esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 LiCSAR System Architecture
### LiCSAR Processing Chain
#### 2.1.4 Unwrapping Interferograms  
The interferogram unwrapping is performed using snaphu in version 2 [53]. Prior to the unwrapping process, the interferogram is spatially filtered using an adaptive power spectrum filter [54] (GAMMA command adf with the parameters _FFT window size_ = 32 and _alpha_ = 1.0). The default filter parameters were chosen based on experience towards monitoring large-scale deformations; however, they can be overridden by a local configuration file per frame. Points with interferometric coherence lower than 0.5 after filtering are removed prior to unwrapping. A map of statistical costs is generated based on a custom approach that searches for phase consistent pixels, avoiding false phase jumps within a threshold distance [55]. The unwrapping process is run on a single core with no tiling (EPT ~15 min/interferogram).
# Defining Feasible Joint and Geometric Workspaces Through Boundary Functions
## 5 Conclusions  
This study introduced a structured mathematical framework for defining feasible joint-space boundaries and their corresponding geometric workspaces through the formulation of boundary functions \(\beta_{i,j}(\mathbf{q})\). Unlike traditional numerical methods that rely on  
Figure 11: Numerical collision detection results. The first plot shows joint space sampling with red points for collisions and blue points for noncollisions. The second plot illustrates the corresponding geometric space, highlighting overlapping regions and the lateral shapes of the feasible workspace.  
extensive sampling and collision detection, the proposed approach constructs continuous workspace boundaries by identifying and connecting critical intersection points derived from geometric and kinematic constraints. The boundary function formulation provides an explicit representation of geometric and collision constraints, offering analytical insights that complement existing numerical approaches.  
The effectiveness of the methodology was demonstrated using a planar robot case study, showing how the feasible joint-space region can be scaled with respect to its centroid to resolve singularities and generate structured trajectories. The analysis of different traversal paths revealed that the final pose and reachability of the manipulator are highly dependent on the selected trajectory and that such paths are generally asymmetric due to the inherent nonlinearities of the system and the scaling process. This formulation contrasts with traditional numerical approaches by offering an explicit, constraint-driven modeling alternative.  
A broader set of applications is being investigated in a subsequent study, in which the present formulation serves as the mathematical foundation. In particular, the extension to spatial manipulators and hybrid architectures is being considered, incorporating data-driven parameterization and visualization of boundary surfaces to support higher-dimensional configurations. These developments build upon the theoretical contributions introduced in this work, establishing a foundation for generalizing the proposed method to a wider class of robotic systems.  
Conceptualization, J.A.L., L.F.L.-V. and H.A.G.-O.; methodology, J.A.L., E.L.-N. and M.E.M.-R.; software, D.M.N. and J.A.L.; validation, L.F.L.-V., L.E.G.-J. and S.C.-T.; formal analysis, J.A.L. and R.C.-N.; investigation, D.M.N., S.C.-T. and M.E.M.-R.; resources, J.A.L.; writing--original draft preparation, D.M.N. and J.A.L.; writing--review and editing, L.E.G.-J., L.F.L.-V. and H.A.G.-O.; visualization, D.M.N. and R.C.-N.; supervision, J.A.L., S.C.-T. and M.E.M.-R.; project administration, M.E.M.-R. and J.A.L.; funding acquisition, J.A.L. and E.L.-N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 5 Evaluation  
The performance of 2 LCCM is closely related to the performance of three aspects: replica selection method, congestion control algorithm, and switching strategy. The performance of the replica selection method has been extensively evaluated in the literature [36] and the parameters of Equation (6) is recommended to be \(\mathrm{w}=\{0.757,\ 0.162,\ 0.081\}\), thus this section focuses on evaluating the performance of switching strategy and the performance of the congestion control algorithm.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.4 Blood Glucose Monitors  
Blood glucose monitoring is essential for individuals managing diabetes. Sensors can measure glucose levels in the blood, providing real-time glucose data that can help to prevent dangerous fluctuations in blood sugar levels [18]. Continuous glucose monitoring (CGM) devices are vital for reducing the risk of diabetic complications such as hyperglycemia and hypoglycemia. By providing real-time glucose data, these monitors empower individuals to make informed decisions about their diet, exercise, and insulin dosage, in turn improving their overall diabetes management and quality of life. Moreover, elevated glucose levels have been correlated with larger final infarct volume and poorer clinical outcomes. This association has been observed in patients undergoing acute reperfusion therapies such as intravenous thrombolysis and endovascular therapy for IS as well as in various studies.
## 3 Studies of PCMs Incorporated in Passive Systems
### Experimental Studies
#### 3.1.4 Discussion  
At the experimental level, it can be seen that the majority of studies are focused on the development of composite materials and their characterization. Different materials and methods of incorporation have been used and the positive effect of PCMs on temperature regulation inside buildings has been demonstrated. However, among the analyzed articles, very few have moved to the application of PCMs at the building or wall scale. This last step is essential to evaluate the effect of PCMs at real scale. Some areas of uncertainty remain, namely the positioning of the PCM in the building envelope, and the melting temperature of the PCM to be chosen according to the climatic conditions, even if many studies have been carried out in this direction.  
The literature also lacks studies on the stability of PCMs in building envelopes over the long term and the effect of aging. A very important aspect that remains to be investigated is the analysis of the hydric behavior of buildings incorporating PCMs necessary for the evaluation of air quality. Finally, as pointed out by [PERSON] et al. [17], no study takes into consideration the internal loads and the existence of the occupant or his behavior (real use of the building).  
Numerical studies carried out under different climatic conditions established criteria for the selection of PCMs as well as their location in the building envelopes. The conclusions of these studies showed that the PCM should be placed near the heat source. Thus, in hot climates, the PCM should be located next to the exterior envelope while in cooler climates, a location near the interior envelope is recommended.  
The findings of the various numerical studies indicated the need to conduct an optimization study on the properties of the PCM to select its location as well as its quantity under various weather conditions.
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G-Mec V2X Testbeds
#### 3.3.5 5g-Drive  
The 5G-DRIVE testbed [58] focuses on developing and evaluating eMBB and V2X scenarios. The network infrastructure built into the 5G-DRIVE testbed is based on Nokia's NetLeap LTE test network. In the 5G-DRIVE testbed, a virtual mobile network is enabled with its own EPC. The eNodeBs are interconnected with the OpenStack cloud and SDN-enabled backhaul. For the experimental tests, a public site in Finland was used. Some of the research activities focused on testing the coexistence of ITS-G5/LTE-V2X [59]. The 5G-DRIVE is equipped with a C-V2X box, a traffic light equipped with a C-V2X RSU, and a tablet in the car. The field test involved the LTE network (2.6 GHz), a 5.9 GHz RSU, and a 5.9 GHz NEBULA OBU. The testbed has been used to experimentally evaluate the latency and packet loss rate between the RSU and OBU [60]. Table 1 presents a categorization of the considered testbeds according to their type and open source accessibility. Furthermore, the equipment of the VUE and RSU are mentioned in Table 1 for the available 5G-V2X and 5G-MEC V2X testbeds. In the case of the 5G-MEC testbeds, the VUE and RSU are not applicable. In addition, Table 1 names the sensors used in the surveyed testbeds for collecting the necessary data. Table 2 presents the surveyed 5G-MEC and 5G-V2X testbeds based on the categorization of 5G usage scenarios such as eMBB, URLLC, and mMTC. Furthermore, the surveyed testbed use case studies are briefly described. The same information is summarized in Table 3 for the 5G-MEC-V2X testbeds. \begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Project/Title of the** & **Type of Tested** & **Open Source** & **VUE** & **RSU** & **Sensors** & **References** \\
**Tested** & & & & & & & \\ \hline
3 CCity & 5C-MICE testbed & Open source & Not applicable & Not applicable & Smartphone camera, CCTV camera, and & [32] \\  & & & & & microphones & \\ \hline Mostask-5C & 5C-MICE testbed & Open source & Not applicable & Not applicable & Mobile sensors for monitoring patient & [6]-1 \\  & & & & & health-related data (specifications not provided) and LDMK sensor & \\ \hline
3 CINFIRE University of & 5C-MICE testbed & Partially open source & Blue rider helmet, Raspberry PT, 360-degree camera, and audio & Not applicable & Camera, microphone, air quality, and noise sensors & [7] \\  & & & & & & \\ EdgeX Foundry & MICE testbed & Open source & Not applicable & & Video cameras, motion sensors, and audio & [34] \\  & & & & & & \\ \hline SCVINNMatch experiment facility & 5C V2X testbed & No & Ambulator & Huawei SC RAN C-Ghard 3.41 GHz, Remote hospital integration & 5C environmental UE, a high-resolution camera, an ultrasound system, Hospital is equipped with a highly mobile camera, Carus, PC, and remote medical expert. \\  & & & & & & \\ \hline SCVINFIRE-Air automotive & 5C V2X testbed & Partially open & Blue and commercial car & Single-board computer (SRC), DSRC wireless interface (852.112), WiFi interface (122 GHz), 502.112/50
## 5 Conclusions  
A new HTP tooth profile design formula, based on the IH tooth profile and the cycloid curve, was proposed, and a corresponding reducer design tool was developed. Using this tool, reducers were designed for cases where the number of teeth in the ring gear was 21 and 100. Simulations were conducted to examine tooth meshing conditions, tooth interference, and related characteristics, confirming that no abnormalities occurred. In the case of a 21-tooth ring gear under a driving torque of 47 Nm applied to the input shaft, bending stress, equivalent stress, and other stress factors were evaluated through analysis. Additionally, a 3D prototype of the R21_z3 reducer with the new HTP tooth profile was fabricated to verify the meshing and operating conditions, thereby validating the proposed HTP tooth profile formula. The conclusions are as follows:  
First, although it is theoretically difficult to design a reducer using the conventional IH tooth profile when the number of teeth is small, the proposed HTP tooth profile formula--incorporating both the IH tooth profile and the cycloid curve--enables reducer design regardless of the number of teeth.  
Second, using the HTP tooth profile design approach, a Python-based reducer design tool was developed. Simulations conducted with this tool, under varying tooth counts, confirmed the absence of issues such as poor meshing conditions or tooth interference, thereby validating the reliability of the design tool.  
Third, the R21_z3 reducer, designed using the proposed HTP tooth profile, underwent strength analysis--including bending stress and equivalent stress in the tooth meshing region--using Ansys 2023 R2. The analysis confirmed the validity of the design results.  
Fourth, a 3D prototype of the R21_z3 reducer was fabricated, and physical verification confirmed that the meshing condition, tooth interference, and other design aspects matched the predictions from the design tool. Consequently, the overall validity of the proposed HTP tooth profile formula--based on the IH tooth profile and the cycloid curve--was confirmed through simulation, strength analysis, and physical prototyping.  
Conceptualization, S.O. and W.J.; methodology, S.O.; software, S.O.; validation, H.J., K.Y. and W.J.; formal analysis, H.J.; investigation, W.J.; resources, W.J.; data curation, W.J.; writing--original draft preparation, W.J.; writing--review and editing, S.O. and H.J.; visualization, W.J.; supervision, S.O.; project administration, H.J.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Planning & Evaluation Institute of Industrial Technology (KEIT); and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20024010).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 2 Experimental Programme
### Specimen Preparation and Test Methods
#### 2.3.4 Wetting-Drying Test  
The WDT is not a standardised test, although it has been performed by various authors [47; 48; 49], in order to simulate tidal conditions (Figure 2). The test consisted of 3-days wetting and 4-days drying cycles at laboratory temperature, as also adopted by [PERSON] et al. [45; 47]. The same saline solution used in the IT was adopted for this test and was renewed every month. Similarly to IT, after 3 months, 6 samples were collected from two 25 mm drill holes in each specimen, at different depths (5,10, 15, 20, 25 and 30 mm).  
Figure 1: IT tank (**a**) and test specimens (**b**).  
2.3.5. Chloride Profiles and Parameter Determination  
Before the IT and the WDT, reference samples were collected from the cubic specimens to determine the initial chloride content (\(C_{i}\)). This parameter ranged from 0.04 to 0.16 wt% of binder, which was lower than the maximum 0.2 wt% of binder, recommended in EN 206 [50], for reinforced concrete under XS classes.  
After the sample collection from the IT and WDT specimens, the total chloride concentration was determined through potentiometric titration, by resorting to a pH/ISE meter and an electrode, similarly to the method suggested in ASHTOO T260 [51], as described in [PERSON] [52]. Knowing the measured chloride content at different depths, a chloride profile was built for each type of concrete. Using non-linear regression (NLR) with the assistance of the software \(R\)_Studio_ (Version 1.1.442), the apparent diffusion coefficient, \(D_{cl}\) (m\({}^{2}\)/s), and the surface chloride content, \(C_{s}\) (wt% of binder), were obtained by curve fitting to Equation (3), where \(C_{i}\) is the initial chloride content (wt% of binder), \(x_{cl}\) is the depth of chloride penetration (m) at exposure time \(t\) (s), and \(er\) is the error function, originated from solving [PERSON]'s second law of diffusion. In this case, since the diffusion coefficient is time-dependent, the \(D_{cl}\) actually represents an average diffusion coefficient during the exposure time.  
\[C(x,t)=C_{i}+(C_{s}-C_{i})\times\left[1-erf\left(\frac{x_{cl}}{2\times\sqrt{D _{cl}\times t}}\right)\right] \tag{3}\]  
In addition, the penetration parameter, \(K_{cr}\), was determined through Equation (4), according to LNEC E-390 [45]. This parameter is useful for comparison purposes and represents the one-year penetration depth of a reference chloride content, \(C_{r}\), if \(D_{cl}\) is constant. A reference \(C_{r}\) of 0.5 wt% of binder was considered [53].  
\[K_{cr}=2\sqrt{D_{cl}}*erf^{-1}\left(\frac{C_{s}-C_{r}}{C_{s}-C_{i}}\right) \tag{4}\]
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 3 System Model and Problem Formulation
### System Model  
#### 3.1.1 Network Model  
As shown in Figure 1, the network is deployed in a two-dimensional area and consists of a UAV, a base station (BS), and randomly distributed sensor nodes. The UAV is assumed to have computing and wireless transmission capabilities, be equipped with a sufficiently charged battery, and be able to obtain its position through the Global Positioning System (GPS). The BS has ample energy and communication capabilities, enabling direct wireless communication with the UAV. The sensor nodes can monitor their remaining energy and data fusion capabilities. Cluster member (CM) nodes send data packets to cluster head (CH) nodes, which aggregate the data and store them in a data buffer. The UAV takes off from the BS and follows a \"fly-hover\" pattern to visit each CH node in a predetermined order continuously. The CH nodes consume energy to upload data packets to the UAV, while the UAV provides charging to the nodes within its coverage range using radio frequency transmission technology.
## 2 Unbiased Minimum Variance Receding-Horizon Fixed-Lag Smoother in Batch Form
### Unbiased Minimum Variance Estimation on the Sub-Horizon \([k-N\ k-h-1]\)
Because the optimal estimator (28) on the sub-horizon \([k-N\;\;k-h-1]\) is satisfied the unbiased condition (30), we can determine the unbiased constraint as [24]  
\[\tilde{A}_{N,h+1}=H_{1,|k-h}\tilde{C}_{N,h+1}. \tag{33}\]  
With the unbiased constraint and following relations [24]  
\[x_{k-h} = \tilde{A}_{N,h+1}x_{k-N}+\tilde{M}_{N,h+1}W_{k-N,k-h-1}, \tag{34}\] \[Y_{k-N,k-h-1} = \tilde{C}_{N,h+1}x_{k-N}+\tilde{C}_{N,h+1}W_{k-N,k-h+1}+V_{k-N,k- h-1}, \tag{35}\]  
the estimation error \(\epsilon_{1,k-h}\) (31) can be expressed as follows:  
\[e_{1,k-h} = \hat{\tau}_{1,k-h|k-1}-x_{k-h} \tag{36}\] \[= H_{1,k-h}Y_{k-N,k-h-1}-\tilde{A}_{N,h+1}x_{k-N}-\tilde{M}_{N,h+1 }W_{k-N-k-1}\] \[= \left(H_{1,k-h}\tilde{C}_{N,h+1}-\tilde{A}_{N,h+1}\right)x_{k-N}+ \left(H_{1,k-h}\tilde{C}_{N,h+1}-\tilde{M}_{N,h+1}\right)W_{k-N,k-h-1}\] \[+H_{1,k-h}V_{k-N,k-h-1}\] \[= \left(H_{1,k-h}-\tilde{M}_{N,h+1}\right)W_{k-N,k-h-1}+H_{1,k-h}V _{k-N,k-h-1}.\]  
Thus, the local estimate \(\hat{\tau}_{1,k-h|k-1}\) and its error covariance matrix \(P_{1,k-h}\) on the sub-horizon \([k-N\;\;k-h-1]\) can be obtained as follows:  
\[\hat{\tau}_{1,k-h|k-1} = H_{1,k-h}Y_{k-N,k-h-1} \tag{37}\] \[= \left[ \begin{array}{cc}\tilde{A}_{N,h+1}&\tilde{M}_{N,h+1} \end{array} \right]\left[ \begin{array}{cc}\tilde{C}_{N,h+1}^{T}\tilde{R}_{N,h +1}^{-1}\tilde{C}_{N,h+1}&\tilde{C}_{N,h+1}^{T}\tilde{R}_{N,h+1}^{-1}\tilde{G} _{N,h+1}\\ \tilde{G}_{N,h+1}^{T}\tilde{R}_{N,h+1}^{-1}\tilde{C}_{N,h+1}&\tilde{G}_{N,h+1} ^{T}\tilde{R}_{N,h+1}^{-1}\tilde{G}_{N,h+1}+\tilde{Q}_{N,h+1}^{-1}\end{array}  \right]^{-1}\] \[\times\left[ \begin{array}{c}\tilde{C}_{N,h+1}^{T}\\ \tilde{G}_{N,h+1}^{T}\end{array} \right]\tilde{R}_{N,h+1}^{-1}Y_{k-N,k-h-1},\] \[P_{1,k-h} = \mathrm{E}\Big{[}\epsilon_{1,k-h}e_{1,k-h}^{T}\Big{]}\] (38) \[= (H_{1,k-h}\tilde{G}_{N,h+1}-\tilde{M}_{N,h+1})\tilde{Q}_{N,h+1}( H_{1,k-h}\tilde{G}_{N,h+1}-\tilde{M}_{N,h+1})^{T}+H_{1,k-h}\tilde{R}_{N,h+1}H_{1,k-h}^{T},\]  
respectively.
## 7 New Approaches to Monitoring and Optimisation of Microalgae Cultivation Systems
### Two-Dimensional and Infrared Spectroscopy
Quantitative determination of biomass composition, including proteins, lipids, carbohydrates, or silicates, by FTIR in the range of 4000-700 cm\({}^{-1}\) has been performed with microalgae biomass or isolated components [229]. Recently, devices have been developed that integrate spectrometers with bioprocessing units. Two classes of such devices are flow-through ATR cells, where the sample stream flows through the measurement chamber of the FTIR spectrometer [230], and autoclayule optical fibres with ATR crystals at the tip [231]. At least two companies, Mettler Toledo International Inc. and Remspec Corp., have introduced fibre-optic ATR probes to the market. The integration of spectroscopic techniques into in situ monitoring systems enables the continuous control of cultivation parameters, such as metabolic changes induced by abiotic stress or the adjustment of environmental conditions to optimise the production of desired secondary metabolites. The use of FTIR spectroscopy in combination with multivariate analysis (e.g., PCA) enables the classification of microalgae based on their biochemical profiles, which is valuable in strain selection for improved production performance [232]. In recent years, advances in two-dimensional spectroscopy combined with multiparametric analysis have opened up new possibilities for advanced monitoring of microalgae cultivation processes. The ability to precisely assess biochemical changes at the molecular level while minimising the invasiveness of the analysis makes these techniques extremely valuable tools in microalgae biotechnology. Further research in this area could contribute to improved cultivation efficiency and higher yields of biofuels, pigments, and other industrially important compounds.
# Multimodal Natural Disaster Scene Recognition with Integrated Large Model and Mamba
## 2 Related Work
### Mamba Selective State-Space Model  
The state-space model (SSM) [30] is a statistical model used to represent dynamic systems, describing the evolution of a system's internal hidden states over time. The objective of SSMs is to infer these hidden dynamic states based on observable data. SSMs typically consist of two fundamental equations.  
State update equation  
\[h_{t}=f(h_{t-1},u_{t},\theta)+\epsilon_{t} \tag{1}\]  
where \(h_{t}\) is the hidden state, \(u_{t}\) represents the input, \(\theta\) denotes model parameters, and \(\epsilon_{t}\) is a noise term.  
Observation equation  
\[y_{t}=g(h_{t},\theta)+\eta_{t} \tag{2}\]  
where \(y_{t}\) is the observable output, \(h_{t}\) is the hidden state, and \(\eta_{t}\) is a noise term.  
The key innovation of the Mamba model [31] lies in the introduction of a selective mechanism into the state-space model, enabling the system to dynamically focus on important states. This selective mechanism can be formalized as  
\[s_{t}=\text{Select}(h_{t},h_{t-1},h_{t-2},\dots,h_{0}) \tag{3}\]  
Unlike standard SSMs that sequentially update hidden states, Mamba's selective mechanism allows the model to update information based on multiple past states, rather than relying solely on the immediate previous state. The updated state can be expressed as  
\[h_{t}=\text{Update}(h_{t-1},u_{t},\theta,\text{Select}(h_{t})) \tag{4}\]  
This selective update mechanism enables Mamba, compared to Transformer models, to efficiently process data over longer time spans while maintaining temporal continuity and dependencies [32]. It also enhances the information extraction efficiency. In this work, we incorporate the Mamba model into multimodal tasks, leveraging Mamba's selective mechanism to dynamically focus on relevant multimodal features. This improves the quality of information fusion, avoiding redundant updates and irrelevant information accumulation. Given the complexity of the scenarios addressed, Mamba's capability to handle long-term dependencies across modalities optimizes the computational resources, significantly reducing the complexity and enabling the rapid identification of natural disaster scenarios, aiding in predictive disaster relief planning.