esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Coverage and Penetration Ratio of Spray  
The analysis of the coverage of droplet deposition by plants and nozzles showed that the coverage of the experiment with the AI nozzle (5.05 and 3.35%) was 2.5 times higher than that of the experiment with the flat fan nozzle (2.11 and 1.44%) in both plants (Figure 6). When the same amount of spray is applied using both nozzles, an increase in deposition indicates a decrease in drift. This means that in drone aerial sprayers, just like in ground-based sprayers, the use of AI nozzles reduces drift compared with the use of flat fan nozzles. The larger diameter of the droplets sprayed from the AI nozzle resulted in less drift, consistent with previous research [25]. However, [PERSON] et al. [49] showed that coverage decreases as the size of the droplet increases. This appears to be caused by downwash from the drone affecting the behavior of the spray droplets. For more accurate analysis, a downwash analysis of the drone should also be performed. According to two-way analysis of variance, there is significant difference only in the effect of the nozzle type (\(p<0.05\)) (Table 4). This means that it is useful to use AI nozzles to deliver more spray droplets to the crop during spraying operations. Although the average deposition of rice was higher than that of soybeans, there was no significant difference by plant [50,51]. The deposition of droplets is affected by the height of the crop, size of the spray drops, and wind speed, but in this study the crops were of similar height, so there does not seem to be a difference in coverage [52].  
The C\({}_{2}\) of the AI nozzle was 1.68% in rice and 1.12% in soybean. And the C\({}_{2}\) of the flat fan nozzle was 0.27% in rice and 0.73% in soybean. The PR averages of the AI nozzle were (27.4 and 33.3%) and those of the flat fan nozzle were (19.0 and 31.5%), respectively (Figure 7). To analyze the effect of plant and nozzle on the PR, a two-way analysis of variance was performed, and it was found that there was no significant difference in the treatment (Table 5). According to previous research, it was expected that larger size spray droplets have a greater PR [25]. However, in this study, spray droplets applied to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Degree of Freedom** & **Sum of Squares** & **F** & \(p\)**-Value** \\ \hline Plant & 1 & 6.15 & 0.764 & 0.3904 \\ Nozzle & 1 & 43.9 & 5.450 & 0.0279 \\ Plant \(\times\) Nozzle & 1 & 1.92 & 0.238 & 0.6300 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Two-way analysis of variance of droplet deposition.  
Figure 5: Layout of field for experiment treatment, sampling zone, and flying path.  
soybeans using the flat fan nozzle resulted in higher-than-expected C\({}_{2}\). After checking the experimental data, the C\({}_{2}\) value was measured to be higher in the downwind direction in two out of three iterations. This may have been caused by a temporary strong wind at the time of the experiment, and we believe that it needs to be improved through additional experiments in the future.
# Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques
###### Abstract  
Recent advancements in Natural Language Processing (NLP) technologies have been driven at an unprecedented pace by the development of Large Language Models (LLMs). However, challenges remain, such as generating responses that are misaligned with the intent of the question or producing incorrect answers. This paper analyzes various Prompt Engineering techniques for large-scale language models and identifies methods that can optimize response performance across different datasets without the need for extensive retraining or fine-tuning. In particular, we examine prominent Prompt Engineering techniques including In-Context Learning (ICL), Chain of Thought (CoT), Retrieval-Augmented Generation (RAG), Step-by-Step Reasoning (SSR), and Tree of Thought (ToT), and we apply these techniques to leading LLMs such as Gemma2, LlaMA3, and Mistral. The performance of these models was evaluated using the AI2 Reasoning Challenge (ARC), HellaSwag, Massive Multitask Language Understanding (MMLU), TruthfulQA, Winogrande, and Grade School Math (GSM8k) datasets across metrics such as BLEU, ROUGE, METEOR, BLEURT, and BERTScore. The experimental results indicate that the most suitable Prompt Engineering technique can vary depending on the characteristics of each dataset. Specifically, for datasets emphasizing mathematical and logical reasoning, Prompt Engineering strategies centered around CoT, SSR, and ToT were found to be advantageous. For datasets focusing on natural language understanding, ICL-centric strategies were more effective, while RAG-based strategies were beneficial for datasets where factual accuracy is crucial. However, it was also observed that the optimal combination of Prompt Engineering techniques could differ depending on the specific LLM, indicating that fine-tuning the Prompt Engineering approach to the model and dataset is essential for achieving the best performance. The findings indicate that as LLMs become more advanced, their reliance on Prompt Engineering (PE) techniques diminishes, yet the magnitude of their performance improvement when PE strategies are applied increases. Furthermore, these advanced models tend to depend less on ICL techniques while exhibiting a greater reliance on RAG strategies. It is also evident that implementing RAG with PE-based preprocessing yields superior performance enhancements compared to the mere application of RAG on raw data.  
large language model; prompt engineering; in-context learning; chain of thought; retrieval-augmented generation; step-by-step reasoning; tree of thought +
Footnote †: journal: Applied sciences
# A Multi-Objective Evaluation Method for Smart Highway Operation and Management
## 3 Establishment of the Smart Highway Evaluation Framework
### Definition of Evaluation Indicators
This study has systematically organized and summarized all electromechanical devices present on intelligent highways, establishing their scores and weights through evaluation of the qualification rate of these devices. The specific classification of electromechanical facilities' equipment is shown in Table 2.  
The definition of the working state for each electromechanical device adopts the indicator qualification rate \(K_{i}\), calculated using the following formula:  
\[K_{i}=\left(1-\frac{nt}{NT}\right)\times 100\% \tag{1}\]  
where \(K_{i}\) represents the integrity index of the \(i\)-th device in the subsystem, ranging from 0 to 100; \(n\) represents the number of equipment failures; \(t\) represents the equipment downtime, the value is 0 if no failures; \(N\) represents the total number of devices; and \(T\) represents the equipment operating time. The evaluation of \"Operation services\" primarily focuses on their characteristics related to \"Efficiency, Safety, Intelligence, and Sustainability\". These aspects manifest in two key facets: \"Traffic operation\" and \"Maintenance management\". Within \"Traffic operation\", there is a focus on elucidating the traits of intelligent highways for enhancing efficiency and managing safety. The aspect of \"Maintenance management\" underscores the intelligence and eco-friendliness of maintenance practices, alongside the precision and effectiveness of emergency response procedures [51]. The precise indicators for \"Operation services\" are detailed in Table 3. \begin{table}
\begin{tabular}{l l} \hline \hline
**Guideline Level B** & **Indicator Level C** \\ \hline \multirow{6}{*}{Monitoring system (B21)} & Central control and management subsystem (C54) \\ \cline{2-3}  & Video surveillance subsystem (C55) \\ \cline{2-3}  & Environmental and equipment monitoring subsystem (C56) \\ \cline{2-3}  & Traffic monitoring subsystem (C57) \\ \hline \multirow{2}{*}{Toll collection system (B22)} & Toll booth subsystem (C58) \\ \cline{2-3}  & Toll settlement center system (C59) \\ \hline \multirow{6}{*}{Communication system (B23)} & Data and video communication subsystem (C60) \\ \cline{2-3}  & Broadcast communication subsystem (C61) \\ \cline{2-3}  & Telephone communication subsystem (C62) \\ \cline{2-3}  & Power distribution system (C63) \\ \cline{2-3}  & Lighting subsystem (C64) \\ \hline Information release system (B25) & Information release subsystem (C65) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specific indicators of electromechanical facilities.
# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 4 Performance Analysis
### Analysis of Communication Overhead  
In our scheme, each sensor node monitors the key parameters of its neighboring nodes at each protocol layer and transmits the trust values toward the monitored nodes to its CH, so the communication overhead of our scheme mainly comes from the packets transmitted from SNs to the CH. As a result, the communication overhead of our scheme is related to the hop count from SNs to the CH. In NBETE, it includes direct evaluation and indirect evaluation. In the direct evaluation, the monitoring node collects the key parameters of the monitored node and calculates the trust factors of the corresponding parameters. There is a factor of availability which evaluates the availability  
Figure 10: Comparison of the false positive probability.  
Figure 9: Comparison of the detection probability. NBETE = Node Behavioral strategies Banding belief theory of the Trust Evaluation algorithm.  
of the neighboring nodes. To obtain this factor, the monitoring node needs to transmit a HELLO packet and its neighboring nodes should reply to it with ACK-HELLO packets. In the indirect evaluation, the neighboring nodes of the monitored node will transmit their trust evaluation results towards the monitored node to the monitoring node as the indirect recommendation values. Thus, the communication overhead of NBETE includes two parts, the HELLO packets for the factor of availability and the indirect trust evaluation from the recommendation nodes, which are related to the average number of neighboring nodes in the network.  
In our scheme, all SNs transmit their evaluation results to their CH once in an observation period. Assuming there are \(n\) SNs in a cluster and the average hop count to the CH is \(N_{h}\), the communication overhead of our scheme, \(CO_{p}\) in an observation period can be expressed as \(CO_{p}=nN_{h}\). As for the NBETE, all SNs broadcast HELLO packets and reply to the HELLO packets of their neighboring nodes once in an observation period. Meanwhile, the neighboring nodes of each SN will send their recommendation values once in an observation period. Assuming the number of SNs is \(n\), and the average number of neighboring nodes of a SN is \(N_{a}\), the communication overhead of NBETE, \(CO_{N}\) in an observation period can be denoted by \(CO_{N}=n(2N_{a}+1)\). We then analyze the communication overhead of the two schemes  
from the angle of computation complexity, the calculation of trust value and the decision approach in the NBETE are more complex than those in our scheme.
# Nonlinear TeraHertz Transmission by Liquid Water at 1 THz
###### Abstract  
The solvation properties of liquid water originate from the transient network of hydrogen-bonded molecules. In order to probe the coupling between the different modes of this network, nonlinear terahertz (THz) spectroscopy techniques are required. Ideally, these techniques should use a minimal volume and capitalize on sensitive field-resolved detection. Here we performed open aperture z-scan transmission experiments on static liquid cells, and detect the THz fields with electro-optical techniques. We show that it is possible to quantify the nonlinear response of liquid water at \(\sim\)1 THz even when large signals originate from the sample holder windows.  
Keywords:terahertz; nonlinear; water; solvation +
Footnote †: journal: Applied
## 1 Introduction
\begin{table}
\begin{tabular}{l l} \hline
**Variables** & **Explanation** \\ \hline \(M\) & The number of coalitions \\ \(N\) & The number of UAVs and slots \\ \(\mathcal{N}\) & The index set of the UAVs \\ \(\mathcal{T}\) & The set of the available time slot \\ \(B\) & The bandwidth of a channel \\ \(R_{0}\) & The fixed data rate \\ \(P_{t}\) & The transmit power of each UAV \\ \(d_{\text{C}}\) & The communication range \\ \(d_{I}\) & The interference range \\ \(P_{r}\) & The received power of the signal \\ \(G_{\text{C}}=\{V,E_{\text{C}}\}\) & The communication graph \\ \(G_{I}=\{V,E_{I}\}\) & The collision graph \\ \(\mathcal{G}_{D}\) & A distributed coalition game with non-transferable utility \\ \(\mathcal{S}\) & The finite set of players (i.e., UAVs) \\ \(\mathcal{V}\) & A partition function \\ \(\prod(\mathcal{S})\) & A partition of \(\mathcal{S}\) \\ \(u_{i}(S_{j},\pi)\) & The individual payoff of UAV i in coalition \(S_{j}\in\pi\) \\ \(v(S_{j},\pi)\) & The utility function of the coalition \(S_{j}\in\pi\) \\ \(v(\pi)\) & The system payoff on \(\pi\) \\ \(T_{n}\) & The corresponding slots selected by UAV-\(n\) \\ \(Th\) & The throughput of the whole multi-UAV network \\ \hline \end{tabular}
\end{table} 
Table 1: Key variables used in this paper.
# Exploring the Cutting Process of Coaxial Phase Change Fibers under Optical Characterization Tests
## 1 Introduction
To the best of our knowledge, this is one of the first studies to investigate in detail the leakage of a phase change material incorporated into the core of a specific PCF structure using the cutting and heat-sealing process. In this work, PCFs containing cellulose acetate (CA) in the sheath and polyethylene glycol (PEG 2000) in the core were cut and heat-sealed. The evaluation of these cuts, seals and possible leakage of the core material was performed through optical and chemical tests. It is worth mentioning that this process is simple, low cost and suitable for applications in Civil Engineering that require large quantities of cut PCFs.
###### Abstract  
This paper proposes a method to reduce seismogram variability as a determining factor in its interpretation, processing, and clustering. By introducing the concept of single fluctuations (SFs), the seismogram can be parsed into a sequence of random impulses with subsequent ordering. This rearrangement of SFs, if they are assigned by positive integers, represents the formal mapping of a regular string of integers into a random one, which can be represented with a bipartite random graph (bigraph). Due to its specific randomness, such a bigraph is considered a new type of random balanced bigraph. The R-envelope and R\({}_{\textrm{Q}}\)-envelope, its equidistant version, are defined by tracing the peak envelope over ordered SFs. The equivalence and complementariness of the R\({}_{\textrm{Q}}\) and bigraph are considered and discussed, forming a combined characteristic of the seismogram. The R/R\({}_{\textrm{Q}}\) provided a considerable reduction in seismogram variability, which was confirmed by creating and analyzing an ensemble of R\({}_{\textrm{Q}}\) from several seismograms. In the R\({}_{\textrm{Q}}\) domain, distance is defined as a possible basis for metrics and clustering, but the ensemble variability was quite narrow and not as suitable for this purpose. Otherwise, the ensemble shows high redundancy hidden in the seismogram population. As for the bigraph, the mesh of its edges is structuralized in bundles, forming a skeleton, which reflects the internal structural content of the seismogram. The distance over the domain of bigraphs is proposed to show the possibility of clustering. This means that only a combined R\({}_{\textrm{Q}}\) and bigraph provides a suitable frame for seismogram representation with reduced variability and, thus, the potential for more effectively organizing seismic databases and a deeper interpretation of seismograms; therefore, R\({}_{\textrm{Q}}\)s and bigraphs can be considered as a transform of a seismogram. Many aspects of these concepts are thoroughly discussed. The similarity between concepts of SF and wavelets is briefly considered. This very complex theme is new and promises broad further research. All issues considered in the paper are abundantly illustrated.  
earthquake; seismogram; earthquake resistance regulative; fluctuation; random bipartite graph; transform; envelope; variability; clustering; database +
Footnote †: journal: Applied Sciences  
1
## 4 Discussion  
This study investigated the relationship between elbow valgus torque during pitching and ball velocity, pitching motion measurements, and physical characteristics, including shoulder joint ROM, in junior high school baseball pitchers. We found that increased ball velocity, age, height, body weight, BMI, and restricted dominant shoulder horizontal adduction ROM compared to that of the non-dominant shoulder were associated with higher medial elbow torque. As far as we know, this is the first study to demonstrate the impact of dominant shoulder horizontal adduction restriction on the higher elbow valgus torque in junior high school baseball pitchers. Excessive torque in the medial elbow's connective tissue is an important factor in elbow joint injuries in overhead athletes [5; 21; 28]. The higher elbow valgus torque and higher shoulder external rotation torque at the time of maximum external rotation of the shoulder in the late cooking phase were significantly correlated with elbow injuries. [29]. The PULSE THROW used in this study measured the peak elbow valgus torque placed on the medial elbow around the time of maximum shoulder external rotation during pitching [21]. The reliability of measurements from wearable sensors, including elbow valgus torque, has been verified by comparison with motion capture [26; 27]. Adaptations in shoulder ROM are found among baseball players as a result of repetitive throwing [30; 31; 32; 33]. A greater ROM deficit in the dominant throwing shoulder reportedly increases the risk of injury 2.5- to 9-fold [34; 35]. In particular, the significant risk factors for the development of pitching-related injuries, including UCL tears, are GIRD and the total arc of motion deficits [23; 36]. Baseball players with posterior shoulder tightness have been reported to be associated with shoulder ROM deficits in glenohumeral internal rotation and horizontal adduction [37]. In the present study, dominant shoulder horizontal adduction deficits compared to the non-dominant shoulder are related to higher elbow valgus torque during pitching. [PERSON] et al. reported that the infraspinatus and lower trapezius contribute to posterior shoulder tightness [25], and [PERSON] et al. indicated that pitching significantly increased muscle elasticity even after 24 h [38]. It was also demonstrated that improving shoulder internal rotation ROM by stretching the posterior shoulder muscles daily was associated with a 36% risk reduction for shoulder and elbow injuries [39]. Modified cross-body stretching and modified sleeper stretching for shoulder ROM and muscle stiffness in baseball players with posterior shoulder stiffness are effective in increasing shoulder internal rotation and horizontal adduction ROM and decreasing muscle stiffness of the infraspinatus or trees minor [40]. As such, improving posterior shoulder tightness and increasing shoulder range of motion may reduce elbow stress and prevent pitching injuries.
# A Novel 3D-Printed Training Platform for Ossiculoplasty with Objective Performance Evaluation
## 4 Discussion
#### Limitations and Future Outlook of the Study  
This study has some limitations. All the participants were medical students without prior surgical experience, which may have influenced the outcomes. It is possible that more experienced individuals might show less improvement with the magnified model, potentially failing to demonstrate the same statistical significance. Furthermore, we did not assess the long-term impact of the training intervention. While a significant difference was observed within a single session, the absence of a learning curve analysis limits our ability to determine whether the training advantages translate into sustained skill improvement over time. A factor to consider is the potential effect of task repetition, as participants in Group A performed the ossiculoplasty twice (first on a 3:1 incus and then on a 1:1 incus), while participants in Group B performed the ossiculoplasty only once on a 1:1 incus. This difference raises the possibility that the improved performance in Group A may be partially attributed to repeated exposure rather than solely to the benefits of magnification. However, when passing from the scaled incus to the real-sized one, the task cannot be considered strictly the same, since it involves substantial operational differences, such as microscope focusing, the grip and handling on the ossicle and the size of the drill. The observed performance improvement is likely mainly due to practicing with the scaled model, which improves understanding of the anatomy and increases awareness of the drilling process, rather than the task repetition itself.  
Moreover, qualitative feedback from the participants strongly highlighted the advantages of using a magnified incus prior to operating under real-size conditions. The subjective assessments consistently indicated that the stepwise approach facilitated better spatial orientation, improved instrument handling, and increased confidence when transitioning to the standard-sized model. These findings suggest that integrating magnified training models into surgical education may enhance skill acquisition, but further studies incorporating longitudinal assessments are necessary to confirm the benefits of this approach. The comparison between learning curves obtained from different training workflows, combining repetitive modelling on 3:1 and 1:1 ossicles, will allow the definition of the most effective training workflow for incus remodeling.  
Eventually, future research should focus on refining the anatomical fidelity of the 3D-printed models and exploring alternative materials to enhance both tactile feedback and visual precision. Expanding the scope of 3D-printed models to encompass diverse anatomies and pathologies could be highly beneficial in developing comprehensive training frameworks for otologic surgery, ensuring that surgeons are equipped to handle a broad spectrum of clinical scenarios.
# A Survey of Secure Time Synchronization
## 3 Current Time Synchronization Methods
### IEEE 1588 (PTP)  
The first version of the IEEE 1588 precision timing protocol (PTP) was released in 2002, and the second version was released in 2008 [52]. It is an application protocol that follows the master-slave architecture and aims to provide high accuracy and a robust synchronization procedure. This protocol allows for synchronization in the nanosecond range. Compared with the previously mentioned protocols, such as GPS and NPT, the PTP is a more robust time synchronization [53]. The IEEE 1588 PTP allows clocks to be distributed across packet-based networks such as the Ethernet. Time-stamping packets should be exchanged between distributed nodes in order to synchronize the network precisely [54].