esa-sceva/satcom-chunk-collection huggingface dataset:
# The Concept of Segmented Wind Turbine Blades: A Review
## 6 Adhesive Joints in Segmented Blades
### Implementations  
Blade segments can be joined using structural adhesive bonds. An overview is given in Figure 5. The efficiency of the joint depends on the chosen geometry. Finger joints were used in the wood-epoxy blades of the MOD-5A turbine (General Electric Company/NASA/DOE, USA) [92]. However, the use of this type of joint in modern fiberglass blades may be impeded by the higher modulus of elasticity and strains, as well as issues with tooling. Similarly, diamond-shaped splice-inserts can be adhered to the segments to form the joint [93]. Likewise, [PERSON] [94] improved upon this approach by using longer connections providing higher stiffness and strength. [PERSON] [95] used finite element modeling to investigate the option of bonded strap plates. For general geometries, scarf joints and stepped lap joints have the highest efficiencies [96]. Concepts using scarf joints were suggested by various authors [84; 85; 86; 89]. To avoid fragile protrusions, [PERSON] [97] proposed using a double scarf joint,  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Adhesive Joint Issue** & & **Suggested Remedies** \\ \hline \multirow{4}{*}{Time of assembly} & \multirow{4}{*}{Alignment of the segments} & -Alignment using laser-positioning \\  & & -Brackets attached to spar cap \\  & & -Alignment pins \\  & & -Overlapping portions \\ \cline{2-3}  & \multicolumn{2}{l}{Curing of the bonds} & -Resistance heated bonds \\ \hline \multirow{4}{*}{Bond-quality} & \multirow{4}{*}{Bond thickness} & -Bonding grid \\  & & -Shims \\ \cline{1-1}  & & -Producing the segments in a single mold \\ \cline{1-1} \cline{2-3}  & \multicolumn{2}{l}{Air entrapment} & -Flooding of a cavity \\ \cline{1-1}  & & -Infusion \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of issues with the use of adhesive joints for segmented blades and the suggested remedies.  
Segmentation using stepped lap joints was suggested by [PERSON] [83]. Further, [PERSON] [98] suggested not infusing the fibers in the joint areas when fabricating the segments, so that they can be joined by overlapping, infusing and curing the dry fibers.
# Technical-Tactical Analysis of Corner Kicks in Male Soccer: A Systematic Review
###### Abstract  
Corner kicks are one of the most frequent and potentially decisive set pieces in soccer, occurring approximately ten times per match. Aim: This systematic review aimed to synthesize the existing evidence on the technical-tactical execution of corner kicks in male soccer, assess the methodological quality of published studies, and provide practical recommendations for coaching staff. Methodology: Following PRISMA guidelines, a systematic search was conducted on 2 March 2025, in the Web of Science and Scopus databases. Studies were included if they focused on the technical-tactical analysis of corner kicks in male soccer, published in peer-reviewed journals or conferences, and written in English. A total of 21 eligible studies were identified. Each study was assessed for quality using a 13-item checklist, and a qualitative synthesis was performed based on key tactical, contextual, and methodological dimensions. Results: The included studies showed that most corners were delivered to the central area between the six-yard box and penalty spot. Dynamic attacking organizations involving 3-4 players and indirect deliveries to the far post were associated with higher probabilities of shots on target. Defensively, mixed marking strategies with players covering the near post yielded lower concession rates. Methodological quality was generally moderate to high. Inconsistencies in zone division and limited reporting of contextual variables were common limitations. Only two studies examined the transition phase after ball loss. Conclusions: Despite their strategic importance, corner kicks remain understudied in certain aspects. There is a need for standardized frameworks in zone division and a greater focus on contextual and transitional elements. This review provides evidence-based recommendations for optimizing set-piece strategy and highlights key areas for future research, including the systematic integration of transition phases and meta-analytical synthesis of performance indicators.  
football; set pieces; tactics; performance analysis; notational analysis +
Footnote †: journal: Applied sciences
# Double-Consensus Based Distributed Optimal Energy Management for Multiple Energy Hubs
## 3 Distributed Algorithm  
### Basic Knowledge  
1. Graph theory: We consider a directed graph \(G=(V,E,A)\) with a set of vertices \(V=(v_{1},v_{2},\cdots,v_{n})\), a set of edges \(E=(v_{i},v_{j})\subseteq V\times V\) and an adjacency matrix \(A=[a_{ij}]\). The in-neighbors and out-neighbors of \(i\)th node are denoted by \(N_{i}^{+}=\{j\in V-\{i\}:(j,i)\in E\}\) with cardinality \(d_{i}^{+}\) and \(N_{i}^{-}=\{j\in V-\{i\}:(i,j)\in E\}\) with cardinality \(d_{i}^{-}\), respectively. Each node can receive information from its in-neighbors and send information to its out-neighbors; meanwhile, it can be also assumed to communicate with itself.
2. Basic definitions: With regard to problem Equations (3)-(6), there are three variables that need to be calculated. To this aim, we define two strongly regular graphs \(G_{1}\) and \(G_{2}\). The first consists of \(n\) nodes, where each node represents the statement related to \(E_{i}^{S,1-\rho}\). The second is built by dividing each node of graph \(G_{1}\) into two nodes which represent the statements related to \(E_{i}^{e}\) and \(E_{i}^{g,\rho}\), respectively. Furthermore, we define two matrices \(\overline{R}=[\overline{r}_{i,j}]\) and \(\overline{S}=[\overline{s}_{i,j}]\) associated with \(G_{1}\); meanwhile, let \(\overline{r}_{i,j}=1/\overline{d}_{i}^{+}\) if \(j\in\overline{N}_{i}^{+}\), \(\overline{r}_{i,j}=0\) if \(j\
otin\overline{N}_{i}^{+}\), and \(\overline{s}_{i,j}=1/\overline{d}_{j}\) if \(i\in\overline{N}_{j}^{-}\), \(\overline{s}_{i,j}=0\) if \(i\
otin\overline{N}_{j}^{-}\). It is not difficult to verify that \(\overline{R}\) is row stochastic and \(\overline{S}\) is column stochastic. Similarly, we define two matrices \(R=[r_{i,j}]\) and \(S=[s_{i,j}]\) for \(G_{2}\); meanwhile, let \(r_{i,j}=1/d_{i}^{+}\) if \(j\in N_{i}^{+}\), \(r_{i,j}=0\) if \(j\
otin N_{i}^{+}\), and \(s_{i,j}=1/d_{j}^{-}\) if \(i\in N_{j}^{-}\), \(s_{i,j}=0\) if \(i\
otin N_{j}^{-}\).
3. Consensus algorithm: Based on the definitions discussed above, we consider two different discrete-time systems--shown in Appendix B-(a)--which will be employed in the design of our proposed algorithm.
# Applications of Magnesium and Its Alloys: A Review
## 2 Production Techniques
### 3D Printing as a New Production Technique  
Additive manufacturing, or 3D printing, is also an emerging production method for magnesium alloys. In general, there has been a growing interest in the 3D printing of metals due to its ability to customize and optimize each alloy to suit its respective applications and yield the best possible outcome. An example would be to print alloys in a more closely aligned configuration to the anatomical geometries of its patient's host tissues [22] to promote more significant cell growth, proliferation, and bone regeneration [23]. Another example would be to print alloys of complex geometries. This was previously not achievable with traditional manufacturing, and the literature has suggested these limitations to have hindered its growth [24]. Further, as opposed to conventional production methods, fabrication with additive manufacturing can also significantly reduce the technical difficulty and cost of customized production while improving its efficiency [25]. Overall, the pursuit and possibilities of additive manufacturing of magnesium alloys are very promising.  
However, investigations on how magnesium alloys can be 3D printed are limited [26] as it is known to come with several challenges. Firstly, it is chemically reactive and flammable. It also has a low vaporization temperature, which increases the difficulty in mixing and printing magnesium alloys with the desired density, strength, biocompatibility, and corrosion behavior [23]. Lastly, the preparation of feedstock is also known to be dangerous as magnesium powders have a high tendency to explode [22]. Today, the two typical additive manufacturing methods to process magnesium alloys are wire arc additive manufacturing (WAAM) and selective laser melting, also known as the powder bed fusion method [26].
# Study on the Seismic Response of Shield Tunnel Structures with the Preload Loss of Bolts
## 1 Introduction
In recent years, high-strength bolts have been widely used in shield tunnel engineering due to their excellent durability and load-bearing capacity. Preload is applied to connect the adjacent segments by applying a specific torque to high-strength bolts, greatly improving the initial stiffness of the shield tunnel. This compensates for the weakening of the overall stiffness of the structure due to connections. However, the preload of bolts would experience varying degrees of loss during the construction and use of shield tunnels [15]. The reasons for preload loss include construction errors, temperature changes, long-term formation load, and long-term vehicle vibration. It is not difficult to see that there is a significant risk of preload loss in the bolts of shield tunnels during long-term operation. [PERSON] et al. [16] obtained the variation laws of bending moments and stress on bolted joints considering preload under static loads by using finite element method. [PERSON] et al. [17] analysed the influence of different bolt forms on the load-bearing performance of joints via full-scale testing methods. [PERSON] et al. [18; 19] found that bent bolts had better overall performance under different bending moments and axial force conditions compared with straight bolts and inclined bolts. The results show that preloading can enhance the mechanical characteristics of bent bolt joints by strengthening the bolt's capacity to secure the pipe seam and minimize the overall structural deformation. [PERSON] et al. [20] analysed the effect of bolt preload on convergence deformation. The results found that insufficient bolt preload could lead to reduced longitudinal seam rotational stiffness, indirectly affecting the overall circumferential stiffness of the tunnel. Currently, extensive research has been conducted on the preload of shield tunnels [21; 22; 23; 24; 25]. Previous studies have shown that the loss of preload significantly affects the overall stiffness of shield tunnel structures. However, the current research primarily focuses on static analysis of preloading. There is still a significant gap in the study of the dynamic response of shield tunnel structures after preload loss. Therefore, in this research, the finite element method was used to establish a three-dimensional refined model of shield tunnels. The relationship curve between bolt preload force and temperature was determined and uses a cooling method to assign different initial preload forces to the bolts. Furthermore, artificial viscoelastic boundaries are introduced to consider the seismic loads from an infinitely distant field. This allows for the investigation of the seismic performance of shield tunnels under preload force loss conditions. This study can provide valuable insights for the seismic design of shield tunnels, enhance the awareness of preload force among engineering professionals, and serve as a reference for the assessment of seismic resilience in shield tunnels.
# An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs
## 3 Multi-Objective Optimization Model for EMRAR
### Constraints  
1. Accuracy constraint Accuracy is used to evaluate the variance between the data collected and sent by the source node and the corresponding data received at the sink. Data aggregation predominantly takes place at path intersections during data transmission. For a given path, as explained in the previous section, the fewer intersections it has with other paths (or less aggregation occurs), the smaller the decrease in accuracy caused by data aggregation during data transmission. To quantify this accuracy decrease, we introduce three metrics: NAL (Node Aggregation Loss), PAL (Path Accuracy Loss), and MAL (Multipath Accuracy Loss), defined as follows:  
* NAL: An NAL \(\delta(0\leq\delta\leq 1)\) means that the approximation transmission error on an aggregation node on path \(mp_{j}(i,\ S)\) is within a factor of \(\delta\).  
* PAL: Suppose a path \(mp_{j}(i,S)\) (\(j\) (\(j\) 1, \(\ldots\), \(|MP(i,S)|\)) has \(c_{j}^{(i)}\) intersections with other paths in \(M\)P(\(i,S\)). If the accuracy loss for each aggregation is \(\delta\), then for each path \(mp_{j}(i,S)\), the PAL \(\Delta_{j}^{(i)}\) is: \[\Delta_{j}^{(i)}=1-(1-\delta)^{c_{j}^{(i)}}\] (11)
* MAL: Suppose the data collected and sent by sensor node \(i\) is \(x_{i}\). After multipath transmission, the data received by sink \(S\) is \(x_{i}\). If the transmission is successful on each path in \(MP(i,S)\), then the MAL \(\Delta^{(i)}\) for \(MP(i,S)\) from \(i\) to \(S\) can be calculated as: \[\Delta^{(i)}=\frac{\left|x_{i}^{\prime}-x_{i}\right|}{x_{i}}=\frac{\sum_{j=1}^ {|MP(i,S)|}\Delta_{j}^{(i)}}{|MP(i,\ S)|}\] (12)  
Hence, for a predefined maximum MAL value \(\Delta\), the accuracy constrain can be represented as:  
\[\Delta^{(i)}=\frac{\sum_{j=1}^{|MP(i,S)|}\Delta_{j}^{(i)}}{|MP(i,\ S)|}\leq\Delta \tag{13}\]  
To satisfy Formula (13), the multipath routing algorithm must try to find multiple paths with fewer intersection nodes. Thus, it also gives the constraint on the value of \(c_{j}^{(i)}\).  
2. Node energy constraint As data transmission and aggregation all consume energy, only the nodes with sufficient residual energy can participate in multipath routing. Suppose the residual energy for a node \(k\) in a path \(mp_{j}(i,S)\) (\(j=1,\ldots,\)\(|MP(i,S)|\)) is \(E_{k}\) (\(k=1,\ldots,\)\(|mp_{j}(i,S)|\)). The initial energy of node \(k\) is \(E_{max}\), and the predefined minimum residual energy required for a relay node in a path is \(E_{min}\). Accordingly, we have  
\[E_{min}\leq E_{k}\leq E_{max},k=1,\...,\ |mp_{j}(i,S)| \tag{14}\]
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 3 Design Overview  
In this section, we introduce the overall design of the SRMCC mechanism. Firstly, we provide a brief description of ICN multicast architecture and basic multicast transmission. Then, in order to implement SRMCC, we designed the functions related to congestion control for the three main network elements in detail. These include the definition of the multicast congestion control (MCC) message, router-assisted awareness of the network congestion state, the MCC transmission and aggregation mechanism, the estimation of the fair shared rate, and rate adjustment. Note that the rate calculation and adjustment method is described in detail in the Section 4.
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 4 Results and Analysis
### Results and Analysis
#### 4.2.1 Analysis of Experimental Parameters
Different classification architectures  
For a performance comparison, we compare the RSSCNet architecture proposed herein with the other architectures in the literature, such as VGG-16-Net [21] and the GSB + LOB model [32]. Figure 11 showed the ranking results of the test accuracy of each model (i.e., RSSCNet: 0.978, VGG-16-Net: 0.973, and GSB + LOB model: 0.97), which indicated that the proposed RSSCNet is the best classification model. Figure 11: Comparison of testing accuracy using different classification architectures. Figure 10: Comparison of overall accuracy with and without two-stage training. Different cyclical learning-rate methods  
In this section, we compare the performances of the two-stage cyclical learning rate method (2-stage CLR, Figure 11) and [PERSON]'s (2019) one-cycle cyclical learning rate method (1-cycle CLR). The results of Figure 12 showed that the two-stage cyclical learning rate method achieved the highest test accuracy of 98.0% at epoch = 134. On the contrary, the one-cycle cyclical learning rate method only achieved the highest test accuracy of 97.0% at epoch = 27. Although the latter could provide quicker access to the best test accuracy for training, the former could achieve a better test accuracy; hence, it was used in subsequent experimental results for model training and performance testing. Figure 12: Comparison of testing accuracy using different cyclical learning rate (CLR) methods: (**a**) cyclical learning-rate policy; (**b**) testing accuracy.
## 4 Comparison and Discussion
### Ablation Study
#### 4.1.2 Differential Processing of Input Variables  
Ablation experiments are performed for the differential processing of model inputs. Figure 12 and Table 7 show the comparison results of the ablation experiments. The model adding the differential features can learn the differential value features better from the results. The main reason for this is that the differential treatment of the input variables conforms to the form of a first-order differential equation, which improves the learning ability of the model for the unit data. Moreover, the differential treatment reduces the effect of the autocorrelation of the input variables on the model.  
Figure 11: The prediction comparison diagram of the differential prediction method.  
Figure 12: The prediction comparison diagram of differential processing of input variables.
# Parallel Load-Bearing and Damping System Design and Test for Satellite Vibration Suppression
## 1 Introduction
This scheme connects viscous dampers in parallel with a specific structure: Payload Adaptor Fitting (PAF). The FE model of the PAF with the damping system was analyzed to design the damping rod. Topology and sizing optimization technology is used for the light-weight support design. Vibration experiments were conducted to verify the effectiveness of the damping system. The experiments were performed by [PERSON] [22]. The results prove that the new system can reduce the vibration response and achieve the integration of load bearing and vibration isolation, as well as maintaining the PAF's stiffness.
## 3 Results and Discussion  
### Scanning Electron Microscope (SEM) Analysis  
Figure 1a,b shows the SEM images of TiO\({}_{2}\) nanoparticle and Figure 1c,d shows the composite photocatalyst with GO.  
The above SEM scanning results showed that, in the absence of GO doping, TiO\({}_{2}\) powder was spherical and some particles agglomerated together to clusters. When the GO doping amount was 3%, the size of GO monolith was slightly larger than that of TiO\({}_{2}\) nanoparticles, so TiO\({}_{2}\) nanoparticles dispersed and adhered to the GO monolith. The agglomeration of spherical particles in different parts was slightly different and relatively dispersed between GO monolith structures with few connections, and the surface of composite material was mainly composed of TiO\({}_{2}\). When the GO doping amount was 23%, GO accounted for a large proportion of the formed structure, and TiO\({}_{2}\) particles adhered to GO monolith of different structures, respectively, and the formed clusters contributed to the GO monolith-like structure.
## 3 Materials and Methods
### Single-Hopped Beer Analysis
#### 3.5.2 HS/SPME/GC-MS Analysis  
The five beers (BC, BT1, BT2, BT3 and BT4) were analysed for their volatile compounds in triplicate. Five mL of beer was transferred into 10 mL headspace vials containing 1.5 g of sodium chloride and 10 \(\upmu\)L of 1-Butanol as the internal standard (25 g/L), which were then sealed with PTFE-silicone septa and stored at 7 \({}^{\circ}\)C in a refrigerated compartment. Analysis of volatile compounds was carried out using the headspace solid-phase microextraction (HS/SPME/GC/MS) technique by means of Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB-CAR-PDMS) fibre (Supelco, Bellefonte, PA, USA) [43]. For SPME analysis, the samples were incubated for 10 min at 40 \({}^{\circ}\)C, after which extraction was carried out exposing the fibre to the headspace for 40 min. Both incubation and extraction were performed by agitation. Fibre desorption was carried out in an injector for 2 min at 250 \({}^{\circ}\)C with a split flow of 7.2 mL min\({}^{-1}\). The fibre was activated each day following the manufacturer's instructions. Chromatographic analysis was performed using TRACE GC coupled with an ISQ single quadrupole (Thermo Scientific, Hudson, NY, USA). The analytes were separated on a SLB-5 ms capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness) (Supelco, Bellefonte, PA, USA) using helium as a carrier gas at a 1.2 mL min\({}^{-1}\) constant flow rate. The oven temperature programme started at 50 \({}^{\circ}\)C and was held there for 5 min and then it was increased at 5 \({}^{\circ}\)C min\({}^{-1}\) to 250 \({}^{\circ}\)C and held there for 3 min [44]. The transfer line and ion source were set at 250 \({}^{\circ}\)C and 270 \({}^{\circ}\)C, respectively, with a quadrupole scan ranging between 33 and 300 amu and with the ionisation energy being 70 eV [45]. Chromatographic data were acquired by means of Tracefinder (Thermo Scientific, Waltham, MA, USA).  
The identification of terpen was carried out by comparing the mass spectra with those of the data system library (NIST, 2005 software, Mass Spectral Search Program V.2.0d, Washington, DC, USA version 2.2 June 2014) [36; 46]. All Identity Spectrum Match factors above 850 resulting from the NIST Identity Spectrum Search algorithm (NIST MS Search 2.0) were considered acceptable for positive identification. After the identification of target volatile compounds (VOCs), peak areas (A\({}_{\text{VOC}}\)) were obtained on the basis of the major ion then normalised with the internal standard peak area (A\({}_{\text{IS}}\)). Data were expressed as a percentage on the basis of the Equation explained in Section 3.3.1.
## 4 Organic Rankine Cycle (ORC)  
The ORC system uses a series of devices in a closed loop in order to recover energy, which can then be fed back to the engine or vehicle as required. It consists of a heat exchanger (the evaporator) that the hot gases pass through and increase the temperature of the working fluid to the extent that it changes states from liquid to superheated vapor. After the evaporator, the working fluid expands isentropically providing work in an expansion device, which is connected to an electric generator. Then the expanded vapor passes through a condenser to change its state back to liquid and reaches the pump which increases its pressure and pushes the working fluid back to the evaporator to repeat the process. Figure 1a represents a temperature-enthalpy (T-S) diagram of the Rankine cycle, while Figure 1b illustrates a schematic view of a typical Rankine cycle with all major components. Theoretically, the ORC systems can offer several benefits to the whole powertrain configuration; at best, power output of the powertrain can be increased by a maximum of approximately 15% depending on the application [26; 27; 28]. As a consequence of the increased efficiency of the engine, the fuel consumption can be decreased, making the ORC systems desirable for various applications [1; 9; 29].  
Furthermore, a significant advantage of the ORC systems is that they do not depend on the pressure of the exhaust gases, as the only thing that affects the efficiency of the system are the exhaust gas temperature and mass flow rate. Comparing a conventional steam Rankine cycle to an ORC, the main difference arises from the fact that ORCs use an organic fluid (instead of water) and do not need a superheater in order for the fluid to reach the desired temperature for evaporating in order to spin the turbine [15; 16; 18].  
Figure 1: Working principle of typical Rankine cycle: (**a**) temperature-enthalpy diagram; and (**b**) schematic of Rankine cycle components.