esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Lightweight Clear Channel Assessment (LW-CCA)
### Time Factors in a Single RSSI Radio Check  
The basis of LW-CCA is the momentary check of RSSI. In this study, we examine the time parameters involved in an RSSI radio check. The relationship between ContikiMAC functions, the CC2420 driver, and the Energest module is shown in Figure 3. In general, each RSSI radio check consists of three phases:  
* Phase 1: Checking permissions for radio driver access by RDC, registering radio hardware to Rx mode by the radio driver, and recording start time of Rx by Energest.
* Phase 2: Validating the RSSI and returning RSSI value from related radio register.
* Phase 3: Set the radio registers to 'off' state, Preparing the radio queue for the next stage of radio activity and also announce the end of Rx state to Energest module.  
Since our goal is to manage WakeUp time based on radio potentials, so analysis and retrieval of time factors affecting WakeUp are inevitable. Figure 3 shows a single WakeUp diagram based on the RSSI check.  
Every radio WakeUp lasts between strobe CC2420_SRXON and strobe CC2420_SRFOFF. The number of CPU ticks in the radio check is saved in Energest. The wait_for_status (CC2420_RSSI_VALID) in phase 2 is known as an important time factor for a single RSSI check. It takes time equal with Equation (3).  
\[T_{\Delta}=\frac{\text{RTIMER\_SECOND}}{10} \tag{3}\]  
Table 6 shows time factors which are resulted from analyzing the relationship between RDC and radio hardware in an RSSI check. The table is extracted from the radio Timeline in Cooja.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(t\_wait\_for\_status\) (ms) & \(t\_RSSI\) (ms) & \(total\_time\) (ms) \\ \hline
0.32 & 0.128 & 0.448 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effective time factors per idle listening based on RSSI under Cooja simulator timeline.  
Every RSSI check involves the different action timings: t_wait_for_status is time spent for RSSI status check based on Equation (3). t_RSSI is time spent for the read of RSSI status check. Total_time is the sum of the time spent in an RSSI radio check. Actually in an RSSI check, the _t_wait_for_status_ takes the longest time 0.32 milliseconds (20 symbols period according to [42]) to doing a reliable RSSI status check-in ContikiMAC. Therefore, it indicates that ContikiMAC devotes more time to RSSI status checking than the eight-symbol periods suggested in CC2420 datasheet. It should be noted that 20 Symbols has no role in RSSI reception. In fact, it is a BackOFF for reliable time to wake the radio.  
Figure 3: Diagram of the relationship between RDC, the radio driver, and the Energest module in performing a single RSSI.
## 4 Experimental Results
### Characteristics of Augmented Essays
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Baseline** & \multicolumn{4}{c}{**Data Augmentation (\(\times\)1)**} & \multicolumn{2}{c}{**Comparison**} \\ \hline \multirow{2}{*}{**Prompt**} & \multirow{2}{*}{**Training Data**} & \multirow{2}{*}{**Random**} & \multirow{2}{*}{**Random\({}_{\text{Order}}\)**} & \multicolumn{2}{c}{**EssayGAN**} & \multirow{2}{*}{**EssayGAN**} & \multirow{2}{*}{**Training Data**} & \multirow{2}{*}{**Training Data**} \\  & & & & & & & + 1 Swapped & + 2 Swapped \\ \hline \multirow{2}{*}{1} & 0.312 & 0.221 & 0.224 & 0.292 & 0.293 & **0.300** & 0.289 \\  & (0.278) & (0.220) & (0.221) & (0.254) & (0.255) & (0.270) & (0.262) \\ \hline \multirow{2}{*}{2} & 0.320 & 0.215 & 0.220 & 0.294 & 0.297 & **0.303** & 0.290 \\  & (0.287) & (0.214) & (0.219) & (0.255) & (0.259) & (0.273) & (0.264) \\ \hline \multirow{2}{*}{3} & 0.252 & 0.190 & 0.189 & **0.233** & 0.232 & 0.227 & 0.211 \\  & (0.244) & (0.190) & (0.185) & (0.214) & (0.217) & (0.221) & (0.207) \\ \hline \multirow{2}{*}{4} & 0.300 & 0.226 & 0.220 & **0.266** & 0.258 & 0.254 & 0.235 \\  & (0.297) & (0.224) & (0.221) & (0.256) & (0.250) & (0.254) & (0.236) \\ \hline \multirow{2}{*}{5} & 0.334 & 0.250 & 0.254 & **0.307** & **0.307** & 0.302 & 0.281 \\  & (0.321) & (0.249) & (0.246) & (0.286) & (0.285) & (0.292) & (0.275) \\ \hline \multirow{2}{*}{6} & 0.292 & 0.256 & 0.258 & **0.282** & 0.278 & 0.278 & 0.269 \\  & (0.288) & (0.256) & (0.255) & (0.271) & (0.268) & (0.276) & (0.268) \\ \hline \multirow{2}{*}{7} & 0.331 & 0.208 & 0.211 & **0.298** & 0.296 & 0.291 & 0.268 \\  & (0.303) & (0.209) & (0.210) & (0.260) & (0.258) & (0.270) & (0.250) \\ \hline \multirow{2}{*}{8} & 0.332 & 0.214 & 0.219 & 0.293 & 0.293 & **0.321** & 0.310 \\  & (0.284) & (0.214) & (0.215) & (0.246) & (0.246) & (0.276) & (0.269) \\ \hline \multirow{2}{*}{AVG} & 0.309 & 0.222 & 0.224 & 0.283 & 0.282 & **0.284** & 0.269 \\  & (0.288) & (0.222) & (0.221) & (0.255) & (0.255) & (0.266) & (0.254) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of local and global coherence between sentences in essays according to the augmentation techniques. ‘Training Data + 1 swapped’ refers to a real essay in which one sentence was replaced with a random sentence. ‘Training Data + 2 swapped’ refers to one with two swapped sentences. The numbers inside parentheses are values of global coherence. Boldface indicates the highest values of coherence, excluding the ‘Training Data’.
## 5 Conclusions  
This study presents a methodology for developing a specialized lexicon for emergency incidents in gas pipeline networks. The method not only integrates standard terminology and risk inventory knowledge but also combines statistical-based approaches with pre-trained models. Analysis shows that the resulting domain-specific lexicon is highly applicable in its intended context.  
This research aims to significantly improve emergency management in gas pipeline networks. The lexicon supports cause analysis, a critical component of emergency response, by encompassing an extensive array of domain-specific terms. The TF-IDF values, a statistical measure of term relevance, indicate the importance of specific terms in this domain, supporting cause analysis. Furthermore, this lexicon facilitates domain knowledge acquisition and aids in decision categorization within emergency decision-making systems. Given its domain-specific attributes, the lexicon is expected to enhance domain knowledge graph development and improve named entity recognition accuracy.  
This study makes significant progress in constructing a domain-specific lexicon for gas pipeline emergencies, though certain limitations remain. The breadth of data sources, challenges in capturing precise semantics, and difficulties in term extraction and analysis highlight areas needing improvement. Future research will address these issues by expanding data sources, improving model precision, and refining semantic analysis. These efforts aim to enhance the lexicon's comprehensiveness and reliability, ensuring its broader applicability and effectiveness in real-world scenarios.  
Conceptualization, X.Z. and Y.H.; methodology, X.Z.; software, X.Z.; validation, T.Q., W.W. and Y.W.; formal analysis, X.Z.; investigation, Y.H.; resources, Y.W.; data curation, W.W.; writing\(-\)original draft preparation, X.Z.; writing\(-\)review and editing, Y.H.; visualization, X.Z.; supervision, Y.H.; project administration, W.W.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2020 YFC1511701, and the Beijing Municipal Science and Technology Plan Project, grant numbers Z221100005222024 and Z221100005222020.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Discussion
One could take a large dataset of unlabeled or partially unlabeled images that are similar, cluster them initially, and re-cluster them to identify final groupings of said images. Additionally, this study limited the initial gene clustering to 100 of the top genes. This could be further tested using more genes, potentially even thousands, to test whether clusters come out stable and of high accuracy. This study also only limited the scope to one type of cancer (lung) and one specific sub-type of cancer, non-small-cell lung cancer (NSCLC). However, as this was a pilot study on a small number of samples, this could easily be expanded. One thing to note is that as this study was able to consistently re-classify clusters into similar groupings based on accuracy, it is possible that if it were to run indefinitely, it could re-cluster everything into an artificially small number of clusters with 100% accuracy and overfit the data. Thus, it is important to apply some sort of threshold or patience to prevent this, as is the case with many machine learning models. For this study, we attempted to address potential overfitting through the use of multiple datasets, by stopping after a relatively low threshold and minimal number of runs (traditionally called epochs), and by not letting the algorithm run to completion. It would be interesting to explore, in future work, at what point an optimal threshold is reached on average for a number of runs, and whether there is some optimal number of runs or whether all machine learning models must vary dependent on the data itself. The genetic clusters provided interesting insight into potential patterns of cancer inheritance as well. KRAS is currently available to be treated by Sotorasib (Lumakras) and adagrasib (Krazati) and was grouped into its own cluster [41; 42; 43]. Similarly ALK, CDKN2, KDR, and NTRK fell into a cluster, as did TP53, EGFR, CDKN2, and BRAF. It would be interesting to see whether these clusters would hold across a larger number of data samples or whether this was specific to the populations of NSCLC subjects present in this data. This study did not attempt to make grandiose claims or thoroughly evaluate these clusters as the final goal was to present a pilot study on a small number of samples.
## Appendix C LLM Implementation Details  
_Appendix C.1. Question Validity_  
**Functionality Overview:**  
* Determines if the user's question is one that we can answer. While DietNerd can recommend general diets that may be suitable for certain health conditions, DietNerd does not answer questions around recipe creation and questions that are asked on behalf of an animal.  
**Input Variables:**  
* query (str): The user's question.  
**Output Variables:**  
* question_validity (str): A string indicating whether the question is valid or not. Possible responses can only either be \"True\", \"False--Recipe\", or \"False--Animal\".  
**LLM Settings:**  
* gpt-4-turbo
* Prompt Strategy: few-shot learning
* temperature = 0.2
* top_p = 0.5  
**Prompt:**  
You are an expert in classifying user questions. Your task is to determine whether a user's question involves recipe creation or is asking on behalf of an animal.  
Recipe creation questions involve detailing specific ingredients, cooking methods, and detailed instructions for preparing a dish.  
Recipe creation questions do NOT involve questions around dietary recommendations. If the user's question is about recipe creation, return \"False - Recipe\". If the question is asking on behalf of an animal, return \"False - Animal\". If the question does not involve any of these topics, return \"True\". Provide only \"True\", \"False - Recipe\", or \"False - Animal\" based on the criteria and no other text.  
Here are some examples:  
User: Can you help me create a weekly meal plan that includes balanced nutrients for a vegetarian diet?  
AI: False - Recipe  
User: How do I make a low-carb lasagna?  
AI: False - Recipe  
User: What are some ideas for healthy snacks I can prepare for my kids?  
AI: True  
User: What are some meals for someone with diabetes?  
AI: True  
User: What are the health benefits of intermittent fasting?  
AI: True  
User: What is the best diet for my cat?  
AI: False - Animal  
User: Can dogs eat raw meat?  
AI: False - Animal
## 2 Islanding Detection Methods
### Computational Intelligent Based Methods
#### 2.6.5 Adaptive Neuro-Fuzzy Inference System Method (ANFIS)  
This method is a combination of both ANN and FL which incorporates the effectiveness of ANN to solve the problem and the flexibility of fuzzy logic to generalize the input values and problem description. An active technique was created for islanding detection that substitutes d-axis injection with an ANFIS controller for the conventional injection with a proportional-integral (PI) controller [46]. The ANFIS method's key benefit is reducing the NDZ while maintaining power quality standards.
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 6 User Satisfaction Ratio
### Intra-Tier and Cross-Tier Cooperation (ITCTC)
#### 6.1.2 Cross-Tier Cooperation (CTC)  
The cross-tier cooperation means that if the requested contents is not found in the local tier then the contents should be forwarded to other tiers for example, if a user from Pico BS does not get the contents from any pico BS then its request will be forwarded to the micro BSs and so on. The micro BSs are also clustered and request is forwarded from one cluster to another cluster.  
```
Input: Xt Output: Yr
1. Cluster the base stations into k = 1, 2, 3... Hk based on past requests in each tier except macro cell
2. Cache the data using CF in each cluster of each tier
3. **For pico BS do**
4. **If** request arrives at pico BS then
5. **Serve the request at associated pico BS**
6. **Else if**
7. **Send the request to neighboring BS but within the same cluster
8. **Else if**
9. **Send the request to pico BS which is in any of the neighboring clusters (Cross- cluster cooperation)
10. **Else if**
11. **Send the request to 1 st Cluster of micro BS**
12. **Else if**
13. **send it to neighboring clusters of micro BS**
14. **Else if**
15. **send it macro BS**
16. **Else**
17. **send it content server**
18. **End**
19. **For micro BS do**
20. **If** request is covered in micro BS then
21. **Serve the requests at associated micro BS**
22. **Repeat steps 5-9**
23. **Else if**
24. **send it macro BS**
25. **Else**
26. **send it content server**
27. **End**
28. **Repeat for all requests**
29. **Return Y**
```  
**Algorithm 2** Intra-tier and Cross-tier Cooperation (MICTC)
# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
###### Abstract  
Plant diseases pose a significant threat to plant and crop health, leading to reduced yields and economic losses. The traditional methods for diagnosing plant diseases are often invasive and time-consuming and may not always provide accurate results. In recent years, there has been growing interest in utilizing Raman microscopy as a non-invasive and label-free technique for plant disease diagnosis. Raman microscopy is a powerful analytical tool that can provide detailed molecular information about samples by analyzing the scattered light from a laser beam. This technique has the potential to revolutionize plant disease diagnosis by offering rapid and accurate detection of various plant pathogens, including bacteria and fungi. One of the key advantages of Raman microscopy/spectroscopy is its ability to provide real-time and in situ analyses of plant samples. By analyzing the unique spectral fingerprints of different pathogens, researchers can quickly identify the presence of specific diseases without the need for complex sample preparation or invasive procedures. This article discusses the development of a Raman microspectroscopy system for disease diagnosis that can accurately detect and identify various plant pathogens, such as bacteria and fungi.  
plant disease detection; rapid identification; Raman spectra; Raman analysis; investigation of plant tissue +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON] Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis. _Appl. Sci._**2024, 14, 9926. [[https://doi.org/10.3390/app14139926](https://doi.org/10.3390/app14139926)]([https://doi.org/10.3390/app14139926](https://doi.org/10.3390/app14139926))
## 3 Field Tests  
The tests were conducted at the Ohio State University (OSU) Don Scott Airport (IATA: OSU, ICAO: KOSU) in Columbus, OH, USA. The airport is used for government and private purposes as well as provides education services. The airport is capable of accepting small-size single/twin engine airplanes, helicopters and corporate jets. Prior to the data acquisition, a local surveying network with three control points was established to support the surveying of the sensor stations, pavement markings on the runway, and for the georeferencing (see Section 2.2). The local network is tied to the UTM (Universal Transverse Mercator) projection with using the positions determined at the control points from GPS observations. All positions and orientations are derived with respect to this global coordinate system.  
Two tests were conducted to demonstrate and evaluate the performance of the proposed system. These tests are summarized in Table 2. The goal of the first test was to assess the performance of the system for tracking taxiing aircraft, i.e., modeling 2D motion. The prototype system was deployed next to one of the taxiways at the OSU airport. The sensor network consisted of two sensor stations. The first station had two vertically aligned VLP-16 sensors, and the other station was equipped with a vertically aligned VLP-16 and a horizontally aligned HDL-32E scanners. Figure 9a shows the sensor configuration and the field of views. The distance between the two sensor stations was about 40 m, and the stations were deployed 20 m from the centerline. This sensor configuration allows for 70 m effective runway coverage, measured on the centerline. During the three-hour data acquisition, 18 taxiing Cessna-type and corporate jet airplanes were tracked; the velocity of the taxiing airplanes varied from 8-12 m/s.  
The goal of the second test was to capture landing aircraft, i.e., to track in 3D. The system was deployed next to the 9L/27R runway; Figure 9b shows the locations of the sensor stations and the field of views. The sensor configuration is slightly different than Test #1, as one of the stations was equipped with an extra VLP-16. The sensor stations were located at 40 m distance from each other, the stations were deployed 33 m from the centerline, and the effective coverage measured along the centerline was 100 m. During the six-hour data acquisition, 34 landing Cessna-type airplanes were tracked, the velocity of the landing airplanes was around 20 m/s.  
Figure 9: _Cont._
## 3 Results and Discussion
### Method Performance  
Under the optimized conditions, the parameters for method-performance evaluation, such as linearity, relative standard deviations (RSDs), limits of detection (LODs), and limits of quantitation (LOQs), are summarized in Table 1. Results show that the proposed method exhibits excellent linearity for analyzing eight CBs in a concentration range of 0.25-2500 ng L\({}^{-1}\), with a linear coefficient (R\({}^{2}\)) of 0.9822-0.9996. The LODs and LOQs were calculated to be 0.002-0.086 ng L\({}^{-1}\) and 0.007-0.288 ng L\({}^{-1}\), according to three and ten times of signal-to-noise, respectively. Method precision was evaluated by the values of RSD. As can be seen from Table 1, the obtained RSDs for one single fiber range from 5.3 to 9.7%, and the RSDs among five SPME fibers are in the range of 7.5-12.6% towards eight CBs. Furthermore, comparisons with other methods that have been reported in the literature for the determination of CBs from water were investigated to verify the advantages of the proposed method. As shown in Table 2, the proposed method presents a higher sensitivity for the determination of CBs in water by comparing with other reported methods in terms of LODs and LOQs. Excellent linearity, high sensitivity, and good reproducibility enable the proposed method to be of great potential in real water analysis. Furthermore, the lifetime of as-prepared SPME fiber based on CDs/g-C\({}_{3}\)N\({}_{4}\) composites was also evaluated in this study. It can be seen from Figure S1 that no significant loss occurred in the extraction efficiency of as-prepared SPME fiber for eight CBs after 120 repeated uses.
## 5 Numerical Simulation and Verification
### The Choice of the Incident (Excitation) Pulse Signal  
The modulated Gaussian pulse was chosen (also known as the Gabor pulse) as the excitation pulse:  
\[g(t)=\exp^{-(t-\mu)^{2}/2\sigma^{2}}\cos(2\pi f_{c}(t-\mu)+\theta) \tag{10}\]  
where \(\sigma\), \(\mu\),\(f_{c}\) and \(\theta\) represent the width of the pulse, time shift, centre frequency and phase, respectively. The main motivation for using Gabor pulse is that the Gabor waveform has the best time-frequency resolution [21].
### Research Significance and Objectives  
The precast industry promoted the beginning of SCC in Europe in the 1990s and applications for ready-mix concrete for bridge structures and buildings. To reduce the demoubling time and accelerate production, high-early-strength concrete compositions are typically preferred by precast companies. If self-compacting high-early-strength concrete mixtures are applied, additional advantages can be reached, namely, reducing the time needed for the demoulding strength by reducing the amount of labour in the compaction process. The recommended demoulding compressive strength ranges from 10 to 17 MPa depending on the final application [11], after a 12 to 24 h waiting period in the mould.  
High-early-strength and self-compacting concrete mixtures may be challenging because they present contradicting mixture design prerequisites and fresh properties from a materials science point of view. For example, a low water/binder ratio (w/b) is crucial to attain high early strength [12]. Conversely, it may affect the mixture's self-compacting capacity, which is especially susceptible to changes in the Vw/Vp.  
This work proposed a methodology for modelling and predicting high-early-strength self-compacting concrete properties by studying key properties at the mortar level (self-compacting high-early-strength cement-based mortars) using the data from [13]. Without any special curing treatment, the goal was to develop self-compacting high-early-strength cement-based mortars with 10 s of flow time and at least 50 MPa of compressive strength at the age of 24 h and with 260 mm of minimum slump flow. The five key parameters for the self-compacting high-early-strength cement-based mortars mixture chosen by [13] were as follows: (i) the water-to-powder ratio by volume, Vw/Vp; (ii) the fine-sand-to-total-sand ratio by volume, Vfs/Vs; (iii) the water-to-cement ratio by volume, Vw/Vc; (iv) the sand-to-mortar ratio by volume, Vs/Vm; and (v) the superplasticiser-to-powder ratio by mass, Sp/p. A total of 50 mixtures were produced.  
As can be perceived, the number of combinations increases considerably between the number of input parameters and factors required for factor planning. Consequently, they also intensify the probability that the interactions will not significantly impact the response. As such, there is a need to reduce the time and experimentation costs [14]. For this reason, fractioned plans may be an exciting option for full factorial plans [15]. Then, this work aims to compare the response models taken from a full, fractioned, and small central composite design to describe, predict and optimise self-compacting high-early-strength cement-based mortars' properties.