esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Features of Stride, Swing, Stance, and Double-Support Interval
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gait Intervals** & **Statistical Parameter** & **CON** & **PD1** & **PD2** & _p-Value_ \\ \hline \multirow{2}{*}{Stride interval} & \(\mu_{st,\ r}\) & \(1.071\pm 0.047\) & \(1.122\pm 0.102\) & \(1.147\pm 0.119\) & 0.194 \\ \cline{2-6}  & \(\sigma_{st,\ r}\) & \(0.001\pm 0.001\) & \(0.005\pm 0.004\) & \(0.007\pm 0.006\) & 0.0005 * \\ \hline \multirow{2}{*}{Swing interval} & \(\mu_{sm,\ r}\) & \(0.381\pm 0.030\) & \(0.368\pm 0.037\) & \(0.361\pm 0.067\) & 0.518 \\ \cline{2-6}  & \(\sigma_{sm,\ r}\) & \(0.0004\pm 0.0002\) & \(0.0009\pm 0.0003\) & \(0.002\pm 0.001\) & 0.00003 * \\ \hline \multirow{2}{*}{Stance interval} & \(\mu_{st,\ r}\) & \(0.689\pm 0.032\) & \(0.753\pm 0.089\) & \(0.778\pm 0.104\) & 0.051 \\ \cline{2-6}  & \(\sigma_{st,\ r}\) & \(0.001\pm 0.001\) & \(0.004\pm 0.004\) & \(0.010\pm 0.017\) & 0.0004 * \\ \hline \multirow{2}{*}{Double support} & \(\mu_{ds,\ r}\) & \(0.301\pm 0.0308\) & \(0.363\pm 0.077\) & \(0.425\pm 0.1106\) & 0.081 \\ \cline{2-6}  & \(\sigma_{ds,\ r}\) & \(0.0020\pm 0.003\) & \(0.00441\pm 0.0032\) & \(0.0090\pm 0.0094\) & 0.005 * \\ \hline \hline \end{tabular} * significance < 0.05.  
\end{table} 
Table 3: Comparison between the mean (\(\pm\)SD) and variance (\(\pm\)SD) of gait parameters for CON, PD1, and PD2 groups, and the _p_-values for statistical significance.
# Ultrasound B-Mode Imaging: Beamforming and Image Formation Techniques
## 1 Introduction  
In the last decade, very active research in the field of ultrasound medical imaging has brought to the development of new advanced image formation techniques and of high-performance systems able to effectively implement them [1]. For years, Brightness (B)-mode, one of the mostly used ultrasound imaging modalities [2], has been based on a time-consuming process, in which focused beams are iteratively sent into the body and the received waves are used to form an image scan-line, covering line-by-line the region of interest.  
\"Image formation\" refers to the whole process of image reconstruction, starting from the transmission strategy to the reception of signals, beamforming, and image processing. The role of the so-called \"beamformer\" is central in this process, as it manages the ultrasound beam generation, focusing, and steering [3]. Image quality is in fact deeply influenced by the beam shape, and thus the beamforming optimization plays an important role in maximizing the signal-to-noise ratio, contrast, and resolution of the final image, while limiting as much as possible off-axis interferences to reject clutter and noise. Additionally, an important goal is to improve the acquisition frame-rate, which, as mentioned above, is limited by the line-by-line acquisition process [4].  
Image enhancement methods play an important role during both the image pre- and post-processing phases [5]. In the former case, these techniques aim at improving the quality of B-mode frames by directly operating on the image generation process, as for example in spatial/frequency compounding, pulse compression, or harmonic imaging. The latter category instead refers to approaches aimed at reducing noise/artifacts, making speckle more uniform, detecting edges, and consequently facilitating the following processing steps, like segmentation or measurement of quantitative parameters.  
Given the above premises, this Special Issue was launched to collect novel contributions on both ultrasound beamforming and image formation techniques. Twenty-one interesting works were consequently submitted and, among them, 10 were selected for publication (i.e., 48% acceptance rate).
# From Security Frameworks to Sustainable Municipal Cybersecurity Capabilities
## 4 Results
### Structure
#### 4.3.5 Communication and Integration Across Functional Areas and Central IT  
Effective communication and integration between central cybersecurity functions and the diverse functional areas of a municipality are paramount for a cohesive security posture. However, significant challenges persist in bridging the gap between central IT security and the operational realities of functional departments like healthcare, water, and education.  
Several contributors described challenges related to the integration between central cybersecurity and functional areas. A recurring issue is the lack of early cybersecurity involvement in new projects and digitalization initiatives. Frequently, cybersecurity's contribution is limited to providing standardized security requirements during the acquisition phase, often late in the project lifecycle. This late engagement has several detrimental consequences, and contributors voiced concerns that security requirements may become generic and ill-suited to the specific context of the project, failing to address nuanced risks. Furthermore, that risk identification often occurs too late, sometimes with a limited understanding from functional areas regarding the potential operational and service consequences of identified vulnerabilities.  
The widely varying nature of ICT within municipalities presents a significant challenge to applying uniform security frameworks and guidelines. While major frameworks and official guidance often focus on general enterprise IT, municipalities operate a diverse range of specialized systems, including SCADA systems for critical infrastructure like water supplies and specialized healthcare/welfare technology solutions. Translating general security requirements, such as asset management and logging, to these specialized domains proves to be complex. As one respondent aptly described it, \"municipalities are 50 different companies in a messy enterprise\". Although central security resources may be involved during the procurement of these specialized systems, the ongoing security management and continual verification of technical and organizational security controls for these systems often falls to the functional areas. These areas, however, often lack the specialized security competencies required to effectively manage the unique security challenges posed by these complex and often critical systems.
## 3 Theoretical Analysis
### Stress Distribution Characteristics of Surrounding Rock in Straight-Wall Circular Arch Roadray  
According to the elasticity theory, a stress distribution calculation model for the surrounding rock of a straight-wall circular arch roadway is constructed. As depicted in Figure 3, \(\sigma_{h}\) represents the horizontal stress in the rock mass; \(\sigma_{v}\) denotes the vertical stress in the rock body; N is the lateral pressure coefficient. Six characteristic points are selected on the roadway cross-apart, namely point A, point B, point C, point D, point E and point F. The values of \(\alpha\) and \(\beta\) in the stress concentration coefficients of each feature point in the figure are shown in Table 2[28]. The tangential stress of each characteristic point is calculated using the following equation [29]:  
\[\sigma_{\theta}=\sigma_{v}(\alpha+\beta N) \tag{1}\]  
where \(\alpha+\beta N\) is called the stress concentration coefficient.  
According to the relationship between the stress concentration coefficient around the roadway and the N value, as shown in Figure 4, the measured consequence of in-situ stress in the Wudong Coal Mine is different. It is clear that the compressive stress concentration at points A, B, E and F are different, when the value of the lateral pressure coefficient is 3.08. Among them, the largest stress concentration coefficient of 13.5 corresponds to point E. When the values of points A, F and B are evaluated, there is a subsequent decrease. It is interesting that points C and D show different degrees of tensile stress concentration, among  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline \(\alpha\) & \(-1\) & \(-0.84\) & \(2.18\) & \(2.26\) & \(3.20\) & \(-1\) \\ \(\beta\) & \(4\) & \(1.21\) & \(-1.08\) & \(-0.98\) & \(3.40\) & \(2.72\) \\ \hline \end{tabular}
\end{table}
Table 2: The value of \(\alpha\) and \(\beta\) in the stress concentration coefficient.  
Figure 3: Cross section of straight-wall circular arch roadway.  
which the stress concentration coefficient at point D is the smallest. It can be deduced that when the transport roadway is excavated, compressive damage occurred at these four points in the roadway envelope. The evidence is that the large stress concentration coefficients occurred at points A, B, E and F, whereas tensile damage was generally observed from these points in the roadway envelope due to the stress concentration coefficients at points C and D being smaller.
## 6 Development Trends and Prospects
### Early Fault Prediction  
Traditional fault detection methods focus on diagnosing faults after they occur, while fault prediction predicts the occurrence of faults through historical data and real-time monitoring information, achieving predictive maintenance, reducing unplanned downtime, and lowering maintenance costs. This trend has driven the evolution of fault detection from diagnosis to prediction, providing support for the lifecycle management of production equipment.  
The early predictive detection of bearings in aircraft engines is of significant importance. Pratt and Whitney specifically developed the Data Collection and Transmission System to obtain high-quality data. They have been upgrading its data ecosystem, data transmission technologies, and analytical tools, accumulating a large number of physics-based predictive algorithms, while continuously iterating on artificial intelligence and machine learning solutions.
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 5 Discussion
### Practical Implementation Considerations
These implementation considerations highlight several promising directions for future research:  
* Development of optimized LRP algorithms specifically designed for real-time GNSS applications;
* Investigation of hardware-specific optimizations and accelerator architectures;
* Exploration of hybrid approaches combining simplified real-time monitoring with more thorough post-processing analysis;
* Study of the relationship between computational precision and detection reliability;
* Development of adaptive processing strategies that balance computational load with detection requirements.
# Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem
## 5 Experimental Studies
### Benchmark Function Problem
#### 5.1.1 Preliminaries  
**A.** **Unimodal functions**  
Table 1 summarizes the unimodal functions utilized in this study. In this regard, the search space, problem dimension, and optimum for 12 benchmark functions, F1-F12, are presented [28]. Functions F1-F3 were separable, and functions F4-F12 were non-separable. The complexity expanded with increasing problem dimensions, which varied up to a maximum value of 100.
# Effect of Track Irregularities on the Response of Two-Way Railway Tracks
## 4 Numerical Results and Discussions
### Track Dynamic Response
#### 4.3.1 Case 01  
To get the general effect of moving train on one track and its effect on the adjacent track, it was considered that the train is moving only on right track, and there is no train moving on the left track. For analysis at different speeds, seven different conditions for the track were considered (one with no irregularities and six different levels of irregularities). The track irregularities were calculated by using the PSD function which is considered as input for train pass-by simulations that are the source from the track. The numerical method explained in Section 3 was used to investigate the effect of different interface conditions between the train and track due to different levels of track irregularities. Vertical displacements were obtained from the above-mentioned track conditions and at different speeds are plotted in Figure 6. Moreover, Figure 7 presents the maximum displacement at the center of both tracks. The results indicated that a train moving on the right track at low speed has little effect on the left track. The vertical displacement on the left track is increased up to 1.88 mm at high speed and worse track conditions on the left track.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case 01 (Left Track/Right Track)** & **Case 02 (Left Track/Right Track)** \\
**Train Moving Only on Right Track** & **Trains Moving on Both Tracks But Opposite in Direction** \\ \hline no train/no irregularities & no irregularities/no irregularities \\ no train/class 01 & class 01/no irregularities \\ no train/class 02 & class 02/no irregularities \\ no train/class 03 & class 03/no irregularities \\ no train/class 04 & class 04/no irregularities \\ no train/class 05 & class 05/no irregularities \\ no train/class 06 & class 06/no irregularities \\ \(\backslash\) & class 01/class 01 \\ \(\backslash\) & class 02/class 01 \\ \(\backslash\) & class 03/class 01 \\ \(\backslash\) & class 04/class 01 \\ \(\backslash\) & class 05/class 01 \\ \(\backslash\) & class 06/class 01 \\ \(\backslash\) & class 02/class 02 \\ \(\backslash\) & class 03/class 02 \\ \(\backslash\) & class 04/class 02 \\ \(\backslash\) & class 05/class 02 \\ \(\backslash\) & class 06/class 02 \\ \(\backslash\) & class 03/class 03 \\ \(\backslash\) & class 04/class 03 \\ \(\backslash\) & class 05/class 03 \\ \(\backslash\) & class 06/class 03 \\ \(\backslash\) & class 04/class 04 \\ \(\backslash\) & class 05/class 04 \\ \(\backslash\) & class 06/class 04 \\ \(\backslash\) & class 05/class 05 \\ \(\backslash\) & class 06/class 05 \\ \(\backslash\) & class 06/class 06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Train and tracks condition on the left and right track.  
Figure 6: Vertical track response at the center of tracks at different speeds (Train moving only on right track): (**a–d**) left track, (**e–h**) right track.
## 1 Introduction
Some clients are unable to timely upload their model updates to the server after performing local updates due to fluctuating communication conditions. Figure 1: Communication-constrained scenarios in FRSs. * Scenario II. Some clients occasionally face limited communication bandwidth, preventing them from timely downloading and uploading item models. Furthermore, the storage configuration, wherein global item model parameters are hosted on the server while updated item model parameters are retained locally on client devices, enables the utilization of local item models for FedRecI2C training. See Section 3 for further elaboration. Compared to other convergence-acceleration methods in FL, FedRecI2C exhibits better compatibility. It can be directly applied to many existing FRSs without the need to modify the client/item models, aggregation method or the network topology, which is different from those methods facing Limitation 3. Moreover, FedRecI2C allows for more severe communication conditions (such as some clients being barely able to upload data) and more complex communication scenarios (see Section 4.2.2 for reference). Therefore, our method, to a certain extent, circumvents the unreasonable assumptions about communication conditions described in Limitation 2. Our effective utilization of communication-constrained clients enables those clients that are typically excluded from training in other FL settings to be fully trained. This addresses the issue described in Limitation 1. The main contributions of this paper are outlined as follows:  
1. Introducing FedRecI2C that integrates communication and computation resources of both communication-capable and communication-constrained clients to expedite model convergence. 2. Demonstrating the broad applicability of FedRecI2C, making it applicable across various FRSs. 3. Demonstrating the effectiveness of FedRecI2C under various communication scenarios through experiments. This paper is structured as follows: Section 2 provides an overview of previous research related to FRS and accelerating convergence in FL. Section 3 describes the general framework of implicit feedback FRSs and the workflow of FedRecI2C. Section 4 presents and discusses the results obtained from our experiments. Section 5 discusses some practical application issues. Finally, Section 6 summarizes the main conclusions and highlights future research directions.
## 4 Results and Discussion
### Simulation of Different Construction Distances
#### 4.2.2 Simulation of Different Burial Depths  
In light of the actual conditions, the determination of the burial depth for oil and gas pipelines must consider a multitude of factors. While a deeper burial can safeguard the pipeline against external disturbances, the economic considerations and the complexities involved in regard to maintenance suggest that deeper is not always better. For this purpose, burial depths of 1 m, 2 m, 3 m and 4 m have been chosen for simulation and calculation to examine the behavior of horizontal displacement and stress responses at varying depths. The curves of horizontal displacement and stress variation under different working conditions are shown in Figure 19.  
Analyzing the first two figures, it can be observed that under different burial depth conditions, the displacement and stress experienced by the pipeline follow roughly the same pattern of change with regard to the construction distance, with both the displacement at the surface of the pipeline and the stress in the X direction increasing as the construction distance decreases. Under different burial depths, the maximum slope values of both the displacement and stress curves increase as the burial depth decreases. In terms of  
Figure 19: (a) Displacement variation curve for different horizontal distances; (b) stress variation curves for different horizontal distances.  
Figure 18: (a) Displacement variation curve for different horizontal distances; (b) stress variation curve for different horizontal distances.  
displacement, overall, as the burial depth becomes shallower, the maximum displacement continues to grow, but the rate of growth becomes progressively smaller. Conversely, in regard to stress, the rate of increase in regard to the maximum stress grows larger as the burial depth decreases. This phenomenon is primarily due to the following reasons: the stiffness of the soil increases with depth because the pressure makes the lower layers of soil more compact. As the burial depth decreases, the soil may offer less resistance to pipeline displacement, leading to a smaller increase in the displacement impact. In terms of stress, under conditions of shallow burial, the soil surrounding the pipeline may not sufficiently transfer and distribute stress. This may lead to stress concentration in the soil around the pipeline, resulting in higher stress on the pipeline. As the burial depth increases, the soil layers can better disperse these stresses, reducing their direct impact on the pipeline. Therefore, stress experiences a faster increase at shallower burial depths.  
In summary, the pattern of displacement change does not show significant variation with decreasing burial depth, while a more pronounced increase in the slope of the stress curve can be clearly seen after the burial depth decreases to 2 m. Therefore, in actual engineering projects encountering pipelines with a burial depth not exceeding 2 m, greater attention should be paid to the stress impacts caused by construction.
## 2 Materials and Methods
### Description of the Surgical Suite and the Experimental Procedure
#### 2.1.2 Experimental Tests  
Two experimental campaigns were carried out in the surgical area. The first data collection period was developed from 00:00 h on Thursday 18 th July 2019 to 09:00 h on Sunday 21 th July 2019. It was conducted during unoccupied periods to avoid interference with the normal activity of the surgical suite and uncertainty of unforeseen events. During these days there were no scheduled operations; therefore, it was tested to introduce directly 100% outside air without air conditioning from 13:30 h on Friday 19 th July 2019. The data of indoor conditions measured during this period were used to calibrate the energy model of the surgical suite. The second data collection period was performed from 10:00 h on Friday 13 th September 2019 to 2:00 h on Friday 20 th September 2019. In this case, the ventilation period with non-conditioned fresh air started at 10:00 h on Saturday 14 th September 2019 and continued for a 24-h period. This second experimental campaign was used to validate the calibrated model, as there were normal periods of surgical activity and periods of inactivity.  
Temperature and RH sensors were used to record the indoor air conditions of the surgical suite, as observed in Table 2. These sensors were located at a height of 1.5 m. Other measured variables were supply airflow rate of AHUs, return airflow rate of AHUs, enthalpy and electrical consumption in the ORs. A five-minute SCADA (Supervisory Control And Data Acquisition) system collected all data. The SCADA system consists of a PXC64-U automation station working with the software DDESIGO INSIGHT V4.1.1, both provided by Siemens (Berlin, Germany). In addition, surgical activity and the number of people present in the ORs were recorded at all times. Further description of the sensors can be found in [21; 38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Number** & **Model** & **Zone** & **Variable** & **Uncertainty** \\ \hline
1 & & OR1 & Temperature & \(\pm\)0.7 \({}^{\circ}\)C, at 15 \({}^{\circ}\)C... 35 \({}^{\circ}\)C \\
2 & Siemens QFA2060 & OR2 & \(\pm\)1 \({}^{\circ}\)C, at \(-\)35 \({}^{\circ}\)C... 50 \({}^{\circ}\)C \\
3 & & OR3 & RH & \(\pm\)5\%, at 0\%... 95\% \\  & & & \(\pm\)3\%, at 30\%... 70\% \\ \hline
4 & & Ancillary rooms & Temperature & \(\pm\)0.8 \({}^{\circ}\)C, at 15 \({}^{\circ}\)C... 35 \({}^{\circ}\)C \\
5 & Siemens QFM2160 & Entrance corridor & Temperature & \(\pm\)1 \({}^{\circ}\)C, at \(-\)35 \({}^{\circ}\)C... 50 \({}^{\circ}\)C \\
6 & & Exit corridor & RH & \(\pm\)5\%, at 0\%... 95\% \\  & & & \(\pm\)3\%, at 30\%... 70\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Temperature and relative humidity (RH) sensors.  
Figure 1: Heating, ventilation and air conditioning (HVAC) system of the surgical suite. In particular, Air Handling Units (AHUs) from OR2 and OR3.