shows the thrust force produced by the MCA and MMA models over the stroke-length range of 0.5 mm for two values of the excitation current when a spring is not fitted. As shown, the values of \(F_{MCA}\) and \(F_{MMA}\) obtained for currents of 1.7 A and 1.8 A, respectively, are lower than the required thrust force over the majority of the stroke length. By contrast, \(F_{MCA}\) and \(F_{MMA}\) exceed 13.3 N for all values of \(z\) given excitation currents of 1.8 A and 1.9 A. Hence, the minimum values of the excitation current required to actuate the jet dispenser with no spring are determined to be \(I_{minMCA}=1.8\) A and \(I_{minMMA}=1.9\) A, respectively. Figure 15 shows the magnitude of the thrust force with the stroke length for the case where a return spring is fitted to the actuation mechanism. For the SA model (Figure 15a), the thrust force produced using an excitation current of 2.1 A exceeds the required thrust force for most of the stroke length. However, for values of \(z\) greater than 0.45 mm, the thrust force falls below the required value. Thus, the minimum value of the excitation current is set as \(I_{minSA}=2.2\) A. Similarly, from Figure 15b, \(I_{minMCA}\) and \(I_{minMMA}\) are set as 3.8 A and 4.1 A, respectively.  
Figure 14: Minimum excitation current for MCA and MMA models (no return spring).  
Figure 13: thrust force, applied current, and stroke length relationships.
## 2 Materials and Methods
### Molecular Docking  
#### 2.6.1 Ligands Selection and Preparation  
VLP in _N. tabucum_ was investigated for its interaction with PAMPs, specifically focusing on peptidoglycan (PGN) and \(\beta\)-1,3-glucan as model ligands. This selection was based on the vittlecogenin (Vg) domain's established role as a PRR capable of binding diverse PAMPs: (1) PGN, a conserved bacterial cell wall component (represented by _Staphylococcus aureus_ and _B. subtilis_); (2) \(\beta\)-1,3-glucan, a fungal cell wall signature (from _Candida albicans_ and _Aspergillus fumigatus_). These ligands were prioritized because of their (i) broad representation across major pathogen groups (bacteria/fungi), (ii) established roles in triggering plant immune responses, and (iii) structural compatibility with the Vg domain's multivalent binding pocket, as demonstrated by its additional capacity to recognize LPS, LTA, and phosphatidylserine in other systems. While \(\beta\)-1,3-glucan binding alone may not directly confer resistance, \(\beta\)-glucans are well-established microbe-associated molecular patterns (MAMPs) that trigger plant immune responses, including salicylic acid signaling, the production of reactive oxygen species (ROS), and the deposition of callose. Thus, VLP's interaction with \(\beta\)-glucan suggests a potential role in fungal recognition and immune modulation, consistent with known PRR-mediated defense mechanisms [53; 54; 55].  
The VLP in _N. tabucum_, which contains a Vg domain, can recognize PAMPs in bacteria and fungi. To investigate this recognition capability, we selected PGN and \(\beta\)-glucan as representative PAMPs for bacteria and fungi, respectively, to evaluate their binding affinities with VLP. The 3D structures of PGN (PubChem Chem CID: 478104487) and \(\beta\)-glucan (PubChem Chem CID: 46173706) were downloaded from the PubChem database ([[https://pubchem.ncbi](https://pubchem.ncbi)]([https://pubchem.ncbi](https://pubchem.ncbi)).  
nlm.nih.gov/ (accessed on 2 August 2024)). These ligand structures were subjected to energy minimization, and their torsion angles were carefully defined to prepare them for the molecular docking procedure.
## 6 Designing a Smart Healthcare Reference Architecture  
IWF Architecture is a standard architectural specification from which a reference architecture for smart healthcare can be drawn analogously [70]. Thus, we designed the Smart Healthcare Reference Architecture (SHRA) based on the IWF Architecture (as shown in Figure 14). This section presents the roles and responsibilities of each layer of the designed architecture.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Responsibility** \\ \hline
1—Edge Devices/Controllers & Physical devices like sensors, actuators, RFID tags \\
2—Connectivity & Sum of all hardware and protocols \\
3—Edge/Fog Computing & Data handling and network security such as the end to end data encryption, Data filtering, \\  & Data scrubbing, Event generation \\  & Data in motion becomes Data at Rest, creation \\
4—Data Accumulation & of database records, allowing data query, \\  & accumulation, and filtering strategies for data optimization \\  & Ensures quality and completeness of data, \\  & Extract, Transform, Load (ETL) functions, Data \\
5—Data abstraction & Processing, Data Comparison and \\  & reconciliation, and many types of data \\  & manipulations \\
6—Applications & Data interpretation, Reporting, Analytics and \\  & System Control \\
7—Users/Collaboration & User interaction is coordinated with all \\  & functions of the system. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layers and respective responsibilities of the IWF Architecture.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**IoT Application** & **Issues in Implementation** & **Corresponding Countermeasures** \\ \hline Smart Health & Handling high dimensional and \\  & weakly-structured data, Handling \\  & physical-digital ecosystems & Knowledge Discovery and Data mining, \\  & Interoperability of different standards, \\  & Connectivity in rural areas, Constrained \\  & devices & Authentication and Access control, \\  & & Privacy-preserving, Block-chain based \\  & & solutions for data integrity, Physical \\  & & countermeasures \\ \hline Smart Wearable & Privacy, Security, Battery life, \\  & Connectivity, Platform standardization, \\  & design, data handling & Data management techniques, \\  & Lack of real-time data, disparate data \\  & systems & Smart asset monitoring, enterprise IoT \\  & systems & platform \\ \hline Smart Transportation in Smart Cities & Designing Sustainable, effective, and \\  & secure transport systems, Autonomous \\  & and Connected vehicles & Mobility As A Service, Intelligent Traffic \\  & Secure transport systems, Autonomous \\  & and Connected vehicles & Management \\ \hline Smart Supply Chain & Optimize inventory and supply chain & Predictive and Prescriptive Analytics to \\  & demands across multiple channels, \\  & Improve quality and speed in supply \\  & chain & Identity and model sales trends, \\  & Improve quality and speed in supply \\  & chain & DRMS, Cryptography, and Key \\  & management. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Issues and corresponding solutions of IoT applications/services.
# Surface Engineering of Woodworking Tools, a Review
## 6 Conclusions  
The history of thin hard coatings goes back to the 1970s. The possibilities of new tool modification techniques were verified in laboratories and in industry, initially for metalworking, and somewhat later also for woodworking. Wood is a specific material; it is characterized by strong fluctuating local anisotropy, porosity, and a hygroscopic nature. Hence, the demands placed on woodworking tools are particularly high. Thanks to the progress in surface treatment, it is possible to obtain tools that meet the requirements of customers to a greater extent than before. Knowledge about the applied modifications of tools is becoming more and more available, as evidenced by the growing number of scientific publications, as well as the multinational composition of research teams. The main difficulty in analyzing the results is:  
* Many articles are related to the processing of fiberboard or very dense homogeneous wood, the most homogeneous and isotropic materials. The processing of other materials, softwood and wood-based materials, does not give unequivocal results in the durability of the tools. It seems that the most important factors in modifying a wood knife are low friction value, good abrasion resistance, good coating adhesion, and thermal resistance. Most of the tested coatings belong to the simplest, two- or three-element systems. Only in some cases have more complex coatings, such as quaternary systems or multi-layer coatings with different structures, been investigated. The test results indicate that the latter have better wear resistance, but standardized tests should be performed to confirm this.  
The analysis of the test results for the properties of coatings and the durability of woodworking tools covered with these coatings allows for the presentation of several conclusions:  
* A comparison of various coatings operating in the same conditions shows the advantage of CrN both in the treatment of beech wood and OSB. This is probably due to thelower brittleness of the CrN coatings, relatively low coefficient of friction, and very good adhesion to the substrate;
* Chromium-based coatings have better wear resistance compared to titanium-based coatings;
* Multilayer coatings are characterized by better wear resistance compared to single-layer coatings. This may be related to the reduction of crack propagation at the phase and grain boundaries. As in the case of a single-layer coating, higher wear resistance is found in coatings that exhibit a lower friction coefficient and are less brittle. The results of the tests confirm that a lower coefficient of friction is more important than hardness;
* The type of tool material used, and its possible thermochemical treatment, have a great influence on tool life. Increasing the hardness of the tool increases its productivity, although not always its durability.
## 4 Use Case Evaluation
### Semantic Reasoning with CYPHER Queries  
To analyze semantic relationships between entities within the Green Route scenario, we employ a dual approach consisting of ontology alignment and Cypher queries on the RDF KG. Cypher is a query language specifically designed for querying graph databases, allowing for the retrieval and manipulation of data represented as a graph, using a concise and expressive syntax. The analysis focuses on three distinct types of social relationships: _co-location_, _co-work_, and _co-semantics_.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 6 On-Field Measurement Campaigns
### Interferences in an Urban Scenario
#### 6.1.2 Case-Study B: Experiment Performed on the Road along Corso Eusebio Giambone and Corso Cosenza  
Another interesting anomaly was recorded on 7 March 2017, at a specific place in Corso Eusebio Giambone. The evident spectrum distortion is illustrated in Figure 9. This event was noticed each time the receiver passes a pharmacy in Corso Eusebio Giambone, 19. The measured TE was 17,056 (dB/Hz)\({}^{2}\), which is still under the TE threshold.
# Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz
## 3 Path Loss Measurements and Models
### Path Loss Propagation Models
#### 3.2.2 The Floating Intercept (FI) Free Space Reference Distance Path Loss Prediction Model
Table 2 presents the LOS parameters of the CI and FI path loss models. Figure 12: CI path loss versus distance at 28 and 38 GHz for the LOS scenario and V-V polarization. Figure 13: CI path loss versus distance at 28 and 38 GHz for the LOS scenario and V-H polarization. Figure 14: FI path loss versus distance at 28 and 38 GHz for the LOS scenario and V–V polarization. \[B=\propto+\beta D+X_{\sigma SF}^{FI}. \tag{16}\]0 FI path loss versus distance at 28 and 38 GHz for the LOS scenario and V–H polarization.
# An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing
## 4 Experimental Analysis
### Data Utility Comparison
#### 4.2.2 Aggregate Query Error  
The effectiveness of aggregate query [60] is evaluated by estimating the data utility in the published datasets. In the experiment, \"COUNT\" operator is computed where the query predicate comprises the start and end locations with time in the following form: \(SELECT\ COUNT(*)FROM\)_Table_  
\(WHERE\ v_{i_{1}}\in V_{i_{1}}\text{AND}\ldots v_{i_{dim}}\in V_{i_{dim}}\text {AND}\ s\in V_{s}\)  
where \(v_{i_{j}}(1\leq j\leq dim)\) indicates the \(QI\) value for attribute \(A_{ij}\), \(V_{i_{j}}\subseteq D_{i_{j}}\) and \(D_{i_{j}}\) comprises the domain for attribute \(A_{i_{j}}\), \(s\) represents the sensitive attribute value and \(V_{s}\subseteq D_{s}\) and \(D_{s}\) implies the domain for the sensitive attribute S. A query predicate can be characterized by predicate dimension \(dim\) and query selectivity \(sel\). Dimension \(dim\) indicating the number of \(QIs\) exist in the predicate and selectivity \(sel\) indicating number of values in each \(V_{i_{j}}(1\leq j\leq dim)\). Specifically, the size of \(V_{i_{j}}(1\leq j\leq dim)\) is randomly chosen from \(0,1,\ldots,sel*|D_{i_{j}}|\). Each query executes on the original table and all anonymized tables. Original count is given as \(org_{count}\) and anonymized count is given as \(anz_{count}\), where \(anz_{count}\) is considered for all anonymization methods, respectively. The average relative error is computed over all queries as [60]:  
\[Relative\ error=\frac{|anz_{count}-org_{count}|}{org_{count}}\times 100\%.\]  
In Figure 6, relative query error is plotted on the \(y\)-axis based on the \(QI\) selection. In the experiment, we have selected birth year, gender, birth year and gender, birth year, gender, start and end location, respectively, as \(QI\) attributes and calculated the relative query error on the anonymized dataset by \(k\)_-anonymity_[13], and the proposed Grouping method. For the experiment, we have selected the January 2016 dataset, and all possible combinations of the query were made and executed through the anonymization table while we calculated the average relative query error. The relative query error is calculated and presented in Figure 6, where the value on the \(y\)-axis denotes relative percentage error and those on the \(x\)-axis stands for different \(QI\) selection. The experimental result shows that proposed anonymization has small relative errors compared with the generalization approach for the \(k\)_-anonymity_ method.  
Figure 5: Data utility on published table.
## 4 Experiments
### Results on CelebAMask-HQ
#### 4.3.3 Comparison of Different Backbone Networks  
To evaluate the impact of backbone network architectures on the performance of facial semantic segmentation, we conducted comparative experiments on the CelebAMask-HQ dataset using two different types of feature extraction backbones: the lightweight MobileNetV2 and the more powerful but computationally intensive Xception [28]. In these experiments, we kept all other architectural components unchanged and integrated the proposed CCR-FE and SimFA modules under both configurations to ensure a fair comparison.  
As shown in Table 4, although using Xception as the backbone yields slightly better results in terms of Mean IoU and Mean F1-score compared to MobileNetV2, the overall performance difference remains minimal. Specifically, the segmentation results obtained with MobileNetV2 are already very close to those of Xception, indicating that the choice of backbone is not a decisive factor in the final performance. This outcome further confirms that the significant performance improvement is primarily attributed to the synergistic effect of the CCR-FE and SimFA modules, which play essential roles in context modeling and semantic enhancement, respectively, handling the majority of feature refinement and integration tasks.  
Therefore, it can be concluded that the core advantage of FP-Deeplab lies in its structural module design rather than its reliance on a more complex backbone. This also validates the proposed method's strong adaptability and architectural independence across different backbones, demonstrating its promising potential for broad transferability and practical deployment.