esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conclusions  
We aimed at the problem of severe energy loss caused by the separate design of the connection topology and the cable cross-section of current wind farm power system. In this pursuit, a coupling optimization method of connection topology and cable cross-section was first established and explained in detail. Based on the proposed method, the power system of a discrete wind farm containing 25 wind turbines was optimized and compared with the results of the traditional step-by-step design method. Our major conclusions of the study are as follows:  
1. In the solution obtained according to the principle of maximum current carrying capacity, the cross-section of each cable segment in the topology is the smallest, which minimized the cable cost. However, the resistance increases as the cross-sectional area decreases, resulting in greater power loss during the service life of the power system, for at least 20 years. In the analysed wind farm power system, the energy loss cost was a major part of the power system expenditure.
2. Based on the proposed method, the coupling optimization of connection topology and cable cross-section achieved optimal matching, thus minimizing the total cost of the power system. The optimal topology obtained tended to use more branches to reduce the current of per branch  
Figure 8: Coupling optimization of the connection topology and the cable cross-sections (Scenario 3).  
Figure 9: Comparison of lengths of different cross-sectional cables used in the topology.  
and to rationally use the cables with large cross-section, thus significantly reducing the energy losses in the power system. In the analysed wind farm power system, the total cost of the power system obtained by the proposed coupling method was reduced by 27.6% compared with the traditional step-by-step method. Furthermore, the power loss was reduced by 72.2%.
3. The proposed method is very versatile and suitable for the optimisation of the power system involving any number of wind turbines, and can be combined with any evolutionary algorithm.  
In future, the work can be improved in the following ways: (1) the proposed method at present is only applicable to the tree topology optimization, and modeling for other topology structures is a subject that needs to be studied further; (2) refined electric analysis models could be included in the optimization to evaluate the impact on the topology structure; and (3) gradient-based evolutionary algorithms could be taken into consideration to further accelerate the convergence process.  
All authors were involved during the development of the article. [PERSON] and [PERSON] conceived and designed the model setup; [PERSON] wrote the article and launched the formal analysis; [PERSON] wrote the codes to compute power output and cost. [PERSON] provided valuable insights throughout the research and writing process.  
This research was funded by [National Nature Science Foundation of China] grant number [51506089, 51506088] And the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
The authors declare no conflict of interest.
# Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection
## 4 Simulation Results  
The radar down-looking target detection introduced in this paper offers the advantage of performing coherent accumulation processing of all pulses in the entire coherent processing interval. It enables coherent processing of the Doppler spectrum to resolve ambiguity by obtaining range-Doppler results using orthogonal signals from multi-PRF. This section assesses the effectiveness of coherent processing in resolving Doppler ambiguity and extends it to typical airborne radar down-looking mode target detection. The proposed method's validity is verified based on the target's track integrity. For comparison, the traditional Multi-MPRF method, LPRF, and HPRF working modes are analyzed in target track detection.
# Integrated System for Official Vehicles with Online Reservation and Moving Path Monitoring
## 2 System Architecture
### Set-Top-Box (STB)  
The set-top box includes a power supply module, GPS module, micro-SD memory storage device, 3-axis accelerometer and gyroscope, and a microcontroller with Wi-Fi communication function (Figure 2). The STB stores the basic information about a vehicle, such as a license plate number. Its lithium-polymer battery is charged in the key cabinet. The user places the set-top box on the charging stand in the vehicle. When the vehicle starts to move, the GPS starts to record the moving path and stores it on the micro-SD card. When returning the vehicle, the user places the set-top box and the key in the key cabinet. The data in the SD card are automatically transferred to the cloud server. The detailed structure of the STB is as follows.
## 1 Introduction
Additionally, each sample of the measurement matrix needs to be calculated through the computation of CAF by adding delays and Doppler frequencies to the reference signal. Hence, this method consumes a significant amount of computational resources during the measurement matrix construction stage, affecting the real-time performance. Therefore, this paper proposes a target detection method for PBR based on batches processing of sparse representation and recovery. This method partitions CAF into blocks, selects blocks that contain targets, and improves the construction and utilization of the measurement matrix. Compared with Method (3), this method maintains the detection performance for weak targets while reducing the memory resource and computational requirements. Hence, this method provides favorable conditions for the parallel execution. The effectiveness of this method is validated in the experimental results. Figure 1: The ambiguity function properties of different signals are not ideal spike-like. (**a**) DVB-T. (**b**) DTMB. (**c**) 5G downlink signal.
# Towards Sustainable Regional Planning: Potential of Commuter Rail in the Madrid Urban Region
## 2 Materials and Methods  
### Case Study: The Madrid Urban Region  
This research studies the Madrid urban region within the limits currently established by the Madrid Regional Transport Consortium, Consorcio Regional de Transporte de Madrid (CRTM), as shown in Figure 1. This exceeds the boundaries of the actual administrative region--i.e., the autonomous Community of Madrid--and extends through municipalities in the neighbouring regions of Castilla-La Mancha (Cuenca, Guadalajara, and Toledo provinces) and Castilla y Leon (Avila and Segovia provinces). Of the 15 Spanish urban regions that have a commuter rail service, Madrid has the largest network regarding commercial lines (9 lines, 2 of them fork into 2 other sub-lines), network length (391 km of tracks), number of stations (94 in total, 59 outside of the municipality of Madrid), daily services (1385 services) and passengers (almost 900,000) [36; 37]. There are 35 municipalities that have a commuter railway station, although this figure increases to 45 when considering a catchment area radius of 1600 m around the station.
## 5 Simulation Method and Results
### Analysis of Experimental Results  
Table 7 shows the summary of the simulation results. The proposed ML-based automatic classification method provides significantly better classification accuracy than the existing methods. The proposed two-step classifier using DNN in the first step and CNN in the second step shows the best classification accuracy with 95.5%. The one-step classifier using CNN and DNN also provides excellent accuracy of 93.3% and 93.0%, respectively. Instead of classifying five topography classes at once, an additional 2.3%p improvement in accuracy is obtained through the proposed two-step classification by separating two classes where mutual confusion frequently occurs. The conventional correlation-based method has 22.3%p lower accuracy than the proposed method, and the human classification also shows 11.2%p lower accuracy than the proposed method. Therefore, it is confirmed that the NN-based ML technology can be effectively used for automatic topography classification.
# Managing a Smart City Integrated Model through Smart Program Management
## 8 Results
### Transversality of the Smart Projects  
Of the total 304 projects included in the final sample, only 35% were considered transversal to two or more domains, with a peak of 63% for Turin and a minimum of 12% for Lecce (Figure 6).  
Overall, 198 projects out of 304 covered only one application domain, 74 covered two application domains, and 25 covered three domains (Figure 7). Six projects were transversal to four and five domains, and only one project had six domains. The results show that Turin is the highest performing Smart city in terms of transversality; out of 65 projects it developed 25 projects covering a single application domain, 21 projects covering two application domains, 14 covering three application domains, three projects covering four domains, another three projects focusing on five domains, and one project covering all six domains.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Smart City** & **Projects Selected** & **Projects Removed** & **Projects Included** \\ \hline Milan & 79 & 4 & 75 \\ Bologna & 10 & 2 & 8 \\ Turin & 76 & 9 & 67 \\ Rome & 18 & 0 & 18 \\ Florence & 32 & 6 & 26 \\ Terni & 37 & 10 & 27 \\ Bari & 49 & 24 & 25 \\ Cagliari & 50 & 18 & 32 \\ Lecce & 27 & 1 & 26 \\ Total & 378 & 74 & 304 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Project distribution.  
Figure 6: Single vs. multiple application domains projects.  
Figure 7: Projects’ transversality for single Smart city.
## 4 Results
### HMM and Binary Segmentation Combined--Comparability Summary  
The corresponding results for the optimal HMM models for Region 1 (Pacific Ocean) are given in Appendix B.1 and for Region 2 (Vic) in Appendix B.2. Note that in Figure 21 the O and A columns have a different meaning to the table reporting the Binary Segmentation change-pointsin Section 4.4. Here, on the RHS of Figure 21, the O columns give the day on which ozone states change according to HMM models which do not include aerosol as a  
Figure 21: Summary of the BinSeg CPs and the HMM derived CPs. In the RHS table, the O columns show the ozone CP (day) based on HMM without covariate adjustment. The A columns show the ozone CP (day) for HMMs with aerosol as a covariate.  
covariate. The A columns report the day on which ozone states change for HMM analyses _with_ aerosol added as a covariate, the so-called covariate-adjusted HMM change-point. On the LHS of Figure 21 we report the BinSeg change-points across the three regions. For visual reference of the total sum aerosol and total sum ozone time-series across the three regions, see Figure 9 and Figure 8, respectively.  
From the combined HMM and Binary Segmentation change-points (CPs) summarized in Figure 21 we observe the following.  
In Region 1 (Pacific Ocean) the exceedance (CP) in Aerosol occurred around 7 November 2019. The associated subsequent decrease in Ozone then occurred around 12 days later on the 19 November 2019. About 40 days later on the 18 January 2020 Ozone levels were still significantly decreasing.  
In Region 2 (VIC) the exceedance (CP) in Aerosol occurred around 13-17 November 2019. Then 18 days thereafter around 7-12 December 2019, there was a significant decrease in ozone; about 25 days later, Ozone levels were still significantly decreasing.  
In Region 3 (Pacific Ocean) the exceedance (CP) in Aerosol occurred around 5 November 2019, followed 13-16 days later by a decrease in ozone around the 30 November 2019. About 40-45 days later, ozone levels were still significantly decreasing around 15 January 2020.
## 3 Results and Discussion
### Simultaneous Protein and Lipid Hydrolysis
#### 3.4.2 Overall Mass Balance  
To obtain a circular economy, it is necessary to recycle materials from meat waste as much as possible to have a material closed loop.  
Most past works on enzymatic hydrolysis of meat have only considered the solubilized or hydrolyzed protein to valorize the proteinic fraction) [41; 62; 75]. However, the valorization opportunities of meat waste are greater if unhydrolyzed collagen is also recovered. Within this concept, the remaining solid fraction, obtained after protein hydrolysis, can also be considered as a source of collagen. Before hydrolysis, the lipids in meat waste had only trace amounts of free fatty acids; but, during hydrolysis of the lipidic fraction, a significant increase in the concentrations was observed; at the end of hydrolysis, the highest concentration of all studied fatty acids was reached [5].  
Therefore, an estimate of the material balance after meat waste treatment is essential to supply information on overall recovery of meat waste components: peptides (hydrolyzed protein), collagen, and free fatty acids.  
Table 15 illustrates the mass balance on a dry basis after enzymatic hydrolysis at the different operating conditions indicated in Table 14. The material balance was calculated for each individual component and included the main components of the solid fraction (collagen), of the water-liquid fraction (protein hydrolysate), and of the lipid-liquid fraction (free fatty acids) with a high added value.  
Figure 6: Recovery percentage of hydrolyzed protein, collagen, and lipids after hydrolysis with a combination of Alcalase and Resinase at different process conditions.  
As expected, the increase in the \(Eo^{\prime}\)/\(So^{\prime}\) ratio significantly affected the total amount of recovered products: the higher that the ratio is, the greater the total amount of products recovered. The same result can be observed with the \(Eo\)/\(So\) ratio. However, modifications of pH and temperature are the reason why, for different \(Eo\)/\(So\) and \(Eo^{\prime}\)/\(So^{\prime}\) ratios, similar total product quantities can be obtained: see experiments no. 1 and 2, 3 and 4, 7 and 8, and 6 and 9.  
Based on the results obtained, the operating conditions of experiments nos. 6 and 9 are identified as the most suitable for the circular economy of the process. In these experiments, 0.8 kg of products are obtained for each kg of dry raw material, with the whole of the proteins and lipids in meat waste being practically recovered.  
Keeping in mind that the price of the enzymes has the most significant impact on the enzymatic reactor's running costs [5] and considering exclusively the price of the enzymes, the operating conditions of experiment no. 6 (pH = 7.5, T = 50 \({}^{\circ}\)C, \(Eo\)/\(So\) = 0.16 AU/g, and \(Eo^{\prime}\)/\(So^{\prime}\) = 1.11 kLU/g) seems to be more advisable for a circular economy.
## 3 System Model and Problem Formulation
### Problem Formulation  
In a typical decentralized edge system, each smartphone prioritizes its own QoS without considering the impact on others. To improve the QoS of collaborative edge intelligence, for any user \(n\), we focus on maximizing the utility \(U_{n}\) of the downloaded global model within the latency requirement \(T_{n}\). Let \(A_{n}=\{a_{n,1},\cdots,a_{n,R}\}\) and \(D_{n}=\{\mathbf{d}_{n,1},\cdots,\mathbf{d}_{n,R}\}\). More formally, considering constraints (1)-(3) and (7), we formulate the QoS optimization problem for any user \(n\in\mathcal{N}\) as follows:  
\[\max_{A_{n},D_{n}} U_{n}(w_{R_{n}}),\] (8) s.t.: (1)-(3) and (7).  
Note that (8) is a variant of the unbounded multiple knapsack problem (UMKP), a classic NP-hard combinatorial optimization problem [37], but differs from UMKP due to additional constraints (3) for a QoS guarantee and (7) for fair reward allocation. It is very challenging to achieve the optimal solution to (8) in DML, as true data distributions in heterogeneous edge networks are largely unpredictable. According to inequality (7), (8) can be relaxed to the following data valuation maximization problem:  
\[\max_{A_{n},D_{n}} \sum_{r\in\mathcal{R}}V(a_{n,r}\mathbf{d}_{n,r}),\] (9) s.t.: (1)-(3).  
We propose \(V\) based on decentralized calculations in Section 4 and discuss an optimized solution to (8) in Section 5.
# Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query
###### Abstract  
Superset query is widely used in object-oriented databases, data mining, and many other fields. Trie is an efficient index for superset query, whereas most existing trie index aim at improving query performance while ignoring storage overheads. To solve this problem, in this paper, we propose an efficient extended Level-Ordered Unary Degree Sequence (LOUDS) index: Ext-LOUDS. Ext-LOUDS expresses a trie by 1 integer vector and 3 bit vectors directly map each NodeID to its corresponding position, thus accelerating some key operations needed for superset query. Based on Ext-LOUDS, an efficient superset query algorithm, ELOUDS-Super, is designed. Experimental results on both real and synthetic datasets show that Ext-LOUDS can decrease 50%-60% space overheads compared with trie while maintaining a relative good query performance.  
Keywords:Ext-LOUDS; ELOUDS-Super; LOUDS; trie +
Footnote †: journal: Applied Sciences
# Neutronic Activity for Development of the Promising Alternative Water-Cooled DEMO Concepts
## 2 WCLL-db Blanket Study
### Development of the WCLL-db MCNP Geometry Model  
#### 2.2.1 CAD to MCNP Geometry Model  
The CAD to MCNP geometry model conversion for particle transport calculations was performed with SuperMC code [7]. To ensure reliable conversion, the CAD models for WCLL-db blankets were developed to avoid, if possible, tori surfaces. Although torus can be successfully used in the MCNP geometry models, the model of the WCLL-db blanket originally includes hundreds of tori, which very likely leads to numerous geometry errors in the converted models.  
The most crucial procedure in the model conversion is the generation of the void cells. In the MCNP code used in the fusion-related tasks, geometry cells must be complemented with the void cells describing a space around them. Void cells are generated in the SuperMC, making use of Boolean operations that are less robust compared to direct processing of the solid surfaces from the CAD model. In the case of numerous tori available in the geometry model, the probability of the malfunction of the code drastically increases. As a result, the model can include geometry errors disabling calculations. A manual post-correction of the geometry model is not possible due to its complexity. This happens because void cells can be too complex, and their boundaries could be formed by many tori surfaces.  
To avoid errors in the MCNP geometry, the CAD models are divided into several (smaller) parts, for example, the first wall, the collectors, and the pipes bundles. This approach was described above. Such treatment has two main advantages: (1) the smaller models, even with tori included, can be reliably converted, and (2) to modify the design, it is enough to modify the small constituent part rather than a whole blanket model. For each blanket, three stand-alone MCNP geometry models were generated representing the FW, the manifold, and the BZ with the bundles of the water cooling tubes. Each model was checked against geometry errors by making use of the MCNP cell volumes calculations. A few insignificant and tiny geometry errors were detected in the models, the number of lost particles not exceeding a few (\(<\)5) per 10\({}^{7}\) source neutrons. If a deviation from this criterion has been found, the location of the geometry errors was fixed, and the initial CAD model was corrected accordingly.
## 4 Results Analysis
### Displacement Field and Crack Type Analysis
#### 4.3.2 Displacement Field Evolution and Crack Types  
Figure 9 presents a displacement field cloud chart of sample \"S-2\" throughout its failure process, where the orientation of the displacement vectors is indicated by arrows, and the presence of cracks is denoted by short black lines. To facilitate a more nuanced understanding of the crack types, several magnified local views are included.  
At point A, the root of the first rock step exhibits RT cracks, indicating that the initial failure is primarily influenced by tensile stress. Upon reaching the peak stress point, both DT and RT cracks are observed within the first step, while the cracks in the rock slab are predominantly DT in nature. This further confirms that the failure of the first rock step is largely driven by tensile stress. At point D, the crack propagation patterns in the second and third rock steps begin to deviate from those observed in the first rock step. Notably, the cracks initiating at the left root are of the mixed failure (MF) type, and those propagating to the right are of the relative tensile (RT) type. During this stage, shear stress starts to exert a more pronounced effect, particularly evident as direct shear (DS) cracks at the bottom of the second step. By point F, RS and MF cracks are observed in rock slab 3, signifying a transition to shearing as the dominant mode of failure. Similarly, the failure of the second rock step is primarily attributed to shear stress, with the cracks therein being predominantly of the RS and MF types. Upon the ultimate failure of the sample, the fourth rock step is characterised by the predominance of RS cracks, which are largely influenced by shear  
Figure 8: Schematics of different crack types (modified from reference [56]).  
stress. Additionally, the third rock step and rock slab 5 exhibit the presence of both RS and mixed failure (MF) cracks. The interplay of these crack types within these regions indicates a complex interaction of tensile and shear stresses leading to the comprehensive failure of the rock mass.  
In summary, the initiation and failure of the first rock step are predominantly influenced by tensile stress, resulting in tensile-type cracks. For the subsequent rock steps, failure occurs after the peak stress point. The lateral and vertical movement of the particles gradually increases, leading to the progressive enhancement of the shear stress influence. As the deformation continues to accumulate, the sample transitions to a state where shear stress becomes the predominant factor in the failure mechanism.  
Figure 9: Displacement field and crack type of sample “S-2” during failure process.