esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed System Overview and Use of Open Link Data Integration for Polystore Databases
### Proposed System  
The proposed system is a web-based information system. HTML/CSS specifies the layout based on the user interface design. JavaScript and PHP both enable dynamic querying and multistage table querying. The proposed system's architecture is represented in Figure 6. The header information for each file is downloaded and saved on the Local Server (RDBMS), while the image file and all other associated data are kept on the IRSA cloud server. Thus, the proposed query system facilitates the conversion of keys to addresses and the migration of queries from the local server to the IRSA cloud server for image retrieval. Users can choose objects and enter values in the input box according to their search criteria in the proposed system.  
The interface provides QBE and recursive query feature for time-based, position-based, and camera-specific ZTF data. Users can perform searches by entering details about a single object or a collection of objects, depending on the user's query. The multi-object search is enabled through the use of the relate and join operations in conjunction with numerous search choices.  
Figure 6: Proposed Web-Based Polystore System architecture.  
The proposed system enables the formulation of queries using an interactive system in which users may select objects and input their associated IDs and predefined object names [23]. A query is created each time a user selects an object when querying. By combining these searches and linking many items, the basic query becomes more complicated. Multistage querying refers to the process of adding queries in many stages [45]. The server then stores the query, and fetches results in a table. Any desired row in the table can be selected so that the FITS image viewer JS9 [46] is connected and the images are visualized. The JS9 image viewer API defines a standard for communicating between local and distant data storage. The query language interface implements a set-theoretic query language for resolving relationships between objects of interest. It is built on the SQL query and is accessible to users who are unfamiliar with database programming.
## 4 Moment Capacity-to-Demand Ratio at the Barrier-Deck Anchorage  
Given the width of the barrier of 900 mm and the height of the applied load over the top surface of the deck slab of 990 mm, the experimental resisting moments are calculated as 186, 201, 143, 180, 176, and 205 kN.m/m, respectively, for specimens B-1, B-2, B-3, B-4, B-5, and B-6. These experimental values were then compared to the analytical resistances calculated using the equations proposed in the existing literature. For the TL-5 barrier wall, which is rigidly connected to a non-deformable concrete deck slab, the experimental findings are compared with those calculated by [64] Conversely, for the barrier wall connected to a slab overhang, the results were compared with those presented by [63], considering a 10 m long continuous barrier in the direction of traffic, as detailed in Table 3. Results show that the capacity-to-demand ratios were 1.51, 1.4, 1.38, 1.43, 1.43, and 1.42 for specimens B-1, B-2, B-3, B-4, B-5, and B-6, respectively. These values are much greater than 1, underscoring the adequacy of the GFRP detailing at the barrier-deck joint.  
Because the long-term exposure to various environmental conditions may reduce the tensile strength, creep rupture, and fatigue endurance of GFRP bars [71], the 2018 edition of the AASHTO LRFD Bridge Design Guide Specifications for GFRP-Reinforced Concrete [72] specifies environmental factors of 0.8 and 0.7 for concrete not exposed or exposed to earth or weather, respectively. [PERSON] et al. [73] determined that an environmental reduction factor value of 0.85 was appropriate for GFRP bars based on a compiled database of 361 accelerated aging tests of unstressed bars. The 2024 Guide for the Design and Construction of Structural Concrete Reinforced with Fiber-Reinforced Polymer (FRP) Bars, ACI 440.1R-24 [74], adopted this environmental reduction factor for concrete both exposed and not exposed to earth or weather. In this research, an environmental reduction factor of 0.85 was applied to the capacity-to-demand ratios in Table 3. This leads to capacity-to-demand ratios of 1.28, 1.19, 1.17, 1.22, 1.22, and 1.21 for specimens B-1, B-2, B-3, B-4, B-5, and B-6, respectively. These values are still greater than 1, underscoring the adequacy of the GFRP detailing at the barrier-deck joint.
# Low-Cost Active Anomaly Detection with Switching Latency
## 4 Numerical Results
### Discussion
Due to the round-robin procedure, the switching costs of the CL-\(\tau cN\) policy are larger than that of the DMSC policy while the observation costs are slightly larger than that of DMSC policy. Since the ratio \(s\) approaches 0, the relative cost of the CL-\(\tau cN\) policy is slightly higher than that of the DMSC policy. As a conclusion, in Scenario 1 where \(s=o(B)\) as \(\alpha\to 0\), the DMSC policy outperforms other policies. Combining Theorem 1 where the DMSC policy is proved to be asymptotically optimal in the finite regime in this scenario, we can conclude that the DMSC policy is the optimal algorithm to solve such an anomaly detection problem in this scenario. In Scenario 2 where \(s=\Omega(B)\) as \(\alpha\to 0\). As shown in Figure 4, the DMSC policy outperforms the CL-\(\tau cN\) policy but underperforms the R-SPRT policy. In this scenario, the ratio \(s\) is sufficiently large such the cost incurred by an additional switch will be higher than the cost of observing and declaring a non-target process. Since switching occurs only when the state of a process is identified, the switching times of R-SPRT are the smallest among any other policy. In this case, the R-SPRT performs better benefiting from the fewerswitching times. Meanwhile, the switching cost of the DMSC policy is much higher than that of R-SPRT, making the performance on the expected cumulative cost inferior. As for the CL-\(\pi cN\) policy, the switching cost is much higher than that of DMSC policy, thus its relative cost is highest among these three algorithms. Besides, the DMSC is order-optimal in terms of the expected cumulative cost in the scenario where \(s=\Omega(B)\) as \(\alpha\to 0\). In a conclusion, since the ratio \(s\) is sufficiently large, any switch will make the cumulative costs much higher. In this scenario, the best method is to minimize switching, i.e., perform SPRT for each process one by one. Thus, in this scenario, the R-SPRT policy is the optimal algorithm since its switching time is the lowest.
# Bumblebee Pollination Enhances Yield and Flavor of Tomato in Gobi Desert Greenhouses
## 3 Results
### Sugars and Organic Acids in Tomato  
Significant differences were found in both sugars and organic acids content tomato fruits by different pollination treatments. Compared with vibrator and PGR treatments, tomato fruits pollinated by bumblebees contained more fructose (Figure 5A, GLM, \(F_{2,19}=72.080\), \(p<0.001\)) and glucose (Figure 5B, GLM, \(F_{2,19}=5.101\), \(p=0.017\)), but less sucrose (Figure 5C, GLM, \(F_{2,19}=7.271\), \(p=0.005\)). Among the organic acids analyzed, bumblebee pollinated tomato fruits contained significantly less citric acid (Figure 5D, Kruskal-Wallis test: \(H=9.000\), \(df=2\), \(p=0.011\)) and malic acid (Figure 5E, Kruskal-Wallis test: \(H=7.167\), \(df=2\), \(p=0.028\)) than vibrator and PGR-treated tomato fruits.  
Figure 4: Fruit weight in relation to seed quantity of tomato fruit by different pollination treatments. Solid and dashed lines indicate significant and nonsignificant partial regressions, respectively.
# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 3 Proposed Method
### Loss Function
#### 3.3.1 Adversarial Loss  
As shown in Figure 3, different from the original GAN, the output of PatchGAN [8] as the discriminator is not a value but a matrix. Each element in the matrix represents the calculation of different receptive fields. Therefore, we can focus on more areas. Here, by using PatchGAN, the adversarial loss \(L_{\text{adv}}\) is composed of the loss of generator \(L_{G}\) and the loss of discriminator \(L_{D}\). Note that the input of the generator is a concatenation of \(\mathbf{I}\) and \(\hat{\mathbf{B}}_{\mathbf{E}}\) (or \(\mathbf{I}\), \(\hat{\mathbf{B}}_{\mathbf{C}}\), \(\hat{\mathbf{B}}_{\mathbf{E}}\)) in this paper, and for the ease of description, denote this concatenation as \(\mathbf{I}_{\mathbf{B}}\). The ground truth for discriminator is \(f(\mathbf{R})\) in this paper. Losses \(L_{G}\) and \(L_{D}\) can be expressed as follows:  
\[L_{G}(\mathbf{I}_{\mathbf{B}})=\mathbb{E}_{\mathbf{I}_{\mathbf{B}}}\Big{[} \log\Big{(}1-\mathrm{D}\Big{(}\mathbf{I}_{\mathbf{B}},\hat{f}(\mathbf{R}),N \Big{)}\Big{)}\Big{]}, \tag{3}\]  
\[L_{D}(\mathbf{I}_{\mathbf{B}},f(\mathbf{R}))= \mathbb{E}_{\mathbf{I}_{\mathbf{B}},f(\mathbf{R})}[\log(1-\mathrm{ D}(\mathbf{I}_{\mathbf{B}},f(\mathbf{R}),N))] \tag{4}\]  
where \(\mathrm{D}(x,y,N)\) represents the PatchGAN discriminator used to discriminate between \(x\) and \(y\) with an \(N\times N\) sized matrix output. The parameter \(N\) is set as 30 in our experiments. In discriminator training, we hope \(\mathrm{D}(\mathbf{I_{B}},f(\mathbf{R}),N)\) is closer to the real label and \(\mathrm{D}\big{(}\mathbf{I_{B}},\hat{f}(\mathbf{R}),N\big{)}\) is closer to the fake label, where the real and fake labels are marked as 1 and 0, respectively. In general, \(L_{D}(\mathbf{I_{B}},f(\mathbf{R}))\) as close to the real label as possible, that is, \(L_{G}(\mathbf{I_{B}})\) should be small enough. In the end, the network parameters are continuously updated in the process of continuous optimization of adversarial loss \(L_{\mathrm{adv}}\).
## 3 Method of Recognizing Line Objects  
### Line Objects to Be Recognized in P&ID  
The type of line objects to be recognized in P&IDs are a continuous line, a line incorporated with a line sign, and a flow arrow, as shown in Figure 1. A continuous line usually represents piping in P&IDs. The lines representing piping serve as a link between the flow of a fluid and the process in the P&ID. In addition to the continuous line, a line incorporated with a line sign typically represents a signal line. The signal lines transmit electrical, pneumatic, or data signals. Finally, the flow arrows serve to indicate the flow direction of a fluid in the direction of the arrow.  
Information identified through the recognition of line objects includes class, location, and flow arrow. The class represents the classification of line type. The location is the coordinates of the start point and the end point of the line object. In representing the location, a start point is at the left side in the case of a horizontal line and at the top in the case of a vertical line. Flow arrows identify the directions, and with reference to the diagram, they are expressed in four orientations: east, west, south, and north.
## 4 Analysis of the Influence of Structural Parameters on the Erosion and Wear of Pipe Elbows
### Influence of the Bending Diameter Ratio on the Erosion Wear of Pipe Elbows  
In the actual layout of the project, the bend diameter ratio of the pipe is one of the important factors affecting the overall erosion and wear rate of the bend. The effect of the bending diameter ratio on erosion wear is shown in Figure 10.  
Figure 9: Angle view of elbow.  
The two arrangements changing the bending diameter ratio of the bend can be seen in Figure 11a. The maximum erosion rate is first decreased, then increased and gradually levelled off. However, it can be seen from erosion cloud diagram 1 that the maximum erosion location is changed. It can be seen that by changing the bend ratio at the inlet end when the bend ratio is less than 1.5, the maximum erosion location is at the bend near the inlet end. When the bend ratio is greater than 2, the maximum erosion location appears in the bend near the outlet end.  
Figure 11: Effect of the bending diameter ratio on the erosion wear of the bends. (**a**) Comparison of the maximum erosion rate of the two-spatial pipeline structure. (**b**) Changing the bending diameter ratio of the inlet end elbow. (**c**) Changing the bending diameter ratio of the outlet end elbow.  
Figure 10: Cloud diagram showing the effect of the bending diameter ratio on erosion wear. (**a**) Erosion cloud after changing the bending diameter ratio of the inlet end elbow. (**b**) Erosion cloud after changing the bending diameter ratio of the outlet end elbow.  
From Figure 11b, it can be seen that when the change in the bending diameter ratio of the bend is at the inlet end, and at the outlet end, there is a constant \(R/D=2/1\) of the bend, the inlet end of the bend at the maximum rate of erosion with the increase in the bending diameter ratio decreases; there is a bend ratio of 1.5-2 when the erosion rate is almost stable and unchanged; and there is a bend ratio of 2-3 when the rate of erosion and wear decreases slowly. Further, when there is a bend ratio of 0.5-1 at the exit end, the erosion and wear rate decline is larger; when there is a bend ratio of more than 1, the erosion and wear rate gradually increases and is gradually stable. When there is a bend ratio in the range of 1.5-2, the two sections of the bend erosion and wear rate close to the change is smooth.  
Figure 11c shows that when the determined \(R/D=2/1\) of the bend is at the inlet end, and the change in the bending diameter ratio of the bend is at the outlet end, in the range of 0.5-1.5 of the small bending diameter ratio, the rate of erosion and wear rate changes, there is a bending diameter ratio of more than 1.5 and the maximum erosion and wear rate of the two bends is at the change in the flat. Therefore, the large bending diameter ratio of the bend causes a reduced erosion wear rate. In the project, the general selection of 1.5-2 for the bend ratio is more favourable.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 5 Experimental Results
### Detection Delay  
Figure 8 presents the results for detection delay in seconds in the logarithm scale. The HDDMW algorithm had the best performance for abrupt drifts, detecting drifts in a much shorter time than the others. HDDMW is followed by HDDMA, DDM, and EDDM, respectively, but with similar times among them. For gradual drifts, HDDMW showed again a significantly shorter time than the other algorithms, followed by HDDMA, DDM, and EDDM. For incremental drifts, the HDDMA algorithm had the best detection delay performance, followed by HDDMW and EDDM with markedly different results. In none of our experiment's samples, the DDM algorithm was able to detect an incremental drift, so there were no delay results.
# BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading
## 5 System Implementation
### Smart Contracts
However, in most cases, the pool price is calculated as a weighted average (i.e., as a sum of the multiplication results of each SMP and its duration in minutes divided by 60) as shown in Equation (5). All pool prices are stored on-chain and indexed by their timestamps in hours. The SMP is posted to the front-end web application in real time, and the pool price is posted after the hour's end to calculate the total amount of spending/earning and facilitate the payment settlement process. ```
0:\(hour\), \(energyOffers\)
0: pool price at a particular hour timestamp
1:\(H\gets 3600\)\(\triangleright\) 1 h = 3600 s
2:\(M\gets 60\)\(\triangleright\) 1 h = 60 min
3:\(sum\)Price\(\leftarrow\) 0\(\triangleright\) initiate sum price
4:\(N\gets sim\)Mintutes_length\(\triangleright\) get the number of system marginal offers
5:for\(k\gets 0\) to \(N\)do
6:\(T\gets sm\)Mintutes\({}_{k}\)
7:if\(T\geq hour\) and \(T<hour+H\)then\(\triangleright\) check offer within previous hour
8:\(id\gets sm\)OfferIDs\({}_{T}\)
9:\(offer\gets energyOffers_{id}\)
10:\(price\gets offer.price\)
11:\(duration\gets 0\)
12:if\(k<N-1\) and \(sm\)Mintutes\({}_{k+1}<hour+H\)then
13:\(duration\leftarrow(sm\)Mintutes\({}_{k+1}-sm\)Mintutes\({}_{k})/M\)
14:else
15:\(duration\gets M-(sm\)Mintutes\({}_{k}-hour)/M\)\(\triangleright\) last offer duration
16:endif
17:\(sum\)Price\(\leftarrow\) sumPrice\(+\) price\(\times\) duration
18:endif
19:endfor
20:\(poo\)Price\(\leftarrow\) sumPrice\(/M\)\(\triangleright\) weighted average system marginal price
21:\(pool\)Price[hour]\(\leftarrow\) poolPrice\(\triangleright\) store pool price
22:\(pool\)PriceHours_add(hour)\(\triangleright\) store current hour as index
23:returnpoolPrice
```  
**Algorithm 2** Pool Price Calculation Algorithm
## 5 Conclusion and Future Works  
This article has presented a routing protocol together with load balance scheme based on energy and link quality (REL) for IoT applications, such as automation of comfortable homes and offices, healthcare, environmental monitoring, and smart parking. REL combines a reliable scheme for route discovery and load balance mechanism, which provides high reliability, QoS-awareness and energy-efficiency. Moreover, it proposes an end-to-end route selection scheme based on cross-layer information with a  
Figure 10: Network lifetime in simulation experiments. (**a**) 20 Nodes, (**b**) 40 Nodes, (**c**) 60 Nodes, (**d**) 80 Nodes, (**e**) 100 Nodes, (**f**) 200 Nodes.  
minimal overhead. Nodes become energy efficient by sending the residual energy to their neighboring nodes with the aid of a piggyback and on-demand scheme. Additionally, REL also uses an event-driven mechanism to provide load balancing as a way to improve the system performance and avoid the energy hole problem.  
REL was evaluated by using simulation and testbed experiments to show its effects and benefits when compared with existing solutions. Testbed results were used to calibrate and confirm the accuracy of the simulation experiments, as well as present the impact of REL in real scenarios. At the same time, simulation experiments were useful to evaluate REL and compare it with well-known AODV and LABILE protocols, in terms of energy-efficiency, latency and data delivery in large-scale scenarios as expected for many IoT applications. In large-scale networks with a high node density, the results showed that REL increases the network lifetime by up to 26.6%, latency by 17.9% and packet delivery by 12%, compared with AODV and LABILE.  
As future studies, we will implement and evaluate REL in a large-scale test-bed, such as Wisebed and SmartSantander. We will also manage the radios/transmissions to improve the energy saving process.
## 3 Finite Element Modeling  
The question about the chemical composition of stainless steel 304 is investigated in this study, which employs 304 stainless steel as the material [14] for simulations and experiments. Briefly, 304 stainless steel is a versatile austenitic stainless steel with a chemical composition comprising the following: carbon (C) \(\leq\) 0.08%, silicon (Si) \(\leq\) 1.00%, manganese (Mn) \(\leq\) 2.00%, phosphorus (P) \(\leq\) 0.045%, sulfur (S) \(\leq\) 0.03%, chromium (Cr) 17.00-20.00%, and nickel (Ni) 8.00-10.50%. This material exhibits excellent corrosion resistance, formability, and weldability, making it highly suitable for precision machining applications.  
Building upon the front cutting face analysis, finite element analysis (FEA) was conducted for simulation and experimental validation. The workpiece material was defined as a 20 mm \(\times\) 10 mm \(\times\) 5 mm 304 stainless-steel cube. Key parameters included a milling speed of 5000 r/min, a milling depth of 3 mm, and an initial material temperature of 25 \({}^{\circ}\)C. The material model parameters and 304 stainless steel properties are listed in Tables 1 and 2. Finite element software: Abaqus 2019 was used to create four tool configurations, non-textured tools, front-cutting-face-textured tools, back-cutting-face-textured tools, and combined front/back-cutting-face-microstructured tools, followed by FEA-based simulations.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**A/mpa** & **B/mpa** & **C** & **n** & **m** \\ \hline
452 & 694 & 0.0067 & 0.996 & 0.311 \\ \hline \end{tabular}
\end{table}
Table 1: Material parameters of 304 stainless steel based on Johnson Cook model.  
This study designates four tool configurations with abbreviations: T\({}_{1}\) (non-microstructured tool), T\({}_{2}\) (front-cutting-face-microstructured tool), T\({}_{3}\) (back-cutting-face-microstructured tool), and T\({}_{4}\) (front-/back-cutting-face-microstructured tool).
###### Abstract  
Wireless sensor networks usually rely on internal permanent or rechargeable batteries as a power supply, causing high maintenance efforts. An alternative solution is to supply the entire system by harvesting the ambient energy, for example, by transducing ambient vibrations into electric energy by virtue of the piezoelectric effect. The purpose of this paper is to present a simple engineering approach for the bandwidth optimization of vibration energy harvesting systems comprising multiple piezoelectric cantilevers (PECs). The frequency tuning of a particular cantilever is achieved by changing the tip mass. It is shown that the bandwidth enhancement by mass tuning is limited and requires several PECs with close resonance frequencies. At a fixed frequency detuning between subsequent PECs, the achievable bandwidth shows a saturation behavior as a function of the number of cantilevers used. Since the resonance frequency of each PEC is different, the output voltages at a particular excitation frequency have different amplitudes and phases. A simple power-transfer circuit where several PECs with an individual full wave bridge rectifier are connected in parallel allows one to extract the electrical power close to the theoretical maximum excluding the diode losses. The experiments performed on two- and three-PEC arrays show reasonable agreement with simulations and demonstrate that this power-transfer circuit additionally influences the frequency dependence of the harvested electrical power.  
vibration energy harvesting; piezoelectric cantilever; array; bandwidth; optimization +
Footnote †: journal: Applied Physics Letters B