esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimisation Results
### Energy Storage System Control Algorithm  
Aforementioned in Section 2.4, by carefully choosing the initial value of energy storage system, the operational cost for energy storage system can be reduced to some extent. Considering the high standby loss of the heat storage system and the flat rate of gas price, it is preferable not to store heat in advance. Thus, the initial heat in HSS is set to 0 kWh. However, considering the electricity tariff can change significantly in a day and the standby loss for electrical storage system is relatively small, storing a reasonable amount of electrical energy in advance can improve overall system cost. Figure 13 shows electricity cost of the domestic building against initial energy stored in the electrical storage system. This was produced using a fixed base case combining the overall management system shown in Figure 1 and the initial parameters in Section 3.  
From Figure 13, the daily electricity cost will be the lowest if initial electrical energy is set as 0.8 kWh. After setting the initial energy for the energy storage system and operating it as demonstrated in Section 2.4, the amount of imported gas, electricity and the state of CHP can be acquired. Figure 14 shows the state of CHP, the amount of imported gas and electricity in a day based on proposed Energy  
Figure 12: The accumulative redundant energy can be stored in energy storage system by applying the modified CHP control algorithm.  
Figure 13: Battery initial energy in (kWh) vs domestic building daily electricity cost.  
Hub optimisation rules in this paper. Figure 15 shows the amount of energy which is stored in battery one, battery two and the hot water tank respectively.  
With the installation of the energy storage system, daily energy cost can yield a further reduction. Based on the control algorithm, total operational cost now reduces to 464 pence, which saves about \(E1.38\) a day. Specifically, the cost of electricity imported from grid is 91 pence, the gas that used to supply heat demand is 74 pence and the remaining cost of 299 pence is used to import gas to supply the CHP. With the proposed optimisation rules, the total cost reduction is 22.9%, which is a 2.9% improvement on previous similar work which reduces cost by 20% [10]. Therefore, the algorithm represents a 14.5% improvement on previous work.
# Corrosion Assessment of Zr2.5 Nb Alloy in Ringer's Solution by Electrochemical Methods
## 3 Results and Discussions  
### Open Circuit Potential (OCP)  
The time variation of the open circuit potential or the free potential evolution was used as a primary criterion for determining the corrosion behavior of a material in a specific used environment. In the case of our protocol, the periods of highlighting the potential in open circuit, Figure 2, were alternated with the analyzes of the polarization resistance and the corrosion rate, where later the collected data were merged and exposed in a single diagram for a more accurate assessment.  
From Figure 2, it can be seen that the immersion potential has a value of E = \(-450\)\(\pm\) 21 mV vs. Ag/AgCl reaching at the end of the 38.5 \(\pm\) 6 h a value of E = \(-241\)\(\pm\) 13 mV vs. Ag/AgCl. This shift of the potential to nobler values demonstrates that a layer of passive oxide is formed on the surface of the Zr2.5 Nb alloy. This tendency to move towards more positive values was also observed by other authors when studying the corrosion behavior of different commercial alloys in R  
Figure 2: Evolution of the free potential (OCP) of the Zr2.5 Nb alloy immersed in [PERSON]’s solution for 38.5 h.
# Space Efficiency in European High-Rise Timber Buildings
## 3 Results
### Space Efficiency
#### 3.3.2 Inter-Relationship of Space Efficiency and Core Planning  
In Figure 7, observed measurements point out the relationship between space utilization and core typology. Bars on the right side symbolize the number of instances grouped by core type. Each orange marker signifies the spatial efficiency of selected cases associated with specific core types. Additionally, a gray bar highlights the frequency of towers in the sample population that have the same core types.  
Figure 6: Inter-relationship between space efficiency and function.  
Within the context of core layouts adopted in high-rise wooden buildings across Europe, the central configuration occurs as the leading choice, observed in 32 instances. This layout demonstrates a range of spatial efficiency levels from 75% to 89%, with an average efficiency of 82%. Conversely, buildings employing a peripheral core, totaling 24 cases, exhibit spatial efficiency levels spanning from 70% to 90%, averaging at 83%. The marginal difference in average spatial efficiency between central and peripheral core typologies, with central cores averaging 82% and peripheral cores at 83%, underscores the subtle interplay of architectural and engineering factors. These findings emphasize that while core layout is a significant determinant of spatial efficiency, the performance outcomes are closely aligned, reflecting the optimized design strategies employed across both configurations.
## 3 Comprehensive Analysis of Issues of CIM-Based DMS Using RDB
### Degradation of Topology Processing Performance
B\(-\)Trees boost more branches compared to binary trees, allowing them to store a greater number of keys. Their nodes accommodate multiple keys, resulting in a relatively low tree height depending on the key count. This feature facilitates the efficient processing of extensive data sets. A B+Tree, an advanced structure derived from a B\(-\)Tree, comprises internal and leaf nodes. Internal nodes exclusively store keys, while leaf nodes store values. In a B\(-\)Tree, all leaf nodes form a linked list, necessitating a search from the root node when seeking adjacent leaf nodes. However, a B+Tree allows for linear search on the leaf nodes, resulting in a significant reduction in time complexity. This structure finds widespread use in large-scale database systems, proving efficient for range searches and sequential access. As shown in Table 4, it is observed that binary trees exhibit a straightforward structure, while B\(-\)Trees and B+Trees are specialized for large-scale data and index structures. In RDBs, correlations for TP can be expressed through foreign key relationships between tables. The TP involves exploring these foreign keys. As shown in Figure 8, the database is designed so that the Terminal class holds information about the ConductingEquipment and ConnectivityNode topology at both ends. Consequently, only Terminals with primary keys can be searched using the index. However, when exploring correlations between foreign keys, ConductingEquipment, and ConnectivityNode, index exploration becomes impractical. This limitation results in extended processing times for TP. In RDBs, indexes are typically set only for primary keys due to their inherent characteristics. An index serves as a method to efficiently manage and swiftly search for data. Although utilizing an index allows for efficient searching of records, there are limitations on the types of data that can benefit from it. In RDB, indexes are automatically generated only for primary keys and not for any other columns. Consequently, searching for data based on the primary key can be swiftly executed using an index. However, when searching for other columns, the index cannot be utilized, necessitating sequential scanning of the entire record. To locate a specific record, the database must sequentially search through all records, resulting in a slow topological search speed. As the volume of data increases, the speed of searching decelerates, and the time complexity increases exponentially, as shown in Equation (1). These constraints contribute to the reduction in the speed of topology
## 6 Experiment Preparation and Results
### Experiment Results  
The experimental results show the classification results for each RIP2 RIP through the threshold, KNN, TSF, and ROCKET algorithms in Table 6. Meanwhile, Table 7 shows the results of applying the system-adapted algorithm (SAA), which was proposed considering the characteristics of narcotic IMS data. The average values of accuracy, precision, recall, and F1_score were used as comparative performance indicators.  
In Table 6, in the case of the threshold algorithm, the accuracy of classification performance for each RIP2 RIP was 0.362, and the F1_score, which was the harmonic mean of precision and recall, was 0.415, indicating the lowest performance among the applied algorithms. Meanwhile, the KNN had an accuracy of 0.7 or more regardless of the value of k, and the accuracy performance decreased as the number of k increased. The TSF algorithm's performance was improved as the confidence level decreased. Lastly, the ROCKET algorithm showed an accuracy of 0.953 and had the best performance among the compared algorithms. Through this, it was confirmed that the KNN, which classifies through similarity, and the ROCKET algorithm, which classifies through kernels, showed good classification performance despite the time-series data deformation, which is a characteristic of the narcotic IMS data mentioned in Section 3.  
Table 7 shows the result of applying each algorithm to machine-learning-based classification in the proposed system algorithm considering the characteristics of narcotic IMS data. When the SAA was applied, the average accuracies of 0.9 for KNN, 0.94 for TSF, and 0.99 for ROCKET were obtained. The algorithm with the highest performance was the ROCKET algorithm. Thus, the proposed algorithm for detecting the most inferred drugs from the data measured once was confirmed to have good performance.
## 3 Discussion
Due to the complexities of multi-tissue breast tissue and different characteristics of tumors (size, location, shape), coupling of experimental measurements with computational modeling allows for the progressive selection, optimization and customization of parameters including NP concentration, irradiation protocols and treatment duration for in-vivo applications. This approach is essential for mitigation or prevention of collateral damage to healthy tissue surrounding the tumor. Here, we used optical absorption coefficient obtained via Mie theory predictions to develop a FEM model and used a temperature-controlled parametric study to demonstrate that the temperatures of different sized fibroadenomas can reach ablative levels leading to complete thermal damage (\(\Omega=100\%\)) during irradiation with different laser powers. Several investigators have shown that the accuracy of FEM models for thermotherapy can be enhanced by using realistic geometries and material properties [20,21,34,44]. Although our model accounted for temperature dependence and blood perfusion effects, the multi layer geometry based on BIRADS [31] is generic and the distribution of the NPs was an assumption. Such simplification can have an adverse effect on integrity of the predicted values. Several reports have shown that using geometries that correlate with real anatomic datasets and include biodistribution data [20,34] have the potential to improve the accuracy of predictions. Elsewhere, such datasets have been obtained via noninvasive techniques such as X-ray computed tomography, sonography and ex-vivo spectrometry [20,34]. Finally, we acknowledge that Au NPs have been the prime candidates for photothermal therapy, however, it still remains an experimental cancer treatment due to issues related to their bio-persistent, which makes them potentially toxic and the use of high irradiation doses to achieve therapeutic temperatures due to the turbidity of biological tissues [28,49]. These issue have led to the recent interest in the photothermal properties of Fe\({}_{3}\)O\({}_{4}\) NPs, which have been approved by the food and drugs administration (FDA). Furthermore, recent studies that have explored the simultaneously application (DUAL-mode) of both NIR laser and alternating magnetic field (AMF) to the Fe\({}_{3}\)O\({}_{4}\) NPs have shown promising and interesting results. The studies found that the amount of heat generated with DUAL-mode equaled the sum of the heating for NIR laser or AMF only [19,26]. The essence of these results is that the use of the DUAL-mode can be used to overcome the challenges associated with the individual techniques.
# Polylactic Acid/Carbon Nanoparticle Composite Filaments for Sensing
## Materials and Methods
### Characterization of PLA Composite Filaments
#### 2.4.8 Vapor Sensing Measurements  
The vapor sensing ability of the composite filaments was investigated by in situ monitoring of their electrical resistance response upon alternating exposure to acetone vapor and dry air for four successive cycles. The exposure time to organic vapor and the drying time in dry air were 100 s and 150 s, respectively. Prior to the exposure to the saturated acetone vapor, the filaments were set in the measuring chamber under dry-air flow for 50 s to reach a stable initial resistance (R\({}_{\text{i}}\)). The filaments for the vapor sensing test were coated with silver ink at the end sides to ensure good contact between the specimen and the electrodes. All samples were tested at 25 \({}^{\circ}\)C. The set-up of the sensing device is a self-made equipment described in previous works [34]. The sample resistance R was measured using a Keithley 2001 digital multimeter (Keithley Instruments Inc., Cleveland, OH, USA), and the experimental results are presented as relative resistance (R\({}_{\text{rel}}\)). R\({}_{\text{rel}}\) is the ratio of the resistance variation (\(\Delta\)R = R \(-\) R\({}_{\text{i}}\)) to the initial resistance. A bubbler evaporation system was used to deliver the desired organic vapor to the detection chamber using dry air as carrier. In the drying run, pure dry air was passed through the chamber. The total gas flow rate was set to 30 L\(\cdot\)h\({}^{-1}\) using a mass flow controller.
# Shielding Grounding Optimization Method for Spaceborne Multi-Cable
###### Abstract  
The coupling relationship between space electronics systems is complex, and the signals of optoelectronic load cables are susceptible to interference, especially the early anomalous weak signals on a ground surface used for immediate remote sensing, which are more susceptible to coupling interference between cables. Ensuring a good grounding state for the cable shield is vital for reducing the interference suffered by cables and increasing the electromagnetic compatibility of the system. In this paper, we propose a shielding grounding optimization method for a spaceborne multi-cable shield, including a cable model and parameter extremization and parameter scanning simulation performed using CST, data processing and truth table transformation conducted using MATLAB, and logic expression extraction carried out using Multisim. The method can sort and classify the shielding effects of all the grounding states of multi-cable shields in batches, and ultimately output logical expressions specifying the mapping relationship between the shield grounding state and shielding effect, allowing the optimal shield grounding state to be quickly identified. Finally, the method was applied to a satellite-borne scanning mirror drive control system, and the effectiveness and accuracy of the method were verified by experimental tests.  
spaceborne multi-cable; cable shield; grounding optimization; logic expression Article
# Stents in Congenital Heart Disease: State of the Art and Future Scenarios
###### Abstract  
Stents are tubular meshed endoprostheses implanted mini-invasively through a transcatheter intervention to guarantee the patency of body conduits, mainly in cardiovascular applications. In pediatric cardiology, stenting has become an accepted procedure in the treatment of congenital heart disease (CHD) as an alternative to open-heart surgery. CHD refers to a range of defects affecting the heart's structure and function arising from abnormal development during pregnancy. While during fetal life, the presence of additional shunts allows for the establishment of parallel circulation and survival of gestation, CHD is not compatible with extrauterine life, and medical intervention is required soon after birth. This review aims to discuss the state of the art of stenting in CHD. Despite the severity of these pathologies, investment from the industry remains limited due to the restricted number of cases, and dedicated devices are still missing. As a consequence, commercially available adult stents are commonly exploited on an off-label basis in newborns without any optimization for the specific anatomy and required function. In this review, a classification of the available stents is provided, resuming the manufacturing technologies, materials, and geometrical aspects to obtain the target biomechanical performance. After analyzing the fetal circulation, different forms of CHD amenable to stenting are considered, collecting the stents currently adopted and discussing the clinical outcomes to outline the features of an ideal device.  
p editatric cardiology; newborn circulation; hypoplastic left heart syndrome; aortic coarctation; pulmonary atresia; pulmonary artery stenosis; ductus arteriosus; transcatheter intervention; orphan device; ideal stent design +
Footnote †: journal:  
Nowadays, almost all stents used in CHD newborns or children have not been approved for such applications and are therefore used off-label [8], meaning that their biomechanical properties might not be targeted for the specific CHD, leading sometimes to a poor outcome of the intervention [9].  
This review aims to present the current state of the art of those CHDs treatable through stenting, both as the gold standard and as an alternative to surgery. Starting from the analysis of independent clinical cases, recalling the devices used, and the treatment outcome, this work wants to summarize the reported experiences toward the identification of the best stent for the specific application. First, a brief introduction recalls the diversity of available stents on the market according to their material, manufacturing process, and specific design. Then different forms of CHD that are currently treated through stenting are discussed, presenting their anatomical anomalies and clarifying the need for such treatment. Although a comparative clinical report is not available on the topic, this review may help interpreting the current stent choices and improving future ones.
## 3 Methodology  
### EMS Design Requirements  
The purpose of this section is to describe the steps followed for designing the EMS. The process is depicted in Figure 2. It shows that the EMS requirements are mainly determined by the characteristics of the system it will operate (system definition), the usage of the forecasting information (system data processing) and the identified operational requirements (system operation requirements).  
Figure 1: Simplified PV-battery–diesel-based microgrid scheme.  
Figure 2: EMS design methodology.  
First, the system characteristics are gathered--mainly the electrical characteristics and the available forecasting data--assuming grid isolated operation. Then, a statistical analysis of the forecasting for PV generation and demand is performed to identify the probability distribution of their errors. This allows generating random forecast scenarios (as detailed in Section 3.5). Next, the operation for the storage system is defined considering long-term variability of PV generation and demand. The minimum number of diesel generating units needed to face the largest demand change expected in the system is also determined. Finally, the EMS is designed, with two main purposes. On the one hand, the optimization problem is formulated based on the steady state equations determining the power balances in the system and limiting system variables. On the other hand, a frequency constraint, which is included in the optimization problem, is formulated (based on dynamic simulation results) relating the PV power generated, the battery power and the number of connected diesels with the minimum allowed frequency after a maximum power imbalance in the system.  
The EMS performance is described in Section 3.2. The execution cycle of the EMS is detailed in Section 3.3. The procedure to determine the frequency constraint is explained in Section 3.4. For the stochastic optimization problem, it is required to generate a number of random scenarios, which is explained in Section 3.5. Finally, the whole optimization problem formulation is addressed in Section 3.7.
# Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking
## 3 Problem Formulation  
Given a multi-domain network and a mobile node in the network, how can 110 paths be found to reduce data transfer delay when this mobile node moves between two different domains? Let the following be the given parameters of the problem:* \(G=(V,E)\): Multi-domain network graph representing nodes and links, where \(V\) is the set of nodes in the network and \(E\) is the set of links (edges) connecting the nodes.
* \(MN\in V\): A mobile node.
* \(D\): Set of domains within the network.
* \(D_{0}\) and \(D_{f}\): Respectively, the initial domain where \(MN\) is located before its motion and the final domain where \(MN\) is located after its motion.
* \(R=\{r_{1},\ldots,|R|\}\): Set of available routes for the mobile node.
* \(B_{e}\): The value of the bandwidth on link \(e\in E\).
* \(T_{e}\): The delay on link \(e\).  
Our objective is to identify a route \(r_{k}\in R\) with minimal data transfer delay cost \(M_{k}\). According to [19], Equation (1) provides the expression for \(M_{k}\):  
\[M_{k}=MF_{k}+MD_{k}+MB_{k} \tag{1}\]  
And  
\[T_{k}=\sum_{e\in E_{k}}T_{e} \tag{2}\]  
With  
\(M_{k}\), the total metric of route \(r_{k}\);  
\(MF_{k}\), the total metric for flow operations;  
\(MD_{k}\), the total metric for the number of domain hops;  
\(MB_{k}\), the total metric for the available bandwidth of the route;  
\(T_{k}\), the total metric for delay of the route;  
\(E_{k}\) is the set of links that constituted the route \(r_{k}\).  
\[MD_{k}=\frac{D_{k}}{D_{max}}\,k\in\{1,2,\ldots,|R|\} \tag{3}\]  
\[MF_{k}=\frac{F_{k}}{F_{max}}\,k\in\{1,2,\ldots,|R|\} \tag{4}\]  
\[MB_{k}=\frac{B_{k}}{B_{max}}\,k\in\{1,2,\ldots,|R|\} \tag{5}\]  
\(D_{k}\) is the number of domains that route \(r_{k}\) goes through;  
\(F_{k}\) is the number of flow operations for switching route \(r_{k}\);  
\(B_{k}\) is the minimum bandwidth of route \(r_{k}\);  
\(D_{max}\) is the number of domains in the network;  
\(B_{max}\) is the largest bandwidth in the network;  
\(F_{max}\) is equal to \(2N\), with \(N\) the number of domains in the network.