esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Robot Modelling
### The Kinematics Modelling  
The legs of the robot have the same structure and mechanical parameters. Here, the right front (RF) leg is used as an example. The single leg model is shown in Figure 2. According to precondition (a), the motions of the rolling hip joint are not considered and the rolling hip joint position is set as 0.  
In Figure 2, the RF leg can be divided into the hip, thigh, and shank parts. Since the motions of the rolling hip joint are neglected, the hip part can be regarded as the base. The origin of the coordinate frame \(\{O_{h}\}\) is set at the rolling hip joint. The \(z\)-axis direction is vertical to the ground facing upward. The \(x\)-axis is pointing to the motion direction of the robot.  
The mechanical parameters in Figure 2 are introduced as follows. The lengths of the hip, thigh, and shank parts are \(l_{0}\), \(l_{1}\), and \(l_{2}\) respectively, where \(l_{0}\) = 45 mm. \(m_{1}\) and \(m_{2}\) are the masses of the thigh and the shank parts, respectively. The joint positions of the pitching hip and pitching knee joints are denoted as \(\theta_{1}\) and \(\theta_{2}\), respectively. The COMs of the thigh and shank parts are defined by \(l_{m1}\), \(l_{m2}\), \(\varepsilon_{1}\), and \(\varepsilon_{2}\). When the thigh and shank parts rotate around their own joint shafts, the moments of inertia are \(l_{1}\) and \(l_{2}\), respectively. The values of these mechanical parameters are listed in Table 1[12].  
Figure 1: The overall structure of the SCalf robot.  
Figure 2: A simplified model of the right front (RF) leg.  
Based on the mechanical parameters, the location of the foot endpoint in coordinate frame\(\{O_{h}\}\) can be written as follows, where \(\theta_{12}=\theta_{1}+\theta_{2}\)  
\[\begin{cases}x=-l_{2}sin\theta_{12}-l_{1}sin\theta_{1}\\ z=-l_{2}cos\theta_{12}-l_{1}cos\theta_{1}-l_{0}\end{cases} \tag{1}\]  
By solving Equation (1), the joint positions of the RF leg can be obtained as  
\[\begin{cases}\theta_{1}=-\phi-arctan(\dfrac{x}{-z-l_{0}})\\ \theta_{2}=\pi-arccos(\dfrac{l_{1}^{2}+l_{2}^{2}-\xi^{2}}{2l_{1}l_{2}})\end{cases} \tag{2}\]  
where  
\[\begin{cases}\xi=\sqrt{(-z-l_{0})^{2}+x^{2}}\\ \phi=arccos(\dfrac{l_{1}^{2}+\xi^{2}-l_{2}^{2}}{2l_{1}\xi})\end{cases} \tag{3}\]  
The Jacobian matrix of the single leg model can also be calculated by Equation (1).  
\[J=\left[\begin{array}{cc}-l_{1}cos\theta_{1}-l_{2}cos\theta_{12}&-l_{2}cos \theta_{12}\\ l_{1}sin\theta_{1}+l_{2}sin\theta_{12}&l_{2}sin\theta_{12}\end{array}\right] \tag{4}\]
# Tensile Properties of As-Built 18 Ni300 Maraging Steel Produced by DED
## 1 Introduction
[27] studied different heat treatments in DED-processed CA6 NM, concluding that a repeated 2 h cycle at 640 \({}^{\circ}\)C led to a set of mechanical properties closest to its cast counterpart. [PERSON] [28] studied the cooling rate influence in phase transformation and interface bonding strength of FV520B produced through wire DED by depositing a clad surface over a rod and subjecting the sample to a tensile quasi-static load, reaching 1046 MPa at a rate of 0.05 mm s\({}^{-1}\). As aforementioned, one of the prominent applications of DED technologies is component repair. Extending a component's life cycle is attractive from both an economic perspective, as large components such as casting dies are expensive to produce [29], but also from an environmental perspective, as it leads to less material waste. Recent research focused on using DED as a repairing tool includes the study of deposition strategy and fill depth in component properties by [PERSON] et al. [30], which studied the monotonic and fatigue behaviour of several specimens with distinct percentages of added 18 Ni300 material, analysing the intergranular cracking as the main crack initiation mechanism. [PERSON] et al. [31] studied the mechanical reliability and environmental impact of repairing an automotive die through DED using H13, inferring a better performance than conventional TIG welding repair as the DED-remanufactured die achieved the same life as the original die. [PERSON] et al. [32] analysed the environmental impact of laser-based DED refurbishment of dies, reaching the conclusion that the larger the solid-to-cavity ratio, the more environmentally costly it becomes to repair through DED technologies. Maraging steels' use within die repairing via DED is justified by its mechanical strength, acceptable corrosion resistance [30] and good weldability [23]. Nevertheless, it is necessary to understand its properties after DED processing, as well as evaluate the adhesion between printed geometries and the substrate. In order to further understand and validate the use of Maraging in die repair, an 18 Ni300 Maraging steel was deposited on an AISI 1045 steel plate, and subsequently electrically discharge machined to obtain three specimen types, enabling the study of 18 Ni300's properties in two distinct directionsin relation to the building direction, as well as a bimetallic specimen comprised of the deposited 18 Ni300 and the AISI 1045 baseplate.
## 1 Introduction
The optimization of lithium-ion battery (LIB) model parameters are distinctly degraded by the unexpected noises in the real-life applications [22; 23]. To discern this labyrinth, instrumental variable (IV) estimation was combined with the bilinear principle in [24] headed for compensating the noise-induced biases of battery parameterization. Luenberger observer was further implemented in the proposed model that resulted in 2.60% and 1.25% SOC estimation error under hybrid pulse experiment (HPE) and federal urban dynamic schedule (FUDS) condition correspondingly. Reference [25] presented backpropagation neural network (BPNN) based SOC estimation technique for Lithium-ion battery at three different temperatures (0, 25 and 45 \({}^{\circ}\)C). The research resulted the reduction of root mean square error (RMSE) for BPNN model at 25 \({}^{\circ}\)C compared to radial basis  
Figure 1: Open circuit voltage (VOC) vs. state of charge (SOC) for the employed 12 V Lead-Acid battery. neural network, extreme learning machine, and generalized regression neural network models. The LabVIEW-DAQ 6008 was implemented in [26] to devise the optimum employment of lead-acid battery through BPNN based SOC assessment algorithm. This research resulted 0.893% average error by recommending the battery life extension for future research. Reference [27] implemented Wohler-curve-based ageing model to improve the battery lifespan that recommended a proper controlling scheme in case of railway application. In [28], a predictive controller system is proposed to manage the SOC of the battery bank and support the critical load demand during grid-blackout condition. The projected model elongated the battery lifetime excluding off-grid atmosphere. According to the above discussion, BPNN based SOC estimation approach is a better solution compared to the existing research in the direction of lengthening the battery lifespan for PV-battery hybrid system. Therefore, the main contribution of this paper is the development of a proper control strategy for a standalone PV-battery integrated system to manage the generation and load-demand operation. Further, an effective SOC estimation technique is proposed to extend the operational life of a lead acid battery. The proposed model is validated through extensive simulation and experimental analyses using MATLAB-dSPACE 1104 interfacing system. The remaining of this paper is organized as follows: The modeling approach for state of charge (SOC) estimation is explained in Section 2. Section 3 represents the numerical simulation and experimental analysis in depth. The significance of the proposed methodology are elucidated based on simulated and experimental results in Section 4. In Section 5, the key conclusion of the paper is drawn.
## 4 Discussion
### Limitations and Challenges  
Despite the strong results, we identified several limitations and challenges:  
* **System-Level Validation.** The observed 75.8% expert agreement rate indicates promising clinical validity of model-generated recommendations but should not be interpreted as an assessment of overall CDSS effectiveness. Further end-to-end validations--including real-time inference and clinician workflow integration--are required.
* **System-Level Validation and Reproducibility Constraints.** A full comparison between the proposed CDSS and established triage tools (e.g., ESI, REITS, or conventional ML models) was not performed due to several constraints. The dataset lacks complete prospectively assigned triage labels, and retrospective inference from clinical notes may introduce inconsistencies. Additionally, fair and reproducible baselines require standardized datasets, open-source implementations, and aligned hyperparameters--many of which are currently unavailable or inconsistent across systems.
* **Dataset Composition Bias.** Because ~91% of the fine-tuning corpus originates from CORD-19, the model is richly exposed to infectious disease terminology, pathophysiology, and treatment guidelines. This skew is advantageous for the present ID triage use-case, boosting accuracy on sepsis, _C. difficile_ colitis, and pneumonia presentations. The trade-off is narrower coverage of under-represented specialties (e.g., dermatology, neurology, multi-morbidity), which may still require dedicated fine-tuning or specialty-specific model variants in future work.
* **Real-World Evaluation Scope.** The proposed CDSS was evaluated on 132 de-identified emergency department encounters labeled with infectious or parasitic diseases (ICD-9 001-139) extracted from MIMIC-III. All 132 records underwent expert review, providing a statistically informative benchmark for real-world infectious disease triage. Nonetheless, external validation across larger, multi-center datasets is still required to fully establish sensitivity, specificity, and robustness in real-world deployment scenarios.
* **Data Quality and Noise.** Our evaluation assumed reasonably good data input. In reality, triage data can be messy--devices might error, notes may be incomplete, or data can be entered in wrong fields. The system currently has basic validation and outlier handling, but it may still be influenced by erroneous data (e.g., a wildly high heart rate reading due to sensor error might trigger an unnecessary alarm). Data quality issues are a limitation; making the system robust to such noise is an ongoing challenge.
* **Infrastructure Requirements.** Optimal performance requires modest IT resources (e.g., GPU for LLM inference, reliable networking for data streams). While the federated setup allows lightweight client deployment, smaller institutions may still face adoption barriers due to hardware or support limitations.
# Performance Analysis of Existing ITS Technologies: Evaluation and Coexistence
## 2 Fundamentals and Related Work  
This section describes mechanisms and protocols at the physical (PHY) and medium access control (MAC) layers used by ITS-G5 and LTE-V2X standards. Then, a summary of previous evaluations of ITS technologies is presented, and issues that need to be addressed are discussed.
## 4 Results
### Comparison with YOLOv12  
In addition to the original YOLOv8 model, this study also trained and evaluated the latest YOLOv12 model to analyze the performance differences and applicability of the two on this dataset. The parameter settings used in the training of the YOLOv12 model are consistent with those of YOLOv8. The core includes the input image size of 512 \(\times\) 512, the batch size of 2, the optimizer using SGD, the learning rate strategy of cosine decay, and the training epoch set to 300 [35; 36]. During training, the early stopping feature in YOLOv12 was activated because the validation set's performance did not improve after several rounds, so training automatically stopped at the 199 th epoch (more details can be found in the YOLOv12_results.csv file in Appendix A). In addition, other key settings such as data augmentation strategies (mixup, copy-paste, randaugment), freeze/thaw phase division, and the training and validation division ratio are also consistent (see the args.yaml file in the Supplementary Material for details).  
Figure 13 displays the comparison results. From the training results, the YOLOv8 model reached its peak performance at the 90 th epoch, with a [EMAIL_ADDRESS] of 0.7128, while the YOLOv12 model reached a maximum [EMAIL_ADDRESS] value of 0.6825 at the 95 th epoch, which is slightly lower than YOLOv8 overall. The two models are relatively close in the trend of the mAP curve, and YOLOv8 has higher detection accuracy and convergence stability in some stages. This result shows that although YOLOv12, as an updated version, has certain feature expression advantages in theoretical structure, in the gray brick damage detection task scenario targeted by this study, YOLOv8 still shows stronger adaptability and robustness; this is especially in the small sample training set, where it can better maintain detection accuracy.  
Figure 13: Comparison of mAP values during YOLOv8 and YOLOv12 model training. (Image source: drawn by the authors).
## 4 Discussion
### The Essential Elements and IoT Architecture Framework to Implement an IAQ Monitoring Study
#### 4.1.2 Sensors Selection
At a minimum, there should be knowledge of constraints at the site that would limit functionality of sensing devices. These constraints may be the size of the site, the day-to-day activities, and access to power outlets. For this study, battery-powered devices were preferred over outlet-powered supplies where possible. There were several scenarios where battery powered was a requirement, namely for the sensors placed at doorways and corridors as these tended to lack power outlets. As outlined in the experimental scenario of Section 1, there were several parameters to be monitored for different purposes. Firstly, doorways were to be monitored to track human movement for a secondary study, specifically the opening and closing of doors. Furthermore, for doorways that lead to larger areas or corridors, the pressure differential needed to be captured to set up boundary conditions for Computational Fluid Dynamic simulations in the next study. Since the pressure difference indicated the direction of air flow, resolution was particularly important. For these areas the Monnit ALTA open/close sensors and SenseCap Barometric Pressure sensors were selected. Both are battery powered, operating within ISM band ranges. Additionally, the SenseCap Barometric Pressure Sensors were purported to have a resolution of 1 Pa, which was one of the lowest available. Secondly, CO\({}_{2}\) and noise levels at workstations were captured. The weather stations were effective in this regard as they also captured temperature, pressure, and relative humidity which are parameters related to occupant comfort. The third and final aspect of this study was to monitor the IAQ parameters commonly measured. These were PM\({}_{2.5}\), PM\({}_{10}\), CO\({}_{2}\), and TVOCs. The Cloud Garden IAQ Sensors were suitable for this purpose.
## 1 Introduction
Sinusoidal phase-shifting fringes were generated in sequence by an optical chopper and projected onto the surface of the object. External signals were generated and sent to synchronize the cameras. Texture was used for rough matching and the wrapped phase calculated by a phase-shifting algorithm was adopted for secondary matching. When PMP is employed in measuring dynamic scene, three or more images are required to reconstruct one 3D frame. The measuring speed of hardware is limited, and the dynamic process has an average effect in a short period of time. The accuracy of the measurement result is affected by motion blur. When FTP is employed, only one fringe is required to reconstruct the 3D shape of the tested object. While weighted filtering operation is carried out in spatial domain, it results in lower accuracy. In order to get the fastest and most accurate 3D reconstruction result, one fringe should correspond to a 3D reconstruction result. A novel 3D shape measurement technology--one that combines FTP and PMP--is proposed in this paper. By projecting and recording the multi-period phase-shifting fringe, this proposed method performs fast Fourier transform (FFT), spectrum filtering and inverse FFT operations along the temporal axis to calculate the wrapped phase value. The result is called temporal Fourier transform profilometry (TFTP). During the whole measurement process, only one sampling moment when the wrapped phase can be correctly unwrapped in spatial domain is required. The wrapped phase is easy to unwrap, and it requires only one deformed fringe pattern to retrieve the 3D shape of the measured object. The proposed method and a developed system were applied to measure the multiple isolated moving objects and experiments verified the performance of this TFTP method.
## 2 Materials and Methods
### Antimicrobial Activity  
The antimicrobial activity of synthesized hybrid materials was performed according to Kirby-Bauer Protocol [20; 38]. To this aim, the hybrid materials were ground, obtaining powders, which were further compressed into 100 mg disks. All the sample disks were sterilized using UV light for 1 h to avoid external contamination during the tests. Antimicrobial properties were assessed against both Gram+ bacteria, such as _S. aureus_ (ATCC 25923) and _E. faecalis_ (ATCC 29212), and Gram- bacteria, such as _E. coli_ (ATCC 25922) and _P. aeruginosa_ (ATCC 27853). These bacteria were chosen as they are known as bacteria that cause nosocomial infections during patient hospitalization [39]. All the peltetized bacterial strains were diluted in distilled saline water (0.9% NaCl), achieving suspensions of 10\({}^{9}\) CFU/mL, that were plated onto the respective agar-based media (whose preparation was reported elsewhere in [20]). After the bacterial plating, all the sterilized samples were left in the middle of the plates and then they were placed in the respective incubators that mimic the growth condition of each bacterium. Indeed, _E. coli_ was incubated at 44 \({}^{\circ}\)C for 24 h, _P. aeruginosa_ and _E. faecalis_ were incubated at 36 \({}^{\circ}\)C for 48 h, whereas _S. aureus_ was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & \
\begin{tabular}{c} **Polyethylene Glycol** \\ **(wt\% in Relation to SiO\({}_{2}\) Matrix)** \\ \end{tabular} &
\begin{tabular}{c} **Caffeic Acid** \\ **(wt\% in Relation to SiO\({}_{2}\) Matrix)** \\ \end{tabular} \\ \hline SA5 & 0 & 5 \\ SA10 & 0 & 10 \\ SA15 & 0 & 15 \\ SA20 & 0 & 20 \\ SP6A5 & 6 & 5 \\ SP6A10 & 6 & 10 \\ SP6A15 & 6 & 15 \\ SP6A20 & 6 & 20 \\ SP12A5 & 12 & 5 \\ SP12A10 & 12 & 10 \\ SP12A15 & 12 & 15 \\ SP12A20 & 12 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compositions of synthesized hybrids with their acronyms. S = SiO\({}_{2}\); Ay= caffeic acid wt%; Py= polyethylene glycol wt%.  
incubated at 36 \({}^{\circ}\)C for 24 h. After the incubation times, all the bacterial plates were removed from the incubators and the inhibition halo diameters (IHDs) were measured.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 5 IoT in 5G Enabling Technologies
### Multiple Access and Waveforms  
**Multiple access** techniques are becoming an important technology in 5G because of their ability to support mMTC activity-based IoT classes with urgent deploy demand [144]. They include several non-orthogonal multiple access (NOMA) forms: multi-user shared multiple access (MUSA) [145; 146; 147; 148],resource spread multiple access (RSMA) [149], sparse code multiple access (SCMA) [150; 151; 152], pattern division multiple access (PDMA) [153; 154; 155], interleave-division multiple access (IDMA) [156; 157], and NOMA by power domain [158]. The NOMA is a radio access technology design for enabling greater spectrum efficiency [159; 160; 161; 162; 56], higher cell-edge throughput, relaxed channel feedback, and low transmission latency [99]. NOMA can be employed to enhance user fairness and to support massive connections with diverse QoS requirements [163]. In NOMA, there are several challenges and open issues, such as dynamic user pairing, the impact of transmission distortion, the impact of interference, resource allocation, NOMA with multiple antennas, heterogeneous networks, outage probability analysis, practical channel model, uniform fairness, NOMA with antenna selection, carrier aggregation and other challenges as discussed in [216]. There are still many challenging issues for SCMA, which need to be solved in future work. For example, there are several open issues in SCMA transceiver design [57], the optimization of algorithms for user grouping and power allocation [164], and further enhancement of the SCMA and MIMO combination.  
**New waveforms** have become a serious candidate for 5G being studied in terms of: (1) modulation based on pulse shaping: filter bank multicarrier (FBMC) [58], generalized frequency division multiplexing (GFDM) [59], pulse shaped OFDM [165] and QAM-FBMC [241]; (2) modulation based on sub-band filtering: universal filtered multi carrier (UFMC) [242], filtered OFDM (f-OFDM) [243] and resource block f-OFDM (RB-f-OFDM) [244]; other modulation format: guard interval discrete Fourier transform spread OFDM (GI DFT-s-OFDM) [245], spectrally-precoded OFDM (SP-OFDM) [246] and orthogonal time frequency and space (OTFS) [247]. The f-OFDM is seen as a potential candidate for IoT applications [60]. Due to narrow sub-bands and thereby pure detection performance, additional processing is needed [60]. Performance of self-interference cancellation (SIC) or filtering on f-OFDMA [60] remains a topic for further study. Additionally, it is interesting to consider the balance of time and frequency dispersion in UFMC, as well as the design of an efficient prototype filter according to application scenarios [60].