esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Multi-Time-Scale Feature Extraction  
We applied a multi-level feature extraction approach, where we progressed from a fine-scale representation to a global one through progressive generalizations [27]. The first level of analysis was the short-term level: we initially divided the signal into windows of 0.25 s (called short-term windows), and for each window, we computed 33 physical quantities, called short-term features. For each feature \(f\), the short-term analysis produced a time series of values \(F=\left(f_{1},f_{2},\ldots,f_{L}\right)\); we extracted higher time-scale properties of the signal from the distribution of these values. The duration of the time windows on which short-term features were computed was set to 0.25 s as a compromise between two opposite needs. On the one hand, as the respiratory cycle in rest conditions lasts around two seconds for an inhalation and three seconds for an exhalation, the duration of the signal needed not to be excessive in order to effectively map the two phases of the respiratory cycle in a detailed way. Moreover, the shorter the time window, the higher the number of samples in the distribution of short-term features, and therefore the more meaningful the statistical indicators built from these distributions and used in the long-term analysis. On the other hand, a lower limit on the duration of the short-term time frames is essential to observe meaningful and informative features on the  
Figure 2: Locations from which respiratory sounds were collected: right anterior (1), left anterior (2), right posterior (3), left posterior (4), right lateral (5) and left lateral (6).  
respiratory cycle, since time windows much shorter than the typical duration of the process would provide information that would be affected by noise and difficult to interpret.  
The second level of analysis was the long-term level and consisted of the calculation of 10 statistical moments associated with the time series of each of the 33 short-term features. At the end of the extraction process, each track was characterized by a total of 330 features. Figure 3 displays a pictorial representation of the feature extraction procedure.
## 4 A Case Study

Table 9: Summary of water supply/demand—Water Balance Urbanisation Detailed Plan. Plant in the area, would enhance community resilience and align the region with global efficiency and sustainability trends. Additionally, it is important to consider that water demand in an urban agglomeration is dynamic, influenced by factors such as climate, water quality and cost, population size, economic and cultural profiles, industrial and commercial presence, building characteristics, and the state of the supply system [64]. Although per capita water consumption has historically shown progressive growth, short- and medium-term forecasts--driven by environmental concerns--indicate the need to re-evaluate this trend [65]. In contexts of water scarcity, several countries have implemented legislation and ambitious targets to reduce consumption in residential buildings. For example, in the United Kingdom, the target for new housing is 110 L/(person.day) [47]; in Melbourne, the target is 100 L/(person.day) [67, 68]; in Denmark, the limit is 105 L/(person.day) for new projects [69]; and the Netherlands aims to reach 100 L/(person.day) by 2035 [70]. Considering the context of the area under analysis, the implementation of the proposed measures--notably the use of greywater and rainwater--could reduce potable water consumption by up to 42%, aligning local indices with international targets and confirming the savings potential highlighted in the literature. Furthermore, the integrated analysis of urban natural resources indicates that by reducing irrigation water demand, up to 87% of non-potable needs could be met exclusively through rainwater during the rainiest months. This strategy not only supports existing infrastructures but also minimises the costs associated with transporting and treating wastewater from centralised public systems, reinforcing the interdependence between water, energy, food, and ecosystem security. Ultimately, the circular and efficient use of water represents a crucial step towards water sustainability, both locally and on a macro scale, by promoting accessible ecological spaces, strengthening social cohesion, and serving as a model for future urbanisations.
## 5 The Query Management Scheme
### The Query Processing Phase (QPP)
#### 5.3.2 Period and Duration Management Theorem  
Given the list of queries, the node scans through the queries and their temporal information to select which queries to respond to at each moment. The following theorem provides a method of selecting queries for generating their responses at a specific time point.  
**Theorem 2.** Period and Duration Management Theorem  
Let \(\Sigma_{t}\) denote the set of all queries arrived at a node up to time \(t\). Then, the set of queries waiting to be responded at t is defined by:  
\[\Lambda_{t}=\left\{q_{i}\in\Sigma_{i}\left|t\equiv\left(I_{i}+S_{i}\right) \bmod T_{i},\,t\leq\left(I_{i}+D_{i}\right)\right\}\right.\]  
where \(S_{i}\), \(D_{i}\), \(T_{i}\) and \(I_{i}\) are the start time, deadline, response period, and query set start time for query \(q_{i}\).  
**Proof**  
Because the query set start time is the zero point for the queries in the set, the first execution time for query \(q_{i}\) is \(t=I_{i}+S_{i}\). Query \(q_{i}\) is replied every \(T_{i}\) since the first execution up to the deadline, which means queries are executed at \(t\equiv\left(I_{i}+S_{i}\right)\bmod T_{i}\) while \(t\leq I_{i}\)\(+D_{i}\). Therefore, the merged query result at time \(t\) is \(M_{\Lambda}\)(S).
## 8 Results
### Classification Accuracy per Habitat Type. Which System is Better for Each Shallow Habitat Type?  
Figure 14 presents the classification accuracy of each individual Natura habitat type when SSS and MBES systems are used alone (_data models m3_ (_SSS_) and _m8_ (_MBES_)) or together (_data model m8_). MBES is further exploited concerning the added gain when just using bathymetry (_data model m4_), bathymetry and backscatter texture (_data model m6_) or a full feature set of bathymetry, backscatter texture and angular responses (_data model m8_). As a rule, MBES was found to be the best single-system choice for separating most habitat types, with high superiority in rough, high relief ones, i.e., \"Reefs\". However, dual-frequency SSS (_data model m3_) was better for textured seabed types e.g., \"Posidonia beds--matte\" or featureless \"Sandbanks\" and \"Unvegetated soft bottoms\". \" Vegetated soft bottoms\" (_C. nodosa_ beds) were better separated by MBES (_data model m8_). This latter observation seems odd, as discussed in Section 8.2, as _C. nodosa_ beds have a very characteristic acoustic signature in dual-frequency SSS records. However, it is the only high-intensity/low-ruggedness seabed type, and as such the MBES bathymetry proved equally important to the backscatter texture. \"Posidonia beds--low relief\" (meadows) were hard to separate using either swath-system, as they were confused locally with other high intensity bottom types (e.g., \"Vegetated soft bottoms\", \"Posidonia beds--matte\" and \"Reefs\"). Nonetheless,  
Figure 12: The classification accuracy results (Cohen’s Kappa coefficient) per _data model_ (described in Figure 5).  
Figure 13: Comparison between the manual habitat map (**a**) calculated as the most frequent class per segment and the best automated one (**b**) corresponding to _data model m10_ (all sonar systems and feature extraction techniques considered).  
the multi-source swath sonars scenario (_data model m10_), with conjunction of statistics on MBES and dual frequency SSS data, proved to provide the best possible classification results in almost all bottom types, with significantly better performance in \"Unvegetated soft bottoms\" and \"Posidonia beds--low relief\", worse in \"Reefs\", and equally good in \"Posidonia beds--matte\".
## 3 Losnus  
### Design Rationale of LOSNUS  
Before explaining the individual components of the LPS _LOSNUS_, the design rationale is presented. As _LOSNUS_ is designed for locating numerous mainly static nodes, the located devices are chosen as passive nodes and the beacons are active for best scalability. TDoA measurements have been chosen instead of ToF to avoid the necessity of a trigger signal between transmitter and receivers which otherwise would increase receiver complexity and costs. Accurate measurement of device positions is important if devices are nearby especially in case of measuring the same quantity (array). Therefore using broadband signals allowing the use of pulse compression techniques is a logical choice. Such techniques demand increased effort on the signal processing side at the receiver and therefore their complexity and number should be kept low. For this reason _LOSNUS_ uses only a single linear frequency modulated chirp marking the signal arrival time with a following transmitter code. To avoid problems in case of signals transmitted in parallel _LOSNUS_ uses a sequence of transmitting beacons, which avoids overlapping of line-of-sight (LOS) signals, but nevertheless delivers a medium fast locating rate. As non-line-of-sight (NLOS) signals are typically weaker than the LOS signals, a good SNR is obtained most of the times. Transmitters are identified using fixed frequency transmitter coding fields. An additional benefit is gained by non-parallel sending as the same amplifier can be used for all transmitting beacons if a low-cost demultiplexer is used. This is especially important in case of transducers requiring high driving voltage and/or large bandwidths, as these amplifiers are quite costly. Further cost reduction is possible by using 1-bit binary correlation at the receivers allowing the use of the synchronous serial interfaces present on modern microcontrollers for signal sampling. A receiver can therefore be realized by a low cost miniature ultrasonic MEMS microphone, a small preamplifier, a bandpass filter, and a 1-bit comparator interfacing to the microcontroller. An extension for low power applications is an optional starting sequence allowing wakeup of the microcontroller when a locating sequence is started. Memory requirements and computational complexity on the receiver can be reduced further by using a short lead-in for each _LOSNUS_ frame time stamping the reception and starting the sampling of the _LOSNUS_ frame. Expected system construction costs at volume quantities are below 5 USD for the receivers.  
Calibration of _LOSNUS_ can be performed directly with the installed beacons and a special receiver arrangement. For achieving high accuracy of beacon localization, the used receivers are synchronized with the transmission sequence, which can be realized with a wired link. This modification allows for measuring ToFs in order to achieve better convergence of the calibration algorithm. As calibration is a one-time process and stays valid unless transmitters are moved, this is the most economical solution. Further verification of the calibration of course can be performed in the TDoA system as well.
## 2 Problem Formulation
The thrusters of the vehicle provide enough power to overcome the ocean current disturbance._  
Moreover, it is assumed that:  
\[0<\bar{U}_{d}<\frac{Y_{2}}{Y_{1}},\quad\ k_{v_{1}}>0,\ k_{p_{i}} >0,\ k_{l_{i}}>0,\ k_{v_{i}}k_{p_{i}}>k_{l_{i}},\ \ i\in\{x,y\},\] \[l>\max\biggl{\{}\frac{m_{22}}{m_{23}},-\frac{X_{2}}{Y_{2}} \biggr{\}},\quad\ \bar{U}_{d}^{*}\leq\frac{2\min\{\underline{a}(\underline{d}-\underline{c}),b \}}{\frac{Y_{1}\bar{U}_{d}}{T}+2\Bigl{(}Y_{1}-\frac{X_{1}-1}{I}\Bigr{)}}. \tag{13}\]  
In controller (12), the new inputs \(\mu_{1}\) and \(\mu_{2}\) are given by:  
\[\mu_{1} =-k_{v_{x}}(\xi_{3}-\xi_{3_{d}})-k_{p_{x}}(\xi_{1}-\xi_{1_{d}})- k_{l_{x}}(\xi_{1_{1}}-\xi_{1_{d}})+\dot{\xi}_{3_{d}}, \tag{14}\] \[\mu_{2} =-k_{v_{y}}(\xi_{4}-\xi_{4_{d}})-k_{p_{y}}(\xi_{2}-\xi_{2_{d}})- k_{l_{y}}(\xi_{2_{l}}-\xi_{2_{d}})+\dot{\xi}_{4_{d}} \tag{15}\]  
\(k_{v_{x}}\), \(k_{v_{y}}\), \(k_{p_{x}}\), \(k_{p_{y}}\), \(k_{l_{x}}\), and \(k_{l_{y}}\) are positive real gains and \(\xi_{l}\) where \(i\in\{1,2,1_{d},2_{d}\}\) are integrals of the appropriate signals), guarantees the achievement of the control objectives (i.e., to make the point track the desired trajectory \(\Gamma(t)=\{(\xi_{1_{d}}(t),\xi_{2_{d}}(t),\xi_{3_{d}}(t),\xi_{4_{d}}(t))|t\in R ^{+}\}\), where \(\dot{\xi}_{1_{d}}=\xi_{3_{d}}\), \(\dot{\xi}_{2_{d}}=\xi_{4_{d}}\)):  
\[\lim_{t\to\infty}(\xi_{1}-\xi_{1_{d}}(t))=0,\quad\quad\quad\quad \lim_{t\to\infty}(\xi_{2}-\xi_{2_{d}}(t))=0,\] \[\lim_{t\to\infty}(\xi_{3}-(\xi_{3_{d}}(t)-V_{x}))=0,\quad\quad \lim_{t\to\infty}(\xi_{4}-(\xi_{4_{d}}(t)-V_{y}))=0. \tag{16}\]  
Specifically, \((\xi_{1},\xi_{2},\xi_{3},\xi_{4})\to(\xi_{1_{d}},\xi_{2_{d}},\xi_{3_{d}},\xi_ {4_{d}})\) globally exponentially and \((z_{1},z_{2})\) are globally ultimately bounded. Furthermore, the steady-state values of the integral variables give an estimate of the ocean current:  
\[\hat{V}_{x}=\lim_{t\to\infty}\frac{k_{v_{x}}(\xi_{1_{l}}(t)-\xi_{1_{l_{d}}}(t))} {k_{l_{x}}},\quad\ \hat{V}_{y}=\lim_{t\to\infty}\frac{k_{y_{x}}(\xi_{2_{l}}(t)-\xi_{2_{l_{d}}}(t))} {k_{l_{y}}}. \tag{17}\]  
The controller proposed in [56] is called in this work classic (CL).
## 5 Discussion  
During urban construction, constructing new underground tunnels is necessary. However, limited underground space inevitably results in a close distance between the new tunnel and the existing structure, leading to interaction. In such situations, while short-term mutual influence is unavoidable, conducting analyses and adopting control measures can minimize the impact on the existing structure. In the long term, new tunnels constructed near existing structures significantly aid urban development by maximizing underground space utilization and forming a comprehensive underground transportation system, which boosts transportation and economic development. However, the influence of the new tunnel on the existing structure may compromise its safety, necessitating analysis to ensure the existing structure's internal forces and deformations remain manageable.  
Using the equivalent radius method, this study improved the application of [PERSON]'s formula for excavating non-circular caverns. It analyzed the impact of irregularly sectioned new tunnel excavations on existing structures. The ellipticity parameter and radial shrinkage were optimized. This method applies to situations with rigid formations or low water levels and short consolidation periods. Long-term effects of pore water pressure dissipation on the underground structure are not considered. As shown through field monitoring and numerical simulations, the proposed method is effective, providing useful guidance for engineers excavating non-circular caves within existing hollow structures.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Retrieval and Processing of Orthoinagery from Geoportal  
High-resolution orthoimagery was crucial for improving ML/CV data quality. The Geoportal service offered access to various orthoimagery resolutions through Web Map Service (WMS) endpoints.
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 2 Methodology
### Generating Global Continuous Multi-Scale Simplified Contours
#### 2.3.2 Estimation of the Scales for the Simplified Contours
To reveal the correspondence between shape similarity and scale, we used an exponential regression function to fit the ideal relationship between the similarity of contours and scale [36; 37]. Ideally, as the scales changed continuously from large to small, the shapes of the contour lines were transformed from complex to simple, and the similarity value gradually changed from large to small. As shown in Figure 7, the relationship between the contour scales and similarity values was fitted with an exponential regression function. The horizontal axis represents the scale, whereas the vertical axis represents the similarity value between the initial large-scale contour lines and the other small scales. As shown inFigure 7, the trend of the relationship between the similarity values and scale is continuous and monotonic in the ideal case. The transitions of gradual simplification from \(L^{\prime}_{0}\) to \(L^{\prime}_{n}\) were emulated by the intersections of the CCS-mesh and the horizontal plane. To generate a simplified contour cluster from \(L^{\prime}_{0}\) to \(L^{\prime}_{n}\), each horizontal plane was assigned the height value \(Z_{i}\) in the equal interval sequence. Given that the relationship between the scale and similarity of different scale contours can be fitted with an exponential regression function and that the trend is continuously monotonic, according to Equation (9) and based on the similarities derived by \(Z_{i}\) values, an interpolation function \(R_{s}(L^{\prime}_{0},L^{\prime}_{n},t)\) was formulated to characterize the transition. \(t\) is a scaling parameter with values ranging from 0 to 1 that can be used to bridge the continuous scales between \(L^{\prime}_{0}\) and \(L^{\prime}_{n}\) by mapping \(Z_{i}\). By referring to the scales of \(L^{\prime}_{0}\) and \(L^{\prime}_{n}\), \(t\) can be linearly converted into common actual scales. When \(t\) is 0, the scale is the initial scale of \(L^{\prime}_{0}\), but when \(t\) is 1, the scale is the target scale of \(L^{\prime}_{n}\). In turn, the contour cluster can be ordered by \(t\). According to the interpolation function \(R_{s}\), the arbitrary scale of the simplified contours can be determined by the \(t\) value and its corresponding \(Z_{i}\) value. In sum, each simplified contour has a determined scale and similarity with \(L^{\prime}_{0}\). Therefore, the proposed method can achieve arbitrary scale simplification.
## 3 Results  
Demographic, clinical, and pathological data of both groups are presented in Tables 1 and 2. BNP and non-BNP groups were comparable according to clinical and pathological characteristics. Out of 170 patients, PSM were found in 42 (24.7%) patients. Only 19 (22.4%) patients with BNP had PSM. No statistically significant difference was noted between patients with or without BNP in terms of PSM (\(p=0.477\)).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **BNP** & **Non-BNP** & \(p\)**-Value** \\ \hline
**Age (years), mean (SD)** & 65.5 \(\pm\) 5.04 & 65.7 \(\pm\) 5.15 & 0.752 \\ \hline
**Clinical stage of disease:** & & & \\ T1c & 10 (11.8\%) & 5 (5.9\%) & \\ T2a & 42 (49.4\%) & 37 (43.5\%) & \\ T2b & 23 (27.1\%) & 29 (34.1\%) & 0.078 \\ T2c & 8 (9.4\%) & 10 (11.8\%) & \\ T3a & 2 (2.4\%) & 4 (4.7\%) & \\ \hline \end{tabular}
\end{table}
Table 1: Patient’s data—Demographic and clinical data.  
We divided PSM at five localizations: base of the prostate, left and right lobes, both lobes, and apex. The most significant part of the prostate with PSM for our study was the base of the prostate. Base-positive margins were detected in eight patients (9.4%) with BNP and five (5.9%) without BNP. No statistically significant difference in the occurrence of PSM at the base or any other localization of the prostate in patients with or without BNP ([PERSON]'s exact test \(p\) = 0.256).  
Using univariate analyses, preoperative PSA (\(p\) < 0.0001), bioptic Gleason score (\(p\) = 0.007), and clinical stage (\(p\) < 0.0001) were statistically significant in correlation with PSM (Table 3). There was a significant difference in pT3 stage distribution between PSM and non-PSM groups (\(p\) < 0.0001). Multivariate logistic regression analysis indicated that, from all preoperative parameters, only preoperative PSA was a statistically significant predictor of PSM (\(p\) = 0.025, OR = 1.06, 95% CI 1.01-1.1).  
The median follow-up was 48 months (IQR, 44.6-51.4). A total of 38 (22.4%) patients had a BCR. BCR was more common in patients without BNP (23.5%) compared to BNP patients (21.2%) (\(p\) = 0.713). Biochemical recurrence-free survival rate in patients with BNP was 49.2 months (95% CI 44.8-53.8), while in patients without BNP it was 46.7 months (95% CI 44.8-53.8) (Log Rank, \(p\) = 0.643) (Figure 1). In a multivariate model with BCR as a dependent variable, the only statistically significant predictor of BCR was PSM (\(p\) < 0.001).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BNP** & **Non-BNP** & \(p\)**-Value** \\ \hline
**Final Gleason score:** & & & \\
6 (3 + 3) & 8 (9.4\%) & 12 (14.1\%) & \\
7 (3 + 4) & 46 (54.1\%) & 36 (42.4\%) & \\
7 (4 + 3) & 23 (27.1\%) & 32 (37.6\%) & \\
8 (4 + 4) & 7 (8.2\%) & 3 (3.5\%) & \\
9 (4 + 5) & 1 (1.2\%) & 2 (2.4\%) & \\ \hline
**Final PH staging:** & & & \\ pT1 & 0 (0\%) & 1 (1.2\%) & \\ pT2 & 60 (70.5\%) & 57 (67\%) & \\ pT3 & 24 (28.2\%) & 27 (31.8\%) & \\ pT4 & 1 (1.2\%) & 0 (0\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Patient\({}^{\prime}\) data—Pathology findings.
## 2 MANTiS-32 Wireless Measurement System Design
### System Architecture and Functional Roles of the MANTiS-32 Wireless Sensing Platform  
The MANTiS-32 wireless sensing system is engineered to acquire structural response data from MANTiS-6050 sensor units and wirelessly transmit the collected data to a Performance-Based Monitoring (PBM) platform via its integrated access point (AP). The transmitted data are then processed to compute real-time damage indices, enabling continuous assessment of structural integrity and facilitating proactive maintenance strategies. Leveraging a high-performance wireless communication framework, the MANTiS-32 system offers rapid deployment, enhanced scalability, and seamless integration into complex structural configurations. Furthermore, the incorporation of GPS-based time synchronization ensures precise coordination across multiple sensing nodes, thereby supporting accurate, distributed, and synchronized monitoring of structural behavior under dynamic loading conditions. As illustrated in Figure 1, the MANTiS-6050 system is configured by connecting up to eight MPU-6050 sensors (InvenSense Inc., San Jose, CA, USA) to a single MANTiS-32 hub. Each MANTiS-6050 module is equipped with a PIC16 microcontroller, which handles sensor control and data acquisition. The measured data are transmitted to the hub via RS485 communication. A total of four MANTiS-32 units are deployed, each managing eight sensor modules, resulting in a synchronized sensing network composed of 32 individual sensors capable of real-time data acquisition and monitoring.  
The sensor data collected by each MANTiS-32 hub is transmitted wirelessly to the Performance-Based Monitoring system via Wi-Fi, utilizing their respective access points (APs). Notably, each MANTiS-32 hub is equipped with a GPS-based time synchronization module, enabling precise temporal alignment of data from distributed sensors. This modular architecture allows for scalable expansion of the sensing network as required by structural monitoring applications. At the top level, the PBM system--implemented as a PC-based analytical platform--performs real-time analysis of the measured vibration data to extract key dynamic characteristics, such as natural frequency (\(\omega\)), the damping ratio (\(\zeta\)), and mode shapes (\(\varphi\)). Using established damage detection equations (e.g., [PERSON], 2019 [24]; [PERSON], 2004 [25]), the system evaluates changes in structural performance and estimates the progression of damage through a computed Structural Health Index (SHI). This information  
Figure 1: System configuration and functional roles of the MANTiS-32 wireless monitoring platform.  
can then be utilized to support data-driven maintenance and decision-making processes for bridge infrastructure.  
Each of the aforementioned modules will be described in detail in Sections 2.2 and 2.3.