esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
These approaches derive treatment policies through the utilization of Deep Q networks, optimizing the critical parameters. The pioneering approach in 2017 introduced the use of Double Q networks to learn the treatment policies [12]. Building upon this foundation, the subsequent research has explored continuous state-space model-based reinforcement learning (RL) to unearth high-quality treatment policies [13]. Additionally, [PERSON], [PERSON] et al. focuses on identifying the salient factors impacting mortality during sepsis treatment [14]. [PERSON], [PERSON] et al. uses the Deep Deterministic Policy Gradient (DDPG) algorithm to learn the policies that recommend dosages of Vasopressors and Intravenous fluids for sepsis patients [15]. Despite these efforts, the management of septic patients remains exceptionally challenging due to the significant inter-individual variability in patients' responses to medical interventions and the absence of universally accepted sepsis treatment protocols. In addition to sepsis treatment, the majority of treatment recommendation methods have adopted reinforcement learning as a fundamental approach for providing treatment recommendations. However, it is important to acknowledge that while reinforcement learning offers evident advantages, it also exhibits certain limitations, particularly when applied to medical datasets. One of the primary limitations arises from the necessity to compute rewards for actions, a process that requires the creation of a virtual environment and a virtual agent. This virtualization is infeasible in clinical settings, where real patients are situated in actual healthcare environments. Second, the restricted number of available actions within reinforcement learning makes it challenging to accommodate sophisticated treatment strategies. Furthermore, aligning sepsis patients' treatment with their current clinical state poses difficulties, as reinforcement learning typically trains policies rather than prescribing specific treatment methods. Finally, the qualitative analysis of learned treatment policies does not furnish unequivocal performance metrics. This paper explicitly concentrates on addressing these aforementioned challenges within the domain of sepsis treatment. The research objectives encompass two key aspects:  
1. An algorithm for SOFA score estimation: We introduce an innovative algorithm for estimating the next Sequential Organ Failure Assessment (SOFA) score. This algorithm leverages a deep ensemble model to project a patient's expected SOFA score progression in response to treatment interventions, addressing the variability in patients' responses. 2. An algorithm for sepsis treatment recommendation: Our novel approach focuses on making modest adjustments to the dosages of sepsis treatments, circumventing the limitations associated with traditional reinforcement learning methods. This patient-specific and data-driven strategy enhances the quality of sepsis patients' care.
## 2 Related Work
### Natural Language to Structured Queries  
In natural language processing (NLP), NL2 SQL (natural language to SQL) is a natural language processing technology designed to translate natural language queries into structured query language (SQL). It enables non-technical users to access and interact with relational databases through natural language. In the context of BIM, NL2 SQL technology offers a more efficient and user-friendly method for retrieving BIM resources, especially when working with large and complex BIM datasets.  
Current research and applications of NL2 SQL in the BIM field are developing rapidly. [PERSON] et al. have introduced the integration of spatial operations into standardized SQL queries, thereby enhancing the accessibility of BIM data for various query functions [32]. [PERSON] et al. proposed a system for construction project cost estimation using SQL, which facilitates the effective exchange of information with BIM elements through an interoperable information management system [33]. [PERSON] et al. developed a medical facility management system based on big data analytics (BDA), BIM, and NoSQL databases, enabling queries and visualization of key performance indicators (FM-KPIs) [34].  
However, NL2 SQL still faces several challenges, including accurately interpreting complex and diverse natural language expressions, managing incomplete semantic information, and generating precise SQL query statements. To support research in this area, various publicly available datasets, such as WikiSQL, Spider, and CoSQL, have been created. These datasets contain numerous real-world examples of natural language queries and corresponding SQL statements. Current NL2 SQL models mainly employ two strategies: they are trained or fine-tuned based on models with sequence-to-sequence (Seq2 Seq) architectures [35] or they inherit the ability of pre-trained large language models such as GPT, ERINE [36], Qwen, LiaMA[37], Mistral[38], etc., with prompt engineering [39]. Specially trained or fine-tuned Seq2 Seq models can perform exceptionally well on the given dataset, while large language models have substantial flexibility to handle the diversity of natural languages.  
Summarizing the development of NL2 SQL research, an intuitive way to optimize the performance of transforming natural language to structured BIM query language is to fuse a domain expert model with a pre-trained large language model to obtain good accuracy and robustness simultaneously.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Chemical Properties and Their Characterization of MNPs
#### 3.2.1 UV-Vis Spectrophotometry  
UV-Vis spectrophotometry is a widely used technique for characterizing nanoparticles, particularly metal nanoparticles. It is used for quantitative analysis of nanoparticle concentration based on the Beer-Lambert Law, which relates absorbance to concentration.  
This method relies on an NP-light interaction, leading to changes in the absorption spectra. When metal nanoparticles, especially noble ones like gold and silver, are exposed to light, they interact with the electromagnetic field, leading to phenomena such as surface plasmon resonance (SPR).  
The absorption of light by nanoparticles leads to the occurrence of characteristic peaks in the UV-Vis spectrum. The position of the SPR peak is sensitive to factors such as theNPs' size, shape, and surrounding medium. This is particularly useful for monitoring the progress of nanoparticle synthesis reactions [106].
## 2 State-of-the-art
### Background  
SSD Architecture  
The Single-Shot MultiBox Detector (SSD) belongs to the One-Step Framework, also known as the Regression or Classification Based Framework, just like YOLO or RetinaNet [43; 44]. With such frameworks, there is an explicit mapping between pixel values, bounding box coordinates and class probabilities, unlike Region Proposal-Based Frameworks, e.g., Faster RCNN. Therefore, compared to Faster RCNN and the same category of architectures, the SSD has lower inference times to the point of achieving real-time performance.  
The SSD architecture, depicted in Figure 2, is composed of two main parts: feature extraction and object detection. For the first one, a state-of-the-art classification model is usually used (e.g., the VGG16 [45] network as in Figure 2), but others like ResNet [46] or MobileNet [47] are also possible. The feature extractor is called the backbone, and its purpose is to generate high-level feature maps from the input image. Besides the backbone, the SSD adds six extra feature maps with a decreasing spatial dimension; see Figure 2[11; 15].  
For the second part, the SSD relies on a set of default anchors (i.e., bounding boxes) (Figure 3), with different aspect ratios and scales, thus reducing the possible amount of shapes that bounding boxes can assume [43]. A convolution layer is responsible for predicting, for each convolution operation and for each default anchor box, the location offset for that anchor and confidence scores for each class on the dataset. This convolution layer is applied to the extra feature maps. In the case of the VGG16 backbone, this layer is also applied to the Conv4_3 output [11]. Fusing the predictions made from the feature maps, each with a different resolution, allows detecting objects of different sizes. From Figure 2, using the feature maps towards the right will result in detecting larger objects, and vice versa [43].  
In the end, many detections will be predicted, so Non-Maximum Suppression (NMS) is applied to keep the highest rated bounding boxes. As concerns training, a weighted sum between localization loss (e.g., smooth L1) and confidence loss (e.g., softmax) is used [43].  
In order to improve SSD performance, the following measures can be used: choose default anchors with scales and aspect ratios according to the problem under analysis; perform data augmentation; and use hard negative mining. Even though, the SSD architecture  
Figure 3: Anchor box shapes used in the SSD architecture. Adapted with permission from ref. [11].  
Figure 2: Scheme for the SSD architecture using VGG16 as the backbone. Adapted from ref. [11].  
performs worst in small object detection as these do not appear in all feature maps. This problem can be mitigated by using better feature extractor backbones (e.g., ResNet) and higher resolution input images [43].
# Next-Generation Blockchain Technology:
## 7 Entropic Differential Barcode (EDB)
However, after the original file has been entropic barcoded, any future changes to the file itself can be detected by barcoding the file again and using the entropic differential barcode (EDB) method to check for the file's integrity. To demonstrate this, we intentionally tampered with the file by changing a single bit (0 into 1), and we ran the EDB program to validate the file. Figure 7 shows the entropic barcodes of the original file, the 1-bit-altered file, and the EDB verification spectrum. The data clearly show that a single-bit alteration, which was a 0 changed into a 1, has been detected via the EDB validation check, showing a spectrum that contains a non-zero value at the location index 522. This means that the window index 522 contains a modification/alteration to the file. The original file's IE value of the window index 522 was 1.831 bits. The change of a 0 into a 1 resulted in a change to the IE value from 1.831 bits to 1.847 bits, hence the non-zero spike in the EDB spectrum which indicates that the alteration has an absolute value of 0.016 bits. Any substantial changes to the file that result in a change in the total number of bits will show up as an EDB spectrum that is non-zero everywhere. This is a powerful technique that could be deployed to perform forensic verification of any form of digital file/asset, including performing financial audits and validating blockchains, crypto tokens, or NFTs. Figure 6: (**a**) Print screen of a random Excel spreadsheet file. (**b**) Example of the spreadsheet Excel file converted into an entropic barcode. Figure 7: Example of the entropic barcoding differential (EBD) technique deployed to detect changes to a digital file. In this case, a single bit was changed. Red (top barcode) corresponds to the original file; Blue (middle barcode) is for the altered file; Bottom graph is the EDB spectrum.
## 3 Proposed Algorithm
### SAR-SIFT Image Registration Algorithm
The direction corresponding to the highest point of the histogram is the main direction. **Generate descriptors. **  
The scale-dependent neighborhood around each key point is divided into sectors. We utilized the ROEWA gradient to calculate the gradient histogram within the key point neighborhood. Then, we consolidated all histograms into a normalized vector to generate SAR-SIFT feature descriptors. **Key point matching and filter matched points. **  
After obtaining the descriptors of the key points, we used the NNDR to perform preliminary matching. We chose the two closest feature points and judged whether the distance between them meets the given threshold. According to the threshold, we selected the matched feature point pairs and stored them. The formula for calculating Euclidean distance is as follows:  
\[d=\sqrt{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}} \tag{11}\]  
The initial matched feature point pairs contain a large number of erroneous matches, which need to be removed. We retained the matched points that are consistent in space and angle using the RANSAC algorithm. After obtaining all the correctly matched point pairs, we used these pairs to calculate the transformation model parameters and obtain the transformation matrix. **Image resampling. **  
We used the obtained affine transformation matrix to resample the registered image, adjusting its position and size to achieve perfect matching. Through the above steps, the registered SAR image was obtained. The flowchart of the SAR-SIFT algorithm operation process is shown below (Figure 6):
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
###### Abstract  
The routing algorithm is one of the main factors that directly impact on network performance. However, conventional routing algorithms do not consider the network data history, for instances, overloaded paths or equipment faults. It is expected that routing algorithms based on machine learning present advantages using that network data. Nevertheless, in a routing algorithm based on reinforcement learning (RL) technique, additional control message headers could be required. In this context, this research presents an enhanced routing protocol based on RL, named e-RLRP, in which the overhead is reduced. Specifically, a dynamic adjustment in the Hello message interval is implemented to compensate the overhead generated by the use of RL. Different network scenarios with variable number of nodes, routes, traffic flows and degree of mobility are implemented, in which network parameters, such as packet loss, delay, throughput and overhead are obtained. Additionally, a Voice-over-IP (VoIP) communication scenario is implemented, in which the E-model algorithm is used to predict the communication quality. For performance comparison, the OLSR, BATMAN and RLRP protocols are used. Experimental results show that the e-RLRP reduces network overhead compared to RLRP, and overcomes in most cases all of these protocols, considering both network parameters and VoIP quality.  
routing algorithms; machine learning; reinforcement learning; intelligent routing; VoIP; QoE +
Footnote †: journal: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
## 3 Result and Analysis
### Effect of DOC on SCR  
The NO-NO2 conversion rate within DOC at different exhaust temperatures and mass flows is given in Figure 11. When the exhaust temperature exceeds 425 \({}^{\circ}\)C, the conversion rate does not change significantly with the exhaust flow, while gradually decreasing with the increase in the exhaust temperature. Under medium load working points (in the range of 325 \({}^{\circ}\)C-425 \({}^{\circ}\)C), the exhaust mass flow has a significant effect on the NOx conversion rate in DOC, and the variety of conversion rates is limited at higher exhaust mass flow. The effect of exhaust temperature on NO-NO2 conversion rate is simple and significant. The high conversion rate mainly occurred between 350 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, with medium to low exhaust flow rate from 300 kg/h to 525 kg/h. The highest conversion rate among all selected operating conditions is about 39.5%; the corresponding exhaust temperature and exhaust mass flow rate are 386 \({}^{\circ}\)C and 336.6 kg/h, respectively. It should be noted that the temperature is also within the optimistic temperature range of the SCR [17].  
Figure 12 shows the NOx conversion rates at three different EAT system schemes of the working points under low- to medium-load conditions of the engine. Exhaust temperature is increased through exhaust electric heating, with or without upstream DOC, which is reflected in the figure as the NOx conversion efficiency at all three operating points being at a high level. At steady state and higher carrier temperature, the NOx generated in DOC still increases the proportion of fast reaction in SCR and improves the conversion rate of NOx, which makes the NOx conversion efficiency of the scheme involving upstream DOC slightly higher than the other two schemes at the three operating points. Under three working conditions, the NOx emissions of the EAT system with upstream DOC decreases by 18.69%, 9.61%, and 14.15% compared to the conventional arrangement, although the NOx conversion efficiency increases to a limited extent compared to the other two EAT systems without upstream DOC. It should be also noted that, at cold start conditions, an  
upstream DOC will consume the heat of the exhaust and delay the catalyst temperature increase of LO-SCR and the effect on the cycle NOx emission may be negative.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project AI  Artificial Intelligence CU/DU  Centralized Unit/Distributed Unit DP  Differential Privacy eMBB  Enhanced Mobile Broadband FL  Federated Learning GDPR  General Data Protection Regulation KPM  Key Performance Measurement mMTC  Massive Machine-Type Communications ML  Machine Learning O-RAN  Open Radio Access Network RBAC  Role-Based Access Control RIC  RAN Intelligent Controller SMO  Service Management and Orchestration SGX  Software Guard Extensions URLLC  Ultra-Reliable Low-Latency Communications xApps/rApps  Near-Real-Time/Non-Real-Time RIC Applications ZTA  Zero-Trust Architecture
## 3 Method and Materials
### Initial Stage  
To successfully build a digital monitoring system for asphalt concrete pavement construction, it was necessary to determine the research objectives and the main challenges of current monitoring [9]. Therefore, this study used the semi-structured interview method to achieve this purpose.  
Semi-structured interviews are suitable for qualitative research, with interviewer-to-respondent conversations, supplemented by follow-up evaluations and surveys, which are conducted within the framework of a pre-defined interview.  
(1) Research purpose  
Through semi-structured interviews with relevant professionals, we can gain an in-depth understanding of the current goals and challenges of digital monitoring of asphalt concrete pavements, to promote follow-up high-quality research.  
(2) Planning the interview  
A total of 25 interviews were conducted in this study, and employees or managers engaged in asphalt pavement construction, pavement quality monitoring, and monitoring system development were selected as interviewees with consideration of their education and working years. The specific selection criteria were as follows. A stratified sampling method was adopted to determine the source of the respondents, and ensure that the respondents came from different work departments. Table 1 contains the data of the respondents.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Unit** & **Age** & **Education** & **Number of People** & **Rank** & **Respondent Number** \\ \hline Quality monitoring unit & 25–40 & Undergraduate & 10 & & A1, A2, A3, A4, A5, A6, & A7, A8, A9, A10 \\ System development unit & 24–35 & Master & 8 & managers and non-manager & A11, A12, A13, A14, A15, & A16, A17, A18 \\ Construction unit & 23–50 & Bachelor, Master & 5 & & A19, A20, A21, A22, A23 \\ Survey unit & 24–42 & Bachelor, Master & 2 & & A24, A25 \\ \hline \end{tabular}
\end{table}
Table 1: Respondent information.  
Figure 1: Flowchart of the methodology employed during the study.  
Working in the same unit or the same job for more than 5 years;  
Working in different types of units;  
Different ages or gender;  
From different professional backgrounds or fields of work;  
Ranks of different positions.  
Conduct interviews  
After the researchers collected the descriptive data of the interviewees, the interview outline was prepared in advance, and the interview was conducted twice; 12 interviews were conducted for the first time, and 13 interviews were conducted for the second time. The entire interview process was recorded using mobile phones, and the transcripts were analyzed using NVivo 12 pro software. Before conducting the formal interview, the research team conducted a pre-interview and revised and improved the interview outline through the pre-interview session. The interviews were conducted informally using open-ended questions. The interview questions could be changed according to the specific circumstances of the interviewees' answers to the questions. See Table 2 for the interview questions.
## 1 Introduction  
Knowledge of seafloor geoacoustic properties is important for the quantification of the reflection, scattering, reverberation and refraction of sound in the marine environment. The compressional speed velocity profile of the sediments in the few meters below the seafloor is an important geoacoustic parameter, which has a significant impact on sound propagation in the water column [1]. This velocity profile is therefore of considerable interest in geophysics, underwater acoustics, ocean engineering, underwater communication and other naval applications [2, 3, 4, 5, 6, 7, 8, 9]. The important factors affecting sound velocity are the sediment physical properties (e.g., water content, porosity, density), sediment composition and texture, overburden pressure, and temperature of the sediment [10].
## 2 Theoretical Analysis Model and Calculation Method
### Effect of Fastener Parameters on System Power Flow Characteristics
#### 3.2.2 Impact of Fastener Damping on Power Flow Transmission Characteristics  
Figure 6 illustrates the spectrum curves depicting the power flow of the track, integral ballast, bridge roof, and bridge bottom at 30 kN\(\cdot\)s/m, 40 kN\(\cdot\)s/m, and 50 kN\(\cdot\)s/m for fastener damping conditions. This analysis aims to further examine the impact of fastener damping on the energy transfer dynamics of the overall system.  
As observed in Figure 6, within the low-frequency range of 0-100 Hz, fastener damping exerts minimal influence on track power flow, and the spectrum curves for the three operating conditions align closely. As frequency rises, the peak values of track power flow emerge at 300 Hz, 600 Hz, 800 Hz, 1200 Hz, and 1600 Hz. Fastener damping significantly impacts the peak values at 600 Hz and 800 Hz, thereby chiefly influencing the power flow transmission characteristics within the high-frequency band for the track.  
The influence of fastener damping on ballast power flow is primarily evident in the 80-600 Hz range, exhibiting a diminishing trend with increasing fastener damping. The  
Figure 6: Power flow of track, integral ballast bed, bridge roof, and bridge floor varies with the damping of coupler: (**a**) Power flow of track; (**b**) Power flow of integral ballast bed; (**c**) Power flow of bridge roof; (**d**) Power flow of bridge bottom plate.  
power flow transfer characteristics of the ballast are inversely proportional to fastener damping. Nevertheless, beyond 600 Hz, the relationship becomes proportional.  
Fastener damping notably affects the power flow of the bridge roof, particularly the vibration energy transferred to the bridge roof within the 0-200 Hz range, which decreases with rising fastener damping. The impact magnitude diminishes with increasing frequency.  
Vertical Vibration Transmission Characteristics of Urban Track Transit Integral Ballast Bed-Box Girder Bridge