esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Design and Specim Preparation  
Two groups of tests, A and B, were designed, in which different qualities and lengths of RS fibers were added to the specimens of group A, as shown in Table 4. Group B was a control group of conventional cemented paste backfill (CCPB) without RS fibers. According to the 4-factor and 3-level orthogonal test \(\mathrm{L_{9}(3^{4})}\), nine formulas were designed for group A. The factors included cement content (_c_: 25, 20, and 17%), solid mass concentration (\(\omega\): 60, 62, and 64%), fiber addition (_m_: 1, 2, 3 kg/m\({}^{3}\)), and fiber length (_l_: 0.8-1, 1-3, 3-5 cm). The cement content of 25%, 20%, and 17% corresponded to cement-tailings ratios of 1:4, 1:5, and 1:6. After permutation and combination, nine types of water cement ratios 3.33 (_c_: 25%, \(\omega\): 60%), 3.06 (_c_: 25%, \(\omega\): 62%), 2.81 (_c_: 25%, \(\omega\): 64%), 4.00 (_c_: 20%, \(\omega\): 60%), 3.68 (_c_: 20%, \(\omega\): 62%), 3.38 (_c_: 20%, \(\omega\): 64%), 4.67 (_c_: 17%, \(\omega\): 60%), 4.29 (_c_: 17%, \(\omega\): 62%), 3.94 (_c_: 17%, \(\omega\): 64%) were calculated. The selection of cement content and solid mass concentration was based on the parameters currently used by tailings source mine enterprise. Orthogonal experiment method was chosen to simplify the complete experiment with multiple factors that require complex manipulations, in order to improve the efficiency of the experiment and to obtain the best formulation quickly. Group B was a full-scale experiment with two factors including cement content and solid mass concentration and three levels, the results of which have been discussed in the literature [4].  
The temperature and humidity of the curing environment are important factors affecting the reliability of concrete structures, and suitable maintenance conditions help to obtain accurate and reliable strength data [34; 35]. After the raw materials were fully stirred by a mortar mixer for 10 min, the fresh slurry was stored in a mold of size \(\upup\)50 mm \(\times\) 50 mm and cured in an environment of 22 \(\pm\) 1 \({}^{\circ}\)C and 90% relative humidity.  
RS fibers showed slight side effects of a decrease of 5% in the flowability of the fresh CTB slurry because RS fibers with high water absorption cause viscous increases in the fresh CTB slurry. In spite of this, the flowability of the RSCPB met the pipeline transportation requirements.  
Figure 2: The morphology of RS fibers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Chemical Constituent** & **Cellulose** & **Hemicellulose** & **Lignin** & **Ash** & **Moisture** \\ \hline Weight Percentage (\%) & 35.6 & 20.5 & 16.8 & 15.2 & 11.9 \\ Wet matter & & & & & \\ \hline
**Chemical Constituent** & **N** & **P\({}_{2}\)O\({}_{5}\)** & **K\({}_{2}\)O** & **S** & **Si** \\ \hline Weight Percentage (\%) & 0.5–0.8 & 0.16–0.27 & 1.4–2.0 & 0.05–0.10 & 4–7 \\ Dry Matter & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main properties of RS fibers [27; 33].
# An Investigation of Applying Large Language Models to Spoken Language Learning
## 1 Introduction
### Related Works
Rather than merely formulating the prompts with input-output pairs as in ICL, CoT incorporates intermediate reasoning steps that guide the LLM's reasoning process from the input to the final output within the prompts. Once appropriately designed, CoT prompts can effectively stimulate the reasoning skills of LLMs. In fact, using diverse CoTs (i.e., sample multiple reasoning paths for one problem) has been shown to be a direct and effective approach for enhancing their performance [33]. Moreover, LLMs are not limited to their internal knowledge and can use external tools when needed. Previous research has demonstrated that the use of API calls to integrate various tools, such as search engines, calculators, and compilers, improves the performance of LLMs in specific tasks [34; 35; 36]. **Deploying LLMs:** For practical applications of LLMs, it is essential to incorporate additional safeguards. They are likely to magnify social biases ingrained in their training data and may produce inaccurate, toxic, biased, or even harmful information [37]. In reality, similar issues have been observed in various language evaluation scenarios, such as native-speaker judgment of the speech of non-native speakers, which is notoriously biased. This phenomenon is known as reverse linguistic stereotyping, whereby a person's speaking performance is evaluated based on the stereotypes associated with their social identity [38]. Therefore, deploying LLMs in sensitive areas such as education must be approached with great care [39; 40]. While LLMs have been evaluated on a variety of benchmarks, such as MMLU [17] and BIG-bench [41], further studies are necessary to apply them to the domain of spoken language learning. We acknowledge that LLMs can assist CALL by helping language teachers create lesson materials or providing students with explanations of grammar and vocabulary in an adaptive and personalized manner [39]. They also have the potential to exhibit spoken language intelligence (SLI), i.e., the ability of a system or technology to understand and process spoken language by leveraging speech recognition, NLP, or conversational AI. These technologies can enable machines to comprehend, interpret, and respond to spoken language in a manner that resembles human-like intelligence. However, it is still questionable whether their capabilities can match the expertise of phonetics specialists or serve as effective language learning assessors. To begin exploring this question, we propose a challenging sub-question: Do LLMs possess adequate spoken language intelligence to handle reasoning questions that require the expertise of human phonetic professionals?
## 2 Imposed Loads  
For tidal range power generation, a barrage is used to control the water level inside an impounded body of water. In simple terms, the water inside the impoundment is _held back_ from high and low tides to create a difference in water levels between the sea and the impoundment; this is called the head of water. For example, if starting from high tide, the gates on turbines, sluices, and locks are closed. The impounded water remains at a high tide level, while the sea level reduces. After two or three hours, the head can be two to five meters depending on the phase of the moon and the location of the barrage. The gates on the turbines are opened so that water rushes through and turns the blades, which are typically 7.5 m in diameter. As the sea level nears low tide, the head reduces, and when it reaches the turbine's low generation limit of about 1.5 m, the turbines are set to run free (no generation), and the sluices and locks are opened to equalise the water levels quickly. Once the water levels are equal, the gates are shut. At this point, the turbines can be used as pumps to lower the impoundment water to low tide levels. This allows for a higher head for the next flood phase. This process is repeated by holding the impounded water at a low tide level whilst the sea rises. This is called two-way generation.  
The head across the barrage creates an imbalance in the horizontal hydrostatic force that must be resisted by the barrage. The level at which the horizontal force acts also creates an overturning moment on the barrage, which must be resisted by bearing pressures and the weight of the barrage.  
Figure 1: Location map for North Wales Coastal Lagoon and Morecambe Bay Estuarine Barrage.  
The loads on the barrage are considered under the two following cases:  
1. Normal operating conditions: The difference in surface water levels between the sea and impoundment creates a horizontal force on the barrage, which will alternate direction with each tide.
2. Storm conditions: The magnitude, timing, and duration of extreme weather events require complex statistical calculations involving site-specific data. The probability of a high storm surge occurring at the same time as high onshore winds and waves is relatively high, but the odds of this occurring during a high spring tide are somewhat less. A simple analysis is included below for this initial comparison of options.  
Tidal range schemes have a minimum design life of 120 years, probably much longer for civil works, which include flood protection. Thus, the design storm will need to be a 1 in 250- or 500-year event. The following analysis is based on characteristic loads and factors of safety for the stability of 1.6 and 1.4 for the operational and storm loads, respectively.  
Two contrasting schemes used in previous papers are used to provide data for the following analysis, namely the North Wales Coastal Lagoon (NW) and Morecambe Bay Estuarine Barrage (MB) [2]. Another paper [3] describes the effects of sea level rise on tidal range generation.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 3 Proposal Architecture
### PlaIMoS Fixed Measurement Station  
The PlaIMoS fixed measurement station, designed and manufactured in Colima, Mexico, uses a PIC18F26K22 microcontroller with a 64-KByte flash memory, a 1024-byte EEPROM memory to process information, a MAX30100 EFD + optical sensor to monitor blood oxygen, a 40-pin data bus to communicate with a Raspberry Pi card, an airflow sensor to monitor the respiration rate,  
Figure 1: PlaIMoS platform architecture.  
two electrodes to monitor healthy heart electrical activity, an RFID card reader to identify individual users, a buzzer and an RGB LED to indicate the working state of the device (Figure 2).
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
###### Abstract  
This paper addresses the challenge of optimizing end-to-end neural diarization systems for conversational telephone speech, focusing on diverse acoustic features beyond traditional Mel-filterbanks. We present a methodological framework for integrating and analyzing different feature types as input to the well-known End-to-End Neural Diarization with Encoder Decoder Attractors (EEND-EDA) model, focusing on Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network (ECAPA-TDNN) embeddings and Geneva Minimalistic Acoustic Parameter Sets (GeMAPS). Our approach combines systematic feature analysis with adaptation strategies, including speaker-count restriction and regularization techniques. Moreover, through comprehensive ablation studies of GeMAPS features, we identify optimal acoustic parameters and temporal contexts for diarization tasks, achieving a reduced feature set that maintains performance while decreasing computational complexity. Experiments on the CallHome corpus demonstrate that our optimized ECAPA-TDNN with Mel-filterbank combination reduces Diarization Error Rate by 29% relative to baseline systems. Our evaluation framework extends beyond traditional metrics, revealing that different feature combinations exhibit distinct strengths in specific diarization aspects.  
end-to-end neural diarization (EEND); acoustic features; ECAPA-TDNN; GeMAPS
## 4 Research Opportunities in O-RAN
### xApps, rApps and dApps Operation  
The O-RAN architecture allows the RIC to host and run applications developed by third party for automation and intelligent orchestration to the network through ML and AI, which will leverage the enormous amount of data generated by the RAN and exposed through the O-RAN interfaces to analyze the current network conditions, forecast future traffic profiles and demand, and implement closed-loop network control strategies to optimize the RAN performance. The add-on xApps, rApps, and dApps will make the monolithic RAN \"black-box\" obsolete and provide open, programmable and virtualized solutions that expose status and offer control knobs through standardized interfaces [68]. The rApps are residence of the Non-RT RIC and control the optimization objectives such as policies, models and slicing that the time scale of the close control loop of the network requires more than one millisecond. The Near-RT RIC hosts the xApps that require the response of control loops time between 10 and 1000 ms for optimizing objectives such as the RRM and session management. However, the notion of dApps, custom and distributed applications can complement xApps/rApps by implementing RAN intelligence at the CUs/DUs for real-time use cases outside the timescales of the current RICs. The control loop response timescale in such use cases is \(\leq\)10 ms to optimize objects such as the beamforming and modulation management. The dApps receive real-time data and KPIs from the RUs (e.g., frequency-domain I/Q samples), DUs (e.g., buffer size, QoS levels), and CUs (e.g., mobility, radio link state), as well as enrichment information from the Near-RT RIC, and use it to execute real-time inference and control of lower-layer functionalities. Such dApps enable network intelligence at the edge of the O-RAN ecosystem [69].  
It is clear that intelligent and dynamic xApps, rApps and dApps are key enablers for future network automation. However, it also introduces novel practical challenges concerning. One of the challenges is the orchestration of the existing xApps, rApps, and dApps in the O-RAN RIC. In addition, the question is how to maintain the orchestration cross domain with core network SON functions in 3 GPP and any newly added xApps, rApps, and dApps to the RAN without creating conflicts between all these apps. As a result, the network intelligence orchestration for the different types such as the xApps, rApps, and dApps is an unprecedented problem that requires innovative, automated and scalable solutions. In [69], the authors formulate an orchestration problem where the orchestration policy variable \(X\) is computed to maximize the total value of requests being accommodated by selecting (i) which requests can be accommodated; (ii) which AI/ML models should be instantiated; and (iii) at what location the AI/ML and requests should be executed to satisfy request performance and timescale requirements to avoid or mitigate the conflict between then and at the same time, complying with the requirements of each request [70].
# Quasi-Experimental Design for Medical Studies with the Method of the Fuzzy Pseudo-Control Group
###### Abstract  
(1) Background: Let the continuous parameter \(X\) be a proxy variable for the outcome of an intervention \(R\). Quasi-experimental studies are designed to evaluate the effect of \(R\) over \(X\) when forming a randomized control group (without the intervention) is impractical or/and unethical. The most popular quasi-experimental design, the difference-in-differences (DID) method, uses four samples of \(X\) values (pre- and post-intervention experimental and pseudo-control groups). DID always quantitatively evaluates the effect of \(R\) over \(X\). However, its practical significance is restricted by several (often unprovable) assumptions and by the monotonic preference requirement over \(X\). We propose a novel fuzzy quasi-experimental computational approach that addresses those limitations.  
(2) Methods: A novel method of the fuzzy pseudo-control group (MFPCG) is introduced and formalized. It uses four fuzzy samples as input, exactly the same as DID. We practically determine and statistically compare the favorability of the differences in \(X\) before and after the intervention for the experimental and the pseudo-control groups in case of the more general hill preferences over \(X\). MFPCG applies four modifications of fuzzy Bootstrap procedures to perform each of the nine statistical tests used. The new method does not use the assumptions of DID, but it does not always produce a positive or a negative answer, as MFPCG results are qualitative. It is not a competing methodology; as such, it should be used alongside DID. (3) Results: We assess the effect of annuloplasty that acts in conjunction with revascularization over two continuous parameters that characterize the condition of patients with ischemic heart disease complicated by moderate and moderate-to-severe ischemic mitral regurgitation. (4) Conclusions: The statistical results proved the favorable effect of annuloplasty on two parameters, both for patients with a relatively preserved medical state and patients with a relatively deteriorated medical state. We validate the MFPCG solution of the case study by comparing them with those from the fuzzy DID. We discuss the limitations and adaptability of MFPCG, which should warrant its use in other case studies and domains.  
**Keywords:** fuzzy samples; fuzzy Bootstrap procedures for statistical tests; medical data analysis; cluster of tests
## 5 Conclusions  
This paper proposes an energy aware clustering grid-based power efficient data aggregation protocol (GB-PEDAP) for WSNs. This scheme builds a grid of cells in the sensing area, selects the sensor with the highest remaining energy in each cell as the cell head, and uses [PERSON]'s algorithm to connect the cell heads to build a tree structure. During data transmission, each cell head is responsible for data collection and data aggregation of sensor nodes in the cell. The cell head will transmit the data along the tree structure's path, and finally transmit it to the BS. In the simulations, the number of rounds executed by GB-PEDAP is better than PEDAP, GSTEB, and TBEEP. The number of rounds executed by GB-PEDAP is approximately 1.2 rounds of TBEEP, 1.3 rounds of GSTEB, and 1.5 rounds of PEDAP. With the initial energy being 0.25 J, the execution rounds of the first node death for GB-PEDAP, TBEEP, GSTEB, and PEDAP are 751, 572, 486, and 339, respectively. The proposed GB-PEDAP can effectively balance and disperse the energy consumption of sensors, thereby prolonging the lifetime of a WSN.  
Conceptualization, N.-C.W.; methodology, N.-C.W. and Y.-L.C.; software, W.-C.L. and C.-Y.L.; validation, W.-C.L. and Y.-L.C.; formal analysis, N.-C.W. and Y.-L.C.; investigation, Y.-L.C. and W.-C.L.; resources, Y.-L.C. and C.-M.C.; data curation, C.-M.C. and Y.-F.H.; writing--original draft preparation, W.-C.L., N.-C.W. and C.-M.C.; writing--review and editing, N.-C.W., C.-M.C. and Y.-F.H.; visualization, C.-Y.L. and Y.-F.H.; supervision, N.-C.W.; project administration, Y.-F.H.; funding acquisition, N.-C.W. and Y.-F.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology of Taiwan under grants MOST-110-2221-E-239-002 and MOST-111-2221-E-324-018.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
Conflicts of Interest: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Protocol** & **GB-PEDAP** & **TBEEP** & **GSTEB** & **PEDAP** \\ \hline Hierarchical architecture & two layers & single layer & single layer & single layer \\ \hline Data transmission structure & direct and tree & chain & tree & tree \\ \hline Energy efficiency & high & medium & medium & low \\ \hline Uniformity of remaining energy & very good & good & good & average \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparisons among different data aggregation protocols.
## 6 Conclusions  
This article obtained several parameters of the HSS model for Xi'an loess through indoor tests, established proportional relationships between the parameters, and vali-dated them using engineering examples. The conclusions are as follows:  
1. The proportional relationship between the small-strain moduli in the HSS model for Xi'an loess was obtained through indoor tests. Numerical simulations were performed for various values of the initial dynamic shear modulus \(C_{0}^{\text{ref}}\). Through a comparison of the simulation results with the field monitoring data, it was found that when the ratio of \(E_{\text{s1-2}}\), \(E_{\text{ood}}^{\text{ref}}\), \(E_{\text{50}}^{\text{ref}}\), \(E_{\text{ur}}^{\text{ref}}\) and \(G_{0}^{\text{ref}}\) in the HSS model is 1:0.96:1.25:6.2:12.4, and \(R_{\text{f}}=0.85\), the simulation results are closest to the field monitoring data. This can provide a reference for similar engineering analyses in the Xi'an loess region.
2. By fitting the data, a functional relationship between the coefficient of earth pressure at rest \(K_{0}\) and the water content \(w\), void ratio \(e\), and over-consolidation ratio OCR for loess was established. The deviation between the values calculated using this fitting formula and the test data is within 15%, providing an empirical method for determining the K value of Xi'an loess.
3. Through numerical analysis of the section between Shen Zhou 2 nd Road Station and Dong Chang'an Street Station on Xi'an Metro Line 15, it was found that compared to the M\(-\)C and HS models, the HSS model results are closer to the field monitoring data. The HSS model is more suitable for projects with stricter settlement requirements, and it also validates the feasibility of applying the HSS parameters proposed in this paper.
4. The above conclusions are derived from the study of loess in the Xi'an area, and further research is needed to determine whether they are applicable to loess in other regions. Future studies will conduct experiments on loess from more regions and at different depths to analyze and summarize the patterns of HSS model parameters for loess in various areas.  
Conceptualization, J.H. and Q.D.; methodology, J.H. and Q.D.; software, J.H. and Q.D.; validation, J.H. and Q.D.; formal analysis, J.H.; investigation, J.H.; resources, Q.D.; data curation, J.H.; writing--original draft preparation, J.H.; writing--review and editing, J.H. and Q.D.; visualization, J.H.; supervision, Q.D.; project administration, Q.D.; funding acquisition, Q.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the findings of this study are available from the corresponding author upon reasonable request.  
The authors thank [PERSON] (Y.C.) for his guidance on indoor tests and thank [PERSON] (X.L.) for providing us with resources.  
The authors declare no conflicts of interest.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 1 Introduction
The required memory is proportional to the size of the network. However, with respect to FPWK, the information required to authenticate a node is smaller. Therefore, PCAT can be applied with larger networks than FPWK. Moreover, the use of authentication tables allows for transmitting less information and computing fewer cryptographic operations with respect to digital certificates, which represent the state-of-the-art for the public-key cryptography. A theoretical evaluation and a comparison with the state-of-the-art shows that PCAT provides an optimal level of security in terms of resilience against an adversary that has compromised at least one node. Moreover, an experimental evaluation, based on the implementation of the protocol on a WSN, demonstrates the feasibility of the proposed approach. The public-key encryption technique used for the implementation of the protocol is elliptic curve cryptography (ECC) [18]. The organization of the rest of the paper is as follows: in Section 2 related works are described. In Section 3 PCAT is detailed. In Section 4, a comparative analysis is presented. Finally in Section 5 a prototype is implemented and tested on real nodes, while in Section 6 some conclusions are drawn.