esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing BIM and Sustainability with Coopetition: Evidence from the Portuguese Stone Industry
## 10 Outcomes Evaluation
### Meeting the 5D BIM Requirements through Coopetition Networks  
The 5D BIM dimension integrates cost data with the 3D model, enabling comprehensive budget and financial management throughout the project lifecycle. This integration allows for more accurate cost estimation, efficient resource allocation, and continuous cost monitoring. By providing a detailed financial overview linked to the 3D model, 5D BIM helps stakeholders make informed decisions, identify potential cost overruns early, and implement cost-saving measures effectively.  
To evaluate how coopetition networks help manufacturing companies meet the requirements of 5D BIM, the KPILP was assessed to determine the gains from CB.P to CN.P.  
Under CB.P, employing an average workforce of 49.9, the OS-PT companies managed a daily shipment of 338.5 stone parts, establishing a labour productivity rate of 6.84 parts per worker. Implementing CN.P enhanced daily output to 415.8 stone parts while maintaining the same workforce size, boosting the KPILP to 8.72 parts per worker. This significant increase showcases the positive impact of competition on labour efficiency.  
To examine the averages more closely, Figure 3 illustrates daily KPILP over the 54 observation days, highlighting in green the consistently higher productivity under CN.P. This provides clear positive insights into the potential contribution of coopetition networks to improving the benefits of BIM.  
The improvement in the KPILP from 6.84 to 8.72 highlights the ability of coopetition networks to enhance labour productivity. This improvement suggests that stone companies can better meet the requirements of 5D BIM. Increasing productivity makes projects more likely to be completed within budget, enhancing project outcomes and greater market competitiveness.  
A paired \(t\)-test was conducted to evaluate the difference in labour productivity between the two datasets, CB.P and CN.P. Since both datasets are derived from the same universe, they are naturally paired. The \(t\)-test yielded a \(p\)-value of 1.7729 \(\times\) 10\({}^{-9}\), indicating that the transition to CN.P has led to statistically significant improvements in labour productivity.  
Under CB.P, the ANOVA Significance F value of 7.4 \(\times\) 10\({}^{-23}\) indicates that the model is highly statistically significant, meaning Labour Productivity under current best practices has a solid and meaningful relationship with KPILP. In contrast, under CN.P, the ANOVA Significance F value of 0.700662 shows that the model is not statistically significant, suggesting that Labour Productivity under coopetition practices does not have a meaningful relationship with KPILP. Given these findings, the performance of KPILP under coopetition practices should be continuously monitored, and adjustments should be made to ensure that the transition from current best practices results in actual performance improvements.  
To achieve the intended benefits of coopetition, a thorough reassessment and refinement of these practices may be necessary.
# Detection of Movement Intention for Operating Methods of Serious Games
## 2 Materials and Methods
### Measurement of the EMG and EEG Signals  
With the balance handle, the subjects performed extension and flexion of their upper extremity and pronation and supination movements of their joints, including shoulder, elbows, and forearms, according to the device tilting movement. In order to collect and analyze the sEMG signal data, an AM530 active surface EMG sensor (Laxtha, Daejeon, Korea) was attached to the deltoid, biceps, and triceps while the reference electrodes were attached to the elbow joint where there was no change in muscle activity (Figure 4). The sEMG signal data were collected at a 1-kHz sampling frequency via serial communication.  
The EEG signal data were collected for analysis of brain activation at a sampling frequency of 1024 Hz and filtered through a band pass filter (BPF) ranging from 0.16 to 43 Hz and a notch filter at 50 and 60 Hz, such that the EEG signal data in the frequency band were selectively collected and the noises induced by the power were removed. Following the 10-20 international system of EEG electrode placement, 32 signal electrodes (hollow white) and 2 ground electrodes (solid black) were configured with two channels (C3 and C4) used to analyze the movement intention, displayed in red in Figure 5.  
The C3 and C4 channels were positioned on the sensorimotor cortex encompassing the motor cortex and somatosensory cortex in the central part of the cerebrum. Activation of the sensorimotor cortex is closely associated with human body movement, which can be directly correlated with the analysis of movement intention [21]. Moreover, the ERD and ERS related to movement occur prominently in the opposite hemisphere of the moving body part, and therefore the EEG data collected in the opposite hemisphere of the moving hand were utilized for analyses of the hand gripping and gamepad operation. For the task of using the balance handle with both hands, arithmetic averages of the collected data from each hemisphere were analyzed.
## 5 Case Study
### Influence of the AC Infeed
For station s\({}_{1}\) and s\({}_{2}\), AC active power can make up part of the power required by the DC side, delaying the discharge rate of the sub-module capacitors. For station s\({}_{3}\) and s\({}_{4}\), there is a rise in the energy of the SMs capacitor for a short period of time without AC infeed. Due to the freewheeling effect of the smoothing reactor, the pre-fault current injecting into the converter will not be reversed immediately. When the AC active power is taken into the calculation, the DC current flowing into the converter station is absorbed by the AC system. When the DC current is reversed, the AC side and the DC side release energy together. Therefore, the total energy of AC system changes very small. And the DC voltage of the MMC drops slower than the response speed of the controller. In this case, the modulation ratio drops very fast, leading to a dynamic imbalance between the modulation ratio and the DC voltage. At this stage, MMC is in a state of undermodulation, which makes the average SM capacitor voltage rises passively and the energy of the capacitor increases. Therefore, accurately estimating the branch currents and node voltages needs considering the AC side power and MMC control. Figure 8: Energy changes between the AC side and DC side: (**a**) station s\({}_{1}\); (**b**) station s\({}_{2}\); (**c**) station s\({}_{3}\); (**d**) station s\({}_{4}\). for converters in the active power control mode are assumed to be constant, leading to small bias in estimating the node voltages. Different from them, a variable current source is adopted in this paper to model the control dynamics of MMCs in active power and voltage control modes. The improved accuracy in estimating the node voltages will be expressed as follows.
# Discrete Element Analysis of Grouting Reinforcement and Slurry Diffusion in Overburden Strata
## 4 Discrete Element Grouting Diffusion Results
### Influence of Formation Confining Pressure on Slurry Diffusion Radius
[25], based on previous studies of grouting diffusion tests in sand layers, compared several typical empirical models andvalidated them against experimental data to derive an empirical formula for the grouting diffusion radius in fractured rock masses, as presented in Equation (7):  
\[R_{1}=\sqrt[3]{\frac{3 Kh_{1}r_{0}t}{\beta\cdot n}} \tag{5}\]  
\[R_{2}=\sqrt{\frac{2 Kh1t}{\beta n\ln\frac{R}{r_{0}}}} \tag{6}\]  
\[R_{3}=226.2\ \cdot p^{0.4306}m^{0.0379}K^{0.6636}t^{0.5611} \tag{7}\]  
This study compares and analyzes the errors between the fitted equation and classical models by substituting relevant parameters into Equations (3)-(6), with the results and associated errors summarized in Table 3. Due to varying assumptions regarding the  
Figure 10: Effect of grouting depth on grout diffusion distance. (**a**) Influence of different formation confining pressure on grouting diffusion radius. (**b**) Influence of grouting pressure on farthest diffusion distance. geological strata, each diffusion model exhibits differing levels of accuracy. The Maag formula demonstrates good accuracy within a grouting pressure range of 1.5-3 MPa, whereas the cylindrical diffusion model is more applicable at around 1 MPa. The Xu formula shows a relatively large deviation from the test results, likely due to its assumption that the grouted body is composed of fractured rock masses with large pores. It is evident that the fitted equation proposed in this study achieves a strong fit, with overall error within 0-10%, which is lower than those of the Maag, cylindrical diffusion, and Xu formulas. This indicates that the empirical equation for overburden grouting diffusion, established through regression analysis, more accurately describes the relationship between grouting pressure and diffusion distance in this test. To verify the accuracy of the proposed formula, we compared the calculation results with the diffusion distances observed in [PERSON]'s [26] grouting test on glacial till soil. In that study, the observed diffusion distances of the slurry were 0.10 m, 0.12 m, and 0.16 m under grouting pressures of 0.4 MPa, 1.2 MPa, and 2 MPa, respectively. By substituting the relevant soil and slurry characteristic parameters into Equation (4), the corresponding predicted diffusion distances are 10.77 cm, 13.22 cm, and 14.54 cm, respectively. The relative errors between the predicted and experimental results are 0.7%, 1.2%, and \(-\)0.1%, indicating good agreement and verifying the reliability of the formula.
## 3 Results and Discussion
### X-ray Crystallography  
The title compound of C\({}_{33}\)H\({}_{30}\)BCl\({}_{2}\)N\({}_{3}\), the crystallographic data and refinement information are summarized in Table 1. The selected bond lengths and bond angles are listed in Table 2. The title compound crystallizes with one cation and anion in the asymmetric unit as shown in Figure 4, that is a 2-(2,6-dichlorophenylamino)-4,5-dihydro-1\(H\)-imidazol-3-ium cation and a tetraphenylborate anion. As typical for tetraphenylborate, the tetraphenylborate anion is in a tetrahedral geometry around the B atom [C--B--C angles of 108.80 (19)\({}^{\circ}\)-110.13 (18)\({}^{\circ}\)]. All the bond lengths and angles are in normal ranges [11]. The crystal structure is stabilized by C2--H2A--Cl2 hydrogen bonds along the \(c\) axis direction (Figure 5, Table 3).  
Figure 4: ORTEP diagram of the titled compound **3** showing the cation and anion parts. Displacement ellipsoids are plotted at the 40% probability level for non-H atoms.  
Figure 3: Differential scanning calorimetry thermogram of the titled compound.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Crystal Data** & \\ \hline Chemical formula & C\({}_{33}\)H\({}_{90}\)BCl\({}_{2}\)N\({}_{3}\) \\ \hline Mr & 550.31 \\ \hline Crystal system, space group & Monoclinic, \(P_{21}/c\) \\ \hline Temperature (K) & 100 \\ \hline \(a,b,c\) (Å) & 9.151 (3), 12.522 (3), 25.493 (6) \\ \hline \(\beta\) (\({}^{\circ}\)) & 105.161 (13) \\ \hline V (Å3) & 2819.5 (13) \\ \hline \(Z\) & 4 \\ \hline Radiation type & Mo \(K\alpha\) \\ \hline \(\mu\) (mm\({}^{-1}\)) & 0.26 \\ \hline Crystal size (mm) & 0.57 \(\times\) 0.25 \(\times\) 0.11 \\ \hline
**Data collection** & \\ \hline Diffractometer & Bruker APEX-II D8 venture diffractometer \\ \hline Absorption correction & Multi-scan \\ \hline Thin, Tmax & 0.867, 0.971 \\ \hline No. of measured, independent and observed [I \(>\) 2\(o\)(I)] reflections & 32,394, 4958, 2969 \\ \hline R\({}_{\rm int}\) & 0.229 \\ \hline
**Refinement** & \\ \hline R [\(F^{2}>\) 2\(o\)(F\({}^{2}\))], wR(\(F^{2}\)), S & 0.068, 0.164, 1.03 \\ \hline No. of reflections & 4958 \\ \hline No. of parameters & 355 \\ \hline No. of restraints & 0 \\ \hline H-atom treatment & H atoms treated by a mixture of independent and constrained refinement \\ \hline \(\Delta\rho_{\rm max},\Delta\rho_{\rm min}\) (e Å\({}^{-3}\)) & 0.43, \(-\)0.36 \\ \hline \end{tabular}
\end{table}
Table 1: Crystallographic data and refinement information for compound 3.  
Figure 5: Molecular packing of titled compound viewed hydrogen bonds which are drawn as dashed lines along \(c\) axis.
# Illegal Logging Detection Based on Acoustic Surveillance of Forest
## 4 Experimental Setup
### Audio Pre-Processing and Feature Extraction  
All evaluated audio signals were initially frame blocked by a sliding window of 20 milliseconds length with 10 milliseconds (50%) overlap between successive audio frames. Each audio frame was parameterized by temporal and frequency domain audio descriptors. Regarding the temporal audio descriptors, the zero-crossing rate, the frame intensity, as well as the root-mean-square energy of the frame were used. The frequency domain audio features that were used were the 12 first Mel frequency cepstral coefficients (MFCCs), the harmonics-to-noise ratio by autocorrelation function, the voicing probability, as well as the dominant frequency. The dimensionality of the resulting feature vector was equal to 18, consisting of three temporal and 15 spectral audio descriptors. In addition, the above-mentioned audio features were calculated utilizing the openSMILE audio processing software tool [22]. Dynamic range normalization was applied as a postprocessing step to all extracted features for equalizing the range of the numerical values.
## 2 Materials and Methods
### Experimental Procedure
#### 2.1.2 Sensors  
Measurement data were generated by a pyrometer, a CCD camera, an emission spectrometer and a laser scanner. The measurement setup is shown in Figure 1.  
Pyrometers allow conclusions to be drawn about the temperature of an object based on the measurement of infrared radiation. The material-specific emissivity must be considered [20]. The use of this sensor system in DED is known from a variety of publications [24, 25]. In this work, the quotient pyrometer Sensortherm Metis H311 was used in a coaxial configuration. A material emissivity of 1.02 was determined for the alloy Inconel 718.  
A CCD sensor (CCD = Charge-Coupled Device) generates an electrical signal proportional to the amount of incoming light and converts it into digital video signals. In the  
\begin{table}
\begin{tabular}{c c c} \hline  & **Lower Limit** & **Upper Limit** \\ \hline Laser power (P) & 200 W & 1000 W \\ Scan speed (v) & 200 mm/min & 1000 mm/min \\ Powder feed rate (m) & 3.5 g/min & 21 g/min \\ Working distance (w) & 7 mm & 11 mm \\ \hline \end{tabular}
\end{table}
Table 1: Process window.  
Figure 1: Measurement setup.  
DED process, this enables an observation of the melt pool [4]. In the context of this work, the monochrome CCD camera module CF 8/5 MX was mounted on the focusing optics of the laser system. The recorded images were processed by a self-developed computer program. In the first step, the contour of the melt pool was extracted. Based on this, geometric parameters could be measured. In this work, information about the diameter and its change over the process time were used.  
Welding processes such as laser beam welding emit process light in the visible, infrared and ultraviolet spectra. Specific changes in this process light can indicate welding defects. We used a process observer from Plasmo GmbH, which enables the detection of these changes [26].  
Methods of laser position detection are based on the principle of laser triangulation. They are usually applied for weld tracking, distance measurement and the detection of simple contours [27]. In this work, the scanControl 2500 laser scanner from Micro Epsilon was used after the build-up process to measure geometric dimensions.
## 5 Operational Descriptions of the Future Distribution Networks with the Sundom Smart Grid
### Protection Use Cases of the Microgrid Phase
The PD0 is connected to the transformers' secondary side relay PD1. PD0 sends a transfer trip command to PD1 to isolate the secondary side of the transformer. Figure 5: The suburban distribution network in the microgrid phase. Adapted from [13]. * The secondary side relay PD1 is part of the MV (or the LV) busbar protection and also operates as the POI of the microgrid. The PD1 includes the loss-of-mains (LOM) protection, an islanding detection algorithm in the F1 situation. When the HV (or MV) is feeding line-voltage drops under the acceptable limit, the PD1 disconnects the microgrid from the utility grid. In addition, the PD1 can receive a disconnection command from the PD0 in the primary substation (or the MV feeder automation (FA) system in the secondary substation). In the F2 or the F3 situations, the PD1 receives an interlocking signal from the PD2 after the pick-up limit is reached. * The PD2s operate only in the F2 situations, and they include directional OC protection, which is selective with the PD1 and the PD3s in the MV grid (or the PD3C in the LV grid). After the PD2 is operated, it sends a disconnection signal to all PD4s of the corresponding feeder. * The PD4 has to be voltage selective with the PD1 and the PD2 and frequency selective with the PD1. In the F2 situation, PD4 receives the disconnection signal from the PD2. In the F1 case, if the microgrid is not capable of islanding, the PD4's LOM protection operates, or the PD4 receives the disconnection signal from the MPMS. * The PD3 Cs are selective with the PD2 and the PD3F, and PD3 Cs protection settings are fixed and based on the microgrid island mode (more critical). Thereby, the communications between the PD3 Cs and the MPMS are not necessary. * The PD3 Fs operate in the F3 situations and the customer grid fault situations, and they are selective with the PD2 and the PD3C. No communications are required. The studied OC protection HL-UCs of the MV and the LV distribution network in the microgrid phase are described in Table 7.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**HL-UC Name** & **Events in the MV and/or LV Distribution Network** \\ \hline Fault in the supplying feeder (F1), microgrid (F1), the island (F2), the island (F3), the island (F4), the island (F5), the island (F6), the island (F7), the island (F8), the island (F9), the island (F10), the island (F11), the island (F12), the island (F13), the island (F14), the island (F15), the island (
## 3 Results
### New Materials and Technologies of Rehabilitation Devices
Hydrogels printed using bioprinting technology enable the creation of tissue \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Key Properties** & **Applications in Rehabilitation and Physiotherapy** \\ \hline PLA & Biodegradable, rigid, easy to print & Temporary orthoses, anatomical models \\ \hline ABS & Impact-resistant, slightly flexible & Orthoses, prostheses, stabilizers \\ \hline TPU & Flexible, abrasion resistant & Orthopedic inserts, dynamic orthoses \\ \hline PA (Nylon) & High mechanical resistance, low weight & Exoskeletons, orthopedic splints \\ \hline PETG & Chemical and mechanical resistance & Limb stabilizers, prostheses \\ \hline Carbon fiber composites & Lightweight, extreme durability & Prostheses, sports orthoses \\ \hline SLA/DLP silicones & Flexibility, comfort of wearing & Prosthesis overlays, elastic dressings \\ \hline Titanium (Ti6 Al4V) & Biocompatibility, high durability & Orthopedic, reconstructive implants \\ \hline Hydrogels & Biocompatibility, ability to regenerate tissue & Scaffolds for muscle and tendon regeneration \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Key material properties and theirapplications (authors’ own elaboration). scaffolds for muscle and tendon regeneration. AI supports the selection of the optimal material by analyzing the patient's biomechanics and predicting the material wear of personalized rehabilitation devices. Thanks to the combination of AI and 3D printing, it is possible to generate multi-material structures with variable mechanical properties, adapted to the dynamic needs of patients.
## 2 Methodology  
In this study, we employed a comprehensive methodology to investigate emotion recognition in a closed-cabin environment using noncontact millimeter-wave radar for respiration signal monitoring. Our experimental setup was specifically designed to simulate real-world closed-cabin conditions, ensuring an environment conducive to reliable data collection. A 77 GHz frequency-modulated continuous-wave (FMCW) radar sensor with a 2-transmitter and 4-receiver MIMO configuration was used to capture respiration signals  
Figure 1: Overall research framework.  
at a sampling rate of 32 Hz. This system, installed within a shielded, soundproof room, allowed us to measure respiration rates accurately, unaffected by external environmental factors such as lighting and airflow.  
The participants were seated in a controlled laboratory setting with ambient conditions maintained at 25 \({}^{\circ}\)C and lighting set to 6500 K \(\times\) 600 lx to support a relaxed atmosphere. They watched a series of emotion-inducing video clips designed to elicit specific emotional responses (e.g., high-arousal positive, low-arousal negative) while the radar recorded their respiration signals. Respiration data were collected in segments of 20 s for a total of 1200 recordings across 20 participants, using pre-screening criteria aligned with operational requirements in closed-cabin environments, such as manned submersibles.  
Signal preprocessing included the application of bandpass filters to remove noise and isolate the respiration signal from mixed physiological data. We then applied the variational mode extraction (VME) algorithm to further refine the signal for analysis, allowing accurate identification of respiration patterns associated with different emotional states. These data were subsequently used as input for a sparse autoencoder (SAE) combined with a support vector machine (SVM) for emotion classification. This methodology not only provided a noninvasive approach to emotion recognition but also enabled a practical implementation framework suitable for closed-cabin applications where real-time, continuous monitoring is critical.