esa-sceva/satcom-chunk-collection huggingface dataset:
# Wastewater Treatment Using Alkali-Activated-Based Sorbents Produced from Blast Furnace Slag
## 3 Results
### Filtration Tests: Effect of the Operating Parameters  
To study and tune operating parameters, monoliths of different volumes (PGM1 and PGM2 in the following) were prepared, which corresponded to monoliths, containing different powder amounts (Table 4). Cu-containing solutions were used to analyze and tune the operating parameters of the filtration process; effects of the monolith volume, of the filtration time, and of the ion concentration in the initial solution were studied. All these parameters are those of choice for practical application; indeed, they could influence metal capture efficiency, results are plotted in Figure 6a-d.  
A rather low effect of the monolith volume was observed (Figure 6a): a 30% increase of the volume corresponded to an increase of 5% in metal capture. Considering that, at this scale, a monolith of lower volume can assure more homogeneity and reproducibility; for all the further experiments, the monolith volume was set at 54.7 cm\({}^{3}\).  
Once the monolith dimension was fixed, no remarkable effect of the filtration time was found (Figure 6b); even a six-fold increase in filtration time did not result in a higher metal  
Figure 6: Effect of the operating parameters: (**a**) monolith volume, (**b**) filtration time, and (**c,d**) initial ion concentration.  
capture, and the observed small differences were within the experimental error. Therefore, the filtration time was set at 10 min, this time being short but better managed by a manual control of the vacuum pump.  
Finally, a linear capture capability of the solids was found on increasing the initial metal concentration (Figure 6c). In the explored range of metal concentrations, no saturation was found, and apparently the solid should be able to capture larger amount of ions. A very high capture efficiency was measured (Figure 6d) for Cu ions which laid the groundwork for the application in treating multi-ion solutions.
## 4 Results and Discussion  
### Validation of the Proposed SDPS Model  
A 201.4-m-long CRH2C passes through two tunnels at a speed of 300 km/h. The tunnel lengths are 1005 m and 2812 m, and the local velocity of sound is 336 m/s (1 standard atmospheric pressure, 283.15 K) [14]. Pressure-measuring points are deployed along the tunnel in the longitudinal direction in the field measurements, the measuring point intervals in the middle section of the 1005-m-long and 2812-m-long tunnels were 100 m and 200 m, respectively. Relative error (RE) is introduced to quantify the differences between the predicted and measured results [51]:  
\[RE=\frac{\left|x_{pre}-x_{maa}\right|}{L_{tu}}\times 100\% \tag{24}\]  
where \(x_{pre}\) is the predicted locations of the extreme pressure, and \(x_{maa}\) is the measured results.  
As shown in Figure 7, in the 2812-m-long tunnel, the measured MPP and MNP both appear at 1400 m from the entrance, and the predicted MPP and MNP appeared at 1450.99 m and 1361.01 m, respectively. The REs were 1.81% and 1.39%, respectively.  
As for the 1005-m-long tunnel, the measured MPP appeared at 500 m from the entrance at 15.70 s, and the predicted MPP appeared at 518.58 m at 15.49 s. The measured MNP appeared at 500 m at 18.45 s, and the predicted MNP appeared at 486.42 m at 18.92 s. The REs of the spatial distribution of MPP and MNP were 1.85% and 1.35%, respectively. The difference between the measured and predicted occurrence times is less than 0.5 s, and it is mainly from the position difference between the measured point and the predicted point of the SDPS model. The difference would be further reduced if the measured data at the predicted location (518.58 m and 486.42 m) were available. In general, the predictedresults were in good agreement with the measured results and proved the validity of the SDPS model.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 4 Experimental Evaluations
### Simulation Results and Analysis with NAIE Channel  
We provide more simulation tests to evaluate the performance of the proposed scheme with various numbers of UEs and higher modulation orders (e.g., 16-QAM) as well as the NAIE channel in practical scenarios provided by the iMaster NAIE platform [25].
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 3 Preliminaries
### Embeddings  
Embedding is a method that aims to vectorize non-numerical data such as words; it involves mapping words or phrases to real number vectors, enabling the capture of semantic meaning and the integration of text information into network embedding. This technique is widely used for natural language processing tasks such as text classification [29]. In this paper, we have used and tested the performance of the following text embedding models, ranging from LLM embeddings to static embeddings.
## 6 Conclusions and Future Directions
### Main Conclusions
#### 6.1.3 Parameter Extraction Results Accurately Reflect Network State  
Extracted traffic parameters comprehensively reflect the operational state of the road network, providing a reliable data foundation for signal control optimization:  
Flow analysis revealed the primary-secondary structure of the road network. Major arterials (formed by intersections 1-8 and 1, 9, 17) carry primary traffic flows with rel atively stable volumes; secondary links have smaller flows but varying volatility. This differentiated flow distribution provides a basis for differential signal timing design.  
Speed and travel time analysis identified unstable links. Links '4,6', '21,22', '16,22' and others exhibited substantial speed fluctuations and travel time variability, suggesting these links may experience severe traffic state transitions requiring special attention in signal control.  
Stop point density analysis intuitively illustrates congestion distribution. Intersections 1, 2, 3, 9, and 17 were identified as congestion hotspots, exhibiting pronounced directional characteristics (such as severe northbound approach congestion at intersections 1 and 2). These findings align with morning peak commuting patterns, validating the reasonableness of analysis results.  
OD matrices provided demand distribution information. Identifying major OD pairs and high-flow corridors provides important inputs for network-level signal coordination. Additionally, edge OD point information contained in detailed OD matrices helps understand network collection and distribution characteristics.
# Secured Real-Time Machine Communication Protocol
## 1 Introduction
**Implementation in Go:** The protocol has been implemented in the Go programming language, leveraging its concurrency capabilities to optimize performance. The implementation is publicly available to foster further research and development. 5. **Comprehensive Evaluation:** A detailed performance evaluation compares SRMCP with widely used protocols such as Modbus and the OPC UA across different scenarios, including local loopback, local networks, and remote networks via a VPN. The results demonstrate SRMCP's superior performance in reading and writing operations while maintaining strong security guarantees. The rest of the paper is organized as follows: Section 2 reviews the major existing industrial communication protocols. Section 3 details the design of the SRMCP protocol. Section 5 describes the implementation and evaluation methods, while Section 6 presents the testing results and analysis. Finally, Section 7 provides the conclusion and outlines future research directions.
## 3 Preliminaries  
In this section, we will introduce the technologies to be used in the initial implementation of our algorithm proposed in this paper.  
**The RPC (Remote Procedure Call)** protocol is a protocol used for remote invocation, allowing client applications to call functions or procedures on a remote server over a network, as if they were calling local functions. The RPC protocol is commonly used in building distributed systems and client/server applications. When implementing RPC, the following guarantees and objectives are typically followed:  
**Transparency:** RPC should be transparent to the developers of client and server applications, who do not need to be aware of the implementation details of remote invocation and can use RPC just like local functions.  
**Interoperability:** RPC should be able to interoperate across different platforms and programming languages. Therefore, it must have a consistent interface and protocol.  
**Reliability:** RPC should ensure the reliability of invocations in case of network or server failures. Therefore, it must have mechanisms such as retry, timeout, and error handling to handle such situations.  
**Efficiency:** RPC should have high-performance capabilities, allowing fast data transmission over the network while maintaining low latency and high throughput.  
**Security:** RPC should have necessary security mechanisms to protect the security and confidentiality of remote invocations. This includes mechanisms such as encryption, authentication, and authorization.
## 7 Discussion
* The hybrid method (which combines the MPR and modified WWA methods) has the ability to account for higher harmonics and the effect of traveling waves; it also works quite well even if only a small number of sensors is available. The hybrid method does not explicitly take into consideration non-stationary characteristics, but reconstruction even with fairly strong non-stationary response is superior to that resulting from use of the POD and MPR methods. The hybrid method needs to assume modes in the second step of the modal weight estimation for the complex MPR modes. Although the hybrid method is the slowest due to the greater computational effort relative to the other methods, it is the most accurate both for a large as well as a small number of input sensors. There are some limitations of this study that need to be acknowledged at this point. First, the results are based on only four NDP data sets and two simulated data sets. The uncertainty or confidence bounds on fatigue damage estimates from each empirical method has not been directly discussed. Second, issues that relate to what constitutes an adequate sample in terms of length of the measured signal and sampling rate when employing each empirical method for reconstruction of strain and fatigue damage estimation have not been directly addressed. Third, in this study, only strain measurements are employed in the fatigue damage estimation; it should be noted that acceleration measurements or a combination of strain and acceleration measurements, often collected in field monitoring, may also be employed to estimate fatigue damage.
# Electronic Fourier-Galois Spectrum Analyzer for the Field \(Gf(31)\)
## 1 Introduction
However, to use the numerical convolution theorem, the most convenient is the basis, which is constructed in Galois fields and corresponds to the Fourier-Galois transform [27]. This approach has not yet found widespread use, although in the future it will make it possible to realize convolutional neural networks with a given transfer function. This is, among other things, due to the fact that the construction of the basis corresponding to the Fourier-Galois transform is subject to rather serious restrictions. It can be constructed for quite certain special cases, and one of them corresponds exactly to circular and circular images (by circular images, we mean images occupying an area bounded by two circles with coinciding centers). There are also other possibilities for using the basis corresponding to the Fourier-Galois transform in measuring equipment. In particular, in [33], the question of its application to the study of rotating objects was considered. One of such objects corresponds to the design of a viscometer, for which we obtained a patent from Kazakhstan [34]. The simplest result of investigating a rotating object is obviously the registration of the rotation frequency. As shown in [33], the measurement of this frequency can be realized by calculating the Fourier-Galois spectrum of a set of signals diagnosing the object under study. The purpose of this paper is to design a specific electronic circuit for a Fourier-Galois spectrum analyzer and to develop the corresponding algorithms. We emphasize that both the algorithms and the spectrum analyzer scheme were developed for a particular field, \(GF\)(31). The choice of this particular field is justified in detail below, but it should be emphasized that different Galois fields have different specificities [35]. For example, the field \(GF\)(257) considered in our paper [36] is of interest from the point of view of reducing the operations of multivalued logic to an algebraic form. However, such fields are not quite convenient for the circuit implementation of Fourier-Galois spectrum analyzers. For this purpose, it is desirable to realize an algorithm for spectrum calculation that would allow the construction of the simplest possible electronic circuit. The approach based on the digital convolution theorem [26; 27] is general, but the electronic circuits realizing it may differ quite significantly.
## 3 Results and Discussion
### Meteorological Factor
#### 3.1.3 Statistical Conclusions on the Meteorological Factor  
Based on the statistical analysis of meteorological data of the Kastoria subregion, no significant difference was observed. The lack of significant differences regarding microclimatic data measurements suggests a non-determinate role of these parameters as factors differentiating isotopic and multi-elemental footprint values in the final product.
# An NDN Cache Management for MEC
## 2 Named-Data Networking Architecture
### NDN Cache Management Overview  
Considering the transmission overheads for forwarding _Interest_/_Data_, the storage overheads for caching _Data_, and the computation overheads for searching the matched _Data_ in-network caches, many CMSs proposed to selectively and gradually cache transmitted _Data_ propositionally to the request frequency of the _Data_[5; 6; 7; 8; 9; 10; 11]. Some CMSs probabilistically/randomly select one or several nodes on a downstream path of the _Data_ and then cache the _Data_ in the selected nodes [5; 6; 7]. Such schemes can reduce the unnecessary redundant caches. However, they cannot correctly predict/evaluate the transmission overheads because they can neither predict nor designate which nodes on a downstream path will cache transmitted _Data_.  
Other CMSs gradually increase the number of nodes caching _Data_ propositionally to the request frequency of the _Data_[8; 9; 10; 11]. They can enhance the transmission overheads and reduce the unnecessary cache redundancy [12; 13; 14]. Both LCD (Leave Copy Down) and WAVE take this approach. Figure 2 shows WAVE [10]. If the \(k\)th node on an upstream path of _Interest_ has cached the matched _Data_ and it forwards the _Data_, LCD/WAVE proposes that only the first node receiving the _Data_ caches the transmitted _Data_. Practically, the node is the \(k\)-\(1\)th node on the upstream path of the _Interest_. While LCD increases the number of newly cached segments of content by one for each time the content is required, WAVE exponentially increases the number of segments that are newly cached, as shown in Figure 2.  
Figure 1: Data networking architecture (NDN) _Interest_/_Data_ Procedure.  
(1) Let the content consist of seven segments {s\({}_{0}\),..., s\({}_{6}\)}. When receiving the first _Interests_ of the content, only the first segment s\({}_{0}\) of the content will be cached in a node (Node\({}_{[3]}\)) on a downstream path. Other segments are transmitted to a requestor without being cached.  
(2) If the second _Interests_ for the content are forwarded, Node\({}_{[3]}\) can respond to the _Interest_ for requiring the cached s\({}_{0}\). Then, while the s\({}_{0}\) is transmitted, only Node\({}_{[2]}\) caches it. The other _Interests_ for {s\({}_{1}\),..., s\({}_{6}\)} are responded to by the provider of the content. Then, only two segments, s\({}_{1}\) and s\({}_{2}\), are newly cached in Node\({}_{[3]}\).  
(3) Finally, if the provider receives the third _Interests_ for the content, four segments {s\({}_{3}\),..., s\({}_{6}\)} are newly cached in Node\({}_{[3]}\).
## 1 Introduction
### Unoccupied Aircraft Systems Estuarine/Marine Applications  
The use of small unoccupied aircraft systems (UAS, or drones) in marine science and conservation applications is on the rise. These portable, affordable and easy to use systems are increasingly used to study and assess at-sea and coastal populations of marine species [20; 21; 22] and to map and evaluate coastal habitats such as saltmarshes and beaches [23; 24]. Applications of UAS now span biological oceanography [25], physical oceanography [26; 27] and atmospheric sampling [28]. In coastal systems, small UAS can provide essentially on-demand remote sensing capabilities, collecting ultra-high resolution (<5 cm) across multiple spectral bands that can be used for near real-time management purposes as well as for validating remotely sensed data collected from occupied aircraft platforms and satellites. Fixed-wing UAS are especially useful for assessing larger areas of coastal habitat due to their increased flight efficiency, and provide researchers studying coastal protected areas the opportunity to resolve fine scale changes in coastal morphology and associated habitats quickly, and at relatively low costs, without significant disturbance of sensitive ecosystems [9; 24].
###### Abstract  
Device-to-device (D2D) technology is a promising technique in terms of being capable of providing efficiency, decreased latency, improved data rate, and increased capacity to cellular networks. Allocating power to users in order to reduce energy consumption and maintain quality of service (QoS) remains a major challenge in D2D communications. In this paper, we aim to maximize the throughput of D2D users and cellular users subject to QoS requirements and signal-to-interference-plus-noise ratio (SINR). To this end, we propose a resource and power allocation approach called optimal power allocation and delay minimization based on the conflict graph (OP-DMCG) algorithm that considers optimal power allocation for D2D multi-users in the cellular uplink channels and minimization of the total network delay using conflict graphs. Based on the simulations presented in this paper, we show that the proposed OP-DMCG algorithm outperforms the greedy throughput maximization plus (GTM+), delay minimization conflict graph (DMCG), and power and delay optimization based uplink resource allocation (PDO-URA) algorithms in terms of both total network throughput and total D2D throughput.  
D2D communication; power allocation; resource allocation; 5G +
Footnote †: journal: Applied Sciences  
_Article_  
Optimal Power Allocation and Delay Minimization Based on Conflict Graph Algorithm for Device-to-Device Communications.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 4 Connectivity Properties of Ship MANET
### Analysis of Movement Situation between Nodes  
For ship encounters, there will be a variety of encounter scenarios. Through the comprehensive inductive analysis of the encounter scenarios, if we regard it as the relative motion between nodes, there will be two kinds of scenarios, namely approaching then leaving scenarios and leaving scenarios.
## 2 Materials and Methods
### Other Parameters Used for Atrial Activity and Heart Rate Evaluation
#### 2.5.3 Spectral Entropy and Renyi Entropy  
The Spectral Entropy (SpEn) is a measure of the distribution of power in a signal. For the atrial activity signal (P-wave) \(x(n)\), the power spectrum is \(S(m)=|X(m)|^{2}\), where \(X(m)\) denotes the discrete Fourier transform of \(x(n)\). The probability distribution \(P(m)\) is given as  
\[P(m)=\frac{S(m)}{\sum_{i}S(i)}. \tag{12}\]  
The spectral entropy is then given as  
\[\text{SpEn}=-\frac{1}{\log_{2}N}\sum_{m=1}^{N}P(m)\log_{2}P(m), \tag{13}\]  
where \(N\) represents all the frequency components. The Matlab function is used to calculate SpEn.  
The Renyi entropy is expressed as  
\[\text{REN}(\alpha)=-\frac{\alpha}{1-\alpha}\sum_{m=1}^{N}\log_{2}(P(m))^{ \alpha}, \tag{14}\]  
where \(\alpha>0\) and \(\alpha\
eq 1\); in this work, \(\alpha=2\).
## 2 Research Methods
### Multi-Objective Optimization Algorithm
[PERSON] and others compared commonly used optimization algorithms in architectural design, including NSGA-II, MOPSO, two-phase optimization using a genetic algorithm (PR GA), elitist non-dominated sorting evolution strategy (ENSES), and multi-objective dragonfly algorithm (MODA), with PR GA showing higher repeatability and a larger solution space, followed by NSGA-II [68]. Ant colony optimization (ACO) is a metaheuristic algorithm inspired by the behavior of ants [69]. It is a global stochastic optimization algorithm capable of solving complex nonlinear problems without the need for derivatives of the objective function. Initially developed for discrete optimization problems, it has evolved to address continuous variables, leading to the development of continuous domain ant colony optimization (ACOr). [PERSON] and others have conducted extensive research on the ant colony optimization algorithm (ACO), developing the continuous domain ant colony optimization (ACOR) algorithm in 2017 for optimizing commercial buildings in Australia, and comparing ACOR with the Nelder-Mead (NM) algorithm, particle swarm optimization with inertia weight (PSOIW), and a hybrid particle swarm optimization with the Hooke-Jeeves (PSO-HJ) algorithm. Results showed that ACOR could find better solutions in less time, saving over 11.4% of energy even with common energy-saving measures [70]. In 2018, [PERSON], and others developed a modified version of the ant colony optimization algorithm for mixed variables (ACOMV-M), which converged to similar quality solutions with about 50% fewer simulations [71]. Subsequently, ant colony optimization was applied in model predictive control directions, with [PERSON], [PERSON] and others using ant colony optimization (ACO) algorithms from different starting points to solve multiple model predictive control (MPC) optimization problems, demonstrating that ACO provides high-quality optimized control sequences while also requiring shorter computation times, achieving fairly good solutions within 15 min [72]. This research faces a classic multi-objective decision-making problem, aiming to find a reasonable balance between minimizing per-unit building area heating carbon emissions and overall cost minimization. Although using efficient insulation materials and incorporating photovoltaic technology can effectively reduce energy consumption and carbon emissions, this typically comes with higher initial investments and maintenance costs. Conversely, aiming to minimize costs may correspond to a high-carbon emission scheme. To address this conflict, this study opts to use the NSGA-II algorithm provided by the Wallacei X 2.7 tool for optimization. This algorithm generates a range of Pareto frontier solutions, offering multiple trade-off options between carbon emissions and costs. Through the visual display of these solutions, decision-makers can more clearly see the trade-offs between different schemes, thereby making a choice between the two objectives.