esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Since the FP structure is written using a fs-laser, it would potentially last longer in harsh or high-temperature environments during continuous monitoring applications, and would be advantageous where precise strain detection is crucial. In this article, the fabrication and sensing characteristics of the FBG-based FP structure using infrared fs-laser exposure in a biconical fiber taper are presented. The simulation of the spectrum of the structure, in a conventional fiber, was performed using Python and Optigrating simulation tool (as discussed in Section 2). Fabrication of the biconical fiber taper (adiabatically) and the sensing structure within its core using fs-laser micromachining are discussed in Section 3. Next, strain and temperature characteristics were investigated for a range up to 3400 \(\mu\)e and 225 \({}^{\circ}\)C, respectively, for both the fabricated sensor and standard FBG and compared (discussed in Section 4). The device was found to have a higher sensitivity to strain and has the potential to be used for structural health monitoring in harsh environments with long-term stability.
# Anti-Cancer Drug Screening with Microfluidic Technology
## 3 Monodrug Screening on a Macroscale Sample  
[PERSON] et al. demonstrated a microfluidic device to study the response of live slice cultures to a range of drug doses in parallel [52]. They confirmed the viability of both the organotypic and GBM xenograft mouse brain slice cultures prior to starting chemotoxicity tests. This PDMS device includes 80 inlet wells connected to downstream microchannels acting as a concentration source or sink. Sources deliver the medium while sinks prevent lateral spread between channels. The delivery channels act as a source, and their adjacent channels act as concentration sinks that prevent lateral spread between delivery channels. A prescribed concentration profile is transported to the tissue slice through the PTFE porous membrane (Figure 2a). Later, [PERSON] et al. manufactured a thermoplastic version of this platform made in PMMA which features less drug absorption. This platform consists of a 40-well plate with an integrated channel network layer for multiplexed drug testing onto GBM xenografts and patient-derived colorectal cancer tumor slices [53].  
[PERSON] et al. described micro-dissected tumor (MDT) tissues on-chip for personalized therapy [54]. The MDTs were trapped by sedimentation into their respective wells in which they are stable and shielded from excessive shear stress. The advantage of their system is long term survival without the need for continuous perfusion due to the small size of MDTs (below 420 \(\upmu\)m). A great advantage of this technique over spheroids on-chip is that it can be used for all types of solid tumors with no dependency on the ability of cells to self-aggregate.  
Recently, a PMMA microfluidic chip to trap cuboidal-shaped microdissected tissues or \"cuboids\" was engineered [55]. The similarly sized cuboids (400 \(\upmu\)m \(\times\) 400 \(\upmu\)m \(\times\) 400 \(\upmu\)m), derived from normal mouse liver and human glioma xenograft tumors (U87), were used and cultured within a collagen hydrogel layer on top of the microwell, with an air interface above and culture medium below (Figure 2b). For proof-of-concept, the cisplatin drug was tested in their 8-well microfluidic device with three different doses.
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 6 Experiment Results
### The Result of Barcode Reconstruction and Recognition  
Completely corrected edge images can reconstruct barcode symbols with no incorrect module. However, a few reconstructed barcode symbols which are reconstructed by the edge images with wrong edges have some incorrect modules. Figure 18 shows their distributions. If the incorrect module number is less than 9, the reconstructed barcode images have a high probability of being recognized by Reed-Solomon error correction. If the incorrect module number is more than 10, the reconstructed barcode images are difficult to identify. The best performance is based on VGG19, which hardly generates the reconstructed barcode images with more than ten modules.  
In Table 4, there are our results compared against competitive DXM decoding software (open-source and also commercial). These software examples do not have a high recognition rate for low-quality barcode images. However, our methods can achieve a recognition rate of more than 96%, and we obtain the recognition rate of 99.5% based on VGG19. One suggestion that can be adopted is to integrate our method into these softwares as a last resort in order to achieve better performance. Before the barcode image is input into the decoding software, [PERSON] et al. [15] binarizes the image to remove the influence of illumination. The binarization result is input into Dynamsoft Barcode, and the decoding  
Figure 17: The performance of the correction algorithm in edge images.  
Figure 18: The distribution of incorrect module numbers in barcode reconstruction images with incorrect modules.  
rate increased from 51.5% to 78.5%. However, its recognition rate is lower than our method because barcode images marked on metal surfaces have worse quality, compared to the printed barcode images in [15]. The barcode pattern and background are often uneven, which is determined by the metal material properties, the surface texture, and the high reflectivity. Figure 19 shows that the grayscale of the barcode pattern is uneven due to texture, which affects the effect of binarization. However, our method has strong stability in extracting features.
## 2 Signal Analysis and Neural Network
### Neural Network (NN)
#### 2.3.2 Back Propagation Neural Network (BPNN)  
The neural network (NN) is an operational model, consisting of interconnected neurons. Each node represents a specific output function, called the activation function [16]. Each connection between two neurons represents a weight. In the initial stage, the weights and offsets of NN are fixed and there is no learning ability. In 1986, the BPNN feedforward neural network model was proposed by [PERSON] et al. [17]. The network refers to the hierarchy of neurons. It consists of input layers, hidden layers and output layers. As shown in Figure 2, this research used the neural network toolbox (NNTOOL) in Matlab to create and train cascaded artificial neural networks. In the experiment, a three-layer feedforward neural network is trained by using the scaled conjugate gradient (SCG) algorithm. The activation function at the hidden layer is a hyperbolic tangent sigmoid transfer function and output layers are log-sigmoid transfer functions in the network. The numbers of hidden neurons applied in the verification are 10. As long as its weight, net-input and transfer function have derivative functions, the network can be trained. Moreover, the reason to choose SCG is that it is based on supervised learning and is comparatively faster than the standard backpropagation model [18].
## 4 Results and Discussion
### The Impact of SLADS on Traffic Efficiency and CO\({}_{2}\) Emissions
#### 4.2.1 The Impact of SLADS on Traffic Delay under Different Driving Styles
Under the five different volume inputs, as shown in Figure 5a-e, when both the aggressive style ratio and the penetration rate of ICVs reached 100%, the average vehicle delay of each vehicle decreased by 0.17 s, 0.86 s, 10.14 s, 10.64 s, 10.40 s separately, that is, 29.64%, 46.85%, 83.29%, and 82.52%, respectively. There were some similar results on the expressway and the secondary motorway. First of all, at the first and the third service level, when the aggressive style ratio of ICVs exceeds the threshold for reducing the traffic delay, the benefit rises at first and then decreases as the ICV penetration rate increases, the inflection points of which appear when the ICV penetration rate is equal to around 50%. This could be attributed to the introduction of ICV making the heterogeneity of traffic flow firstly decrease and then increase. Next, at the oversaturated service levels, as the aggressive style ratio of ICVs reached 100%, the average traffic delay increased inversely with the ICV penetration rate being around 80%. Although the difference in vehicle distribution in VISSIM may account for this phenomenon, it can also be inferred that the stability of ICV's ability to reduce traffic delay is reduced at oversaturated service levels due to the deterioration of traffic flow.
## 3 Results and Discussion
### Investigation of Thermal Transitions (DSC)  
Figure 4 presents the conventional DSC thermograms of neat PLA alongside the newly synthesized PLA-based polyesters. For neat PLA, a T\({}_{\mathrm{g}}\) value was recorded at 57 \({}^{\circ}\)C, while a distinct melting endotherm appeared at 176 \({}^{\circ}\)C (Table 2). Similar to the neat linear PLA, all copolymers exhibited single thermal events, including glass transition steps and melting peaks. However, unlike the single melting peak of PLA, the star-shaped polymers displayed a 'double-peaked' melting behavior, though within a similarly narrow temperature range. This phenomenon is likely due to the coexistence of crystals with varying sizes or thermal stability. Overall, the findings indicate relatively homogeneous material properties, which are essential for achieving consistent drug release profiles. The primary factors influencing these thermal effects appear to be the branches number and the Mn reduction. Given that glycerol, sorbitol, and pentaerythnitol contain 3, 6, and 4-OH groups, respectively, 3-, 6-, and 4-branched star-like PLA copolymers were produced through ROP.  
Figure 4: Conventional DSC traces during (**a**) 1 st heating and (**b**) quenching for all samples.  
Figure 3: Comparative ATR-FTIR spectra for all PLA-based copolymers samples.
## 4 Intensity Map Derived in This Study
The former director of the Seismological Survey of Serbia, [PERSON], came to similar conclusions at the beginning of the 20 th century and mentioned this while describing his Modified Mercalli scale in the newspaper \"Politika\" from 18 May 1927, no. 6845, year XXIV, Belgrade. Our reevaluated macroseismic intensities of 8 April 1893, Svilajnac earthquake are illustrated in Figure 6. The map was created on the basis of the currently available 538 data for various locations in 9 different countries (Serbia, Hungary, Romania, Bulgaria, North Macedonia, Bosnia and Herzegovina, Croatia, Austria, and Slovakia). The inset in Figure 6 shows a physical map in which a bounding square indicates the territory from which the reports on the manifested effects of the 8 April 1893, Svilajnac earthquake were observed. Our estimate of the location of the epicenter is also shown. The locations depicted in Figure 6 were given integer intensities (e.g., I = IX, VIII, VII, etc.) when we were confident that we had sufficient descriptive data to reach a reliable intensity assessment. When the data were such that we could not decide on one or the other intensity degree, we marked the intensity with an interval value (e.g., I = VIII-IX, VII-VIII, VI-VII, etc.). Finally, when there were reports with insufficient information to assign any intensity, or when it was data taken from the literature without an accompanying report on the effects of the earthquake, we also entered them into the map and marked them with an empty black circle, indicating an undefined intensity degree at those locations. In this way, the map shows all the locations where there were reports that the earthquake was felt. Based on the estimated intensity values, we defined the isoseismal lines to separate the areas with different intensities. Figure 6 shows isoseisimals for the intensity degrees IX to V. By a simple visual comparison of the macroseismic map with the relief map, our preliminary conclusion is that the shape of the isoseisimals is defined by geological formations, namely by the spatial arrangement of the deep geological sediments that are present in the Pannonian plain and the rocks of the mountain regions (Dinaric Alps, Hellenides, Rhodopes, Balkan Mountains, and Carpathian Mountains). The latter (spatial distribution of mountains and plains, i.e., deep geological rocks and sediments) was, in our opinion, most probably the reason why the maximum effects of the earthquake were directed towards the northwest, north, and northeast, less towards the west and east, and least towards the south. The effects of the regional geological formations on the spatial distribution of different earthquake intensity degrees have recently been observed also for the strongest earthquakes in Vrancea (Romania) [19]. Figure 6: Reevaluated macroseismic map of 8 April 1893 earthquake—this study.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Soil** & **TOC, \%** & **pHH2O** & **pHCaCl2** & \begin{tabular}{c} **CEC,** \\ **CMP+/kg** \\ \end{tabular} & \begin{tabular}{c} **Cr,** \\ **mg/kg** \\ \end{tabular} & \begin{tabular}{c} **Ni,** \\ **mg/kg** \\ \end{tabular} & \begin{tabular}{c} **Cd,** \\ **mg/kg** \\ \end{tabular} & \begin{tabular}{c} **Zn,** \\ **mg/kg** \\ \end{tabular} & \
\begin{tabular}{c} **Cu,** \\ **mg/kg** \\ \end{tabular} &
\begin{tabular}{c} **Pb,** \\ **mg/kg** \\ \end{tabular} \\ \hline Longyugan, mat & 1.20 & 6.3 & 4.2 & 3.8 & 0.10 & 1.71 & 0.17 & 1.43 & 0.71 & 0.10 \\ \hline Longyugan, rec & 1.45 & 6.4 & 4.5 & 1.9 & 2.77 & 2.28 & 0.16 & 9.16 & 2.44 & 1.34 \\ \hline Longyugan, res & 1.71 & 7.2 & n.d. & 1.8 & 1.65 & 1.71 & 0.24 & 3.60 & 2.56 & 0.43 \\ \hline Longyugan, ind & 1.90 & 6.7 & 4.1 & 3.6 & 4.05 & 3.20 & 0.26 & 14.12 & 1.67 & 2.05 \\ \hline Nadym, mat & 0.65 & 4.5 & 2.8 & 6.0 & 1.54 & 0.10 & 0.02 & 1.10 & 2.40 & 0.10 \\ \hline Nadym, recr & 0.70 & 5.0 & 3.9 & 3.2 & 6.25 & 4.47 & 0.19 & 24.92 & 2.02 & 4.43 \\ \hline Nadym, res & 1.20 & 5.6 & 4.4 & 3.6 & 3.38 & 3.26 & 0.08 & 8.22 & 1.95 & 1.01 \\ \hline Nadym, ind & 0.95 & 5.7 & 4.2 & 4.1 & 5.63 & 3.26 & 0.15 & 7.21 & 2.32 & 1.10 \\ \hline Kharsaim, mat & 3.56 & 7.2 & n.d. & 4.0 & 16.19 & 26.53 & 0.48 & 43.24 & 12.81 & 5.48 \\ \hline Kharsaim, ind & 3.68 & 7.5 & n.d. & 4.5 & 23.28 & 23.31 & 0.46 & 32.35 & 12.68 & 5.99 \\ \hline Muravlenko, mat & 0.78 & 6.0 & 4.4 & 3.0 & 4.01 & 1.18 & 0.32 & 2.64 & 0.32 & 0.65 \\ \hline Muravlenko, res & 1.05 & 6.9 & 4.6 & 3.2 & 5.63 & 5.28 & 0.36 & 6.94 & 3.85 & 0.53 \\ \hline Tarko-Sale, mat & 2.50 & 5.9 & 3.7 & 5.1 & 1.05 & 0.58 & 0.15 & 1.23 & 0.10 & 0.10 \\ \hline Tarko-Sale, ind & 2.80 & 6.4 & 4.4 & 4.5 & 3.43 & 2.30 & 0.30 & 6.30 & 1.02 & 3.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical characteristics of the soil superficial layer (mature—mat; recreational—rec; restoration—res; industrial—ind). **Funding:** This work was supported by the Russian Science Foundation, project No. 24-44-00006. **Data Availability Statement:** The dataset is available upon request from the authors. **Acknowledgments:** The authors are thankful to the Government of Yamal region for their management and logistical help. The authors are thankful to the Arctic Research Center (Salekhard) for their help in the organization of the field trip. The authors are also grateful to [PERSON] for her help in the preparation of expeditions. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods  
### Data Processing  
#### 2.1.1 GNSS Data and Pre-Processing  
The data integrity rate of the 24 continuous observation stations of the Crustal Movement Network of China (CMONOC), GNSS, selected in this study was >95%. The observation period of most stations was January 2011-June 2021. GAMIT/GLOBK software was used for calculations [19]. First, the single-day relaxation solutions of the International GNSS Service (IGS) station and regional GNSS stations were obtained using GAMIT. Antenna phase center was corrected using the absolute antenna phase center model. The atmospheric refraction model used Vienna Mapping Functions 1. Global Earth tide and ocean tide corrections followed the IERS 2003 Conventions. The IGS station was used as the fixed-frame station, and GLOBK was used to adjust and estimate the time series and errors of point coordinates of regional stations under the International Terrestrial Reference Framework 2014. Then, the steps of the time series caused by antenna replacement and co-seismic displacement of strong earthquakes were repaired, and outliers were eliminated. Finally, the surface loading model [20; 21; 22] is used for atmospheric and non-tidal ocean load model corrections, but not for hydrographic load corrections.
# ARAAC: A Rational Allocation Approach in Cloud Data Center Networks
## 2 ARAAC: A Rational Allocation Approach in Cloud-Service Networks
### Benchmark Mechanisms
#### 2.3.1 SBP: Second Best Price  
To be consistent, we chose to use the same set of tenants' profiles as was chosen for the ARAAC proposal. Hence, both models deal with the same set of bidders and have the same parameters. However, in the SBP mechanism, the selection algorithm is somehow different. As for that in ARAAC, the model receives the bidders' profiles and then sorts them according to the offered price unit (\(p_{i}\)) values. Having the bids sorted, the model excludes those who have their price unit values that are less than the threshold value \(P^{*}{}_{t}\). It then checks the for resource availability according to the \(\alpha_{t}S_{t}\) value. However, it _does not_ perform any checks for the bidder's resource utilization record, it only seeks those bidders that maximize its current utility \(U_{P}\). Hence, it expects high profit values but low utilization rates. Being less selective compared with ARAAC, blocking rates may also be lower.  
In ARAAC, we believe that profit gains are certainly important, but we also believe that _resource utilization_ in such cloud-DCNs environments is important too. Eventually, those allocation models that do not stress the importance of resource utilization reach a depletion point that forces expensive updates in the cloud-DCN structures. Aside from being very expensive, such updates may require massive changes in the whole network infrastructure. With the absence of an efficient utilization mechanism, it is unlikely that such updates will satisfy the rapidly increasing growth for long. It is more likely that such updates will be frequent sooner than expected.
# Enhanced Message Replication Technique for DTN Routing Protocols
## 2 Related Works
### Quota-Based Protocols
Another work in the field of quota-based routing protocols is Encounter-Based Routing (EBR), proposed by [PERSON] et al. [21]. EBR creates a limited number of replicas for each message based on the history of a node's encounters. In EBR, nodes that have encountered one another frequently have the best chance of successfully transmitting messages to the destination. However, a destination may never receive transmitted messages if it has low encounter rates in a low-density network. To address this issue, [PERSON] et al. [22] proposed the Destination-Based Routing Protocol (DBRP). DBRP assigns weights to nodes based on the rate of encounters with the destination and other nodes, with nodes that have encountered the destination receiving higher weights. In networks with limited node buffer space, the DBRP has been shown to perform better than other quota-based routing protocols, such as Spray and Wait and EBR. However, the DBRP does not consider a node's capability in determining the appropriate number of replicas, which can lead to either buffer congestion due to lack of space or inefficient use of network resources when a small number of replicas are transmitted by a node with sufficient resources. Both EBR and the DBRP have been shown to have good performance through simulation, but blindly forwarding replicas to nodes with high encounter rates without considering their capability can lead to delivery delays, message drops, and inefficient resource utilization. In [32], a novel method was proposed to address the issue of resource consumption caused by message replication. This method determines the dissemination of DTN data based on the expected path of a node, transmitting duplicates to nodes that are close to the destination. The method was evaluated by comparing the number of arrived data to the number of generated data for both the proposed method and an existing method, and the former was found to achieve a higher message arrival rate. However, this method focuses on where duplicated messages should be sent but does not consider how many replicas of each message should be sent to encountered nodes. Although quota-based protocols are more resource-friendly than flooding-based protocols, they still suffer from low delivery ratios and high delivery delays. To address this issue, we propose the Enhanced Message Replication Technique (EMRT) protocol--a dynamic quota-based technique that considers not only encounter-based routing metrics, but also network congestion and capacity, to minimize overhead, maximize the delivery ratio, and efficiently utilize network resources. Table 1 provides a summary of the features and characteristics of DTN routing protocols [33]. The table includes details on both flooding-based and quota-based DTN routing protocols.
# A Novel Optimization Layout Method for Clamps in a Pipeline System
## 2 Materials and Methods  
### Process of the Optimization  
Because of the multi-support and long-span characteristics of the pipeline, the increase in the number of clamps means that the amount of calculation increases exponentially. This study, based on the neural network, proposed a surrogate model for optimizing the layout of the clamps, in which the nonlinear optimization of the genetic algorithm could be used to find the optimal solution for the model. First, the density of the finite element calculation was determined through the sensitivity analysis. Neural network was used to build relationships between the position of clamps, input and output data. The finite element analysis result was used to train the neural network to improve its accuracy. Finally, based on the output data, the optimal solution was found. The flow chart of the optimization is shown in Figure 1.
# Assessment of Self-Supervised Denoising Methods for
## 1 Introduction
However, acquiring these resources is costly and may result in the development of large models that are inefficient to utilize [16; 17]. In addition, the speaker's identity may not be retained. Moving to unsupervised learning for ES enhancement, the most used methods are based on traditional filtering, such as the Kalman filter [18] and the Wiener filter combined with wavelet-based methods [13]. These classical methods are known for producing artifacts called musical noises [19]. To address these challenges, our contribution involves utilizing Only-Noisy-Training (ONT) [20], an approach that enables the denoising of noisy wave files without requiring clean counterparts. To the best of our knowledge, this marks the first application of a self-supervised denoising technique to enhance ES while preserving the speaker's identity. In this paper, we have conducted experiments using two ONT-based methods, DCUNET and DCUNET-cTSTM [20], to determine whether these methods are effective for denoising and enhancing ES. Additionally, to compare and calculate the loss and metrics of the ONT systems, the pre-trained VoiceFixer model (VF) [21] was used to restore the clean sound files of ES. Our results demonstrate the feasibility of denoising tasks without conventional targets. Furthermore, DCUNET-cTSTM exhibited particularly notable performance. The remainder of this manuscript is structured as follows: Section 2 describes the materials and methodologies used for denoising and comparison tasks. Section 3 provides a comprehensive summary of the experimental results. Section 4 engages in a detailed discussion of the outcomes. Lastly, Section 5 concludes the work.