esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Human-Machine Interfaces for Data Acquisition and User Feedback
### Data Acquisition and Processing
#### 3.1.2 Data-Acquisition Tools  
The types of sensors that acquire data must be chosen carefully, taking several factors into account (see Table 2). A varied set of sensors will increase system accuracy, which is crucial in obstacles and object-detection tasks. On the other hand, a huge amount of diverse data needs to be processed and fused, thus increasing the computational cost and achievement time for a task. When designing their systems, researchers had to find the right balance between those points, depending on the purpose at which they were aiming. One of the most common solutions is to use smartphones equipped with RGB-D or monocular cameras and position sensors (accelerometers, gyroscopes, and magnetometers), as shown in examples [27; 28; 29; 33; 42; 44]. Another solution is the introduction of infra-red or laser sensors in the acquisition system, a choice made in articles [23; 32; 45]. For outdoor navigation, GPS is almost always chosen as the navigational support, being easily accessible, cheap, and having very wide coverage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Acquisition Device** & **\% of Papers** & **Ref. to Papers** \\ \hline smart glasses & 30 & [19; 24; 25; 26; 27; 28; 29; 30; 31] \\ smart cane & 6.7 & [32; 33] \\ smartphone & 36.7 & [18; 34; 35; 36; 37; 38; 39; 40; 41; 42] \\ other wearable device & 26.7 & [21; 22; 23; 43; 44; 45; 46; 47] \\ \hline \hline \end{tabular}
\end{table}
Table 1: The type of acquisition devices used in each paper.
## 3 Data Fusion and Multimodal Integration
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Technique** & **Key Features** & **Advantages** & **Drawbacks** \\ \hline \multirow{4}{*}{Deep learning} & Automatically extracts complex features from raw data using models like CNNs, RNNs, and YOLO. & Performs well with complex data (images, time series) and needs little feature engineering. & Needs large labeled datasets, can be computationally expensive, and lacks interpretability. \\ \hline \multirow{4}{*}{Transfer learning} & Transfers knowledge from a source task to a related target task, requiring minimal retraining. & Works well with limited labeled data and reduces training time. & Performance drops if the source and target tasks are too different. \\ \hline \multirow{4}{*}{Hybrid models} & Combines different machine learning models (e.g., SVM and Neural Networks). & Improves robustness and combines the strengths of different models. & Designing and tuning can be complex, and training time may increase. \\ \hline \multirow{4}{*}{Ensemble learning} & Combines predictions from multiple models (e.g., bagging, boosting, stacking). & Helps reduce overfitting and improves accuracy. & Can be computationally expensive and less transparent. \\ \hline \multirow{4}{*}{Fusion strategies} & Integrates data from multiple sources at different levels (input, features, or decisions). & Leads to richer representations and more accurate diagnostics. & Requires careful data synchronization and choosing the right fusion method. \\ \cline{1-1} \cline{2-2}  & & & \\ \cline{1-1} \cline{2-2}  & & & \\ \cline{1-1} \cline{2-2}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary comparing key techniques used in multimodal fault diagnosis. The table provides an overview of each method’s main features, advantages, and drawbacks, making it easier to choose the right approach for practical applications. Figure 4: The intermediate fusion approach combines features from different modalities at a middle stage within the model. This method helps the model learn more complex interactions between modalities while still keeping each modality somewhat independent.
# Semantic Lidar-Inertial SLAM for Dynamic Scenes
## 3 Our Method
### State Prediction and Pre-Integration  
There are several IMU frames between two consecutive lidar frames. Within a short time, the state of body can be updated by:  
\[\mathbf{p}_{j} =\mathbf{p}_{i}+\sum\limits_{k=i}^{j-1}\Bigl{[}\mathbf{v}_{k}\Delta t+ \tfrac{1}{2}\mathbf{g}^{W}\Delta t^{2}+\tfrac{1}{2}\mathbf{\mathsf{R}}_{k}\bigl{(} \hat{\mathbf{a}}_{k}-\mathbf{b}_{a_{k}}\bigr{)}\Delta t^{2}\Bigr{]} \tag{1}\] \[\mathbf{v}_{j} =\mathbf{v}_{i}+\mathbf{g}^{W}\Delta t_{ij}{}^{2}+\sum\limits_{k=i}^{j-1} \mathbf{\mathsf{R}}_{k}\bigl{(}\hat{\mathbf{a}}_{k}-\mathbf{b}_{a_{k}}\bigr{)}\Delta t\] \[q_{j} =q_{i}\otimes\prod\limits_{k=i}^{j-1}\delta q_{k}=q_{i}\otimes \prod\limits_{k=i}^{j-1}\left[\begin{array}{c}\frac{1}{2}\Delta t\bigl{(} \hat{\mathbf{\omega}}_{k}-\mathbf{b}_{g_{k}}\bigr{)}\\ 1\end{array}\right]\]  
where \(\mathbf{p}\), \(\mathbf{v}\), and \(q\) are the position, velocity, and orientation of the IMU body. \(\mathbf{b}_{a}\) and \(\mathbf{b}_{g}\) denote acceleration bias and gyroscope bias, respectively. \(\hat{\mathbf{a}}_{k}\) and \(\hat{\mathbf{\omega}}_{k}\) are the raw measurements of IMU at timestamp \(k\).  
The raw lidar data \(S_{j}\) are skewed because of the lidar sensor's motion during scanning. So, we de-skew each lidar frame \(S_{j}\) by linear interpolation of \(\overline{\mathbf{\mathsf{T}}_{Lj^{\prime}}}\) and obtain de-skewed point cloud \(\overline{S}_{j}\).  
At the same time, the raw IMU outputs \(\hat{\mathbf{a}}_{k}\) and \(\hat{\mathbf{\omega}}_{k}\) can be converted to pre-integration measurements (\(\Delta\mathbf{p}_{ij}\), \(\Delta\mathbf{v}_{ij}\), and \(\Delta q_{ij}\)) by:  
\[\Delta\mathbf{p}_{ij} =\mathbf{\mathsf{R}}_{i}^{T}\Bigl{(}\mathbf{p}_{j}-\mathbf{p}_{i}-\mathbf{v}_{i} \Delta t_{ij}-\tfrac{1}{2}\mathbf{g}^{W}\Delta t_{ij}^{2}\Bigr{)}=\sum\limits_{k=i }^{j-1}\Bigl{[}\mathbf{v}_{k}\Delta t+\tfrac{1}{2}\mathbf{\mathsf{R}}(\Delta q_{ik}) \bigl{(}\hat{\mathbf{a}}_{k}-\mathbf{b}_{a_{k}}\bigr{)}\Delta t^{2}\Bigr{]} \tag{2}\] \[\Delta\mathbf{v}_{ij} =\mathbf{\mathsf{R}}_{i}^{T}\Bigl{(}\mathbf{v}_{j}-\mathbf{v}_{i}-\mathbf{g}^{W} \Delta t_{ij}\Bigr{)}=\sum\limits_{k=i}^{j-1}\bigl{[}\mathbf{\mathsf{R}}(\Delta q_{ jk})\bigl{(}\hat{\mathbf{a}}_{k}-\mathbf{b}_{a_{k}}\bigr{)}\Delta t\bigr{]}\] \[\Delta q_{ij} =q_{i}^{-1}\otimes q_{j}=\prod\limits_{k=i}^{j-1}\delta q_{k}= \prod\limits_{k=i}^{j-1}\left[\begin{array}{c}\frac{1}{2}\Delta t\bigl{(} \hat{\mathbf{\omega}}_{k}-\mathbf{b}_{g_{k}}\bigr{)}\\ 1\end{array}\right]\]  
The pre-integration measurements are used for joint optimization.  
Figure 1: Overall framework of the proposed semantic lidar-inertial SLAM.
## 3 Results  
### Lake Chilwa Paleo-Environment Control  
#### 3.1.1 Relict Paleo-Shorelines  
The former extent of Lake Chilwa may be estimated from relict paleo-shorelines associated with lake high-stands, where wave and wind energy were sufficient to build landforms persisting into the modern landscape [5; 6; 7; 48]. Those mapped within the literature are summarised in Figure 1. Both erosional bench (e.g., 652 m asl (above sea level)) and depositional (e.g., 631 m asl) relict shorelines are observed. Documented shorelines are somewhat discontinuous except for the pronounced 631 m asl shoreline arc to the north at + 9 m relative elevation compared to the modern lake level set at 622 m asl corresponding to the 1950-1976 mean lake altitude [5] (noting some works reference a lake level of 627 m asl [31]). To the west and towards the south, further relict shorelines occur at 627 m asl (at + 5 m), 637 m asl (at + 15 m) and 652 m asl (at + 30 m) [5; 7] (noting'm asl' is henceforth abbreviated to'm'). Relict shoreline interpolation together with borehole log data underpins system reconstruction as a series of five raised lacustrine terrace deposits [5; 31], terraces 1 to 5 shown in Figure 2(b) for the southern Basin. Lithologies are mostly characteristic of lacustrine deposits with a predominance of fine-grained clay, silt and mud deposits as expected for low-energy deposition environments with occasional sand and pebble layers.  
Our contouring of the modern-day land surface corresponding to the relict shoreline elevations allows estimation of the former areal extent of Lake Chilwa (Figure 2(a)) (recognising possible tectonic land movements since). Older paleo-lake size lengthens along the north-south axis with 637 and 652 m contours extending significantly to the south into the Phlaombe District study area, and far to the north beyond current Lake Chiuta. Lower-elevation, 631 and 627 m, contours are bounded to the north by the prominent beach ridge shoreline, the Chilwa-Chiuta sandbar. The 627 m contour delimits the clay-rich deposits associated with basin floor [7]. The reference 623 m contour added illustrates the influence of a 1 m lake level rise from the current mean, causing a significant 10 km lake expansion northwards over extensive marsh wetland, very shallow gradient land (Figure 1).  
The mapped terrace deposits are seen to follow the lake stage elevation contouring traced on current topography in the Phalombe District (Figure 2(b)). For instance, (Lake) terrace 1 approximately follows the 627 m contour, terrace 3 the 637 m contour, and terrace 5 the 652 m contour. The high-level lacustrine deposits outline does not follow a specific elevation outline attributed to subsequent formation tilting and varies in the study area from _c._ 660 m in the east up to _c._ 720 m in the west. The high-level deposit boundary in the east remains close to the terrace 5 boundary in this area of steep topographic change, in contrast to the west where the high-level deposits are extensive and form the Chilwa-Phalombe Plain.
# Implementation Aspects in Regularized Structural Equation Models
## 2 Estimation of Regularized Structural Equation Models
### Standard Error Estimation  
We now describe the computation of the variance matrix of parameter estimates \(\widehat{\Theta}\) from penalized ML estimation for a fixed regularized parameter \(\lambda\) or the direct BIC minimization approach. Both estimation approaches minimize a differentiable (or differentiable approximation) function \(F(\theta,\xi)\) with respect to \(\theta\) as a function of sufficient statistics \(\xi\) (see also [6]). The vector of sufficient statistics \(\widehat{\xi}\) is approximately normally distributed (see [3]); that is,  
\[\widehat{\xi}-\xi_{0}\sim\mathsf{MVN}(\mathbf{0},\boldsymbol{V}_{\xi}) \tag{19}\]  
for a true population parameter \(\xi_{0}\) of sufficient statistics. We denote by \(F_{\Theta}=(\partial F)/(\partial\theta)\) the vector of partial derivatives with respect to \(\theta\). The parameter estimate \(\widehat{\Theta}\) is given as the root of the non-linear equation  
\[F_{\Theta}(\theta,\widehat{\xi})=\mathbf{0}. \tag{20}\]  
General M-estimation theory (i.e., the delta method [18]) can be applied to derive the variance matrix of \(\widehat{\Theta}\). Assume that there exists a (pseudo-)true parameter \(\theta_{0}\) such that  
\[F_{\Theta}(\theta_{0},\xi_{0})=\mathbf{0}. \tag{21}\]  
We now derive the covariance matrix of \(\widehat{\Theta}\) by utilizing a Taylor expansion of \(F_{\Theta}\). We denote by \(F_{\Theta\Theta}\) and \(F_{\Theta\xi}\) the matrices of second-order partial derivatives of \(F_{\Theta}\) with respect to \(\theta\) and \(\xi\), respectively. We obtain  
\[F_{\Theta}(\widehat{\Theta},\widehat{\xi})=F_{\Theta}(\theta_{0},\xi_{0})+F_{ \Theta\Theta}(\theta_{0},\xi_{0})(\widehat{\theta}-\theta_{0})+F_{\Theta\xi} (\theta_{0},\xi_{0})(\widehat{\xi}-\xi_{0})=\mathbf{0}. \tag{22}\]  
As the parameter estimate \(\widehat{\Theta}\) is a non-linear function of \(\widehat{\xi}\), the Taylor expansion (22) provides the approximation  
\[\widehat{\Theta}-\theta_{0}=-F_{\Theta\Theta}(\theta_{0},\xi_{0})^{-1}F_{ \Theta\xi}(\theta_{0},\xi_{0})(\widehat{\xi}-\xi_{0}). \tag{23}\]  
By defining \(A=-F_{\Theta\Theta}(\theta_{0},\xi_{0})^{-1}F_{\Theta\xi}(\theta_{0},\xi_{0})\), we get by using the multivariate delta formula [18]:  
\[\mathsf{Var}(\widehat{\Theta})=\boldsymbol{AV}_{\xi}A^{\top}. \tag{24}\]  
An estimate of \(A\) is obtained as \(\widehat{A}=-F_{\Theta\Theta}(\widehat{\Theta},\widehat{\xi})^{-1}F_{\Theta \xi}(\widehat{\Theta},\widehat{\xi})\). This approach is ordinarily used for differentiable discrepancy functions in the SEM literature [3; 7; 42]. Standard errors for entries in \(\widehat{\Theta}\) can be obtained by taking the square root of diagonal elements of \(\mathsf{Var}(\widehat{\Theta})\) computed from (24).
###### Abstract
(2018) integrated fuel economy and durability into a dual-loop framework to identify Pareto-optimal solutions, recommending a battery capacity of 150 Ah and an FCS maximum net output power of 40 kW. [PERSON] et al. (2018) incorporated durability costs into energy management and component sizing optimization using convex programming, determining that the optimal battery rated power and energy capacity for a plug-in fuel cell city logistics vehicle were approximately 54 kW and 29 kWh, independent of driving cycle variations. Previous analyses suggest that the optimization of component sizing is strongly coupled with the design of the EMS. But these EMSs may only focus on fuel economy or fail to account for the fact that fuel economy and system durability vary with different sizing configurations. In terms of component sizing, researchers often caution against oversized batteries due to cost implications. However, most existing analyses remain qualitative and fail to quantify the economic impact of an increased battery capacity. Limited research has quantitatively addressed the balance between fuel efficiency and system durability. It is essential to design an optimization framework that reintegrates the interdependent parameters in component sizing and energy management, where optimization variables include both the power source size and weighting factor. Additionally, a quantitative assessment of potential cost variations over time must be considered to ensure accurate results. This paper proposes an optimization framework for the optimal component sizing and energy management of an FCHV. The structure of the weighted multi-objective optimization is shown in Figure 1. The sizing problem determines the fuel cell size based on vehicle system design goals, effectively reducing the component sizing issue to a single-parameter representation of the battery size's impact on the power source capacity. To transform the multi-objective optimization into a single-objective form, a cost function is developed to unify economic and durability metrics by weighting factors, and the weighted total cost is optimized to derive the optimal weighting factors and component sizing. The remainder of this paper is organized as follows: Section 2 introduces mathematical models for fuel economy, system durability, and the powertrain of the FCHV. Section 3 defines the component sizing problem and details the energy management strategy, highlighting the use of DP to optimize the weighting factor and evaluate its impact under various powertrain configurations. Section 4 presents the simulation results, including a comparison of optimization outcomes with different sizing parameters and a sensitivity analysis of power source parameters and energy costs across various power source prices. Finally, Section 5 provides a summary of the key findings.
## 2 A Brief Introduction of MMC  
Figure 1(a) shows a general structure of a three-phase MMC. Each bridge arm of the MMC consists of an arm reactor \(L_{\mathrm{arm}}\) and \(N\) series-connected sub-modules (SMs). \(R_{\mathrm{arm}}\) is the equivalent resistance of the bridge arm. \(U_{\mathrm{dc}}\) is the DC voltage of MMC. The multilevel output voltage is generated by controlling the number of inserted SMs in the bridge arm.  
Different topologies of SMs for MMCs have been proposed and investigated, including the half-bridge (HB), the full-bridge (FB), the clamp-double (CD). and the three-level/five-level cross-connected (3 LCC/5 LCC) SMs [16; 17; 18] as shown in Figure 1(b)-1(g). These SM topologies are available for the MMC structure that is shown in Figure 1(a). The features of each topology are summarized, as follows:  
* HB circuit: the HB-SM is inserted or bypassed by turning on T\({}_{1}\) or T\({}_{2}\), respectively. When the SM is switched-on or in inserted state, its output voltage equals to its capacitor voltage \(U_{\mathrm{c}}\). Otherwise, when the SM is switched-off or in bypassed state, the output voltage is 0 [18].
* FB circuit: the operation state of FB-SM is determined by the switching states of the four switches T\({}_{1}\) to T\({}_{4}\). When in switched-on/inserted state, the output voltage equals to the capacitor voltage \(u_{\mathrm{c}}\). Otherwise, it is 0. The costs of an MMC based on the FB-SMs are higher than that of an MMC based on the HB-SMs due to the increased number of switches in each SM [16].
* CD circuit: a CD-SM consists of two HB-SMs, two additional diodes, and one extra integrated gate bipolar transistor (IGBT) with its anti-parallel diode. During normal operation, the switch S\({}_{5}\) is always in conduction state and the dynamics of CD-SM are equivalent to two series connected HB-SMs [16].
* 3 LCC circuit: there are two different types of 3 LCC-SM, three level neutral-point-clamped (NPC), and three-level flying capacitor (FC). For a three-level FC-SM, its power losses are similar with a  
Figure 2: Structure of a three-phase MMC with various sub-modules (SMs).  
HB-SM. The power losses of NPC-SM are higher than the HB-SM and lower than the FB-SM [3]. The 3 LCC-SM based MMC is not very attractive from the perspective of manufacturing and control [17].
* 5 LCC circuit: there are two HB-SMs connected back-to-back by two extra IGBTs with their anti-parallel diodes in a 5 LCC-SM. Its power losses are the same as the CD-SM [17].  
Table 2 provides a comparison of different SM topologies, in terms of output voltage levels, DC short circuit fault ride through capability, and power losses. Section 4 will discuss the DC short circuit fault ride through capability of each SM topology.  
There are different dynamic characteristics and operational demands for various SM topologies. It is necessary to establish the MMC model that covers different SM topologies for further dynamic investigation and fault analysis.
# An IoT-Based Solution for Intelligent Farming +
Additionally, SheepIT exhibits a few distinctive requirements that, when combined with the aforementioned ones, preclude the use of existing IoT solutions. The complete and detailed set of requirements can be found in [22], but they can be briefly summarized as follows:  
* The presence of irregular slopes with several obstacles;
* Unforeseeable node mobility (sheep);
* Variable node densities due to the flock movement;* Need for real-time localization, preferably resorting to Received Signal Strength Indicator (RSSI)-based localization;
* Need to ensure the coexistence of an effective control posture mechanism;
* Frequent system reconfigurations by non-technical personnel. In order to fulfill these needs, an architecture was designed composed by a Wireless Sensor Network (WSN) [22] and a Computational Platform (CP) [23] accessible via the Internet. The WSN comprises a set of instrumented mobile nodes carried by sheep (collars); a set of statically placed nodes for defining the grazing area and collect sheep's data (beacons); and a gateway that, besides connecting the WSN to the CP, delivers local services. Due to the application constraints, all WSN components were customized, avoiding the use of IP-based solutions up to the gateway level. The gateway was also developed specifically for this application, allowing a smooth integration between the local WSN and the computational platform (IP-based), as well as the integration of a local management service, to enable users to easily interact with the system for management and supervision procedures. In order to support other intelligent farming solutions with similar requirements of SheepIT, a new M2M protocol stack was proposed by [PERSON] et al. [24]. This paper extends that work significantly, by improving the state of the art review and presenting a more complete and thorough discussion of the proposed stack. Furthermore, a special focus is given to gateway, detailing its design, implementation and validation. The remainder of this paper is organized as follows. Section 2 surveys the related work and analyzes the suitability of the existing protocol stacks in the context of SheepIT. Then, in Section 3, we present the network stack architecture and discuss the differences with respect to the state-of-the-art approaches. A special focus is given to the messages exchanged on the local network domain and to the gateway design. The feasibility of the approach is evaluated in Section 4, particularly in what concerns the gateway scalability. Finally, Section 5 presents the conclusions.
# Low-Profile Dual-Band Reflector Antenna for High-Frequency Applications
## 2 Antenna Configuration and Design
### Main Reflector, SRA, and Higher-Band SR
Because the \(D_{S}\) is much smaller than the \(D\), the shadow influence of the higher-band subreflector can be ignored and the lower-band SR can be designed without considering the influence of the higher-band subreflector and support dielectric.
## 3 Results and Discussion  
### Physicochemical Parameters of Spine Grapes  
To examine the differences between spine grape clones, physicochemical parameters--including soluble solids content (\"Brix), pH, titratable acid, and total anthocyanin content--were analyzed. As Figure S1 (Supplementary Materials) shows, the soluble solids content of spine grapes ranged from 12.2 g/L to 15.4 g/L, which was lower than _Vitis vinifera_ cultivars such as Cabernet Sauvignon (about 21 g/L-27 g/L in China), Merlot (about 21 g/L-25 g/L in China), and Chardonnay (about 20 g/L-25 g/L in China). This was consistent with the report by [PERSON] et al. [7], which reported that the soluble solids content of four spine grape varieties ranged from 14.7 g/L to 16.0 g/L. The titratable acid content ranged from 2.26 g/L to 3.46 g/L. G4 grapes had the highest titratable acid content, while BY grapes had the lowest. The pH values were not always consistent with the titratable acid content. For example, the pH value was 3.86 for XZZ grapes with titratable acid at 3.11 g/L, while the pH value was 3.80 in BY grapes with titratable acid at 2.26 g/L. [PERSON] et al. [24] also found that pH values were not consistent with the titratable acid content in _Vitis vinifera_ cultivars such as Cabernet Sauvignon. The pH value of grape must does not change significantly during the fermentation process, so the pH of the grape berries determinesthe pH of the wine. In general, the pH value of _V. vinifera_ wine ranges from 2.8 to 3.8, and appropriate pH value is important for the stability of the color of the wine [14]. Anthocyanins are crucial quality factors for grapes and wine [17]. In the present research, the higher total anthocyanin contents were found in spine grapes (20 g/kg-33 g/kg) than Cabernet Sauvignon grapes (12 g/kg-16 g/kg) [17; 25]. XZZ grapes had the highest total anthocyanin content, while anthocyanins were not detected in BY grapes. The higher total anthocyanin content in spine grapes might give richer anthocyanins in its wines, which, in turn, give a more attractive color for consumers than _V. vinifera_ wines [26].
## 5 Proof-of-Concept Results  
We found that BPv6 and BPv7's complex encoding scheme made building a protocol translator which encodes and decodes these protocols a difficult task. Although we were not able to produce a full set of BPv6 and BPv7 capabilities, we were able to demonstrate a bundle translator that would be useful for an initial SDDTN node. In Section 5.1 we discuss the high-level algorithm used for our translator. In Section 5.2 we discuss the constraints of our proof of concept and their causes. In Section 5.3 we showcase the capabilities and test the behavior of our proof of concept in different scenarios.