esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The advent of 6G networks and beyond calls for innovative paradigms to address the stringent demands of emerging applications, such as extended reality and autonomous vehicles, as well as technological frameworks like digital twin networks. Traditional cloud computing and edge computing architectures fall short in providing their required flexibility, scalability, and ultra-low latency. Cloud computing centralizes resources in distant data centers, leading to high latency and increased network congestion, while edge computing, though closer to data sources, lacks the agility to dynamically adapt to fluctuating workloads, user mobility, and real-time requirements. In-network computing (INC) offers a transformative solution by integrating computational capabilities directly into the network fabric, enabling dynamic and distributed task execution. This paper explores INC through the lens of information-centric networking (ICN), a revolutionary communication paradigm implementing routing-by-name and in-network caching, and thus emerging as a natural enabler for INC. We review state-of-the-art advancements involving INC and ICN, addressing critical topics such as service naming, executor selection strategies, compute reuse, and security. Furthermore, we discuss key challenges and propose research directions for deploying INC via ICN, thereby outlining a cohesive roadmap for future investigation.  
in-network computing; information-centric networking; named data networking; compute reuse; edge computing +
Footnote †: journal: Computer Science
## 1 Introduction
### Previous Work
In particular, it was shown in [26] that the required user scaling law to achieve the optimal DoF is given by \(\text{SNR}^{2M-1}\). However, the result in [26] is pessimistic in practice in the sense that too many MSs in a cell are necessary to guarantee the DoF optimality even if the optimal DoF under a certain user scaling law was originally characterized in the full-duplex system with partial CSIT [26]. Such a high user scaling law in [26] stems from the scheduling role imbalance between downlink MSs and uplink MSs since a set of downlink MSs is selected with strong responsibility to eliminate both the downlink interference and MS-to-MS interference, whereas a set of uplink MSs is arbitrarily chosen. It remains an open challenge how to significantly reduce the user scaling law without extra CSIT in the full-duplex system using opportunistic transmission. Moreover, there have been extensive studies on scheduling and resource optimization in a variety of network scenarios including wireless networks with energy harvesting [27; 28; 29] and cognitive networks [30].
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 4 Methodology
### Altimetry Data Processing
#### 4.1.1 Sea Level Anomaly (SLA) Estimation  
From the altimeter range, once all the propagation and geophysical corrections mentioned below are removed, we can deduce the sea surface height (SSH, i.e., sea level referred to a reference ellipsoid). If we further remove a mean sea surface in order to overcome the problem of geoid estimation, we obtain sea level anomaly (SLA) data. In this study we use SLA data, computed as follows:  
\[SLA=H-R-\Delta R_{iuno}-\Delta R_{dry}-\Delta R_{wet}-\Delta R_{ssb}-\Delta R _{tilde}-\Delta R_{DAC}-MSS \tag{1}\]  
In Equation (1), \(H\) is the orbital height, \(R\) is the Ku-band altimeter range, \(\Delta R_{iuno}\) is the ionospheric correction, \(\Delta R_{dry}\) and \(\Delta R_{wet}\) are the dry and wet tropospheric corrections, respectively, \(\Delta R_{ssb}\) is the sea state bias, \(\Delta R_{tilde}\) is the tide correction (composed of the ocean tide, pole tide and solid Earth tide), \(\Delta R_{DAC}\) is the dynamic atmospheric correction, and \(MSS\) is mean sea surface (in practice we used the MSS_CNES_CLS-2011 model).  
In some cases, \(R\) is not directly available (for instance, there is no specialized range parameter in the ALES product), so it can be computed as follows:  
\[R=T+E\times(c/2)+D+M+0.180 \tag{2}\]  
where \(T\) is the onboard tracking range in the Ku band, \(E\) is the retracked offset (with time dimension), the factor \"\(c/2\) (\(c\) is light velocity)\" is the scaling factor from traveling time to range, \(D\) is the Doppler correction in Ku band, \(M\) is the instrument bias due to PTR and LPF features ([23; 24] and 0.180 is a bias (in meters) due to wrong altimeter antenna reference point [25].  
In this study, we computed SLA time series using the altimeter ranges issued from six retrackers: ALES, MLE3, MLE4, RED3, ICE1, and ICE3. For the MLE3, MLE4, and ALES retrackers, we first used Equation (2) to compute \(R\), and then applied Equation (1) to compute the SLA (because \(R\) is often flagged in the GDR and ALES products). To validate our calculation method, we compared the MLE4 SLA obtained with the equivalent official \"_ssha_\" parameter provided in the GDRs, and found good consistency. For RED3, ICE1, and ICE3, we applied Equation (1) to directly compute the SLA, because there were no valid E values in the PISTACH product.
# Simplified Calculation Model for Contact Resistance Based on Fractal Rough Surfaces Method
## 3 Results and Analysis
### Surface Morphological Analysis
To verify the accuracy of the proposed contact model, the contact load and contact area data calculated from the model were compared with the G-W model [9], M-B model [2], and [PERSON] experimental data [27], as shown in Figure 11. The surface morphology and material parameters were set to \(D=1.53\), \(G=4\times 10^{-13}\) m, \(L=10^{-4}\) m\({}^{2}\), and \(E^{*}=50\) GPa. The predicted data of the model proposed in this paper and the experimental data were found to be in general agreement. Under light loads of \(F^{*}\), which were no greater than \(2\times 10^{-3}\), the M-B model agreed well with the experimental data. However, as the load increased, the M-B model deviated from the experimental data to some extent. The reason for this deviation is that in real contact, the protrusions on two rough surfaces may disappear or merge into larger contact spots, which indicates strong coupling between contact areas. The contact model with asperity as the smallest unit is difficult to effectively  
Figure 10: Variation in contact area and mean separation with \(l_{max}\). Figure 9: The effect of different fractal parameters on the mean separation. (**a**) \(G=10^{-11}\) m; (**b**) \(D=1.5\). characterize the contact morphology state under heavy load. The G-W model mainly focuses on elastic contact, ignoring local plastic deformation and underestimating the real contact area. Similar to the M-B model, the model proposed in this paper ignores the interaction between contacting asperities, which is reflected in the calculation results with high accuracy for medium and light loads. However, its simplified contact analysis helps to extend the model to different applications.
## 6 Results and Discussion
### Training Data Size vs. Precision  
Figure 4 illustrates the effect of a varying number of training samples on the precision performance of the FL setup of the proposed FT-CID model by comparing the CNN and MLP algorithms in the centralised setup with the average score. The performance of the intrusion detection model improves with increased size of training samples. Precision performance is obtained for 20 communication rounds in the AMI-associated federated environment. The proposed FT-CID achieves a precision value of 85.18%, and the precision values of the CNN and MLP models are 78.92% and71.09%, respectively. As the size of the training data increases, the precision of both the proposed intrusion detection model and the baseline algorithm-based intrusion detection model increases. Compared with the CNN in the centralised setup, the CNN model in the proposed federated setup shows a 14.33 percentage-point improvement in precision, even with only 40% of the local learning model, with the assistance of the transfer learning model associated with parameter transfer and relational knowledge transfer. By inherently analysing the multiple sets of training data, the proposed intrusion detection model learns the attack patterns from the heterogeneous IIoT network by updating the global model and optimising the local learning model with the assistance of the TL model associated with parameter transfer and relational knowledge transfer. By analysing the multiple attack patterns with the global and local knowledge, the FT-CID model shows greater accuracy in distinguishing the intrusion samples from the normal samples and has higher precision than the existing CNN and MLP models. Moreover, the FT-CID model customises the learning model based on the gradient of the local learning model and the knowledge from the global model, which tends to improve the performance of the comparative models. As a result, the customised modelling of the IIoT network tends to yield better TPs in the IDS, even when there is a lack of available intrusion patterns in the training data of the local IIoT network. Table 1 presents the numerical points of Figure 4.
###### Abstract  
Replacing estimates of \(C_{20}\) from the Gravity Recovery and Climate Experiment (GRACE) monthly gravity field solutions by those from satellite laser ranging (SLR) data and including degree one terms has become a standard procedure for proper science applications in the satellite gravimetry community. Here, we assess the impact of degree one terms, SLR-based \(C_{20}\) and \(C_{30}\) estimates on GRACE-derived polar ice sheet mass variations. We report that degree one terms recommended for GRACE Release 06 (RL06) data have an impact of 2.5 times more than those for GRACE RL05 data on the mass trend estimates over the Greenland and the Antarctic ice sheets. The latest recommended \(C_{20}\) solutions in GRACE Technical Note 14 (TN14) affect the mass trend estimates of ice sheets in absolute value by more than 50%, as compared to those in TN11 and TN07. The SLR-based \(C_{30}\) replacement has some impact on the Antarctic ice sheet mass variations, mainly depending on the length of the study period. This study emphasizes that reliable solutions of low degree spherical harmonics are crucial for accurately deriving ice sheet mass balance from satellite gravimetry.  
GRACE; polar ice sheets; mass change; low degree spherical harmonic coefficients +
Footnote †: journal: _J. Accepted: 17 December 2020; Published: 20 December 2020_
# Evaluation of the Radiological and Chemical Risk for Public Health from Flour Sample Investigation
## 3 Results and Discussion
### The Annual Effective Dose for Food Ingestion  
Table 3 reports the estimation of the annual effective dose due to the ingestion of investigated samples, \(D_{ing}\), as calculated by Equation (2), for the age category higher than 17 years.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Activity Concentration (Bq kg\({}^{-1}\))**} \\ \hline
**Sample ID** & \({}^{40}\)**K** & \({}^{137}\)**Cs** \\ \hline S1 & \(285\pm 46\) & \(<\)4.1 \\ S2 & \(144\pm 26\) & \(<\)3.9 \\ S3 & \(155\pm 30\) & \(<\)5.1 \\ S4 & \(345\pm 51\) & \(<\)9.8 \\ S5 & \(248\pm 40\) & \(<\)2.9 \\ S6 & \(443\pm 68\) & \(<\)2.6 \\ S7 & \(70\pm 15\) & \(<\)2.2 \\ S8 & \(152\pm 29\) & \(<\)6.7 \\ S9 & \(99\pm 19\) & \(<\)5.1 \\ S10 & \(300\pm 41\) & \(<\)6.4 \\ S11 & \(370\pm 56\) & \(<\)7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The activity concentration (mean value \(\pm\) standard deviation) of \({}^{40}\)K and \({}^{137}\)Cs in the analyzed flour samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample ID** & \(D_{ing}\) **(mSv y\({}^{-1}\))** & \multicolumn{2}{c}{**Percentage (\%) with Respect to Worldwide Average Natural dose to Humans (2.4 mSv y\({}^{-1}\))**} \\ \hline S1 & 0.15 & 6.2 \\ S2 & 0.07 & 2.9 \\ S3 & 0.08 & 3.3 \\ S4 & 0.18 & 7.5 \\ S5 & 0.13 & 5.4 \\ S6 & 0.23 & 9.6 \\ S7 & 0.04 & 1.6 \\ S8 & 0.08 & 3.3 \\ S9 & 0.05 & 2.1 \\ S10 & 0.15 & 6.2 \\ S11 & 0.19 & 7.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Annual effective dose due to the ingestion of investigated samples, \(D_{ing}\), for the age category higher than 17 years, together with its percentage with respect to worldwide average natural dose to humans (2.4 mSv y\({}^{-1}\)).  
Reported values take into account the average yearly consumption of flour in Italy, assumed to be of about 85 kg, taking into consideration its use to produce bread, pizza, and baked goods [49; 50], making the hypothesis that this need was satisfied from a single type of flour. For each type of investigated flour, Table 3 also shows the percentage values of the aforementioned doses with respect to the total (external + internal) natural radioactivity value for humans, expressed in terms of effective dose, equal to 2.4 mSv y\({}^{-1}\)[51]. Noteworthily, the calculated annual effective doses for all investigated flour types were under the threshold level of 1 mSv y\({}^{-1}\) imposed by the legislation [51] (see Figure 5).  
Therefore, no risk to humans due to ionizing radiations can be associated with the flour ingestion.
# Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization
## 5 Results
### Validation  
In this subsection, six different validation cases are analyzed based on the subcase \(\mathrm{R}=3\) and \(\mathrm{p}=6\), whose data and shape can be seen in Appendices B.1 and B.2. All the figures are colored rainbow-like in the machining direction \"i\", starting in blue with the elements (1, y, z) and finishing in red with the elements (80, y, z). This is done to give a clearer view of the results and the difference between the resultant shapes of the validation cases.  
* The case (a) of Figures A1-A4 of Appendices C.1-C.4 represents the standard case without any additional filter added.
* The case (b) of Figures A1-A4 of Appendices C.1-C.4 is the resultant shape of the standard case in combination with the Heaviside step filter, with \(\mathrm{\eta}=0.5\).
* In Figures A1c, A2c, A3c and A4c of Appendices C.1-C.4, the ellipsoid-shaped density filter is applied to the standard case. Fixing the column of \(\mathrm{p}=6\) in Appendices B.2-B.4, the radius of each Cartesian direction is chosen in order to obtain the features in that  
Figure 13: OC routine with the machining filter highlighted in pink and the filtering steps colored in green.  
axis of the case in the sensitivity analysis. In this case \(\mathrm{R}_{\mathrm{x}}^{\mathrm{min}}=6,\mathrm{R}_{\mathrm{y}}^{\mathrm{min}}=5\), and \(\mathrm{R}_{\mathrm{z}}^{\mathrm{min}}=3.5\) are used as filter radii for obtaining a more manufacturable result with the features of \(\mathrm{R}=6\) and \(\mathrm{p}=6\) (Appendix B.4) in the x direction, \(\mathrm{R}=5\) and \(\mathrm{p}=6\) (Appendix B.4) in the y direction, and \(\mathrm{R}=3.5\) and \(\mathrm{p}=6\) (Appendix B.3) in the z direction.
* In the (d) case, the machining filter is applied to the standard case.
* The case (e) showcases the combination of case (b) and case (c); this is the standard case, with a Heaviside step filter and ellipsoid-shaped density filter combined.
* Finally, the case (f) evidences the resultant shape of the combination of case (e) with the machining filter. In this case, the three additions described in Section 4 are applied.  
For cases (c), (d), (e), and (f), which are the cases where the Heaviside filter is applied, the searching methodology described in [40] is applied, updating the design variables every 25 iterations or when the updating conditions are met. Finally, case (g) is showcased, where the number of iterations for updating the design variables is reduced to 10 with the conditions of case (f). This is done to reduce the total number of iterations and search for an optional different design. A video showcasing case (g) and the AVD described in [40] is available in the Data Availability Statement section.  
Relevant validation data are presented in Table 2, which shows the number of iterations before convergence, the measure of non-discreteness, the machinability, and the compliance (as objective function) of all the verification cases.
## 3 Results
### Correlation Analysis  
A Pearson correlation analysis was conducted to examine the relationships between various soil properties and pesticide concentrations. The results are presented in Figure 2, which illustrates a comprehensive matrix of correlations among pH, SOM, available nitrogen, available phosphorus, available potassium, atrazine, acetochlor, and alachlor.  
pH showed a strong positive correlation with SOM (0.9), indicating that as soil pH increases, so does the content of organic matter. pH also had a moderate positive correlation with available potassium (0.82).  
SOM exhibited a very strong positive correlation with available potassium (0.81), suggesting that higher levels of organic matter may enhance potassium availability. SOM also had a strong positive correlation with acetochlor (0.92), implying that organic matter content could influence the concentration of this pesticide. A moderate positive correlation was observed between SOM and available phosphorus (0.38).  
Available nitrogen showed a strong negative correlation with available phosphorus (\(-0.78\)), indicating an inverse relationship between these two nutrients. It had a moderate positive correlation with pH (0.25) and SOM (0.21). There was a weak positive correlation between available nitrogen and acetochlor (0.47).  
Available phosphorus had a strong positive correlation with available potassium (0.78), suggesting that factors affecting phosphorus availability might similarly influence potassium. It showed a moderate positive correlation with pH (0.3) and SOM (0.38). There was a weak positive correlation between available phosphorus and atrazine (0.28).  
Available potassium exhibited a strong positive correlation with pH (0.82) and SOM (0.81), indicating that both pH and organic matter content can significantly influence potassium availability. It had a moderate positive correlation with available phosphorus (0.78) and acetochlor (0.58).  
Figure 2: Correlation matrix illustrating the relationships between pesticides and soil physical and chemical properties.  
Atrazine showed a strong positive correlation with alachlor (0.9), suggesting that these two pesticides may share similar environmental fate mechanisms. Acetochlor had a strong positive correlation with SOM (0.92) and alachlor (0.9), indicating that organic matter content and other pesticide concentrations could influence its presence. It showed a moderate positive correlation with available potassium (0.58) and pH (0.82). There was a weak positive correlation between acetochlor and available nitrogen (0.47). Alachlor exhibited a strong positive correlation with atrazine (0.9) and acetochlor (0.9), suggesting that these pesticides may be influenced by similar environmental factors.
# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
## 3 Results  
### Data Collection and Preprocessing  
#### 3.1.1 Data Collection  
Two urban road intersections, a roundabout and a cross intersection, were selected for this study, as shown in Figure 5. Due to the limited field of view of lightweight LiDAR, the location and number of survey stations were determined based on the actual scene. The positions of the survey stations for the two experimental areas are shown in Figure 5a,b as A, B, C, and D.  
The selection criteria for the survey points were as follows:  
(1) The survey points were selected in relatively safe areas within the intersection, such as pavements, to minimize the impact of traffic.  
(2) The point cloud data collected from multiple survey points should collectively cover the entire intersection as comprehensively as possible.  
During the data collection for the roundabout intersection, some areas were under construction, making it difficult to set up the LiDAR system. As a result, the survey points are distributed on one side of the intersection.  
Additionally, data collection is typically conducted during low-traffic hours of the day. The Livox Mid-40 LiDAR (Livox Technology Company Limited, Shenzhen, China) has a limited field of view (Section 2.1); thus, efforts were made to avoid situations where vehicles were in the field of view. This approach fundamentally reduces the impact of vehicle noise on data processing, highlighting one of the advantages of using lightweight LiDAR. The data collection process is shown in Figure 6.
## 2 Background and Literature Study
### Enhancing Data Security and Confidentiality in Smart Healthcare Settings  
Understanding the necessity of data security and privacy pushes us toward the need to enhance data security and confidentiality in smart healthcare settings. With this new generation of data collection methods, it also poses the question of who the owner of the data is. The work of [58] states that there is a \"lack of agreement about who the final data owner should be and uncertainty about what ownership exactly entails\" when it comes to medical data in healthcare. This underscores the necessity for the healthcare sector to advance towards higher levels of data security, which can be achieved by addressing this issue. One potential approach is to establish precise definitions and clarifications concerning data ownership. NHS users are composed of various groups of healthcare professionals, local authorities, academics, auditors, commissioners, patients, health startups, and pharmaceutical companies. So, it is critical to know who can access what information and at what level. The data should be secured in such a way that access from unauthorised individuals is prevented. The system should ensure data Confidentiality (keeping information secret), Integrity (maintaining accuracy of information), and Availability (ensuring access to information by authorised users) as highlighted in [59]. Enhanced levels of data confidentiality can be enforced, where symmetric and asymmetric encryption keys can be used to allow varying levels of data security [60]. Research conducted by [61] has highlighted that high data latency can significantly impair the availability of data, emphasising the critical need for efficient Public Key Infrastructure (PKI) implementations to mitigate this issue. Another example includes Identity and Access Management (IAM). Such a system can perform different functions including authentication, authorisation, verification, and storage provision [62]. Security features are critical and have a significant impact on customers' trust and adoption of such technology [63]. One of the concerns regarding the IoMT security system is the leakage of personal information leading to a critical risk to patient privacy, but the IoMT system must ensure data confidentiality and preserve user data privacy [64; 65; 66] and the system must ensure non-repudiation [67]. However, due to the device's constraints, it is very challenging to incorporate security features. So, the encryption mechanisms must support low-powered devices [68], and a performance analysis of security features is conducted in [69]. Moreover, storage and processing are challenging when dealing with big data, so cloud computing can be combined with IoMTapplications to enhance the system's performance. Cloud services provide the essential scalability factor, make the system flexible, and provide the necessary processing power needed to analyse vast datasets generated by IoT devices. This seamless integration enables real-time monitoring and reporting, transforming raw data into meaningful insights readily accessible to end users.