esa-sceva/satcom-chunk-collection huggingface dataset:
# Generating Process Models by Interacting with Chatbots--A Literature Review
## 3 Structure of the Systematic Literature Review
### Research Questions  
The overarching research question was formulated as 'How can natural language and process models be integrated, including the use of chatbots?'. Starting with this research question, we conducted a SLR and subsequently divided this primary research question into four detailed research questions. The latter were obtained after having gained an overview of the collected studies, which we categorized according to their method, distinguishing between (1) studies generating process models from natural text input, (2) studies generating text from modeled process models, (3) studies encompassing both directions, and (4) studies that investigate the use of chatbots in the context of (1)-(3).  
* RQ1: How to generate process models from natural language text input (Text-to-Model)? Research question RQ1 aims to explore and understand the methodologies, techniques, and approaches utilized in transforming unstructured natural language text intoprocess models (cf. Figure 1). Through this investigation, the goal is to identify the various methods employed, and any emerging trends or advancements in the field.
* RQ2: How to generate natural language text from process models (Model-to-Text)? The aim of RQ2 is to investigate and comprehend the methodologies, techniques, and strategies employed in generating natural language text output from process models (cf. Figure 2). Through this inquiry, the objective is to discern the diverse approaches utilized.
* RQ3: How to get from natural language text input to process models and backwards? The aim of RQ3 is to explore methodologies, techniques, and frameworks that enable bidirectional conversions between natural language text and process models. By investigating this bidirectional transformation process, the goal is to understand the various approaches utilized.
* RQ4: How are chatbots included in the processes of RQ1-RQ3? The aim of RQ4 is to explore which of the collected studies for RQ1-RQ3 use a chatbot for generating process models from natural language text or for generating natural language text from process models.
# Lightweight Object Detector Based on Images Captured Using Unmanned Aerial Vehicle
## 3 Results
### Experiments and Analysis of Results
#### 3.3.3 Ablation Experiments  
To verify the effectiveness of each proposed module for the overall model, ablation experiments were conducted. These experiments demonstrated the effectiveness of various modules within the overall model, using Yolov8s as the baseline. Specifically, we evaluated the lightweight C2f-Faster, C2f-SWCA, and C2f-Faster-SWCA modules, with the final module combining both the lightweight C2f-Faster and C2f-SWCA modules. The ablation experiments are shown in Table 4.  
The data indicate that, after introducing the C2f-Faster module, the overall number of model parameters was reduced by 25.4% but the accuracy decreased by 0.6%. Compared to the introduction of coordinate attention, the SWCA module improved its effectiveness by  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **mAP50** & **mAP50:95** & **GFLOPs** & **Params/M** \\ \hline Baseline & 39.9 & 23.8 & 28.5 & 11.13 \\ +C2f-Faster & 39.3 & 23.8 & 21.4 & 8.30 \\ +C2f-CA & 40.4 & 24.4 & 28.4 & 11.15 \\ +C2f-SWCA & 40.6 & 24.4 & 28.5 & 11.56 \\ +C2f-Faster-SWCA & 40.0 & 23.8 & 21.3 & 8.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Impact of individual modules (C2f-Faster and SWCA) on model performance relative to the baseline (Yolov8s).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Image Size** & **Params/M** & **mAP50** & **mAP50:95** \\ \hline Yolov3-tiny & 640°640 & 8.68 & 13.5 & 5.8 \\ Yolov5s & 640°640 & 7.03 & 26.4 & 14.2 \\ VA-Yolo & 640°640 & 6.56 & 23.6 & 12.6 \\ PP-Yolo & 640°640 & 52.2 & 39.6 & 24.6 \\ FasterRCNN ResNeXt101 & 640°640 & — & 40.2 & 22.6 \\ YoloX-X & 640°640 & 99.1 & 43.2 & 25.8 \\ Swin-T & 640°640 & 38.6 & 42.5 & 23.1 \\ DDETR & 640°640 & 39.8 & 42.7 & 24.8 \\ Yolov8s & 640°640 & 11.13 & 39.9 & 23.8 \\
**Yolo-CFS** & 640°640 & **8.61** & **40.0** & **23.8** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of Yolo-CFS with other object detection models on the Visdrone2019 dataset.  
40% on one side and increased accuracy by 0.2%. The C2f-Faster-SWCA module mitigated the significant accuracy drop caused by the C2f-Faster module in the overall lightweighting process, and the improved model's accuracy matched that of the baseline model. Notably, the number of parameters increased by only 4%, proving that the overall model maintained accuracy without a significant decrease during the lightweighting process.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 3 Results
### Wireless Link Emulation
#### 3.1.3 CWND Evaluation  
For the CWND evaluation, we started a TCP connection between both terminal equipment and transmitted the maximum possible raw bit rate through the emulated link for 25 seconds.
## 2 Method
### Specific Steps of Eliminating Gross Errors of the Dam Monitoring Data  
For a given data-fit segment \(\text{N}=\left\{(x_{i},t_{i})|x\in(1,n),i\in(1,Q)\right\}\), the initial number of the hidden level nodes is \(s\), the maximum number of hidden layer nodes is \(s_{\text{max}}\), the expected accuracy is \(\varepsilon\).  
Step 1: The dam environment factors connection weight vectors \(\omega_{i}\) between the input layer and the hidden layer and the bias vectors \(\mathbf{b}_{i}\) of the hidden layer are randomly generated in the ELM network structure \((i=1,2,\cdots,s)\);  
Step 2: Let \(\mathrm{s}=1\), calculate the hidden-layer output matrix \(\mathrm{H}_{0}\) and the connection weight of fitting effector factor of concrete dam \(\beta_{0}\) between the hidden layer and the output layer, and calculate learning accuracy \(\mathrm{e}_{\mathrm{s}}=\mathrm{e}_{0}\);  
Step 3: Let \(\mathrm{s}=s+1\), calculate the output matrix \(H_{s}=[H_{s-1},\upsilon_{s}]\) and the connection weight of fitting effector factor of concrete dam \(\beta_{s}\) until \(s\geq s_{\mathrm{max}}\) or \(\mathrm{e}_{s}<\epsilon\), the optimal number of hidden layer nodes \(s^{*}\) is obtained;  
Step 4: Utilize the LMS method to calculate the unit weighted variance of deformation monitoring data of concrete dam \(\sigma\), then the initial equivalence weight matrix \(\overset{\cdot}{P}\) is also obtained;  
Step 5: According to \(H_{s^{*}}\) in Step 3 and \(\overset{\cdot}{P}\) in Step 4, use Equation (12) to calculate \(\overset{\cdot}{\beta}\);  
Step 6: Calculate by iteration successively until \(\overset{\cdot}{\beta}^{i+1}-\overset{\cdot}{\beta}\)\(\leq\delta\) stopped;  
Step 7: Output the monitoring deformation weight of concrete dam in the continuous time series \(P_{i}\), the sample series of \(P_{i}\) to 0 are judged as gross errors and eliminated.  
The flow of gross errors identification by LMS-IGG-ELM is shown in Figure 3:
## 2 End-of-Life of CRC  
End-of-life (EoL) describes the end of a product's life cycle and starts when the product no longer accomplishes its function and is considered as waste. The EoL contemplates different routes, such as reuse without structural changes, remanufacturing to like-new condition, recycling treatment of waste products for use as raw material in the same or a different product cycle, as well as incineration with or without energy recovery or landfill [15]. For the EoL of CRC, a distinction is made between two types:  
1. Separation of the demolition material into individual fractions that can be disposed of or recycled individually;
2. Non-separation of the demolition material into individual fractions, resulting in the reuse, recycling, or disposal of a heterogeneous material [3] (Figure 1).  
Figure 1 shows all currently possible EoL scenarios for CRC--excluding landfilling, as this is not allowed in Germany for carbon fibers with an EP matrix, according to the Deponieverordnung (DepV) (09/1027). It represents both the complete separation of the materials and their continued use as a complete component. The carbon fiber fraction can be used for energy recovery, reinforcement in concrete and plastic, as well as a carbon  
Figure 1: EoL of the CRC building part (own illustration).  
source for industrial processes. These processes and applications are further discussed in Section 2.2. For the separated concrete fraction, the recycling and use of a filling material in road construction are possible scenarios which are discussed in more detail in Section 2.3. Furthermore, in Figure 1, a distinction between preferred, less preferred, and not preferred. EoL scenarios is made--shown graphically with different frames. The terms \"preferred\" or \"not preferred\" refer to the properties of the materials at the end of the EoL scenario, based on whether or not their further use avoids downcycling. In general, scenarios are preferred in which the material properties are maintained.  
[PERSON] [16] rules out the reuse of textile-reinforced concrete components. According to [PERSON], reuse should be chosen over material recycling, but in practice a damage-free dismantling is not yet possible. Moreover, the current legal and political system of the construction sector does not promote the dissemination of reuse practices [17]. With the demolition and separation of the CRC components, it is expected that the highest-quality outputs for the processed materials (processed concrete and processed carbon fibers) will be achieved [3].
## 2 Materials and Methods
### Data Processing  
In this paper, ALS forest canopy height and CASAL full-waveform forest canopy height were used for comparison, and the study was carried out in accordance with the following methodological steps: (1) accuracy analysis of all CASAL observation data; (2) accuracy analysis of different forest types; and (3) screening and analysis of effective factors affecting the performance of CASAL in forest areas. Data processing in this study can be divided into two parts, namely data preprocessing and statistical analysis. The data processing and statistical analysis were performed using Python v3.8, and the mapping visualization was achieved using ArcGIS v10.7. The data processing flow is illustrated in Figure 3.
# Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges
## 2 Fog and Edge Computing: Modelling and Simulation Challenges
### Application Level Modelling  
There is an infinite range of potential applications for fog and edge computing, ranging from simple-IoT based sensor monitoring to the complex data processing systems inherent in Industry 4.0, e-health, smart cities etc. Consequently, underlying applications vary in their needs based on the degree of (i) contextual location awareness and low latency, (ii) geographic distribution, (iii) scale and coordination of end-point networks, (iv) heterogeneity, interoperability and functionality of end points, (v) real-time vs batch processing, (vi) mobility of end points, and (vii) interplay between the edge, the fog and the cloud layers [4; 22]. Provisioning for such heterogeneity requires significant planning upfront and ongoing optimisation throughout the C2T continuum including application design.  
The majority of current fog and edge services that support applications can be further divided into three main categories - Content Distribution Networks (CDN), IoT and Virtual Network Functions (VNF). While all three use the same infrastructure, the functional aspects of each type of service are fundamentally different. CDN services focus mostly on static content replication and distribution across multiple locations. IoT services are used to offload data processing and storage from sensors to edge locations selectively pushing some of the data up the network stack to the cloud. VNFs are chains of network functions that handle mobile network protocol traffic (e.g., LTE stack) or provide network traffic filtering and routing functions, such as enterprise, firewall and VPN services. Law [23] suggests \"_a simulation model should always be developed for a particular set of objectives. In fact, a model that is valid for one objective may not be for another._\" Modelling all of the applications deployed within a fog/edge network can be beneficial for infrastructure providers but constructing a simulation solution that can efficiently handle a set of such broad objectives is a challenge and needs careful consideration.
## 2 Literature Review
### Quantitative Analysis  
A quantitative empirical analysis was conducted on the literature, involving the examination of a dataset comprising 2500 papers. The analysis aimed to determine various statistical indicators, with a focus on the frequency of common terms in research papers and their relevance to the topic. The study utilized bibliographic research methods, employing the Dimensions.ai scientific database [15] and visualization software (VOSViewer [16]). The analysis proceeded through the following steps:  
**Step 1:**: Keyword search: A direct search in the Dimensions.ai database using the keyword \"education AND digital technology\".
**Step 2:**: Export of search results: export the search results using the database's configuration options.
**Step 3:**: Mapping of exported results: utilize mapping software (VOSViewer 1.6.20) to visualize the exported results.  
The threshold for the number of occurrences was set to 100, resulting in the identification of 88 terms. After the data processing step, the term map in various forms was obtained in Figure 1. The terms are listed and can be previewed in Table 1.  
\begin{table}
\begin{tabular}{l l r r} \hline \hline
**Rank** & **Term** & **Relevance Score** & **Occurrences** \\ \hline
54 & older adult & 4.2628 & 130 \\
82 & teaching & 3.0753 & 423 \\
46 & learner & 2.9500 & 167 \\
81 & teacher & 2.7435 & 377 \\
80 & systematic review & 2.4310 & 159 \\
19 & digital health & 2.4148 & 134 \\
60 & patient & 2.3371 & 319 \\
64 & population & 2.3194 & 256 \\
85 & university & 2.2092 & 307 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis of the top 9 obtained terms after the bibliographic study sorted by their relevance score.  
Figure 1: The term occurrence map: **(a)** The term density. **(b)** The cluster organization.  
The terms mentioned in the bibliographic study may suggest a concern for the education of older adults, with an emphasis on the process of learning and teaching, especially in the digital context of health and medical services.
# Case Study of an Integrated Design and Technical Concept for a Scalable Hyperloop System
## 4 Functional Definition
### Safety
To minimize disruption to operations while ensuring the safety of the passengers, one of the three previously mentioned error procedures is deployed based on the measured leakage rate and the resulting time constraint. Gates are crucial to prevent the propagation of failures, effectively isolating sections of the tube to avoid the spread of issues due to a single error. Also, they allow for the extension of the main lane without filling the entire section with air. Manufacturing these large and heavy yet reliable mechanisms will be challenging but feasible. Inflatable rubber gates used in the pipeline industry present an interesting alternative but seem difficult to implement with the guideway geometry. Therefore, conventional steel gates have been chosen. Several sliding door designs have already been presented by the industry. For the  
Figure 6: Schematic overview over the three major error procedures for safe handling of different hazard scenarios with their severity and probability. \begin{table}
\begin{tabular}{l c} \hline \hline
**Description** & **Value** \\ \hline Max time to hub & 13 min \\ Max distance between hubs & 109,327 m \\ Number of pods in this track segment & 245.57 pods \\ Required rescue lane length & 3684 m \\ Emergency acceleration & 8 \(\frac{m}{s^{2}}\) \\ Braking length emergency & 1736 m \\ Braking duration emergency & 20.83 s \\ Max affected pods at sudden collapse (max speed) & 6.89 pods \\ Max affected passengers at sudden collapse (max speed) & 147 pax \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Safety characteristics. Values with a grey background are calculated, while others are input values. TUM Hyperloop demonstrator, a hanging door was featured to allow for inaccuracies in the angle and position of the concrete. Another challenging aspect concerning safety is the communication system between pods and infrastructure. Both 5G and Wi-Fi with leaky cables or normal antennas are feasible options for low-latency and high-bandwidth communication. However, since low-latency communication is safety-critical and difficult to guarantee with wireless systems alone, a secondary communication system is proposed. Intelligent substations along the track will detect the positions of the pods, communicate simple status signals to the pods, and relay information between them and with operation control. This redundancy ensures that the failure of any single system can be managed by other systems.
## 6 Discussion and Future Challenges
### The Complex Electromagnetic Environmental Effects Evaluation System  
Generally, the current software for electromagnetic environmental effects research is mainly for simulation modeling. Evaluation and prediction software is more limited to a single source and frequency of interference, as well as a specific space and equipment. The evaluation of the electromagnetic environmental effects of any equipment under the combined action of multiple sources and frequencies is not available. Therefore, it is not suitable for evaluating the interference effects of a complex electromagnetic environment. The evaluation system for the electromagnetic environmental effect is the convergence point of previous studies on the effect evaluation model. This model is the basis of the evaluation system. Therefore, building a comprehensive evaluation model for the complex electromagnetic environmental effects is key to the evaluation system. However, the evaluation model for complex electromagnetic environmental effects is bound to be more complicated than that for a single frequency and source of electromagnetic environmental effects. Hence, it is necessary to simplify the model from an engineering perspective and improve its adaptability [95]. Table 8 shows the possible solutions and key issues for each challenge.
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
###### Abstract
In order to extract semantic information and transform code into vectors we propose the concept of _semantic slice_ for malicious JavaScript and leverage specific key functions as the slicing criterion to generate program slices. * We studied the influence of obfuscation technique on neural classifier, which is one of the key steps in data processing. Through the comparison of multiple sets of experiments, we observe that the obfuscation can reduce the performance of the BSLTM neural network. Then we comprehensively discuss the reasons for this situation. * We implemented the BLSTM neural network and compared it with four other machine learning-based detection models and a traditional antivirus software. Experimental results show that our model achieves the best performance. The rest of this paper is organized as follows. We introduce related work in Section 2. The abstract code representation of JavaScript is proposed in Section 3 and the BLSTM neural network for malicious JavaScript detection is introduced in Section 4. In Section 5, we introduce experimental designs and experimental results. We discuss the limitations in Section 6 and the conclusion in Section 7.
# Enhancing the Integration of Protein-Rich Oat Waste Material into Meat Formulations
## 4 Discussion
### Assessing the Possibility of Using OPH as a Meat Additive
Another contributing factor could be the natural presence of iron in oats [58], which when combined with the iron present in meat, accelerates the oxidation. This aspect of the research will be further explored in subsequent investigations. It is established that proteins play an important role in the process of emulsified sausage production. The batters are considered oil-in-water emulsions, in which fat globules covered with protein layers are immersed in a myofibrillar protein gel matrix. Heating allows the formation of a stable gel, in which disulfide bridges are formed between fat and protein [59]. Additional proteins (both animal and plant) usually increase the stability of meat emulsions, as well as the hardness of the meat products [60]. Among the most popular plant proteins used in the meat industry are soy proteins, along with others such as wheat, peas, etc., which possess gelling properties and allow for the improvement of the water holding capacity and texture of meat products. Proper quality meat is evaluated in terms of WHC and gel-formation; however, meat defects occur quite often. Plant proteins may support meat protein functionality, working with them to retain moisture and assure proper texture [61]. Incorporating unknown additives into a meat system may result in unexpected results [62]. Although the observations made during the performance of these tests led us to conclude that hardness increased along with OPH addition, the differences were negligible and not statistically significant. Hardness increased in all the samples along with storage time, probably due to drying, despite the containers being closed. OPH improved the cohesiveness and resilience of the meat samples, which were especially visible in samples containing 2 and 3% of OPH. This may be explained by the presence of carbohydrates, part of which were fibres (data not published). This may also suggest the occurrence of plant and meat protein interactions. [PERSON] et al. [63] stated that no chemical interactions between salt-soluble meat proteins and carrageenan or soy protein isolate mixtures were detected in the formulations. Nonetheless, this additive may have changed the conformation of meat proteins, activating functional groups and forming additional disulfide bonds. These changes may have strengthened the gel. It has also been suggested that the myosin light chain could be protected by the additive preventing its destruction during thermal treatment and increasing gel stability. The same effect was achieved in our research via OPH. Small molecules obtained due to hydrolysis may have the ability to penetrate the system and improve the structure. OPH could also change a proportion of \(\alpha\)-helices to \(\beta\)-sheets of meat proteins, as such an outcome was achieved in research on potato and meat protein mixtures [62]. The formation of \(\beta\)-sheets by unfolding \(\alpha\)-helices was proved to be essential in protein network formation [64].