esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Geodetic Methods Details
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Ascending Track 102**} & \multicolumn{4}{c}{**Descending Track 102**} \\ \hline
**Pick No.** & **Long. (\({}^{\circ}\)E)** & **Lat. (\({}^{\circ}\)N)** & \
\begin{tabular}{c} **Picked** \\ **Value (mm)** \\ \end{tabular} & **Model (mm)** & **Long. (\({}^{\circ}\)E)** & **Lat. (\({}^{\circ}\)N)** &
\begin{tabular}{c} **Picked** \\ **Value (mm)** \\ \end{tabular} & **Model (mm)** \\ \hline \(1\) & \(23.42948\) & \(38.29764\) & \(-28\) & \(-24.65\) & \(23.43559\) & \(38.29896\) & \(-28\) & \(-30.6\) \\ \hline \(2\) & \(23.42241\) & \(38.30126\) & \(-28\) & \(-27.1\) & \(23.44081\) & \(38.29666\) & \(-28\) & \(-26.66\) \\ \hline \(3\) & \(23.41931\) & \(38.30648\) & \(-28\) & \(-26.86\) & \(23.44709\) & \(38.29781\) & \(-28\) & \(-25.91\) \\ \hline \(4\) & \(23.41975\) & \(38.31011\) & \(-28\) & \(-21.29\) & \(23.44665\) & \(38.3025\) & \(-28\) & \(-25.4\) \\ \hline \(5\) & \(23.42462\) & \(38.31091\) & \(-28\) & \(-26.33\) & \(23.44187\) & \(38.30489\) & \(-28\) & \(-29.78\) \\ \hline \(6\) & \(23.42966\) & \(38.30967\) & \(-28\) & \(-32.45\) & \(23.43745\) & \(38.3071\) & \(-28\) & \(-28.54\) \\ \hline \(7\) & \(23.43577\) & \(38.30728\) & \(-28\) & \(-29.29\) & \(23.43364\) & \(38.30834\) & \(-28\) & \(-28.84\) \\ \hline \(8\) & \(23.44116\) & \(38.30436\) & \(-28\) & \(-26.68\) & \(23.42975\) & \(38.30931\) & \(-28\) & \(-24.49\) \\ \hline \(9\) & \(23.44293\) & \(38.30153\) & \(-28\) & \(-32.24\) & \(23.42798\) & \(38.30772\) & \(-28\) & \(-28.14\) \\ \hline \(10\) & \(23.44222\) & \(38.29826\) & \(-28\) & \(-29.64\) & \(23.42966\) & \(38.30454\) & \(-28\) & \(-35.32\) \\ \hline \(11\) & \(23.43992\) & \(38.29657\) & \(-28\) & \(-24.78\) & \(23.43161\) & \(38.30171\) & \(-28\) & \(-32.77\) \\ \hline \(12\) & \(23.43506\) & \(38.29649\) & \(-28\) & \(-24.84\) & \(23.43656\) & \(38.30445\) & \(-56\) & \(-47.43\) \\ \hline \(13\) & \(23.42825\) & \(38.28428\) & \(0\) & \(3.83\) & \(23.4378\) & \(38.30356\) & \(-56\) & \(-48.61\) \\ \hline \(14\) & \(23.41329\) & \(38.28844\) & \(0\) & \(1.83\) & \(23.43338\) & \(38.28746\) & \(0\) & \(1.24\) \\ \hline \(15\) & \(23.4048\) & \(38.29516\) & \(0\) & \(-0.88\) & \(23.44444\) & \(38.28481\) & \(0\) & \(2.17\) \\ \hline \(16\) & \(23.39967\) & \(38.30259\) & \(0\) & \(-2.14\) & \(23.45921\) & \(38.28799\) & \(0\) & \(0.22\) \\ \hline \(17\) & \(23.39914\) & \(38.30949\) & \(0\) & \(-2.41\) & \(23.46646\) & \(38.29127\) & \(0\) & \(0.07\) \\ \hline \(18\) & \(23.40356\) & \(38.31542\) & \(0\) & \(-2.43\) & \(23.46885\) & \(38.29702\) & \(0\) & \(0.32\) \\ \hline \(19\) & \(23.41214\) & \(38.31763\) & \(0\) & \(-2.94\) & \(23.46098\) & \(38.30224\) & \(0\) & \(0.68\) \\ \hline \hline \end{tabular}
\end{table} 
Table A1: Sampled values (fringe pickings) from the ascending interferogram with track nr. 102 and the descending one track 7, both spanning the dates 21 November 2020–3 December 2020, along with their modeled values. **Table A1. **_Cont._
## 3 Results
### Biofilm Development in Time for Two Inoculum Levels
#### 3.4.1 Study of Biofilm Population Density: Viable Cell Counts  
The influence of the inoculum levels on the biofilm population density was studied and it was represented as a function of time in Figure 4. The [PERSON] and [PERSON] (1994) model was fitted to the data and allowed the estimation of the growth parameters (Section 2.6) ([PERSON] and [PERSON], 2013). The resulting lag time (\(\lambda\)), maximum specific growth rate (\(\mu_{max}\)), initial and maximum cell densities (\(ln(N_{0})\) and \(ln(N_{max})\), respectively), the standard errors (SE) and the Root Mean Squared Errors (RMSE, indicating the quality of the fit) are listed in Table 1. First, a longer lag phase (\(\lambda\)) was needed for the biofilm to grow on the polystyrene when the inoculum level was \(10^{4}\) CFU/mL compared to \(10^{7}\) CFU/mL. Then, the relative maximum growth rate (\(\mu_{max}\)) was higher for \(10^{4}\) CFU/mL inoculum level compared to \(10^{7}\) CFU/mL. Finally, the stationary phase reached similar densities of approximately 18 ln(CFU/cm\({}^{2}\)) for both inoculum levels. The absolute increase of the biofilm population was calculated as \(ln(N_{max})-ln(N_{0})\). These values are also displayed in Table 1.  
Figure 3: Optical density at 590 nm (violet bars, right y-axis) after CV staining and biofilm cell densities (blue bars, left y-axis) measured for 24-h-grown biofilms obtained with different inoculum levels: \(10^{4}\), \(10^{5}\), \(10^{6}\) and \(10^{7}\) CFU/mL. The error bars indicate standard deviation on three independent replicates. The statistical analysis was carried out separately on the two sets of data. Data bearing different letters (no letter in common) are significantly different (\(p\leq 0.05\)).
## 5 System Description
### System Pre-Conditions  
To implement our web application, we utilized a series of programs and tools to ensure the efficiency and stability of our project. Specifically, to support cross-platform deployment and system portability, the AUORA MINDS framework was implemented using containerized microservices with Docker Engine v26.0.0 and developed in Java 1.8.0_312 using Maven 3.8.7 managing project dependencies. Front-end services were built with Node.js. The implementation details, the source code, and environment configuration are publicly available on GitHub for transparency and reproducibility. All performance evaluations of the AUORA MINDS system were conducted on a Lenovo ThinkSystem SR950 8S server. The evaluation environment consisted of a virtual machine running Ubuntu 22.04.3 LTS, allocated 16 GB of RAM, and orchestrated via Docker v26.0.0 for containerized deployment. For the Federated Learning modules and data processing pipelines, Python 3.10 was employed. These specific versions were selected based on their maturity, performance characteristics, and compatibility with the modular, microservice-based architecture of AUORA MINDS. This environment was chosen to emulate typical enterprise-grade infrastructure used in institutional or clinical settings.  
The various components of the platform, including the virtual Identity Providers (vldPs), web-facing service provider (Relying Party), client application, and Federated Learning aggregator, were containerized to ensure modularity and portability. Containers were launched with specific resource constraints to reflect realistic operational conditions, in particular:  
* Each container was limited to 2 GB of memory using the memory = 2 g flag.
* CPU usage per container was capped at 1.5 cores using the cpus = 1.5 option.
* Docker bridge networking mode (network = bridge) was used to simulate isolated communication between services.  
Figure 2: Aurora Mind’s identity management system.  
Persistent volumes (e.g., -v aurora_data:/app/data) were mounted to manage secure and persistent storage of user credentials and encrypted datasets.
* All containers were orchestrated using a Docker Compose v2.24.0. configuration, which defined the setup of multi-service communication, startup dependencies, and environment variables for each component.
* The base images used included OpenJDK 8 for Java-based identity management modules and Node.js v14.21.3 for server-side scripts and web services, ensuring compatibility and minimal resource overhead.  
This setup provided a controlled and secure environment for benchmarking latency, registration time, and Federated Learning performance, enabling an accurate evaluation of AURORA MINDS in conditions comparable to production deployment.
# Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing +
## 4 Energy-Efficient Scheduling Scheme
### Scheduling Scheme Based on Backtracking Method (SSBM)  
For the Algorithm 1, the time complexity increases exponentially with the expansion of sub-tasks, which can be reduced by some heuristic. In this subsection, we propose a heuristic algorithm that minimizes the power consumption and guarantees the users' deadline as well as lower complexity.  
The main idea of the algorithm is to select the best node in every layer. Generally, it seems that the composition of the global optimal solution is not necessarily optimal in every part of the solution. May be multiple sub-optimal solutions forming the global optimal solution. However, with the perspective of reducing complexity, this algorithm significantly reduces the complexity (reducing the exponential level to the linear level), and it is very suitable for scenarios that require the low complexity of the algorithm.  
As shown in Algorithm 2, the input and output are the same as Algorithm 1. The first two steps of the algorithm are also completely consistent with Algorithm 1. We perform the same operations as Algorithm 1 lines 5 and 6, in line 3 and 4.The algorithm sorts the calculation results of tasks performed by different nodes by power consumption and selects the set of data with the lowest power consumption in line 5. It is determined whether the cumulative delay sum is less than the maximum tolerable delay of the task in line 6. If it is less than the value, then the power consumption value is recorded as the power consumption set S of the current sub-task in line 7. Otherwise, execute line 10 of the algorithm, that is, delete the solution in the topology tree, and then jump to step 5 to re-select the smallest value. By analogy, the feasible solution and the node path that can realize the power consumption value are output after traversing a feasible solution for the first time. The worst time complexity of SSBM is \(O(n^{3})\).
## 4 Case Study: A LNG Refueling Station for Maritime Applications  
In this section, the calculation procedure presented in the previous paragraphs is applied to a real case study, consisting in the project--funded by the Emilia Romagna region and developed by the University of Bologna--to realize a LNG refueling station at the port dock of Ravenna (Italy) for maritime applications [31]. At present, a ferry is used to carry cars from one side to the other of the main canal of the Ravenna port (400 m wide) at a maximum rate of 10 runs per hour, for a total of 19 h per day in the whole year. The ferry is equipped with two diesel engines of 162 kW each.  
The developed idea entails the modification of the two engines into dual fuel engines working with LNG and diesel fuels. From the design of the new propulsion system [31], the single-engine power is supposed to decrease down to 125 kW at 1500 rpm. The ferry will be further equipped with four storage tanks of 3300 L each, operating at a pressure equal to 3 bar, refueled approximatively every week. Therefore, the LNG system must produce less than 4000 kg per week.  
Starting from this idea, and accounting for these constraints, the first objective was to properly design the required LNG production plant. Furthermore, it is supposed that all the electrical energy required to move the compressors and the compression chiller is provided by renewable energy sources (in particular, wind). In this respect, the evaluation of optimal size of the wind plant, which allows to weekly produce the required amount of LNG, is also object of the presented analysis. In more detail, in the next paragraphs, the definition of the LNG production plant configuration and operating parameters--based on the optimization analysis presented in the Section 2 of this paper--and the application of the developed calculation model in order to define the optimal coupling with a wind plant will be presented and discussed. To this respect, it should be highlighted that the integration between the LNG production process and a wind plant is particularly interesting in order to promote the renewable sources exploitation without posing issues to the national electrical grid (such as frequency instability, capacity challenges, etc.). However, this has to be intended just as a possibility, while the proposed LNG production process and the design and performance methodology are valid independently from the source of the electricity which feeds the process.  
Future works, after the realization of the proposed integrated plant, will analyze the real plant operation and the relative experimental results.
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 6 Experimental Results
### Case Study: Adaptive Behavior Through Purpose  
We include a mini case study to illustrate how the system adapts to different Purpose settings. Patient #003 had a moderate heart condition but expressed dislike for constant alarms. Initially, Purpose sensitivity was set to \"low\" to reduce false alarms. During a test, the patient had several borderline tachycardia episodes at night, none of which triggered an alert (by design, to let them rest unless it got very severe). Later, during a follow-up, the doctor decided that was too risky and remotely changed the profile to \"high sensitivity\". On the next night, a similar episode occurred and this time an alert was triggered promptly. The system essentially shifted threshold and delay parameters internally as per the Purpose. This demonstrates a policy change in action without redeploying code--a clear win for the software-defined approach. It also validates that such purposeful tuning is feasible and effective. Of course, one must be careful; too low sensitivity and you miss events (in our test none were serious so it was okay).
## 4 Light Intensity- and Spectrum-Associated Adjustment of Metabolism
### Regulation of Nucleic Acid Metabolism by Light Intensity and Spectrum
#### 4.5.1 Effect of Light Intensity on Nucleic Acid Metabolism  
Many key enzymes of NTP and dNTP synthesis are affected by light intensity by the involvement of the redox system (Table A3) [202]. In rice (_Oryza sativa_), the ribonucleotide reductase (RNR) is required for chloroplast biogenesis in early leaf development since it affected the phenotype of OsRNR large and small subunit-deficient mutants, _v3_ and _st1_, respectively, and it is light intensity-responsive [200]. The effect of light on RNR may be mediated by Trx based on its stimulating effect on RNR in _Arabidopsis_[203]. In addition, many other enzymes involved in nucleic acid metabolism are putative Trx targets in land plants, such as those ones participating in purine salvage, NTP and dNTP synthesis, pyrimidine de novo synthesis, and DNA remodelling (Table A3) [204].  
During purine and pyrimidine salvage, nucleosides and nucleobases are recycled from metabolism or uptake into nucleotides. Proper salvage and/or degradation of nucleotides and related molecules may be essential during development [202] or under stress conditions and should be adjusted to the changes in the light conditions [205]. Accordingly, under extended darkness or increased leaf age, xanthine dehydrogenase (XDH, related to purine catabolism) is needed to delay senescence and to avoid excessive ROS accumulation of dark-stressed plants after re-exposition to light [205]. In addition, the expression of the _uracil phosphoriosyltransferase_ (_UPRT_, related to uracil salvage) gene was found to be light-induced and peaked in the middle of the light period; furthermore, UPRT showed a pronounced function in the establishment of photosynthesis [206].  
Not only nucleotide metabolism but also RNA synthesis and DNA repair processes exhibit light and redox dependency (Table A3). The presence of light can alter the nucleotide content of tRNAs [207]. Plastid-encoded RNA polymerase (PEP) is a principal RNA polymerase in chloroplasts and is affected by light-induced redox signals [201]. Proteins tightly associated with PEP core subunits, called polymerase-associated proteins (PAPs), are required for PEP activity. Most of the _PAPs_ and _sigma factor_ (which determines the promoter specificity of the major RNA polymerase) genes are light-induced. Furthermore, PEP and associated proteins (PEP complex) are major targets of photosynthesis-derived redox signals mediated by Trx-Z through the fructkinase-like proteins FLN1 and FLN2 and ECB1/MRL7 (EARLY CHLOROPLAST BIOGENESIS 2/MESOPHYLL-CELL RNAi LIBRARY LINE 7) in _Arabidopsis_. During light-dark transition, Trx-Z mediates redox signals to the PEP complex through FLN2. Fe-SOD2, Fe-SOD3, and PRDA1 (PEP-RELATED DEVELOPMENT ARRESTED 1) were reported to participate in the redox regulation of the PEP complex.
## 1 Introduction
Therefore, this study focuses on this _PV_ module mounting system. The operation modes of a horizontal single-axis tracker are defined to track the daily movement of the Sun and to avoid shadowing between adjacent solar trackers. There are three modes of operation as follows [9]: (i) backtracking mode, (ii) static mode, and (iii) normal tracking mode. Avoiding shading between adjacent solar trackers is one of the most important premises in the design of a _PV_ power plant. Sunrise and sunset, characterised by low solar elevations, are the critical periods from this point of view. Backtracking algorithms are used to avoid shading during these periods. These algorithms determine the tilt angle of the _PV_ field that avoids shadows on adjacent solar trackers and also maximises the incident beam solar irradiance. This period of operation is called the backtracking mode. The operation static mode is characterised by the limit of movement (\(\pm\beta_{\max}\)) of the _PV_ field. Thus, if the position of the Sun defines the _PV_ field arrangement with a tilt angle greater than the movement limit to obtain the maximum incident beam solar irradiance, the tracking algorithm will set the tilt angle to \(\beta_{\max}\). The normal tracking mode is the longest period of operation of the solar tracker. In this mode, the tracking algorithm places the _PV_ field in the position that maximises the incident beam solar irradiance by maximising the cosine of the solar incidence angle [10]. Once the _PV_ module mounting system has been chosen, the next step is to determine a number of parameters that define the design of a _PV_ power plant. These parameters are the following [9]: the shape of the available land, tilt angle of the terrain, the azimuth angle of solar tracker, the pitch, the configuration of _PV_ modules, the movement limit, etc. This paper analyses the influence of the movement limit of a horizontal single-axis tracker on the incident energy on the _PV_ field. Table 1 shows several _PV_ power plants in Spain that use single-axis solar tracking technology. The vast majority of these _PV_ plants are characterised by the use of \(\pm 60\) (\({}^{\circ}\)) as the limit of movement. A question arises with respect to this choice; is this movement limit suitable for all locations from the point of view of the incident solar irradiance on the _PV_ field? The limit of movement is a parameter that is not usually taken into account in studies related to _PV_ power plant design. There are many studies that do not consider this parameter, and other studies consider this parameter but without analysing the consequences of its variation. Several studies that corroborate this statement will be discussed below.
## 3 Simulation Results and Discussion
### Progressive Internal Stress State of the Soil Plug  
As the open-ended pile penetrates deeper, the internal state of the soil plug experiences significant changes throughout its depth at each impact-driving stage. The fluctuations of the internal state were captured in terms of horizontal and vertical stress. Within the twelve steps of progressive penetration, three specific depths--292 mm, 555 mm, and 596 mm--were highlighted, representing the unplugged state, fully plugged state, and partially plugged state, respectively, as presented in Figure 10.  
As the penetration proceeded to 292 mm, as shown in Figure 10a, a soil arch that was formed temporarily in the preceding step broke down with the following penetration. The formerly obstructed area by the arch was filled with particles, resulting in a reduction in the overall length of the soil plug compared with the current depth of penetration. Additionally, the main contact force chain was formed vertically beneath the pile, so the plugging effect was not observed at this stage. The corresponding stress state throughout the soil plug remained at the overburden stress level, with the maximum horizontal stress within the soil plug observed at 2.2 kPa, and the _IFR_ at this stage is 0.62. All these observations lead to the  
Figure 9: Plug length indices from the DEM simulation and model pile test ([PERSON] et al. [32]): (**a**) _PLR_; (**b**) _IFR_.  
conclusion that the pile is in an unplugged state. The soil plug and its internal stress state at the penetration depth of 555 mm are shown in Figure 10b. The impact driving in this step induces a strong contact force chain inside the open-ended pile, showing a particularly robust soil arch at 1 \(D_{in}\) from the pile tip. The resulting stress state consists of horizontal stress of 378.4 kPa, which corresponds to seventy times the overburden stress. This can be concluded as a fully plugged state, which is also supported by an extremely low _IFR_ of 0.006 at this stage. Figure 10c shows the contact force chain and distribution of the internal stress at a 596 mm penetration depth. Unlike the unplugged state (e.g., at 292 mm), the contact force chain was formed horizontally beneath the tip even though the internal soil arch had diminished. This can be interpreted as the soil plug behaving as a closed-ended pile. This behavior offers considerable resistance from internal skin friction between the pile and the plug. The _IFR_ at this stage increases to 0.44, and the internal horizontal stress is reduced significantly from 378 kPa to 84.3 kPa; however, it is still over five times the overburden stress. Considering various factors, this stage is determined to be a partially plugged state.