esa-sceva/satcom-chunk-collection huggingface dataset:
# Strengthening Design of RC Columns with Direct Fastening Steel Jackets
## 2 Review of Previous Work
### Seismic Strengthening of RC Columns by Direct Fastening Steel Jackets  
To investigate the seismic performance of strengthened RC columns using direct fastening steel jackets, an experimental study was conducted [25]. Attention was given to the enhancement of flexural stiffness and strength.  
On the basis of comparisons between predicted and measured effective flexural stiffness, the expressions recommended in EN 1994-1-1 [30] are advised to calculate effective flexural stiffness:  
\[K_{i}=\left(EI\right)_{s}+0.6\left(EI\right)_{c} \tag{17}\]  
where \(\left(EI\right)_{s}\) is the flexural stiffness provided by the steel jacketing; and \(\left(EI\right)_{c}\) is the flexural stiffness provided by the RC column.  
Buckling of the steel plate on the compressive side was observed. The corresponding buckling stress of steel plate under a compressive state should thus be embedded within the theoretical model used to predict lateral capacity. Using the Rayleigh-Ritz method and assuming the initial bowing and subsequent deflection as a trigonometric function, the buckling strength of steel plate is given as:  
\[\sigma_{p,critical}=\frac{4\pi^{2}Dd_{p}}{s_{d}{}^{2}A_{p}}(1-\alpha_{i}) \tag{18}\]  
where \(D\) is the bending stiffness of the steel plate per unit width; \(d_{p}\) is the plate depth; \(E_{p}\) and \(\mu_{p}\) are elastic modulus and Poisson's ratio of steel plate; \(A_{p}\) is the cross-sectional area of steel plate; and \(\alpha_{i}\) is the imperfection factor.  
The imperfection factor \(\alpha_{i}\) is introduced to consider the initial bowing effect induced by the welding and is calibrated using the experimental results:  
\[\alpha_{i}=1.046-\frac{0.73\lambda_{sr}}{100} \tag{19}\]  
where \(\lambda_{sr}=s_{d}/t_{p}\) is the slenderness ratio of steel plate. As the empirical imperfection factor was calibrated with limited available data, it is useful to assume a slenderness ratio of the steel plate between 14 and 39.  
It is worth noting that the steel plate detached from the RC column at the bottom of the tension fiber. This was largely due to the strain incompatibility between RC column and the steel jacket on the tension side during bending. As a result, the assumption that the plane section would remain plane after deformation throughout the entire length of the column is inaccurate. Hence, a reduction factor of \(\eta_{i}=0.6\) was introduced for the tensile component of the steel plate when the lateral resistant capacity was estimated.
## 4 Verification  
The results concerning the required seismic gap preventing bounding between adjacent structures (Table 3 and Figure 4) have been extensively verified for different structural arrangements and earthquake records. In this section, a representative example of the verification analysis is presented. It concerns a two-story building (\(h_{i}\) = 6 m, \(T_{i}\) = 1.24 s) located close to a five-story structure (\(h_{i}\) = 15 m, \(T_{j}\) = 2.93 s) which are exposed to the Imperial Valley (PGA = 284 cm/s\({}^{2}\)), Kocaeli (PGA = 369 cm/s\({}^{2}\)), and Tabas (PGA = 821 cm/s\({}^{2}\)) earthquakes. The peak lateral displacements of independently vibrating buildings are equal to 14.15, 39.58, and 38.72 cm for the two-story structure, and 43.57, 23.73, and 57.52 cm for the five-story building under the Imperial Valley, Kocaeli, and Tabas earthquake records, respectively (see Table 2). The required seismic gap preventing bounding between adjacent structures obtained from the ANN analysis is equal to 29, 58, and 67 cm, respectively (see Table 3). The response time histories for both buildings are shown in Figure 5 for all three seismic excitations. It should be explained in this place that the initial distance, equal to the calculated seismic gap (i.e., 29, 58, 67 cm), has been introduced between two time histories of both structures in the figure in order to trace their relative displacement easily and check whether they collide during the time of the earthquake. It can be seen from Figure 5 that the calculated seismic gap is really large enough to prevent structural collisions, and it is just appropriate for the three cases of ground motions. The same conclusion has been obtained for all other structural arrangements and seismic excitations. In some cases, the seismic gap has been calculated to be so small that adjacent buildings are able nearly to touch each other during the time of the earthquake, and also zero gap sizes have been determined for identical structures. It has to be underlined, however, that the possible overlapping between the time histories of the two adjacent  
Figure 4: Required seismic gap with relation to the peak ground acceleration (PGA) of earthquake records for different structural arrangements.  
buildings has never been observed, indicating that bounding has not taken place. The above confirms the accuracy of the methodology based on the ANN which has been applied in the verification analysis.  
Figure 5: Examples of the results of the verification analysis for a two-story building located close to a five-story structure (arrangement 2-5) under three earthquake records: (**a**) Imperial Valley earthquake, seismic gap: 29 cm, (**b**) Kocaeli earthquake, seismic gap: 58 cm, (**c**) Tabas earthquake, seismic gap: 67 cm.
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 6 Results
### Processing Environmental Reports
The time spent for the other activities not relevant for the processing of the environmental report are negligible, with a contribution of an average of 0.57%. The figures for the average time are provided in Table 8.  
From the point of view of the MTRR, the storage of the state vector and the salinity information are undertaken as one unique, indivisible action provided by the Data Access Manager component in the SWARMs architecture. Thus, from the MTRR perspective we can only assert that storing the information in the ontology took longer. Considering that the state vector always has the same size, the average time for storing it in the semantic repository was 12,339 ms with a standard deviation of 13,435 ms. As for the relational database, average time for storing the state vector was 2283 ms with a standard deviation of 2564 ms. Figure 36 illustrates the distribution of both series of time. \begin{table}
\begin{tabular}{c c c} \hline
**Activity** & **Average Time (ms)** & **Contribution (\%)** \\ \hline Storing vector in the database & 9.36 & 10.24 \\ Storing vector in the ontology & 50.61 & 55.34 \\ Storing salinity in the ontology & 30.96 & 33.85 \\ Other activities & 0.52 & 0.57 \\ TOTAL AVERAGE TIME & 91.45 & 100.00 \\ \hline \end{tabular}
\end{table} 
Table 8: Average time per activity for processing the state vector reports. Figure 35: Pareto chart for the average total time required for each part in processing the state vector. Figure 36: (**a**) Boxplot for times required to store the state vector in the SWARMs ontology; (**b**) boxplot for the times required to store the state vector in the historical database. In terms of efficiency we can affirm that storing the information in the relational database represents only an average of 18.50% of the time required to store the same information into the semantic repository.
## 5 Simulation
### Localization Accuracy  
For the accuracy evaluation, we calculate the root mean square error (RMSE) of the localization. For the simplicity, AUV moves with constant velocity and the same direction during the localization process. In other words, the \(x\)-coordinate of the AUV varies with the velocity, but the other coordinates are fixed to the half of network size. To clarify the problem of MAC delay in localization, it is assumed that ranging error just occurs only due to the MAC delay unless there is specific mention. Meanwhile, the MAC delay is determined by the length of the preamble, propagation delay, and transmission delay. Among the delay sources, we ignore the transmission delay because it is relatively trivial as compared with the others. For the comparison with the existing system, we consider the RMSE of TDMA-based localization together.  
Figure 9 shows the impact of MAC delay and AUV movement on the localization accuracy in a network with a size of 300 m. As shown in the figure, the higher the AUV's velocity is, the greater the RMSE is in TDMA-based localization. In contrast, FDMA-based localization does not affected by the MAC delay; consequently, it provides the stable localization performance regardless of AUV movement. Most of localization only focuses on how to improve the ranging accuracy. Therefore, the adopted message scheme and the corresponding influence on the localization are totally ignored. However, the simulation result reveals that the MAC delay should be considered for practical system design. Furthermore, the result implies that the simultaneous transmission is obligatory condition in localization for mobile node.  
As the communication distances increase, the MAC delay grows because the propagation delay is proportional to the distance. After increasing the network size to 1500 m, we calculate the RMSE, and the result is illustrated in Figure 10. In both cases, the localization performance decreases as compared with the former simulation conducted the small size network. One notable thing is that the proposed localization slightly affected by AUV's velocity. This is due to the fact that the packets transmitted by each AN are arrived at the AUV with different times and the jitter acts like a MAC delay. Nevertheless, our system still provides high and stable localization accuracy.  
Figure 8: BER according to the Doppler factor, assuming that \(N\) is 4.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 2 Cognitive Radio Networks
### Cognitive Radio Functions
#### 2.1.2 Spectrum Decision  
Once the accessible spectrum bands and the associated potential estimated channel capacity are identified, according to their Quality of Service (QoS) requirements, appropriate operating spectrum bands can be selected [51; 52]. Before selecting the appropriate spectrum band, the CR nodes need to know issues such as interference, the characteristics of path loss, the amount of wireless link errors and delay at the link layer based on the local observations and the primary network information. In CRAHN, the spectrum result includes mutual responsibility of spectrum selection and the route establishment.
## 4 Challenges and Considerations
### Metal AM Costs  
To successfully apply metal AM in industry, AM products need to be feasible from a technical and economic perspective. Feasibility analysis is one of the necessary analyses, which facilitates enterprise decisions for manufacturing system selection [172]. Production of metal AM is not simple, nor cheap. This topic has received attention from researchers recently. For example, [PERSON] et al. [173] developed a cost model for selecting the most optimal metal AM technologies. [PERSON] et al. [174] presented cost prediction models for metal powder machines. Additionally, [PERSON] et al. [175] believed that the total production cost of metal AM must be reduced; accordingly, they applied a process-based cost modeling (PBCM) to the metal AM topology optimization to reduce the total production cost, including material, labour, power, and equipment costs. The literature review has revealed that the major contributor to the total metal AM production cost is the capital equipment cost [115]. Nowadays, there are many manufacturers that offer different types of metal AM systems. The price of metal 3D printers varies from AU$ 170,000 (US$ 115,000) to approximately AU$ 2.8 million (US$ 1.9 million) [5]. Therefore, prior to large-scale investment, the metal AM production costs for future products must be estimated and the products must be cost competitive against conventional manufacturing processes such as forging. However, the case for metal AM is a lot stronger for on-demand and/or on-site production, if waiting for replacements may have the potential to cause large delays and significant production losses. Increased competition has resulted in a downward trend in machine prices, but only marginally thus far. In addition, the need for new gas lines, electrical works, industrial compressors, post processing machines, heat treatment furnaces, and annual service and maintenance costs must all be factored in. Additionally, if the weight of the machine exceeds five tons, special structural changes to the floor are required. The total cost also includes running costs such as buildings, materials and other consumables, energy consumption, personal protective equipment, and designing, testing and inspection, and post processing costs. Therefore, it is critical to identify all costs including the above items as well as any hidden elements related to the production of metal components for successful implementation. In addition, the development of a cost analysis model that estimates the production costs of a metal AM produced part versus a traditionally manufactured product is required, in such a model, using a sensitivity analysis to investigate various alternatives in the presence of uncertainty may lead to more robust results [176]. Impacts on supply
# Flexible Antennas: A Review
## 4 Applications of Flexible Antennas under Different Frequency Bands
### Below 12 GHz
Later various shapes of the conducting patch were designed to improve the efficiency of the antenna [153; 154; 155; 156]. A compact, the high efficient polymer-based flexible antenna was proposed in [157]. In this research, the authors used sticky tape and PEDOT as the substrate and the conducting material, respectively. Many other polymer-based flexible UWB antennas were reported in the literature. These include liquid crystal polymer [158; 159; 160; 161], polydimethylsiloxane (PDMS) [162; 163; 164], graphene-assembled film [165], artificial magnetic conductor (AMC) [166; 167; 168; 169; 170], PET [171; 172; 173], paper [174], and polyamide [175; 176; 177].
# Management Platforms and Protocols for Internet of Things: A Survey
## 7 IoT Device Management Protocols
### Things Management Protocol (TMP)  
The TMP uses the operations get/set, similar to the SNMP operations, to enable default interface for communication between the _\"things with things\"_ and _\"things with the applications\"_[84].  
[PERSON] et al. [85] describes that the motivation for creating TMP was the need to manage the heterogeneity devices independently. TMP is SOAP-based, as shown in Figure 19, and uses key technologies such as HTTP, XML, and SOA for information integration and connection application based on independent protocols.  
Figure 18: OMA LWM2M standard management architecture.  
In Table 4, the TMP creates the connection between protocol and transport layer protocols and includes several operation request/response messages in the protocol, e.g., GetInformationObject and SetInformationObject.  
TMP supports three operations: GetInformationObject, GetNextInformationObject, and SetInformationObject. The basic requirements for operating \"things information\" are satisfied in these operations.  
_Pros: Simple connection and request information._  
_Cons: No robust and secure._  
Use Case  
The Smart Street Lighting System [86] can be managed remotely using Thing Management Protocol and some tasks can be automated with the objective of reduction of the power consumption, which has an ecological implication.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 4 Experiment Setup
### Network Setup  
To evaluate our approach on FIT IoT-LAB, the experiment was performed on Contiki OS using M3 nodes. The topology consists of 67 client nodes that send UDP packets to the server repeatedly on an interval of 1 to 60 s between two subsequent transmissions in order to differentiate the sending rate between the two instances. Experimentation parameters are presented in Table 3. Multiple RPL instances are not fully supported in Contiki, we used an implementation ([[https://github.com/jeremydub/contiki](https://github.com/jeremydub/contiki)]([https://github.com/jeremydub/contiki](https://github.com/jeremydub/contiki))) [40] where multiple instances are supported. We implemented it on FIT-IoT lab in order to evaluate our proposition. In this new RPL implementation, nodes can participate in multiple instances with different objective functions and metrics. A specific instance can be set at application layer, allowing traffic differentiation. It also supports new constraints in DIO metric container object. Also, a root can now be a sink for multiple applications that have different route requirements. For our experiments, we considered the upward traffic with two instances: \(OFQS\) with critical and periodic traffic (Instance 1 and Instance 3 resp.) as presented in Section 3.4 compared to RPL with \(MRHOF/ETX\) for critical traffic and \(OF0/HC\) for periodic traffic. All experiments results are measured within a 90% of confidence interval.
# Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
## 3 Materials and Methods
### Proposed System by Using Yolov3 and GrabCut  
This paper proposed an automatic GrabCut based segmentation pipeline using Yolov3 deep convolutional neural network. The proposed pipeline for segmentation of the skin lesion consists of four stages. In the first stage, hairs are removed from the image for more accurate detection and segmentation. The second stage is the detection of the lesion location in the image. For this purpose, some modifications were made on Yolov3 to accurately locate the lesion in a dermoscopic image. The original Yolov3 with 1000 class output was trained on the ImageNet data. Therefore, the modified version of Yolov3 used in this study has a single class output. The original Yolov3 gives three different size detections by applying \(1\times 1\) detection kernels to the feature maps at 79 th, 91 th and 106 th layers. The shape of detection kernels is \(1\times 1\times(\text{B}\times(5+\text{C}))\). B is the number of bounding boxes of a cell, 5 is the number of the parameters the bounding box has, and C is the number of classes. In this study, there is only one class, so the filter size at 79 th, 91 th and 109 th layers is 18 according to the filter formula, filters = (classes + 5) \(\times\) 3 [13] (see Figure 11). After the modifications, Yolov3 model was trained with dermoscopic images and used for detection of the lesion location in the image. Using this location information, a rectangle was drawn around the lesion automatically for creating a start point for GrabCut algorithm. After the location detection, GrabCut segments the image at the third stage. At the final stage, the morphological opening and closing have been applied to the segmented image using a \(5\times 5\) kernel for removal of the noise in the segmented binary image. The detailed system is presented in Figure 11.
# Mechanistic Insights into the Bornyl Diphosphate Synthase from _Lavandula angustifolia_
## 3 Results  
### Bioinformatics Analysis  
_Lavandula angustifolia_ bornyl diphosphate synthase (LaBPPS) possesses two conserved domains: the terpene synthase domain (aa 73-250) and the terpene cyclase domain (aa 276-601) (Figure 1a and Figure S1). LaBPPS catalyzes the conversion of geranyl diphosphate (GPP) to bornyl diphosphate (Figure 1b). The LaBPPS sequence was retrieved from the UniProt database (accession number A0A185 NNC6). The molecular weight of LaBPPS is estimated to be approximately 70.67 kDa, with the molecular formula C\({}_{3162}\)H\({}_{4889}\)N\({}_{841}\)O\({}_{933}\)S\({}_{33}\) and an isoelectric point (pl) of 5.16.  
The effectiveness of codon optimization was evaluated by analyzing the codon adaptation index (CAI) and GC content. The optimized CAI value for LaBPPS was found to be 81.6% (Table 2). The optimal GC content range typically spans from 30% to 70%, and the optimized GC content for LaBPPS was measured at 51.9%, which falls within this optimal range (Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Codon Optimization** & **Codon Adaptation Index (CAI) Value** & **GC Content Value** \\ \hline Before codon optimization & 46.2\% & 43.0\% \\ After codon optimization & 81.6\% & 51.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Codon optimization of _LaBPPS_.  
Figure 1: **The organization of LaBPPS clusters and the reaction.****(a)** A schematic representation of LaBPPS. **(b)** The chemical equation describing the bornyl diphosphate synthase reaction.
## 3 Analytical Model of Prototypes
Two new versions of A1 are considered: the former with increased length (\(L_{a}=17.11\) mm) and decreased trimming frequency (\(f_{t}=108.3\) Hz), the latter with decreased length (\(L_{a}=14.00\) mm) and increased trimming frequency (\(f_{t}=146.4\) Hz). Figure 5 shows that, when the trimming frequency decreases, both the peaks move towards lower frequency, and the amplitude of the second peak significantly increases. Whereas, when the trimming frequency increases, both the peaks move towards higher frequencies and the amplitude of the second peak decreases. Figure 3: Open circuit FRFs (frequency response functions) of the prototypes calculated by means of the analytical model. When the harvester is connected to a resistive load, the FRF between the generated voltage and base acceleration (\(FRF_{L}\)) is related to the generated power. For this reason, \(FRF_{L}\) was calculated by means of the analytical model considering the optimal load resistance [36], which maximizes the generated power at the frequency of the first resonance (\(\omega_{r}\)):  
\[R_{opt}=\frac{1}{\omega_{r}C_{pu}} \tag{15}\]  
Figure 6 shows the predicted FRFs. The load resistance significantly decreases the amplitudes of the resonance peaks. If PPA 1001 alone is considered, the peak frequency slightly decreases (from 125.5 to 124.6 Hz). This effect is in agreement with the results presented in [6; 37]. Also, when the harvester is coupled with the trimming device the frequencies of the resonance peaks decrease, owing to the resistive load. This effect is more important for the first peak than for the second. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{3}{c}{**1 st Mode**} & \multicolumn{3}{c}{**2 nd Mode**} \\ \cline{2-5}
**Harvester** & \begin{tabular}{c} **Frequency \(f_{1}\)** \\ **(Hz)** \\ \end{tabular} & \begin{tabular}{c} **Peak Amplitude** \\ **(V/ms\({}^{-2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Frequency \(f_{2}\)** \\ **(Hz)** \\ \end{tabular} &
\begin{tabular}{c} **Peak Amplitude** \\ **(V/ ms\({}^{-2}\))** \\ \end{tabular} & \(\Delta\)**F (Hz)** \\ \hline PPA 1001 & 125.5 & 1.69 & - & - & - \\ PPA 1001 + A1 & 97.1 & 1.57 & 161.5 & 0.39 & 64.4 \\ PPA 1001 + A2 & 102.8 & 1.34 & 152.6 & 0.46 & 49.8 \\ PPA 1001 + A3 & 107.7 & 1.18 & 145.6 & 0.52 & 37.9 \\ \hline \end{tabular}
\end{table} 
Table 1: Analytical results: resonance frequencies and corresponding amplitudes of the prototypes in open circuit condition. Figure 4: Phases of the displacements of the harvester and of the DVA calculated by means of the analytical model.