esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Prompt Optimization for Relevance Evaluation via LLM-Based Confusion Matrix Feedback
## 5 Experimental Results
### Performance Comparison of Different Methods
#### 5.2.2 Comparison of Automated Optimization Methods  
Examining the results of automated prompt optimization methods, we observe that APO-CF achieves the highest [PERSON]'s \(\kappa\) score of 0.553 on GPT-4o-mini, demonstrating its strong ability to refine prompts effectively. On GPT-4o, APO-CF also achieves a notable improvement over initial prompts, reaching 0.548, although slightly lower than on GPT-4o-mini. More importantly, it outperforms all other automated methods, including APO, APE, and OPRO, indicating that our confusion matrix-based update process performs effectively, even with a single iteration. Notably, APO-CF performs better than APO while requiring significantly fewer LLM calls, proving that a single well-calibrated update can be more beneficial than multiple iterative updates.  
However, when tested on DeepSeek-Chat, APO CF does not perform as well and, in fact, results in lower performance than some initial prompts. This suggests that the confusion matrix-based refinement process may not align well with DeepSeek-Chat's interpretation of relevance. One possible explanation is that DeepSeek-Chat may not effectively utilize the confusion matrix for prompt refinement, leading to suboptimal adjustments. Further analysis of this issue is provided in the Section 6.  
Another intriguing result is that APE performs comparably to, and in some cases even better than, APO, which is counterintuitive given that APE does not allow the LLM to modify the prompt itself, only structuring it differently. The expectation was that direct LLM-driven updates (APO) would be superior, yet our findings challenge this assumption. This suggests that not all LLM-generated prompt modifications lead to improvements, and simpler structural refinements may sometimes be more effective than complex model-driven optimizations.  
Additionally, OPRO does not perform as well as expected, showing that its approach of generating new prompts based on past iterations does not necessarily result in an optimal refinement. This aligns with our hypothesis that merely iterating over previously generated prompts without deeper analysis may not be an effective way to optimize prompts for relevance evaluation tasks.  
Overall, our findings reinforce that confusion matrix-driven prompt updates are highly effective for relevance evaluation, as evidenced by APO-CF's strong performance across GPT-based models. However, its lower performance on DeepSeek-Chat highlights the need for model-specific tuning in certain cases. Importantly, our results demonstrate that a single, well-informed update can outperform more iterative methods, offering an efficient approach to optimizing prompts with minimal LLM calls.
# Removal of Antibiotics by Biochars: A Critical Review
## 4 A Critical Review of Biochar-Based Adsorption Processes
### Thermodynamic and Kinetic Considerations
Similarly, the adsorption of tetracycline, quinolone, and sulfonamide antibiotics onto H\({}_{3}\)PO\({}_{4}\) activated biochar was described well by the Elovich and pseudo-second-order kinetic models, as well as by the Langmuir isotherm [172]. Interestingly the results of this latter study indicated that the adsorption of antibiotics is an endothermic and spontaneous process with negative Gibbs free energy and a positive entropy change. Both chemical and physical adsorption occurred simultaneously [172]. The endothermic character of antibiotic adsorption on activated carbon was also reported for the adsorption of heavy metals [178]. On the other hand, the adsorption of sulfonamide antibiotics onto H\({}_{3}\)PO\({}_{4}\) activated biochar resulted in a spontaneous and exothermic process that was favorable at low temperatures [179]. The adsorption was described by the Langmuir isotherm and the pseudo-second-order kinetic model, similar to previous studies [179]. The study of [PERSON] et al. (2002) also showed a similar trend. The adsorption of quinolone and tetracycline antibiotics onto modified biochar was exothermic and was described by the Langmuir isotherm and the pseudo-second-order kinetic model [180]. The above examples were the modified biochars, which are the biochars most frequently used as adsorbents. The kinetic models and isotherms for the adsorption of antibiotics onto pristine biochars seem to follow the same trend (i.e., Langmuir isotherm and pseudo-second-order kinetics). However, there are still few studies on pristine biochars providing insights into their adsorption mechanisms. For instance, the adsorption of tetracycline antibiotics onto wheat-stalk biochars was described well with the Langmuir isotherm as well as the pseudo-second-order and intraparticle diffusion kinetic models [148]. A similar kinetic model was reported for the adsorption of sulfonamide antibiotics onto a biochar based on spent coffee grounds. The adsorption kinetics of sulfadiazine (SDZ) and sulfamethoxazole (SMX), two common sulfonamide antibiotics, was better described by a pseudo-second-order model, implying that the adsorption of antibiotics onto biochars is controlled by the chemisorption mechanism [171]. The overall results indicate that the adsorption of antibiotics onto pristine or modified biochars predominantly occurs as a monolayer through surface reactions and intraparticle diffusion mechanisms. Thermodynamic studies of the adsorption of different antibiotics have shown that the adsorption of antibiotics on biochars can be exothermic or endothermic and should be determined for each adsorption case to improve the adsorption efficiency.
# Realtime Tsunami Prediction System Using Ocean Floor Network for Local Regions
## Real-Time Tsunami Prediction Technique
### Tsunami Database  
The approach used to construct the tsunami database was basically similar to the above approach [14]. We prepared many fault models along the Nankai Trough areas with a specified magnitude, depth, dip, and location and calculated tsunami waveforms using many fault models and JAGURS code [21]. The locations necessarily had nothing to do with the distribution of the plate boundary. There are many branching faults on the Nankai forecar region, and proof of high-speed ruptures along branching faults has been obtained [22]. [PERSON] et al. (2014) [14] have prepared fault models of over 3000 cases. However, such a large number of fault models is unnecessary to grasp the relationship between observed tsunami height at the ocean floor network observatories and predicted tsunami height at the predicted points of landfall. We, therefore, halved the number of fault models and were left with somewhat more than 1500 cases. The rationale is that the entire system should be no more complex than necessary to enable implementation by many kinds of users.  
However, there were few high-magnitude fault models. The highest M earthquake in the tsunami database was 8.5. [PERSON] et al. (2017) [9] have indicated that tsunami prediction with the tsunami database is also focused on very large earthquakes (e.g., M9 [23]) and that the prediction is valid. However, users may desire a greater number of fault models with a high M. In our system, it is easy to increase the number of fault models. We have added some fault models with the fault slide constant increased severalfold compared to the fault models with M9 as above. [PERSON] et al. (2016) [16] have applied extrapolation using a Gaussian process to estimate the height of a very large tsunami based on a small number of fault models in the database, but we increased the fault models because we needed models to visualize tsunami inundation areas along with the depth of inundation.
# Remote Spectral Identification in the THz Band with Reflection Spectroscopy in an Open Atmosphere
## 6 Conclusions  
We presented a concept of a practical solution to the problem of the remote identification of materials. A technical analysis of the commercially available equipment was shown. Based on the conclusions, a THz radiation source and detector were selected. The detailed description of the setup for the transmission and reflection configuration was provided. We reported the terahertz transmission and reflectance spectra of selected materials, measured in a normal atmosphere with about 40% RH at room temperature. The methods of data acquisition and processing were presented. Reference data measurements were conducted in the TDS spectrometer and compared with the experimental measurement results. The comparison showed a satisfactory auto-correlation of the obtained results to be used in the process of material identification after applying more sophisticated numerical methods. Suggestions for improving the performance and directions toward more practical implementation were given. This paper emphasizes the hardware and setup configuration with the aim of providing proof of concept that THz reflection spectroscopy can be used for the remote identification of some materials. This research did not advocate that only the highest-performing equipment was suitable for use in practical applications. The presented approach aimed to show opportunities for and find the physical limitations of the remote identification of selected materials in the THz range to enable more suitable future technology developments.  
Conceptualization, M.W. and N.P.; methodology, M.W. and N.P.; software, M.M.; validation, M.W., M.M. and N.P.; formal analysis, M.W. and N.P.; investigation, M.W.; resources, N.P.; data curation, M.W. and M.M.; writing--original draft preparation, M.W.; writing--review and editing, M.W., M.M. and N.P.; visualization, M.W; supervision, N.P.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Military University of Technology; grant number UGB/22-726/2022/WAT.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We are grateful to [PERSON] from the Faculty of Chemistry, Military University of Technology, for preparing and providing the samples of the materials used in the research. We would like to thank [PERSON] from Warsaw University of Technology for his help and suggestions with the design and manufacture of the 8\" parabolic mirror.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## 3. Robust Optimization for Household Load Scheduling  
### Robust Optimization  
In the view of mathematics, the above scheduling problem is a mixed integer programming (MIP) problem with uncertainties. A nominal mixed integer programming is given as Equation (24).  
\[\begin{split}\text{minimize }&\mathbf{c}^{\prime}\mathbf{x}\\ \text{subject to }&\mathbf{A}\mathbf{x}\leq\mathbf{b},\mathbf{l}\leq\mathbf{x}\leq\mathbf{u} \\ & x_{i}\in\mathbb{Z},\ \ \ \ \ i=1,\cdots,k\end{split} \tag{24}\]  
However, the existence of the uncertain parameters (some elements in \(\mathbf{b}\) of the Equation (24)) hinder the available methods like branch and bound method from solving the problem. Therefore, the robust optimization technique proposed by [PERSON] et al. is applied to make the uncertain programming problem solvable and to make the solutions robust.  
In the robust optimization, if the element \(a_{ij}\) in the matrix \(\mathbf{A}\) is uncertain, it should be replaced by \(\bar{a}_{ij}\) that takes value in \(\left[\bar{a}_{ij}-\bar{a}_{ij},\bar{a}_{ij}+\bar{a}_{ij}\right]\) of which \(\bar{a}_{ij}\) and \(\hat{a}_{ij}\) are the mean value and range of the coefficient (the elements in \(\mathbf{b}\) can be seen as \(a_{ij}\) through some transformations [12; 13]). A new parameter is then defined as\(z_{ij}=\left(\bar{a}_{ij}-\bar{a}_{ij}\right)/\bar{a}_{ij}\), so \(z_{ij}\) is a bounded random variable (but with unknown distribution) which is within \([-1,1]\).  
In order to deal with the uncertain but bounded parameters, [PERSON] et al. also introduced a number \(\Gamma_{i}\) that takes value in the interval \([0,|I_{i}|]\), where \(J_{i}=\left\{j|\hat{a}_{ij}>0\right\}\) for each constraint. The purpose of intruding this parameter \(\Gamma_{i}\) is to adjust the robustness against the level of conservatism of the solution. Specifically, the number of the coefficients that are considered as uncertain equals to \((\lfloor\Gamma_{i}\rfloor+1)\). For the former \(\lfloor\Gamma_{i}\rfloor\) uncertain coefficients, the coefficients are considered to vary in the interval \([\bar{a}_{ij}-\bar{a}_{ij},\bar{a}_{ij}+\bar{a}_{ij}]\); for the final uncertain coefficient, it is considered to vary in \(\left[\bar{a}_{ij}-(\Gamma_{i}-\lfloor\Gamma_{i}\rfloor)\hat{a}_{ij},\bar{a}_ {ij}+(\Gamma_{i}-\lfloor\Gamma_{i}\rfloor)\hat{a}_{ij}\right]\). The larger \(\Gamma_{i}\) is, the more uncertainties will be considered during the optimization, and the more robust of the solution will be. When \(\Gamma_{i}\) equals to \(|J_{i}|\), it means that the solution can satisfy all the situations regardless of what value the uncertain parameter takes.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 2 Interference in Hotspot Scenarios  
Mobile networks face many challenges that can limit channel capacity. For example, the lack of available bandwidth is a common issue that can be addressed by increasing the spectrum reuse, as a way to increase the capacity limit. However, that may lead to a higher co-channel interference, which happens when more then one user is using the same radio resource at the same time.  
The solution presented in this paper can be applied on an Orthogonal Frequency-Division Multiple Access (OFDMA)-based system (e.g., 4G LTE or 5G NR) in a Downlink (DL) direction, whose users may experience interference from multiple neighboring cells reusing the same frequency at the same time. This interference may be a severe limiting factor in terms of capacity, especially with a reuse factor of 1.  
In such scenarios, users from different cells can interfere with each other, and users located in the cell edge are the most affected by co-channel interference [4]. These users are far from the transmitting cell and closer to adjacent cells, increasing the probability of receiving any interfering signals with higher power levels and signals from its serving cell with lower power levels. Hence, user distribution is another aspect that can have a negative impact on system performance.  
Therefore, the existence of regions with high user concentration (hotspots), especially if close to the cell edge, can lead to a lack of available resources and a higher probability of users being allocated to the same frequency. Regardless of its location, the appearance of hotspots may increase interference, leading to lower SINR. This results in higher Bit Error Rate (BER) and, consequently, lower traffic capacity.  
All of these challenges are well known and can degrade performance; however, they are partially predictable. On the other hand, some modern, urban hotspot scenarios bear a more dynamic aspect. For example, the number of people flowing through a big city does not merely repeat weekly, since extraordinary situations that are hard to predict often happen in large urban areas.  
For instance, a certain neighborhood that consistently had a uniform distribution of users may change significantly because a new food park has opened, a soccer league changed its venue, a protest is taking place or an accident stopped the traffic. In such situations, mobile networks that were planned and deployed statically will not be able to serve its users well. Besides, the traffic demand per user is also likely to increase as these situations or events induce a different behavior in users. It leads them to consume more bandwidth by sharing photos and videos, aggravating the high traffic demand.
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 4 Impacts of PM on the Health and Welfare of Chickens and Farm Workers
### Impacts on Birds' Health, Behaviors, and Welfare  
High PM concentrations have been linked to higher avian mortality rates [84]. Particulate matters contain various airborne bacteria and endotoxin, which negatively impact health and welfare issues of birds. When birds inhale dust particles with dust-borne pathogens (especially Mycoplasma species) damage occurs to mucosal surface cilia present in the trachea [85]. Particulate matter of size PM\({}_{2.5}\) was found to have lots of harmful microorganisms and endotoxins (Table 6) [86]. Long-term exposure to PM\({}_{2.5}\) has been linked to impaired lung function, and fraction size up to PM\({}_{10}\) has increased mortality risk [83]. According to [PERSON] et al. (2015), the endotoxin of dust helps decrease the percentage of cell-mediated immunity B cells (CD3-la+ B cells) in layers [87]. A decrease in cell-mediated immunity B cells causes birds to have difficulty fighting against poultry pathogens and several health issues. In addition, birds raised on litter floors evinced a higher incidence of lung damage due to higher PM emissions [88].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**PM Sizes/Types**} & **Effects of PM on Health, Behavior, and Welfare** & **References** \\ \hline \multirow{2}{*}{PM\({}_{2.5}\)} & Consists of a high level of microorganisms and & \\  & endotoxin, which affects health & [86] \\ \hline \multirow{2}{*}{PM\({}_{2.5}\)} & Induces developmental cardiotoxicity in chicken & \\  & embryos and hatchling chickens & [89] \\ \hline \multirow{2}{*}{PM\({}_{2.5}\)} & Impaired lung function & [83] \\ \hline \multirow{2}{*}{PM\({}_{10}\)} & Increased risk of mortality rates & \\  & & \\ \end{tabular}
\end{table}
Table 6: Effects of various PM sizes or types on health, behavior, and welfare of birds.
# Security and Independence of Process Safety and Control Systems in the Petroleum Industry
## 2 Aspects of Independence  
In petroleum and process industries with major accident potentials, the strategy has been to establish multiple countermeasures to prevent or mitigate the consequences of loss of control with hazardous materials and processes. This approach is referred to as defence-in-depth [4]. The Norwegian Petroleum Safety Authority (PSA) advocates the use of barriers to control the risk of major accidents. Barriers are safeguards (measures,counter-measures, and solutions) whose function is to offer protection in failure, hazard, and accident situations.  
Their function is provided by barrier elements that may be technical, organisational, or operational. Where more than one barrier is necessary, there shall be sufficient independence between barriers. The requirement for independence entails that it should not be possible for multiple important barriers to be impaired or malfunction simultaneously, e.g., as a result of a single fault or a single incident. Thus, OT systems performing safety functions shall be able to perform the intended functions independently of other systems and in addition to systems for management and control.  
The safety systems may have an interface with other systems if it is not adversely affected as a consequence of system failures, errors, or isolated incidents in these systems. Safety systems generally have a high level of resilience against internal failures and loss of power and communication (as required by design specifications), but the resilience against potential influences from external devices and threats is less mature since safety systems have historically been well-isolated.  
The ideal situation, from a reliability perspective, is that safety systems are fully independent. Yet, safety systems in offshore facilities generally share network infrastructure with process control systems, and achieving independence in safety systems is therefore about identifying, analysing, and reducing dependencies. Some aspects of independence are elaborated in the following.
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 3 System Model
### Power Consumption Model  
The wind powered EH-WSN system operates under a cyclic workload with period \(T_{W}\). The energy consumption is calculated by both the power mode of the hardware (e.g., wireless communication mode) and the active operating time in that mode. Duty cycle \(D_{\text{C}}\) is just the ratio of active time \(t_{active}\) to period \(T_{W}\), as defined in Equation (5). By duty cycling, the power consumption of load \(\overline{P}_{Load}\) is scaled, as defined in Equation (6). \(P_{active}\) and \(P_{sleep}\) are the active power and sleep power of the system, respectively. Here \(P_{active}\) is a variable subject to the wireless transmission power level, which would be determined by the TPC module in Section 4. (For simplicity we consider \(P_{active}\) is completely determined by the radio unit, whereas the power consumption of MCU, sensors, and other modules are out of the scope).  
\[D_{\text{C}}=\frac{t_{active}}{T_{W}} \tag{5}\]  
\[\overline{P}_{Load}=D_{\text{C}}\cdot(P_{active}+P_{D\_{system}})+(1-D_{ \text{C}})\cdot(P_{sleep}+P_{D\_{system}})+P_{leak} \tag{6}\]  
and \(P_{leak}\) represents the leakage power of supercapacitor: \(P_{leak}=V_{\text{SC}}^{2}/R_{leak}\), where \(R_{leak}\) is its leakage resistance. Correspondingly \(P_{D\_{system}}\) is the total power dissipation of DC-DC converters that will be analyzed below.