esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Protein Labelling and Two-Dimensional Fluorescence Differential Gel Electrophoresis  
The extracted proteins from the recombinant _M. microti_ strains were fluorescently labeled and analyzed by two-dimensional fluorescence differential gel electrophoresis (2-D DIGE) in duplicate. A CyDyeTM DIGE Fluor Labelling Kit (Amersham Biosciences, Buckinghamshire, UK) was used to differentially label the cell extracts of _M. microti_-PMV261, _M. microti_-PstS-1 and _M. microti_-Ag85B with the following dyes: Cy3 (orange), Cy5 (red) and Cy2 (green), respectively. Briefly, the samples were incubated in 400 pmol/\(\upmu\)L of dimethylformamide at 4 \({}^{\circ}\)C in the dark for 30 min. The reaction was stopped by adding 10 mM of lysine, followed by incubation at 4 \({}^{\circ}\)C in the dark for 10 min. The proteins were dissolved in a buffer solution containing 9 M of urea, 4% CHAPS (3-[(3-cholamidopropyl) dimethyllammonio]-1-propane sulfonate), 100 mM of dithiothreitol, 0.1% Bio-LytE(r) 3/10 Ampholyte (Bio-Rad) and 0.001% bromophenol blue. They were then placed on immobilized pH gradient ReadyStripTM IPG Strips of pH 4-7, 11 cm (Bio-Rad) for 16 h. The isoelectric focusing was performed under the following conditions: first, a current of 500 V, 1 mA, 5 W, 1 V/h; second, 2500 V, 1 mA, 5 W, 1 V/h; and lastly, 2500 V, 1 mA, 5 W, 49,500 V/h were applied with a MultiPhTTM Electrophoresis Unit (Pharmacia Biotech, Uppsala, Sweden) within 8 h. Then, the strips were placed in 70 mM of dithiothreitol and a buffer solution composed of 6M of urea, 30% \(v/v\) glycerol, 50 mM of Tris, 2% SDS and 0.002% bromophenol blue. It was then immediately placed in 193 mM of iodoacetamide. The proteins were separated by electrophoresis in a 12% SDS-PAGE using a SE600 Standard Dual Cooled Vertical Electrophoresis Unit (Hoefer SE60 TM unit; Pharmacia Biotech, Uppsala, Sweden) and a buffer solution consisting of 192 mM of glycine, 25 mM pf Tris, and 0.1% SDS at 50 V for 30 min, 100 V for 2 h, and 200 V for 3 h. Finally, the images of the gels were captured using a TyphoonTM 9400 scanner imager (Amersham Biosciences, Piscataway, NJ, USA) and analyzed using the Quantity OneTM Image Analysis Software (Bio-Rad, Hercules, CA, USA).
## 2 Methodology
### Study Area and Climatological Analysis  
Variables such as temperature, humidity, radiation and wind impact AWG use by controlling their capacity to capture water and the power source for operation. In this work, the climatological study is relevant for detecting the high, low and average temperature, humidity and radiation values in the study region in order to analyse the feasibility of using AWGs. The study was conducted in the municipality of Matehuala, San Luis Potosi, Mexico (Figure 1), which is located between 23 deg38'49\" North and 100 deg38'37\" West, with a land area of 1165.87 km\({}^{2}\), 223 towns and approximately 99,015 inhabitants. Matehuala is at an average height of 1570 m above sea level, with calci-gypsiferous, clayey soil poor in organic matter (0.2%). The city's main economic activities are mining and commerce. Climatological data were obtained from the meteorological station of the National Water Commission (CONAGUA), records were used from the network of automatic weather stations (EMAs) managed by the General Coordination of National Weather Service (CGSMN) with satellite transmission. Thesedata were taken daily, every 10 min, over a period of 8 years spanning from 2010 to 2017 in Matehuala, San Luis Potosi, Mexico.  
Figure 2a shows temperature behaviour throughout the year, with an average high temperature of 22.88 \({}^{\circ}\)C in June and an average low temperature of 13.14 \({}^{\circ}\)C in January. Based on the results, the high temperature is most frequently registered after noon, while the low temperature occurs mostly in the early morning hours. Humidity behaves differently to temperature, decreasing from January to April and increasing until September. The minimum average humidity is 48.49% and the maximum average is 70.29% (Figure 2b). Figure 2c shows no significant relationship between temperature and relative humidity since, as mentioned above, the maximum amount of water vapour that air can contain depends on the temperature, though the amount of water is not guaranteed to be the same for each temperature point. Here it is observed that humidity does not follow the temperature pattern nor vice versa, since each is independent of the other.  
Figure 1: Map of México, which shows Matehuala, SLP.  
Figure 2: Statistical data of (**a**) temperature, (**b**) relative humidity (RH) and (**c**) humidity and temperature ratio in Matehuala, SLP, Mexico (2010–2017).  
Temperature analysis corresponded with the area's type of climate whose temperature range is not extreme despite being located in a desert region. Moisture analysis results reveal that, despite being a desert area, the region has the potential and necessary resources for water extraction from the environment, using air humidity with a minimum average of 48.49% humidity as a source, sufficient for the operation of an AWG [42].
## 3 Distributed Mission Scheduling Model
### DMSA Based on Nash Equilibrium  
For the generalization of the problem, in distributed mission scheduling algorithm (DMSA, as shown in Algorithm 1), we set \(n\) finite memory tables of length \(L\) that record the historical actions adopted by each player \(s_{i}\) and denote them by \(Mem_{i}{}^{t}=(mem_{i}{}^{1};mem_{i}{}^{2};\ldots,mem_{i}{}^{L})\) and \((i=1,2,\ldots,n)\), respectively, whose initialization is defined to be empty. When optimization begins, every player will be randomly allocated several targets and generate its own action space \(A_{i}\). In each round of gaming, all the players exchange information, and each player selects its action; \(a_{i}\)\(\in\)\(A_{i}\) is by the adaptive particle swarm optimization algorithm (APSOA) in static scheduling or by the adaptive tabu-search algorithm (ATSA) in dynamic scheduling, both of which will execute constrains check and deconflict process and thus calculate current payoff \(U_{i}(t)\) in (12). As Equations defined in (9)-(11) are the overall optimization sub-objectives of scheduling, when calculating current payoff \(U_{i}(t)\) of each satellite in (12), the variable \(n\) is set as (1) and the best response \(BR_{i}{}^{t}\) as  
\[BR_{i}{}^{t}=\operatorname*{argmax}_{a^{t}_{i}\in A_{i}}U_{i}(a^{t}_{i},a^{*}_ {-i}) \tag{14}\]  
Then, each individual will compare current \(BR_{i}{}^{t}\) with last \(BR_{i}{}^{t-1}\) (or \(a^{t-1}_{i}\)) recorded in the memory table and follow the greedy strategy to select the one with higher payoff as current action \(a^{t}_{i}\). After recording \(a^{t}_{i}\) into the bottom of memory table as newest \(mem_{i}\), the DMSA will check whether the number of elements in the current memory table is greater than length \(L\), and if so, \(s_{i}\) will delete the oldest element (the \(mem_{i}{}^{1}\)) to obtain an updated fixed-length \(Mem_{i}{}^{t}\) and then reallocate those allocated but unscheduled targets to neighbors (neighboring satellites refer to satellites with the same payload, and we assume they can send information to each other directly at any time). The gaming will continue to process until recorded actions of all players' memory tables stay consistent for \(L\) rounds, which means all satellites are unable to obtain higher returns through their own plan adjustments.  
```
Input: Satellites Set \(S\), Targets Set \(T\), memory length \(L\), Observation Windows Set \(W\). Output: Overall Scheduling Plan Procedure:
1.for each round of Game, g = 1,2,3..., do
2. for each satellite \(s_{i}\)\(\in\)\(S\), simultaneously do
3. Receive information from neighbors;
4. Calculate and select \(U_{i}\), \(BR_{i}^{\prime}\) by APSOA/ATSA;
5. Find received targets that haven't been scheduled;
6. Send unscheduled targets to neighbors;
7. Update \(Ment_{t}^{\prime}\) of each \(s_{i}\);
8. endfor
9.endfor
```  
**Algorithm 1** DMSA
# A Study on the Feasibility of Natural Frequency-Based Crack Detection
## 1 Introduction
On the other hand, to simplify the problem, the load-bearing capacity, the design load, and the impact of crack growth are generally excluded from consideration when devising natural frequency-based crack detection methods. Throughout the course of this work, the following questions have emerged. What is the range of structures and materials to which the crack detection method can be applied? How is the natural frequency shift due to a crack affected by the level of loading the structure is subjected to (or in other words, the safety factor under design loading)? The fundamental question that has not been addressed in the literature is whether measurable natural frequency changes occur before a crack develops to a critical stage where crack propagation or brittle failure begins. This is the focus of the current study. In this study, the first challenge is to choose an appropriate object for research. For the chosen object, it should be convenient to determine the crack severity corresponding to brittle fracture or the onset of crack growth using fracture mechanics. Meanwhile, it should be possible to evaluate the variation of natural frequencies as the crack grows. To compare both aspects directly, a single edge notched tension (SENT) specimen (also known as a common fracture toughness test specimen) [25] is used (see Figure 1). The advantage of using fracture toughness test specimens is that the solution of stress intensity factor \(K\) for these specimens is readily available in the literature [25; 26]. The stress intensity factor \(K\) is a measure of the severity of a crack situation as affected by the crack size, stress, and geometry. Its critical value indicates the critical state in which brittle fracture occurs or crack extension begins. As for vibration analysis, the SENT specimens under uniform uniaxial tension are essentially single-edge cracked rectangular plates under uniaxial in-plane load with the crack lying along the centreline. The boundaries of the cracked plates can be considered as CFCF where C represents a clamped boundary and F represents a free boundary. Multiple methods are available in the literature for the natural frequency calculation of loaded cracked plates. In this study, the stress resultants caused by the in-plane load are calculated using the extended finite element method (XFEM). The stress results are then substituted into the natural frequency calculation of the loaded cracked plates. Based on the classical thin plate theory, natural frequencies are calculated using the Rayleigh-Ritz method (RRM). To describe the stress singularities at the crack tip and the discontinuity of displacement and slope across the crack surfaces, corner functions are incorporated into the admissible functions. The methodology of the current study is further elaborated in Section 2.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 5 Numerical Results
### Power Distribution  
In Table 5, for a given pair of \(\lambda_{a}\) and \(\lambda_{b}\), we elaborate the power distribution with the different transmission modes in the typical cell. The transmission power of each RR is fixed to \(P_{\text{RR}}=25/6\) mW (\(6.3\) dBm), so the maximal transmission power is \(N_{\text{max}}P_{\text{RR}}=25\) mW (\(14\) dBm) with \(N_{\text{max}}=6\). For each transmission mode, Table 5 shows the specific percentage share of each transmit power. Where off represents a number of nodes to which no RR is assigned and therefore have a transmission power of 0 mW. From \(P_{\text{RR}}\) to \(6P_{\text{RR}}\) corresponds to the transmission power of the nodes that are assigned the corresponding number of 1 to 6 RRs, respectively. As marked in the light grey part of the table, it can be noticed that SIMO with MRC mode has the least number of off nodes, meanwhile with 99.92% of the nodes being transmitting information at \(P_{\text{RR}}\) power. Therefore, it has the best power distribution. MU-MIMO with \(n_{u}=2\) also has a good power distribution, with only 0.05% of nodes off and 99.80% of nodes transmitting at \(P_{\text{RR}}\). SISO has the worst performance, with up to 3.97% of nodes unable to transmit information and only 90.76% of nodes transmitting at low power.
## 4 Discussion
### FE Model for the PSC Girder Bridge  
As shown in Figure 10a,c, the PSC I-girder bridge used in this study has three spans, where each span has a width of 12 m and a total length of 100 m, and the maximum inter-span distance is 40 m. The FE bridge model is detailed in Figure 10b,d,e. The FE model for I-girders, beams, and piers was built using beam elements, and the number of beam elements used was 331. Details of material constitutive models used for the I-girders, beams, and piers are shown in Table 2. The boundary conditions of the FE model are fixed at the abutment and the bottoms of the piers. Furthermore, in order to consider the bridge shoes installed at the connection of the bridge deck and the pier cap, the bridge deck and the pier cap were connected with a spring element.  
Figure 10: PSC girder bridge model for seismic analysis: (**a**) section properties; (**b**) section for the FE model; (**c**) side view of the PSC girder bridge; (**d**) side view of the PSC girder bridge for the FE model; (**e**) isometric view of the PSC girder bridge for the FE model.
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 3 Model Architecture
### Loss Function Optimization  
Traditional contrastive loss functions inadequately control the similarity differences between positive (same-class) and negative (different-class) sample pairs, potentially causing  
Figure 5: The AugODNet_BRA backbone network structure.  
overlapping or adjacent embeddings. Additionally, imbalanced gradient contributions from positive and negative pairs during training hinder convergence and performance. To address this, an optimized strategy incorporating margin intervals, class-center regularization, and dynamic loss weight is proposed.  
Margin Interval: To enhance class separability, a margin parameter enforces a minimum distance between positive and negative pairs, preventing class confusion in the embedding space. The optimized loss function is as follows:  
\[L_{ij}=\max\left(0,d(e_{ij},c_{i})-\min_{\begin{subarray}{c}1<k<N\\ k\
eq i\end{subarray}}d(e_{ij},c_{k})+\alpha\right). \tag{13}\]  
Class-Center Regularization: This maximizes the distances between class centers to avoid overlap, improving separability. The class-center regularization loss function is  
\[R=\sum_{i=1}^{N}\sum_{k\
eq i}\max(0,\beta-d(c_{i},c_{k})). \tag{14}\]  
where \(\beta\) is the minimum inter-class margin, and \(d(c_{i},c_{k})\) denotes the distance between classes \(i\) and \(k\).  
Dynamic Loss Weight: The weights of the positive and negative pairs are adjusted adaptively during training. Initially, negative sample pairs typically generate larger gradients, which have a more significant impact on model updates, while positive pairs contribute less. Dynamic weight balances their influence, enhancing training efficiency and stability.  
The final loss function is as follows:  
\[L_{ij}=\lambda_{1}d(e_{ij},c_{i})-\lambda_{2}\min_{k\
eq i}d(e_{ij},c_{k}), \tag{15}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are weight factors for positive and negative pairs, respectively.  
Combining these, the optimized loss function is  
\[GL_{ij}=\max\left(0,\lambda_{1}d(e_{ij},c_{i})-\lambda_{2}\min_{k\
eq i}d(e_{ ij},c_{k})+\alpha\right)+\gamma R. \tag{16}\]  
where \(\gamma\) is a hyperparameter adjusting the regularization term \(R\).
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 6 Application and Evaluation
### Execution Time Monitoring  
Execution time monitoring is an important part of maintaining the performance in service-based systems, such as AWS Step Functions. The historical monitoring data provide a baseline, against which the workflow administrator can compare the current performance of the workflow system. A monitoring plan includes a set of baseline metrics and current performance metrics, which are determined by historical and current data, respectively. If the current performance falls below the baseline, the administrator can undertake various countermeasures, ranging from simply reconfiguring some workflow parameters to purchasing more cloud resources to run the tasks. In JobDeciderWorkflow, parameter reconfiguration can involve updating the criterion of running the job with Lambda or ECS and reseting the maximum number of job resubmissions to ECS.  
Figure 5: JobDeciderWorkflow in AWS Step Functions  
In this case study, we focus on the metrics of execution times. AWS Step Functions supports the monitoring of the task execution time and the end-to-end workflow execution time. However, it does not provide the time metrics in the level of traces. Consider the following trace component for JobDeciderWorkflow:  
_The \"Submit Job to ECS\" stage is traversed up to \(i\) times, where \(i\) is a parameter, until the\"Emit 'Successful\" stage is reached._  
The time metrics of trace components similar to the above one can help to define a performance baseline and the current performance, and to create a thorough monitoring plan.
# Physics-Guided Neural Network-Based Feedforward Control for Seamless Pipe Manufacturing Process
## 2 Materials and Methods
### Experimental Setup
For the purposes of testing and synthesizing the control system for the seamless pipe manufacturing process from a continuously cast material, a scaled-down model of the entrance device was developed, as shown in Figure 9. This model consists of an electro-hydraulic system with two cylinders, grippers, control valves, a pump unit, a reservoir, and accompanying electronics to implement the methods discussed in this paper. Figure 9: Experimental hardware setup. The control system depicted in Figure 8 has been implemented using a Siemens S7-1517T PLC, which includes a neural processing unit (NPU) module for real-time neural network evaluation. The PLC acts as the system's central controller, managing the primary control loops, such as the PID-based force regulation. It also handles sensor inputs (e.g., pressure transducers) and actuator outputs (e.g., valve control). The PLC interfaces with distributed sensors and actuators via PROFINET, connected to an ET 200S I/O station for efficient field-level communication. To control the hydraulic cylinders, the SimaHydTO library [32] is employed, offering modular function blocks for precise position and force control. This library also facilitates real-time monitoring and compensation for valve nonlinearities. While a technology CPU is not required for most basic motion operations, it is essential for advanced synchronization tasks such as camming and cam track operations. The PGNN is executed on the technology module neural processing unit (TM NPU), which is powered by the AI-capable Intel Movidius Myriad X processing unit. The NPU contains two LEON CPUs and 16 SHAVE parallel processors, capable of accelerating neural networks in hardware, with a peak compute capacity of up to 4 TOPs [33]. As shown in Figure 10, data exchange between the PLC and NPU occurs over the backplane bus, enabling real-time communication. In our setup, the execution time for the neural network within the NPU was approximately 650 \(\upmu\)s, with data transfer between the PLC and NPU occurring at intervals of 4 ms.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 2 Defining Quantum Money and Quantum Wallets
### Quantum Wallets  
A _quantum wallet_ would serve as a secure storage device for quantum money, relying on the principles of quantum memory to maintain the coherence and integrity of quantum states representing currency [19]. Quantum wallets will be designed not only to hold quantum states but also to ensure their long-term stability against decoherence and interference. To achieve this goal it is essential to implement quantum memory technology, as it preserves quantum information by minimizing interactions with the environment.  
In quantum wallets, stability is achieved by storing quantum states in DFS, where specific types of noise do not impact the state. By hosting quantum states in DFS, quantum wallets ensure the durability of quantum money over time. Quantum memory in this context provides the structural support for quantum currency, protecting against rapid decoherence that would otherwise render the stored quantum states unusable [20]. For quantum money to be practical, it must retain coherence long enough for standard, everyday transactions, making the robustness of DFS-based quantum memory essential.  
The feasibility of quantum wallets depends on the balance between coherence timescales and transaction speeds. In typical systems, coherence times (\(\tau_{c}\)) of quantum memories can range from microseconds in superconducting qubits to seconds or longer in atomic systems or rare-earth doped crystals. On the other hand, transaction times (\(\tau_{t}\))--which include transmission, verification, and user interaction--must remain well within \(\tau_{c}\) to prevent decoherence. For example, a transaction involving quantum teleportation over satellite links may require several hundred milliseconds, which is feasible only if the coherence time of the wallet exceeds this window. Therefore, practical implementation requires quantum memories with \(\tau_{c}\gg\tau_{t}\), and ongoing advances in low-noise materials and error mitigation techniques are essential to maintain this condition.  
The use of DFS in quantum wallets will allow quantum money to remain stable and secure over extended periods, much like a traditional currency stored in a vault. The DFS-based quantum memory mitigates noise effects such as collective dephasing, which commonly lead to information loss. This approach is critical for creating a currency system that users can reliably store and access over time, addressing a primary challenge in practical quantum currency applications.  
A quantum wallet's reliance on quantum memory further enhances security, as the no-cloning theorem inherently prevents unauthorized duplication of quantum states. Additionally, any unauthorized attempt to measure or interact with the quantum state would collapse the stored information, rendering it unusable and thwarting counterfeiting attempts. In this way, the quantum wallet provides a tamper-proof solution where only authorized users can access or transfer quantum money without disturbing the state. Therefore, by acting as a stable vault and a security
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 7 Routing for Multi-hop WSNs with a Single Immobile Sink
### Discussions of the Results  
The fully distributed algorithm requires less communication overhead per iteration, but more than five times the number of iterations that the partially distributed algorithm requires to converge and stop. The aggregate overhead and computational effort over all the iterations were not shown in the results in [2]. It could still be that the partially distributed algorithm requires a lower total communication overhead and computational effort since it requires lower iterations to converge. In this case the fully distributed algorithm would loose its appeal. Therefore, both algorithms should be evaluated for the total computational effort and the total required communication overhead for all the iterations in order to make an accurate comparison.