esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**M1**} & \multicolumn{3}{c}{**M2**} \\ \hline  & **LNGHG** & **AFFORD** & **INFR** & **USAD** & & **REN** & **AFFORD** & **INFR** & **USAD** \\ \hline InGHG & \(1.0000\) & & & & REN & \(1.0000\) & & & \\  & \multicolumn{3}{c}{——} & & & & \multicolumn{1}{c}{——} & & & \\ AFORD & \(-0.4929\) & \(1.0000\) & & & AFFORD & \(-0.3093\) & \(1.0000\) & & \\  & \(0.0002\) & \multicolumn{3}{c}{——} & & & \(0.0257\) & \multicolumn{3}{c}{——} & \\ INFR & \(-0.1710\) & \(-0.1123\) & \(1.0000\) & & INFR & \(0.4030\) & \(-0.1123\) & \(1.0000\) & \\  & \(0.2255\) & \(0.4278\) & \multicolumn{3}{c}{——} & & \(0.0031\) & \(0.4278\) & \multicolumn{3}{c}{——} \\ USAD & \(0.4341\) & \(-0.4161\) & \(0.3545\) & \(1.0000\) & USAD & \(-0.0733\) & \(-0.4161\) & \(0.3545\) & \(1.0000\) \\  & \(0.0013\) & \(0.0022\) & \(0.0099\) & \multicolumn{3}{c}{——} & \(0.6055\) & \(0.0022\) & \(0.0099\) & \multicolumn{3}{c}{——} \\ \hline \hline \end{tabular} Source: Own processing, 2024.  
\end{table} 
Table 4: Correlation matrix for the two models. In Table 6, the results highlight the relationship between digital transformation and the share of renewable energy in total energy consumption. The positive and statistically significant coefficient for digital infrastructure (0.2828, \(p\)-value = 0.0000) suggests that improvements in digital infrastructure directly support the integration of renewable energy sources. This aligns with theoretical arguments that digitalization enhances energy grid efficiency, improves demand forecasting, and facilitates the adoption of smart energy solutions, thereby increasing the share of renewable energy in the energy mix. However, the negative and statistically significant coefficient for affordability (\(-\)0.2158, \(p\)-value = 0.0006) suggests that as digital services become more affordable, the immediate impact on renewable energy adoption may be limited. This could be due to the fact that lower digital costs drive broader adoption of technology, which initially increases overall energy demand before renewable sources can sufficiently meet that demand. This finding underscores the importance of aligning digital expansion with investments in clean energy infrastructure to ensure that affordability does not inadvertently lead to greater reliance on fossil fuels. Similarly, the negative coefficient for user adoption (\(-\)0.1426, \(p\)-value = 0.0012) indicates that increased digital usage does not necessarily correlate with a higher share of renewable energy. This suggests that, in many emerging economies, digital expansion is still largely dependent on conventional energy sources, reinforcing the need for targeted policies that incentivize the integration of renewables into digital ecosystems.
## 3 Wake-Up Radio Communication Protocols  
The passive wake-up radios help in mitigating the energy consumption of the sensor nodes at the hardware level as we explained in the previous section. However, this hardware needs MAC protocols to control its communication functions. These strategies are employed to coordinate the wake-up radio activities as well as its trigger functionality while simultaneously putting the main radio in a deep sleep until when it is required to be active. The MAC layer mainly coordinates how broadcast channels are shared between nodes to prevent collisions as well as reducing delays in accessing the channel, increasing energy consumption efficiency and guaranteeing fairness between the nodes. Disadvantages of RF energy harvesting include the decreased wake-up range as well as increased latency. This occurs due to the time required to harvest sufficient power from the RF signal.  
A sensor node with passive wake-up radio utilizes two channels; the channel for the main radio and that of the wake-up radio communications. The wake-up radio channel can be classified for use in the two basic wake-up communication types: the single hop and multi-hop wake-up networks. In this section, the basic design for RF energy harvesting network and communication protocols is described. Subsequently, we reviewed the existing MAC protocols and compared them.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 1 Introduction
### Study Objectives  
This study aimed to act as a pilot for further integrative impact-analysis assessment in the Mozambique Channel. As such, it focused on tropical cyclone (TC) Ana which occurred in January 2022 between the genesis on 20 January until 25 January over Madagascar and Mozambique. Note that \"tropical cyclone\" is the preferred concise term used in this study, although it was classified as such for one day and is generally considered a'severe tropical storm'.  
The main questions that the study attempted to answer included the following:  
* Are there differences in the climatic and anthropogenic impacts after TCs in coastal Mozambique and Madagascar?
* Specifically, were mangrove forests degraded after the TC? Were there regional differences in the degradation extent and pattern? For example, do areas in the vicinity of the mangrove hit by the TC fare better than non-magnrove areas?
## 3 Case Study  
### The TIDS  
The Train Intelligent Detection System (TIDS) [43] is a new IRDAS, and has been placed into operation in the Tsuen Wan Line of the Hong Kong MTR. The TIDS is equippedwith cameras, LiDARs, and a millimeter-wave RADAR to obtain real-time data from the operational environment. The environmental data is processed by the AI algorithm and the precise electronic map. In the processing, the data from cameras is used to recognize objects, and the data fusion of cameras and LiDARs is used to measure the distance of the objects. The millimeter-wave RADAR is used to measure the speed of the train. The combination of speed and sign recognition supports localization. Based on that, the TIDS can adjust the real-time speed limit to the situation of the railway line for the driver and provide an alarm when there is an obstacle or a human ahead, as is shown in Figure 3.  
The onboard ATC failure is a typical kind of signaling failure in Metro operation. If the onboard ATC fails, there is no automated protection except for some signal lights that protect stations and switches. The drivers can only drive on sight, and most of the time, the drivers do not know the distance of the trains, signal lights, or other infrastructures in front of them. Since the braking distance of a train is very long for a driver's sight, the driver is only permitted to drive at a very low speed. The situation of onboard ATC failure is a typical operational scenario where the TIDS can provide assistance to drivers and promote the driving speed. Thus, this operational scenario is chosen to be analyzed in the case study of this paper.  
In this operational scenario, based on the location and electronic map, the TIDS can possess the information of the infrastructure (e.g., switch, station), including the corresponding signal light in front of the train. The type and distance of the infrastructure ahead are then shown to the driver with a real-time calculated speed limit. The driver is also reminded in advance to watch the signal light. When there is a train, a human, or an obstacle ahead, the TIDS will recognize the type and provide the type, distance, and corresponding speed limit to the driver. If something urgent happens, for example, the driver ignores the speed limit, or the intrusion of an obstacle requires the speed limit to be lower than the current speed, the TIDS will provide an alarm.  
The TIDS needs to obtain information directly from the operational environment to provide assistance for operation. To carry out hazard analysis of the system-environment interaction, it is necessary to identify the environmental variables in advance, including the variables that the TIDS needs to detect and those that may affect the performance of the detection. The identified environmental variables are shown in Table 1.  
Figure 3: A brief description of the Train Intelligent Detection System.
## 5 Conclusions  
This study represents a proof of concept for using transfer learning from CNN, pre-trained on non-medical images, for maxillofacial fracture detection on CT images. In the literature, the use of transfer learning applied to CT scans to detect maxillofacial fractures of injured patients has not yet been explored. Our system proved to be capable of predicting maxillofacial fractures in patients with an accuracy of 80%. MFDC can become a valuable technology in assisting radiologists with prompt diagnosis and treatment that could reduce medical error risks and prevent patient harm and stress by minimizing maxillofacial trauma's diagnostic delays. An AI-based system assisting radiological investigation in non-specialized clinical wards can reduce incongruuous hospitalization's socio-economic burden for the patient, society, and health system.  
Conceptualization, V.A., P.A., R.C., G.D.O., R.P. and L.U.; methodology, M.A. and R.P.; software, M.A.; validation, M.A.; investigation, M.A.; data collection, V.A.; data curation, R.C. and L.U.; writing--original draft preparation, M.A.; writing--review and editing, V.A., P.A., R.C., G.D.O., M.M., M.P. and R.P.; visualization, L.U.; supervision, P.A.; project administration, P.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of \"Federico II\" University, Naples, Italy.  
Informed Consent Statement: Informed consent was waived because of the retrospective nature of the study and the analysis used anonymous clinical data.  
The data presented in this study are not available due to privacy restrictions.  
This work was carried out as part of the \"ICT for Health\" project, which was financially supported by the Italian Ministry of Education, University and Research (MIUR), under the initiative \"Departments of Excellence\" (Italian Budget Law no. 232/2016), through an excellence grant awarded to the Department of Information Technology and Electrical Engineering of the University of Naples Federico II, Naples, Italy.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract  
This paper explores a solution to safeguard buried pipes located near constructions in the Al-Bisri region of Assiut Governorate by utilizing the concept of the characteristic damage state. This issue has escalated due to the increasing rate of construction activity near pipelines, resulting in a multitude of legal disputes. This study investigates the behavior of buried pipes when influenced by newly constructed buildings using the finite element method. The paper employs two-dimensional models of a 12-story reinforced concrete building with a raft foundation and a series of piles supporting the buried pipe. In this study, we used the PLAXIS software, a 2D plane strain program, to conduct numerical investigations. The soil was idealized using the Mohr-Coulomb model with a 15-node triangular element, while the piles and structures were idealized with a five-node isoperimetric beam element. The point of contact between the beam and the soil was represented by the interface element. Our research examined the distance between the pipe and the footing edge and the distance between the piles and the footing edge. The finite element model results provided nodal displacements and element straining actions for analysis. The results shed light on the behavior of the sheet pile wall and sewage pipe in various situations. The largest bending moment in the sewage pipe was seen in the absence of piling, in contrast, to pile support at Rx = 0.75. The bending moment in the pipe expanded and always occurred at the same location as Rx rose. The clay layer next to the pipe's lateral deformation was significantly reduced after piling, with the greatest deformation occurring at Rx = 0.  
buried pipe; sewage pipe; medium clay; lateral deformation; finite element Article
## 2 Materials and Methods
### Methods
#### 2.2.3 Simulation Experiments  
Four simulation scenarios were designed depending on the types of V\({}_{\rm cmax}\) used in the FGM. First, we defined a reference setup that included changes in all the factors (i.e., simulation \"All\") to drive the FGM in a straightforward manner. In the \"All\" simulation, the FGM was operated with the spatially and temporally explicit V\({}_{\rm cmax}\) derived from the LCC in a 500 m grid cell and 8-day temporal interval from 2001 to 2016 (Table 2). By considering the spatial and temporal changes in V\({}_{\rm cmax}\), the European GPP for the contemporary climate was estimated based on the \"All\" simulations.  
While the other input data were kept fixed for all the runs, three other simulation scenarios (Table 2) were designed with different types of photosynthetic capacity (PC) parameterization methods, as follows: (a) In simulation \"PC1\", the FGM was operated with 500 m, 8-day V\({}_{\rm cmax}\) data derived from the LCC in 2001 by considering spatial and seasonal changes in V\({}_{\rm cmax}\), neglecting the interannual variations in V\({}_{\rm cmax}\) from 2001 to 2016. (b) In simulation \"PC2\", the FGM was parameterized with PFT-specific V\({}_{\rm cmax}\) constants retrieved from flux tower observations. This typical V\({}_{\rm cmax}\) parameterization method is widely adopted for TBMs. (c) In simulation \"PC3\", the FGM was parameterized with PFT-specific V\({}_{\rm cmax}\) constants provided by the TRY database (which includes 723 data points of V\({}_{\rm cmax}\) field measurements) [49]. [PERSON] et al. (2009) compiled data on qualitative traits, climate, and soil to subdivide terrestrial vegetation into PFTs and set V\({}_{\rm cmax}\) to different empirical values for different PFTs.  
We then compared the simulations \"PC1\", \"PC2\", and \"PC3\" with the reference to quantify the effects of the changed photosynthesis capacity on the magnitude and spatial pattern of, as well as the temporal variation in, GPP. Thus, the simulation difference between \"All\" and \"PC1\" (i.e., \"All\"\(-\)\"PC1\") represents the impacts of interannual changes in V\({}_{\rm cmax}\) on GPP. The simulation difference between \"All\" and \"PC2\" (i.e., \"All\"\(-\)\"PC2\") or \"PC3\" (i.e., \"All\"\(-\)\"PC3\") represents the uncertainties regarding GPP using two typical parameterization methods by fixing V\({}_{\rm cmax}\) as a PFT-specific constant.
## 2 Material and Methods  
### Dataset  
The dataset used in this study was originally from a 2020 Kaggle competition, i.e., the Kaggle Prostate Cancer Grade Assessment (PANDA) Challenge ([[https://www.kaggle.com/c/prostate-cancer-grade-assessment](https://www.kaggle.com/c/prostate-cancer-grade-assessment)]([https://www.kaggle.com/c/prostate-cancer-grade-assessment](https://www.kaggle.com/c/prostate-cancer-grade-assessment)) (accessed on 20 March 2021)). The PANDA dataset is regarded as the most extensive multi-center dataset that consists of around 11,000 images (roughly eight times the size of the CAMELYON17 challenge ([[https://camelyon17.grand-challenge.org/](https://camelyon17.grand-challenge.org/)]([https://camelyon17.grand-challenge.org/](https://camelyon17.grand-challenge.org/)) (accessed on 20 March 2021)) of digitized H&E-stained biopsies collected and labeled by two different centers, i.e., the Karolinska Institute and Radboud University Medical Center. Furthermore, in contrast to the previous challenges, this dataset uses full diagnostic biopsy images (whole-slide images) rather than small tissue micro-arrays, which brings new challenges to the learning task. The statistical information of the dataset is shown in Table 1.  
It is worth noting that the labels in the training set were not perfectly annotated (not the gold standard). Only part of images had segmentation masks that marked which parts of the image led to the ISUP grade, and there may have been false positives or false negatives among them. Images were labeled in both the ISUP and Gleason scores, with the ISUP grade being the prediction target. The images in the dataset were quite large, with sizes ranging from 15 to 60 MB per image. Besides, the associated labels were also not 100% correct, because even experts with years of experience do not always agree on how to  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**ISUP Grade** & **Train** & **Test** \\ \hline
0: background (non tissue) or unknown & 2314 & 578 \\
1: stroma (connective tissue, non-epithelium tissue) & 2133 & 533 \\
2: healthy (benign) epithelium & 1074 & 269 \\
3: cancerous epithelium (Gleason 3) & 996 & 248 \\
4: cancerous epithelium (Gleason 4) & 1000 & 249 \\
5: cancerous epithelium (Gleason 5) & 979 & 245 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistical information of the PANDA dataset.  
interpret a slide, which adds difficulty to the training of DL models. However, the noise introduced during the annotation process increased the potential medical value of training a robust model.
## 3 Results and Discussion
### Industrial-Scale Phase
#### 3.2.3 Production of Chemical Sludge  
The production of chemical sludge underwent evaluation during the industrial-scale phase. The significance of this data comes from its relevance to the future design of the dewatering process for the sludge. Samples of chemical sludge were collected the quantity of dry matter (DM) has been evaluated as 4.5-5%.  
On average, 25.5 L of sludge was made per m\({}^{3}\) of BWW treated. Since 1.33 kg of COD was taken out of every m\({}^{3}\) of BWW that was fed, when the BWW was treated chemically and flocculated, 1.01 kg\({}_{\text{DM}}\) of chemical sludge was released per kilogram of chemical oxygen demand removed (Figure 4).  
The possible forms of reuse of this sludge are dependent on the chemical and chemical-physical characteristics of the sludge itself. A complete characterization of the sludge and additional tests will be carried out in the future to determine (i) the effectiveness of the dewatering process to reduce the volume and therefore the disposal costs, and (ii) the possible forms of management appropriate to its characteristics. For example, the Czech Republic, as well as other EU countries, have very stringent rules on the quality of sludge that is disposed of in agriculture, and for this reason, a complete analysis of its characteristics is necessary to be able to choose the most appropriate option [45]. Furthermore, research on the possibility of recovering coagulants from the chemical sludge to minimize its production is giving promising results, providing a possible alternative to the more traditional forms of disposal [46].  
Figure 4: Amount of chemical sludge produced in the industrial-scale tests. Avg. indicates the average value of sludge production.  
A quantitative comparison of the present results with previous studies remains difficult considering that the previous literature is mainly based on tests carried out on a laboratory scale, and for this reason, sludge production has not been estimated.
## 4 Future Directions
### Build Energy-Efficient MAC and Routing Protocols to Reduce Energy Consumption in UIoT Networks
#### 4.8.2 Auto Mobility for Data Transfer in UIoT Routing Mechanism  
UIoT devices are deployed in the deep-sea environment, however, some UIoT device mobility modules are not managed accurately due to the various factors that affect the device mobility, such as internal waves, mammal activities, water pollution, water density, etc. In this case, it is necessary to adapt the auto-mobility module in UIoT devices during the data transfer. This can avoid wastage of energy by selecting exact devices through routing in UIoT networks.
## 5 Experiments and Discussion
### Image Motion Compensation Simulation
rises to around 3 \(\upmu\)m/ms, and the corresponding maximum exposure time is reduced to approximately 2 ms. Through comparative analysis, it is evident that the variation in scanning angle has a significantly smaller impact on compensation performance than the variation in pitch tilt angle. In conclusion, these simulation results validate the effectiveness of the proposed compensation algorithm and comprehensively analyze the effects of scanning angle, scanning speed, speed-to-height ratio, and pitch tilt angle on compensation performance. Among these factors, the pitch tilt angle and scanning speed are identified as the key limiting factors, as they have the most significant impact on residual deviation and allowable exposure time. By selecting an appropriate pitch tilt angle and scanning speed, the compensation performance can be notably improved. The algorithm enables image motion compensation under wide-field backward-squint imaging conditions with a flight altitude of 3000 m, a roll scanning rate of 40\({}^{\circ}\)/s, and a scanning field of view of 90\({}^{\circ}\).
# Inertial Parameter Identification in Robotics: A Survey
## 7 Enforcing Physical Consistency within Inertial Parameter Identification
### Preventing Marginal Physically  
As highlighted in [25; 26], imposing parameter physicality using a set of rigid constraints within an optimization process may eventually lead to situations where the parameter estimates lie at the very border of the physical consistency spectrahedron. This phenomenon, referred to as _marginal physicality_, typically occurs when the unconstrained estimate lies outside the physicality region, as it might for instance, be the case when the observation matrix \(\mathbf{W}\) is ill-conditioned, due to a lack of excitation or to excessive noise in \(\mathbf{q},\mathbf{\dot{q}},\mathbf{\ddot{q}}\). In [16; 94] for instance, the authors proposed adding a relaxation term within the IDIM-OLS cost function, minimizing the Euclidean distance between the current parameter estimate \(\mathbf{\chi}\) and the set of initial standard parameters \(\mathbf{\chi}_{0}\) of the robot. In this case, (62) would be reformulated as  
\[\begin{split}\mathbf{\hat{\beta}}_{OLS}=&\underset{ \mathbf{\beta},\mathbf{\underline{\chi}}}{\arg\min}\|\mathbf{W}\mathbf{\beta}-\mathbf{y}_{\top}\|_{ 2}^{2}+\underbrace{\lambda\|\mathbf{\chi}-\mathbf{\chi}_{0}\|_{2}^{2}}_{\text{ Euclidean relaxation}}\\ & s.t.\ \mathbf{\chi}=G^{-1}\Big{[}\mathbf{\beta}^{\top}\quad\mathbf{\underline{ \chi}}^{\top}\Big{]}^{\top}\\ &\mathbf{D}_{j}(\mathbf{\chi})\succ 0\ \forall j=\{1\cdots n\},\end{split} \tag{65}\]  
where \(\lambda<<1\) is a tuning quantity. In practice, this term allows \"shifting\" the current parameter estimates toward the CAD original estimates which, by essence, are physically consistent. Observing that the set of physically consistent parameters, verifying (61) could be given a Riemannian manifold structure, with a well-defined metric, refs. [25; 26] proposed to replace the Euclidean distance used in the relaxation process by a non-Euclidean distance, eventually leading to  
\[\begin{split}\mathbf{\hat{\beta}}_{OLS}=&\underset{ \mathbf{\beta},\mathbf{\underline{\chi}}}{\arg\min}\|\mathbf{W}\mathbf{\beta}-\mathbf{y}_{\top}\|_{ 2}^{2}+\underbrace{\lambda\mathbf{d}(\mathbf{\chi}-\mathbf{\chi}_{0})^{2}}_{\text{Geometric relaxation}}\\ & s.t.\ \mathbf{\chi}=G^{-1}\Big{[}\mathbf{\beta}^{\top}\quad\mathbf{\underline{ \chi}}^{\top}\Big{]}^{\top}\\ &\mathbf{D}_{j}(\mathbf{\chi})\succ 0\ \forall j=\{1\cdots n\},\end{split} \tag{66}\]  
where \(\mathbf{d}(\cdot)\) refers to a distance function on the set of physically consistent parameters.