esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Wood Densification and GFRP Reinforcement on the Embedment Strength of Poplar CLT
## 2 Materials and Methods  
### Materials and CLT Fabrication  
Three-ply CLT panels made of air-dried poplar (_Populus alba_) wood with moisture content of around 12% and oven-dry density of 400 \(\pm\) 10 kg/m\({}^{3}\) were used in this research. CLT panels were produced in four groups. The thickness of the boards was 2 cm. One group was reinforced with GFRP using a bidirectional (0\({}^{\circ}\)/90\({}^{\circ}\)) E-glass fiber fabric (fiber tensile modulus and density of 70 GPa and 2.55 gr/cm\({}^{3}\), respectively), as shown in Figure 1. Three GFRP layers were added to each surface. Two groups were manufactured with densified wood in two densification ratios (25% and 50%). In order to manufacture densified wood, lumber panels were placed between heated platens in a hydraulic press and compressed. Then, CLT panels were made from the densified wood. The last group consisted of CLT with non-densified wood, considered as a control group (0). A summary of the sample groups is detailed in Table 1. All components were cold-pressed for 150 min and pressure of 1 MPa with one-component polyurethane glue.  
Embedment Strength Modelling and Experiments  
After preparing the CLT panel, specimens with dimensions of 15 cm \(\times\) 8 \(\times\) cm \(\times\) 6 cm (length \(\times\) width \(\times\) thickness) were cut from the panel for the embedment strength test (Figure 2). A lag screw (s) with a diameter of 8 mm was used as a fastener. The characteristics of the fastener are detailed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Groups** & **Repetitions** & **Description** \\ \hline Control & & No reinforcement \\ (unreinforced) & 10 & (Loading direction: L = longitudinal, T = transverse) \\ \hline
25L & 10 & Reinforcement with 25\% densification (loading direction: longitudinal) \\ \hline
25T & 10 & Reinforcement with 25\% densification (loading direction: transverse) \\ \hline
50L & 10 & Reinforcement with 50\% densification (loading direction: longitudinal) \\ \hline
50T & 10 & Reinforcement with 50\% densification (loading direction: transverse) \\ \hline GFRPL & 10 & Reinforcement with GFRP (loading direction: longitudinal) \\ \hline GFRPT & 10 & Reinforcement with GFRP (loading direction: transverse) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample groups and repetitions.  
Figure 1: GFRP arrangement in CLT layers.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 4 Performance Analysis
### Queuing Delay Test  
The CUBIC protocol, which is based on packet loss, can increase the link delay. This is because the CUBIC protocol gradually ramps up the data transmission speed until there is a packet loss due to buffer overflow. Consequently, when the link buffer contains many packets, the limitation of router forwarding speed results in an increase in queuing delay. The BBR protocol will cause an increase in queuing delay when it is in the PROBE_BW state, while the BRF protocol will not cause an increase in queuing delay. The queuing delay calculation formula can be expressed as follows.  
\[\mathrm{D}_{\mathrm{queue}}=\mathrm{RTT}_{\mathrm{measurement}}-\mathrm{RTT}_{ \mathrm{empty}} \tag{20}\]  
In this study, we evaluate the impact of different congestion control (CC) algorithms on queuing delay in a network. Specifically, we measure the queuing delay (\(\mathrm{D}_{\mathrm{queue}}\)) using the test topology shown in Figure 7a, where \(\mathrm{RTT}_{\mathrm{measurement}}\) represents the current measured round-trip delay and \(\mathrm{RTT}_{\mathrm{empty}}\) is the round-trip delay when the routing queue is empty. The CC algorithms tested include the BRF protocol, the BBR protocol, and the CUBIC protocol. We conduct tests by establishing connections from S1 to C1 and sending data while measuring round-trip delays once per second for 30 s under each algorithm. Figure 12 shows the queuing delay test results. Our results show that the CUBIC protocol causes a significant increase in queuing delay, while BBR's queuing delay increases periodically. In contrast, the BRF protocol does not add any additional queuing delay.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 6 Results
### Discussion  
From the presented results, it is evident that a significant statistical difference between the MPTCP throughput results of the ns-3 simulation and testbed is noticed in the following cases.  
* In the case of uplink traffic, the simulator underestimates the MPTCP throughput with short-lived flows irrespective of the existence of a bottleneck link. However, with long-lived flows passing through a bottleneck link, the simulator slightly underestimates the throughput of LIA and OLIA, but it matches wVegas, and gives a noticeable lower throughput than the testbed with Cubic.
* In the case of downlink traffic, the simulator overestimates wVegas throughput when no bottleneck link exists. However, a slight underestimation of the throughput with all algorithms, except wVegas, is noticed when a bottleneck link is present.
* In the case of LTE handover, the simulator overestimates throughput when no bottleneck link exists. However, only the throughput of wVegas is overestimated when a bottleneck is present.  
The main cause of the discrepancy observed with uplink and downlink traffic, as indicated in Section 6.3, is the different RTT values measured by the simulator compared with the RTT values obtained by the testbed. This is attributed to the fact that the ns-3 simulator does not implement the full control plane of the LTE stack, but a simplified one [46], which implies that it does not truly represent the delays caused by the control plane procedures. Indeed, having a different RTT over one network interface impacts the RTT of the other interface as the MPTCP scheduler first fills the congestion window of the interface with the lowest RTT. Moreover, the congestion window calculations of each subflow depend on the other for LIA, OLIA, and wVegas algorithms.  
Moreover, the bottleneck presence impacts the RTT. This affects the difference that already exists between its values in the simulator and testbed. In addition, the performance of wVegas is highly affected by the accuracy of the RTT values [34], which affect the minimum measured RTT (\(base\_RTT_{i}\)) parameter, and hence influences the calculation and evolution of the subflow weights (\(\alpha_{i}\)). For most of the studied algorithms, ns-3 simulation results tend to better approach the testbed results for long-lived flows as the difference in RTT between the subflows (i.e., Wi-Fi and LTE) of an MPTCP connection measured in the testbed becomes closer to its simulator counterpart when averaged over a long time scale of data transmission.  
The reason behind the discrepancy with LTE handover is because the ns-3 simulator implements only the X2-based handover, which is a shorter procedure compared with the S1-based handover. The rates of the different subflows of the same MPTCP connection often take a shorter time to stabilize (after an S1-based handover disturbance) when they share a bottleneck link as they directly affect one another, which leads to a less impact on the overall throughput.
## 4 Discussion
### Influence of SRR (with Same SRF and Good SNR)  
In Section 3, we found that single-sensor fusion has a good visual quality for VI. For objective quality, the pattern was almost uniform for CC for all fusions, and RMSE was quite different for different image fusions and VIs. However, the general trend is that by increasing SRR, the CC graph is falling slowly and the RMSE graph is increasing slowly (Figure 3). This good quality might be due to the high-resolution PAN (1 m) and MS (6,8,10,12 m) images, the good SNR and the same SRF. The CC and RMSE values are almost the same for all image fusion methods, which indicates that image fusion is insensitive with VI. However, the HPF has a scattered graph and seems to be sensitive. ARVI and GNDVI have quite low RMSE values and are well separated in all image fusion algorithms and all SRR. However, CC seems to be almost the same for all VI and are almost overlapped. [PERSON] et al. [55] found that the GS spectral sharpening algorithm gives better results than PC spectral sharpening, according to the classification results. Similarly, in our result, the GS fusion has a good quality (low RMSE and high CC) for all VIs in all SRR (1:6 to 1:12) (Figure 3). In reality, such same-sensor fusion does not exist in such large SRR (1:6 to 1:12). We did this arbitrary image fusion to analyze how much SRF and SRR could affect the fusion result as long as there is good SNR between PAN and MS images. In general, the FVI quality is decreasing with decreasing SRR very slowly as long as there is a good SNR and a minimum SRF effect (Figure 3).  
Figure 3: Trend of FVI quality with Image Fusion and SRR (same SRF and good SNR).
## 5 Conclusions  
In this paper, an ECDL configuration with a wide interference filter and diffraction grating is characterized. Additionally, we analyzed and tested the laser performance. The experiment result shows that the external cavity structure has a good compression linewidth characteristic. The Lorenz linewidth at a wavelength of 1555 nm is 13.6 kHz. Additionally, we attribute it to the suppression of the noise floor by the interference filter and the mode selection of high reflectivity diffraction grating. In addition, the optical performance of the ECDL is tested at an injection current of 200 mA. The ECDL has a wide tunable range of 40 nm from 15,250 nm to 1565 nm and a high SMSR of 57 dB at a wavelength of 1555 nm. Additionally, an output power of 25.6 mW is achieved.  
Figure 7: The output power as a function of wavelength with an injection current of 200 mA.
## 4 Applications in Clinical Assays  
### Detection of Infectious Diseases Caused by Bacteria  
Infectious diseases caused by bacteria are common in our life. MIPs/SIPs based electrochemical biosensors have been used as rapid diagnostic tools for these diseases. As a branch of MIPs/SIPs, CIPs are special for cell biomarkers. CIPs based electrochemical biosensor were reported for _Staphylococcus epidermidis_ (_S. epidermidis_) detection [28]. 3-aminophenylboronic acid was used as a functional monomer for the electrochemical fabrication of the CIPs. EIS signal was shown to respond linearly to concentrations of _S. epidermidis_ in the range of 10\({}^{3}\)-10\({}^{7}\) cfu mL\({}^{-1}\). MIPs fabricated by polyphenol was used as an artificial receptor for the detection of flagellar filaments from _Proteus mirabilis_ by [PERSON] and coworkers [61]. EIS and square wave voltammetry (SWV) were applied to measure the interaction of flagellar filaments with the MIPs that was fixed on their home-made paper-printed electrodes. Their results showed that the limit of detection (LOD) for the flagellar filaments was as low as 0.6 ng/mL.
# Probing Ferroic States in Oxide Thin Films Using Optical Second Harmonic Generation
## 1 Introduction
### Ferroic Domain Engineering in Single Layers and Superlattices
By tuning ferroelectric layer thicknesses and controlling the nature of the charge screening environment in heterostructures and superlattices, it is possible to engineer the ferroelectric domain architecture in three dimensions [61; 65]. A prominent example is the formation of ferroelectric vortex domains in PTO/STO superlattices, as shown in Figure 3a,b [48] or the stabilization of flux-closure domain patterns in PTO single layers inserted between two STO dielectric layers; see Figure 3c [65]. The direct observation of three-dimensional ferroelectric and multiferroic domain architectures is however not trivial. The conventional SPM technique usually requires the contact with the functional layer and is inherently surface sensitive, therefore lacking the demanded depth analysis of the structure. A particular challenge arises in probing domain structures in a multilayer configuration where the ferroic layers are capped by an impenetrable layer. In active devices, the functional layer is often capped by a conducting electrode which limits the piezoresponse force analysis to the out-of-plane polarization component [4; 66; 67].
## 3 Proposed Methodology
### Bert Model Structure
#### 3.2.5 The Output Layer  
The output layer, situated at the pinnacle of the model architecture, plays a pivotal role in transforming the voluminous feature representations acquired throughout the network into actionable predictions. Let us now examine the functionalities and mechanisms employed within the output layer in greater detail.  
* The integration of the mechanism output is as follows: the output of the preceding layers, which frequently includes the contextual embeddings derived from Bert as well as the aggregated information from the residual network, serves as the input to the output layer.
* Fully Connected Transformation: The input from the preceding layers undergoes transformation through one or more fully connected layers within the output layer. These fully connected layers serve to further distill and refine the learned representations, enabling the model to extract high-level features and patterns relevant to the classification task at hand.
* Softmax Activation: Following the transformation through fully connected layers, the output of the final fully connected layer is passed through a softmax activation function. The softmax function computes the probability distribution over the possible classes or categories, assigning a probability score to each class. This probability distribution reflects the model's confidence in each class, with higher probabilities indicating stronger predictions.
* Classification Output: The final step in the output layer involves the classification of the input text based on the computed probability distribution. The class with the highest probability score is selected as the predicted class label for the input text. This classification output serves as the model's prediction for the sentiment, category, or label associated with the input text, enabling downstream applications to utilize the model's insights for decision-making or analysis.
* Training and Optimization: During the training phase, the output layer is optimized to minimize the discrepancy between the predicted probability distribution and the ground truth labels. This is typically achieved through the use of a loss function, such as cross-entropy loss, which quantifies the difference between the predicted probabilities and the true labels.  
The output layer serves as the final stage in the model's decision-making process, transforming the learned feature representations into probabilistic predictions. By integrating information from preceding layers, applying transformations through fully connected layers, and computing probability distributions via softmax activation, the output layer enables the model to make informed classifications and predictions based on the input text.
# Numerical Simulation Study on the Performance of Buried Pipelines under the Action of Faults
## 4 Analysis of Parameters Affecting Pipeline Response under Fault Action
### Influence of Fault Angle  
It can be seen from the literature [28] that the faults crossed by the pipeline along the project are basically in a range from 40 degrees to 90 degrees. Therefore, this study selects the strike-slip reverse faults with fault angles equal to 45 degrees, 60 degrees, 75 degrees, and 90 degrees. The variations in the axial strain and the von Mises stress of the pipeline under different fault angles are shown in Figure 9.  
As can be seen from Figure 9a, the maximum axial tensile strain and compressive strain of the pipeline rise with the increasing fault displacement. The axial tensile strain  
Figure 8: The response of pipeline under different fault types: (**a**) Axial strain; (**b**) von Mises stress.  
Figure 9: The response of pipeline under different fault angles: (**a**) Axial strain; (**b**) von Mises stress.  
increases more slowly under different fault angles, and the growth rate is as follows, 90 degrees > 60 degrees > 75 degrees > 45 degrees. Before the fault displacement reaches 2 m, the pipeline does not reach the tensile damage standard under all fault angles. The changing trend of the axial compressive strain is different, it is fastest at a fault angle of 60 degrees and reaches the shell buckling damage criterion first.  
As can be seen from Figure 9b, the evolution of the maximum Mises stress with the increase in fault displacement is similar to that of the compressive strain. The maximum von Mises stress grows the fastest under the fault at 60 degrees, and the growth rate is as follows, 60 degrees > 75 degrees > 45 degrees > 90 degrees. The growth rate of the von Mises stress remains unchanged in the four cases after the pipe yields, and the differences under the four fault angles are not significant. From the comprehensive stress and strain analysis, it can be determined that the risk of pipe failure is the highest under a fault angle of 60 degrees.
# Nucleotides as Bacterial Second Messengers
## 5 The c-di-AMP Nucleotide  
The c-di-AMP dinucleotide was initially come across bound to the DNA integrity scanning protein (DisA) of the bacterium _Thermotoga maritima_[72]. Subsequently, intracellular pathogenic bacteria (such as _Listeria monocytogenes_) were observed to secrete c-di-AMP to the cytosol of infected host cells [73]. The bacteria _Bacillus subtilis_[74], _Chlamydia trachomatis_[75], _Staphylococcus aureus_[76], and _Streptococcus pyogenes_[77] produce c-di-AMP.  
Like the other nucleotides, c-di-AMP binds to a specific set of receptor or target proteins; this binding modifies the functions of these proteins or may influence other effector proteins regulated by the receptor, thereby controlling cellular pathways. The c-di-AMP participates in several processes, including bacterial growth under low potassium conditions [78], regulation of fatty acid synthesis in _Mycobacterium smegmatis_[79], cell wall homeostasis [80], and sensing of DNA integrity in _B. subtilis_[81] (Figure 4).
## 2 Materials and Methods
### Experimental Procedures  
The test scheme consisted of three parts. Part 1 involved investigating the effects of the zeolite content and contaminant concentration on the swelling properties of the bentonite at different ZnCl\({}_{2}\) concentrations (0, 0.01, 0.03, 0.05, 0.10, and 1.00 mol/L). Part 2 investigated the impacts of zeolite content on the permeability performance of a soil mixture (made with bentonite-stone chip mass ratio of 11%) at various ZnCl\({}_{2}\) concentrations (0, 0.01, 0.03, and 0.10 mol/L). The Zn\({}^{2+}\) concentration of the leachate at the end of the permeability tests was measured. Referring to the scheme of bentonite and zeolite adsorption tests on Zn\({}^{2+}\) in the relevant literature [19; 20], we determined the concentration of Zn\({}^{2+}\) ions for the adsorption tests in this study. Part 3 investigated the adsorption characteristics of mixed soils with different zeolite doping (0, 12.50, 25, and 50%) for different concentrations of Zn\({}^{2+}\) ions (0, 3, 5, 10, 20, 30, and 50 mmol/L) by batch adsorption tests, allowing isothermal adsorption in static equilibrium at the soil to water ratio of 1:20. The test soil to water ratio was determined by referring to the Zn\({}^{2+}\)[21; 22] adsorption test with bentonite and zeolite alone as adsorbent materials. Electric potential analysis was conducted after adsorption to explain the relationship between the adsorption capacity and the electric double layer. All tests were conducted at 25 \({}^{\circ}\)C.  
Figure 1: XRD diffraction diagram of bentonite.  
Figure 2: The appearance of stone chips.
# A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System
## 4 The Predictive Analytics Infrastructure and the Early Warning System
### The Predictive Analytics Infrastructure  
#### 4.1.1 Microservices Infrastructure  
The infrastructure design is crucial for an effective implementation but also for the success of a TAI system. Although monolithic infrastructures are still used and have some advantages, infrastructures based on microservices are recognized as a growing good practice [62] because of their major benefits. The system is transformed into packages of small services, each one independently deployable, horizontally scalable, and running its own processes. Moreover, the benefits can be seen during development because there is flexibility to use different technologies and a reduction in development cycles and time-to-market [63]. However, some challenges must be faced because system complexity increases. Microservices need to communicate, and lightweight and secure channels must be provided. Moreover, testing each microservices in an isolated way is more complex, and fault tolerance must be enforced while interacting with other microservices.  
Currently, many technology companies are following this paradigm to develop their products. Some relevant examples can be found in [64; 65]. The developed predictive analytics infrastructure follows the microservices infrastructure style supported by DevOps practices:  
* Microservices: Each service has been developed independently and scoped on a single purpose. Docker technology [66] has been used to containerize each service.
* Continuous integration and delivery: The code is managed with GitLab [67], and containers are created automatically within the GitLab server and stored in a private Docker Registry to allow continuous delivery [68].
* Infrastructure as a code: Complete infrastructure is managed by Docker Compose [69] with a single configuration file obtaining the latest version of each service from the Docker Registry.
* Monitoring and Logging: Although Docker Compose provides a simple logging process to monitor the services, the client service provides several dashboards to supervise batch tasks run by the different microservices.  
We used an on-premise instance to deploy the infrastructure due to the research nature of the project. Nevertheless, the system can be easily reproducible on any cloud environment supporting Docker, Docker Compose, and GitLab technologies.
### Non-Uniform Mesh Density  
The time required to solve an FEM model largely depends on the number of elements involved in analysis. Rolling is a highly nonlinear contact problem, which usually involves large deformation and requires fine mesh for numerical convergence [33; 40; 41] and accurate prediction [48]. Non-uniform mesh density, actively used in most of the FEM simulationsof rolling, keeps the dense mesh in the contact region and the coarse mesh in other regions far from the roller, thereby reducing the total number of elements with minimum impact on accuracy. The creation of non-uniform mesh density does not require special techniques or algorithms and can be done using standard finite element analysis (FEA) software tools. Such a strategy to generate efficient models has been widely adopted in 2D [40; 49; 50] and 3D [33; 34; 38; 41; 42; 43; 45; 46; 47] simulations of rolling and burnishing.