esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Focused Beamforming in Three-Dimensional Space  
Generally, conventional beamforming (CBF) is to search on a reconstruction surface with a preset depth, and the Z coordinate of the reconstruction point is the same as the Z coordinate of the sound source. The three-dimensional spatial focused beamforming searches in a spatial region composed of multiple reconstruction planes of different depths, as shown in Figure 1.  
A planar array containing M microphones is used to locate the point sound source S in the space. The coordinate of the \(m\)-th microphone is \((x_{m},y_{m},z_{m})\) (\(m=1,2,3\ldots,\) M). The signal strength of the point sound source S received by different microphones attenuates as the distance increases [9]. The sound pressure signals measured by the \(m\)-th microphone is \(P_{m}(\omega)\). The first microphone \((x_{1},y_{1},z_{1})\) is taken as the reference. Point \(f\left(x_{f},y_{f},z_{f}\right)\) is a reconstruction point in the sound pressure reconstruction area in three-dimensional space. \(\mathbf{r}_{1}f\) is the position vector of the reference microphone to the reconstruction point \(f\), and \(\mathbf{r}_{mf}\) is the position vector of the \(m\)-th microphone to the reconstruction point \(f\), then the delay time \(\tau_{m}\left(x_{f},y_{f},z_{f}\right)\) of the \(m\)-th microphone to the reference microphone is as follows [9]:  
\[\tau_{m}\left(x_{f},y_{f},z_{f}\right)=\frac{\left|\mathbf{r}_{1}f\right|- \left|\mathbf{r}_{mf}\right|}{c} \tag{1}\]  
In formula (1), \(\left|\cdot\right|\) is the vector modulus, and \(c\) is the speed of sound.  
According to the principle of delay-and-sum beamforming, the sum of the phase delay \(\tau_{m}\left(x_{f},y_{f},z_{f}\right)\) of each microphone signal is compensated. The delay-and-sum beamformer output of point \(f\) is as follows [9]:  
\[B\left(x_{f},y_{f},z_{f},\omega\right)=\frac{1}{M}\sum\limits_{m=1}^{M}w_{m}P _{m}(\omega)\mathrm{e}^{-j\omega\cdot\tau_{m}\left(x_{f},y_{f},z_{f}\right)} \tag{2}\]  
where \(w_{m}\) is the weighting coefficient of the \(m\)-th microphone [9].  
Then, the sound pressure reconstruction points in all 3D reconstruction areas are calculated. The complex output of the sound pressure at each point \(B\left(x_{f},y_{f},z_{f},\omega\right)\) constitutes the acoustic imaging map. The reconstruction point with the maximum sound pressure output \(B_{max}\left(x_{f},y_{f},z_{f},\omega\right)\) is the sound source position.
# High-Throughput Bit-Pattern Matching under Heavy Interference on FPGA
## 6 Summary  
In this article, we have presented a novel circuit for matching bit patterns in the presence of heavy interference (BER = \(10^{-1}\), BER = \(10^{-2}\)). The circuit accepts one pattern of 1200 bits that suffered errors during transmission as the input and outputs the pattern that was most likely to be transmitted. This consisted of a shift-register memory structure with 64 1200-bit entries and 64 pattern-matching basic-unit circuits. The structure provides throughput in the range of 800 Gbps while consuming a reasonable number of resources, which allows for its implementation on small commercial platforms.  
To achieve the maximum operating frequency and low latency, the detection method was implemented through the utilization of a comparator-augmented adder tree-based circuit called the cascaded pattern identifier. Each pattern matching the basic unit received the input pattern of the circuit, along with its respective memory entry. With the use of the cascaded pattern identifier, the process of comparison was achieved with a latency of 23 clock cycles and an operational frequency of 667 MHz. The outputs of all units were then checked, and the detected entry was outputted.  
Contemporary methods of pattern matching revolve around the efficient use of memory to provide pattern matching at high speeds while providing the function of updating the memory of the circuit in real time. In its present form, the proposed circuit is the only circuit that can perform pattern matching under interference at such high data rates and that has the potential to be expanded indefinitely without compromising its operational frequency. It also can update its memory, albeit at a much lower pace.  
Inherently, apart from bit-pattern matching, the design can also be used without major modifications to detect a series of analog measurements. One example is the case of a group of sensors that produce measurements of a certain bit length. By using comparators and comparing their outputs to a threshold, we can turn each multi-bit measurement into a single bit. This, in turn, transforms the series of events into a bit pattern that can be directly detected by the circuit.  
Conceptualization, D.N.; methodology, D.N.; software, D.N.; validation, D.N.; formal analysis, D.N.; investigation, D.N.; resources, D.N.; data curation, D.N.; writing--original draft preparation, D.N.; writing--review and editing, D.N. and P.G.; visualization, D.N. and P.G.; supervision, P.G., C.K. and H.A.; project administration, H.A. and C.K.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The APC is covered by a waiver.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Simulation Methodology
### Simulation Model and Setting Variables
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Building Envelope**} & \multicolumn{2}{c}{**HVAC System**} \\ \cline{2-5}  & **Wall (U-Value)** &
\begin{tabular}{c} **Glazing and Solar** \\ **Shading System** \\ \end{tabular} & **Infiltration** & **Air-Conditioning System** & **Plant System** \\ \hline Case 11 *** & & & & HW Boiler (Eff.90\%) \\  & & & & + Cent. Chiller \\  & & & & (COP 4.0) \\ \hline Case 12 *** & 0.47 W/m\({}^{2}\cdot\)K & Double Low-E (No Blind) & 3.0 ACH & CAV System & HW Boiler (Eff.80\%) \\  & & & & + Cent. Chiller \\ Case 13 *** & & & & HW Boiler (Eff.90\%) \\  & & & & + Cent. Chiller \\  & & & & (COP 6.0) \\ \hline Case 14 *** & & & & GSHP \\  & & & & (Heat Exchanger: \\  & & & Vertical \\  & & & ground-coupled, \\  & & & & GSHP Capacity: \\  & & & 280 RT, System COP \\  & & & & 3.75 (Cooling), \\  & & & & System COP \\  & & & & 3.15 (Heating)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 4: Simplified diagram of energy saving technologies. Passive systems selected as variables were as follows: The improved wall U-value was 0.15 W/m2.K (Case 1), and the improved air tightness was 0.2 ACH (Case 2). Windows and glazing system alternatives were double tinted low-e (Case 3) and triple low-e (Case 4), which reflect improved U-value, SHGC, and VLT. Venetian blinds were selected as a solar shading system. Blinds were installed on the outside of windows in Case 5, and their influences were analyzed on energy saving. The active system was classified as the air-conditioning and plant system. When evaluating the air-conditioning system, the base performance of the absorption chiller-heater was COP 1.0 for cooling, and COP 0.8 for heating. The CAV system, which was the base air-conditioning system from Cases 1 to 5, was changed to the VAV system in Case 6. The VAV + economizer system was applied in Case 7, and the VAV + rotary type ERV was designed in Case 8. The VAV + UFAD system (Case 9), and active chilled beam with DOAS (Case 10), were also used as alternatives to evaluate the air-conditioning system's contribution to energy saving. The plant systems were modified to evaluate their contribution to energy saving from Cases 11 to 13. The base absorption chiller-heater (cooling COP 1.0, heating COP 0.8) was replaced by the hot water condensing boiler and centrifugal chiller with the same CAV systems. The primary energy consumption was examined when either the efficiency of the boiler or the COP of the chiller was changed (Cases 11 and 12), and also when both were changed simultaneously (Case 13). For renewable energy technologies, geothermal energy was used in Cases 14 and 15. When using geothermal energy from the GSHP, the air-conditioning system applied was the CAV system in Case 14, and the active chilled beam with the DOAS in Case 15.
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 3 2\(\pi\) Ambiguity Problem  
The AMCW method has a limitation on the maximum range of the distance due to the 2\(\pi\) ambiguity problem. In the AMCW method, the maximum measurable distance, known as the unambiguous range, is expressed as follows  
\[d_{\max}=\frac{c}{2f}. \tag{8}\]  
The actual distance of an object at a distance exceeding the unambiguous range is expressed as  
\[d_{act}=d+\frac{c}{2f}\times k,\;k=0,1,2,3,\ldots, \tag{9}\]  
where \(k\) is a non-negative integer. The 2\(\pi\) ambiguity relies on \(k\), which cannot be determined for as long as single modulation frequency \(f\) is used. If \(k\) can be estimated, the actual distance can be found, and the 2\(\pi\) ambiguity problem can, thus, be solved. There are several ways to solve this problem, as described in the subsequent subsections.
# A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning +
## 4 Proposed Framework
### Proposed Method  
The proposed method considered in the framework for automated monitoring in IoT is illustrated in the Figure 1. It must be used by a team consisting of different stakeholder roles:  
* IoT developer: the technical staff, individual, or company that is held responsible by the implementation of the IoT solution. It must have necessary hardware, software and architectural expertise to develop and deploy the IoT solution;
* Business specialist: usually the client, a team, or individual that wants to deploy IoT solutions to reduce costs and create value in Smart City, Industry 4.0, Smart Home, and other application fields. It has a deep understanding of the business needs related to the IoT solution.  
We consider that the IoT developer and the business specialist are individuals or teams that participate in an IoT project in a critical field. The quality aspects must be controlled to make sure that the resulting technical solution adheres to the non-functional requirements modeled considering the business drivers. The method may be applied in the start of an IoT project to enhance the resulting IoT solution quality attributes. It is comprised of the following steps:  
1. Business drivers: related to business view from the RM-ODP. Business needs must be specified by the business specialist to the IoT developer;
2. Non-functional requirements: according to the ISO25010, specify the non-functional requirements and prioritize these requirements considering the business drivers described in the first step;
3. Engineering view: related to the engineering view present in RM-ODP. The IoT developer must present the architectural solution that support the non-functional requirements monitoring;
4. Dynamic system evaluation: real time monitoring and data collection from testbeds that enable automated tests and analysis with unsupervised machine learning mechanisms. The automated tests are used by the IoT developer in a TDD methodology;
5. Recommendations: the IoT developer must provide suggestions based on the analysis performed to the business specialist, considering how the business drivers are modeled in non-functional requirements. The suggestions may be related to the architecture (e.g., monolithic or microservices, cloud or edge data processing).  
Figure 1: Proposed method for the framework for automated monitoring in IoT.
## 3 Experiment Results and Analysis
### Specim FW-2  
The schematic diagram of the tested phenomena in the FW-2 group is shown in Figure 13. When the displacement angles reached 1/600 (\(\Delta\) = 5 mm), 1/500 (\(\Delta\) = 6 mm), and 1/400 (\(\Delta\) = 7.5 mm), there was no obvious phenomenon in the overall specimen. The change of strain was within a reasonable range, the panel did not produce cracks, and a grating appeared during the experiment when the upper connector bolt and the long circular hole slid. When the displacement angles reached 1/350 (\(\Delta\) = 8.6 mm), 1/300 (\(\Delta\) = 10 mm), and 1/250 (\(\Delta\) = 12 mm), there were no obvious phenomena. Only the two kinds of friction  
Figure 11: Loading history.  
noise could be heard (the friction noise among the panel, bolt, and connector, and the friction noise between the enclosure reinforcement and the loading plate). When the displacement angle reached 1/200 (\(\Delta\) = 15 mm), vertical cracks appeared at the bottom of panel No. 2 (Figure 13a). When the displacement angle reached 1/100 (\(\Delta\) = 30 mm), the cracks at the bottom of the panels were extended. When the displacement angle reached 1/75 (\(\Delta\) = 40 mm), a number of small fragments came off, and the bonding mortar cracked between panel No. 1 and panel No. 2, and panel No. 4 and panel No. 5. (Figure 13b). When the displacement angle reached 1/50 (\(\Delta\) = 60 mm), a corner of the back of panel No. 4 was broken (Figure 13c). When the displacement angle reached 3/10 (\(\Delta\) = 90 mm), new cracks appeared at the bottom of the panel, and vertical cracks appeared near the bolt holes at the connector on panel No. 4. When the displacement angle reached 7/20 (\(\Delta\) = 105 mm), the ALC panel corner was broken severely, and the relative sliding between the bolt and the bolt hole could be clearly observed during the loading process (Figure 13d).  
Figure 12: Crack pattern of specimen FW1: (**a**) splice mortar crack; (**b**) transverse crack in mortar; (**c**) dislocation of wall panels; (**d**) crack of the corner; (**e**) weld fracture; (**f**) large area of broken panel corner.
## 1 Introduction
Given the fast-paced development of diagnostic algorithms and the diversity of power plant architectures and sensing systems, the existing literature on fault detection and diagnosis (FDD) has become increasingly fragmented. While numerous studies focus on specific fault types, algorithms, or plant components, relatively few offer a comprehensive, systematized comparison of current practices across publication sources, methods, and application domains. This article aims to address that gap by presenting a structured review of 185 peer-reviewed publications related to FDD in power plants, indexed in MDPI, Elsevier, and Google Scholar. Unlike previous reviews that are either domain-specific or algorithm-centric, this work combines a quantitative analysis of publication patterns with a thematic synthesis of diagnostic frameworks, fault types, sensor technologies, and algorithmic strategies. The novelty of this review lies in its multidimensional classification of FDD studies, based on the following:  
* The type of power plant (e.g., thermal, nuclear, PV, hybrid);
* The sensor technology employed;
* The algorithmic paradigm (traditional, ML, DL, hybrid);
* The degree of diagnostic pipeline (detection, localization, resolution). In addition to identifying methodological and technological trends, the review proposes the following:  
* A taxonomy of diagnostic system configurations;
* A methodological checklist for evaluating the robustness and completeness of FDD studies;
* A summary of key findings, supported by tables and figures. Moreover, compared to existing review papers, this study introduces a cross-mapped taxonomy and a methodological checklist to assess research completeness--elements rarely found together in prior surveys. These features position the paper not just as a summary, but as a diagnostic framework development tool. Unlike prior reviews, which often covered broader periods or specific algorithmic techniques, this study focuses exclusively on publications from 2019 to 2025 to ensure topical relevance. It introduces a four-dimensional taxonomy that integrates plant type, sensor technology, algorithm class, and pipeline completeness, along with a cross-platform meta-analysis that contrasts publication patterns across MDPI, Elsevier, and Google Scholar. These features aim to provide a current and functionally structured resource for diagnostic research and practice. The paper is organized as follows: Section 2 presents the methodology and data selection criteria; Section 3 outlines the key findings; Section 4 discusses implications and research gaps; and Section 5 concludes the review.
# Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks
## 1 Introduction  
Concrete is a mixture that has revolutionized construction. Standard [1] defines it as a material composed of a binding component with embedded particles and aggregates. However, producing high-quality paving blocks on a large scale poses challenges. The batch-type paving block manufacturing process involves mixing, vibro-compacting, curing, and palletizing. In process control, a company with high production volumes in Quito-Ecuador has quality controls with specialists in raw materials, intermediate goods, and finished goods for each batch of 30,000 paving blocks. The vibro-compacting machine compacts the mixture of cement, water, aggregates, and additives that enters the mold and uses a tamper that shapes the fresh paving blocks, for which the dimensions, the weight of the vibro-compacted paving block, and the water absorption test are means of controlling the product quality initially.  
The tensile splitting strength test was described in standard [2], but its scope is limited to concrete cylinders. The Ecuador standard [1] is applied for paving blocks and replaces standard [3], where the compressive strength test is omitted from the tensile splitting strength test. The article described by [4] indicates how the paving blocks break on site intheir daily use, not by compression but by fracture (splitting); for this reason, the tensile splitting strength test was recommended to measure its ability to resist stresses.  
The tensile splitting strength is a crucial quality characteristic for customers, indicating the material's breaking point and useful life. However, this property is tested 28 days after manufacture. Predicting tensile strength earlier would enable manufacturers to avoid nonconforming paving blocks due to limited vision regarding this property. This study investigates how tensile strength depends on two groups of predictor variables in the production process. The first group is the thickness in mm, width in mm, length in mm, mass of the fresh paving block in g, and percentage of water absorption; the second group of predictor variables is the density of the fresh paving block in kg/m\({}^{3}\) and the percentage of water absorption. Predictive modelling with statistical validation enables acceptable error when forecasting tensile strength using these explanatory variables.  
A statistical sampling is carried out to infer 30,000 units of the rectangular paving block with 6 cm thickness (study population) representing a production batch. Specific objectives are identifying multivariate techniques for this case study, implementing predictive models, and comparing methodologies to determine the best model for predicting tensile splitting strength. To perform accurate statistical analysis predicting a continuous dependent variable from multiple continuous independent variables, we must verify hypotheses, infer the population, take representative samples, process data, identify outliers, build models, and check assumptions.
# Future Wireless Networking Experiments Escaping Simulations
## 2 Theoretical Comparison of the Testbeds
### Architecture Comparison
The edge cloud provides the computing needs for the SDR of the BSs, and the general cloud, which connects the testbed to the internet. A mix of radio nodes and hardware components are in Layers 2 and 1. In COSMOS, the hardware components are SDR nodes, while the radio node components include large, medium, and small nodes [17]. In Layer 1 POWDER architecture includes tens of wireless endpoints, including radio, antennas, and mobility patterns. In Table 1, we have listed IoT sensor units, mobile couriers with controlled and predicted mobility, and fixed location endpoints. Local computed nodes provide the needed computing capacity to the general endpoints and Massive Multi-Input Multi-Output (mMIMO) Base Stations (BSs) compose the second layer. Together with the SDR, the RF frontend, and antennas, common with the general BSs, the mMIMO BSs have a dedicated configuration to support MIMO. The third layer comprises cloud computing and edge computing nodes. Finally, in Layer 1 and 2, CityLab has several nodes divided into indoor units, active outdoor units, and passive outdoor units, as shown in Figure 1.  
To control, monitor, and configure the testbed hardware and provide services to the users, the testbeds are equipped with a control framework. In CityLab, testers can use jFed ([[https://jfed.ilab.timec.be/](https://jfed.ilab.timec.be/)]([https://jfed.ilab.timec.be/](https://jfed.ilab.timec.be/))) to access the experiment management system based on Emulab, which is also used in POWDER [18]. An Orbit Management Framework (OMF) testbed control framework is instead exploited in COSMOS. In Figure 2, the W-iLab testbed architecture is shown. There are two W-iLabs, W-iLab1.t, and W-iLab2.t. Both labs are located in Ghent, Belgium. W-iLab1.t has nodes spread over different floors, both with Wi-Fi and sensor nodes. Wi-Lab2 is an industrial room with 150 fixed and 20 mobile nodes with Wi-Fi sensors, LTE, and SDR [23]. The nodes  
Figure 1: Overview of POWDER, COSMOS, and CityLab. deployed in these environments can be exploited to create advanced interference and test scenarios. The testbed is equipped with environment emulators that help to emulate the behaviour of any type of sensor. In addition, another relevant metric is the battery depletion, that gives an indication of the power consumption in the sensor node. The testbed management is performed mainly using Emulab, used to implement networking protocols, load OS images, mount directories, etc. OMF takes care of controlling the experiment flow such as the startup applications, Wi-Fi interfaces, and script running. Finally, OML collects the data. In addition, in Wi-Lib2.t the Emulab deployment consists of two servers, BOSS and OPS. BOSS hosts the web interface and cares for network protocols and images used by Emulab. OPS allows a user to host files for their projects and can be accessed through SSH.
# Learning-Assisted Rain Attenuation Prediction Models
## 6 Open Research Issues and Challenges  
Significant open research problems, difficulties, and strategies explored in the sense of rain attenuation are covered in this section. Three types of machine learning approaches can aid the LARA model, and these are the machine learning-assisted rain attenuation model, rain type prediction model, and prediction of rainfall from rain attenuated dataset for microwave links. The prediction models of rain attenuation aided by machine learning are still in the process of growth and subject to a comprehensive study. We discussed in Section 2 that there are several possible parameters for the rain attenuation model. However, as mentioned earlier, among these parameters, rainfall and the distance between the transmitter and receiver antenna are very important parameters as most of the models include these factors. Among these two parameters, distance represents comparatively easily collectible information from the link dataset. However, rainfall is a random event that is hard to predict. Fortunately, there are many available techniques to predict rainfall as per weather communities' interests. Besides, accurate rainfall and rain type determination is another fine-estimated data feeding approach that can aid the rain attenuation model.