esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Implementation of HMM Analysis  
Here, we investigate the possible effects of bushfires-induced emissions of aerosols on the levels of ozone in the total column. This is achieved by observing and analyzing changes in states rather than by analyzing changes in actual observed values (e.g., ozone values), as in traditional time series analysis [71].  
We use the depmixS4 package in R [18]. DepmixS4 specifies and fits hidden Markov models, optimized with the EM algorithm. Models can also fit multiple sets of observations and covariates (e.g., aerosol levels). The performance of the HMM can be evaluated using various metrics, such as AIC or BIC when using R depmixS4 by [PERSON] et al. [18], or accuracy, precision, recall, or F1 score when using Python hmmlearn--a set of algorithms for unsupervised learning and inference of HMMs.  
In modeling we add covariates to the HMM models as follows: Let \(S\) denote the states, and \(N=T\). The joint likelihood of observations \(O_{1:T}\) and latent state \(S_{1:T}=(S_{1},\ldots,S_{T})\), with model parameters \(\theta\) and covariates \(z_{1:T}=(z_{1},\ldots,z_{T})\), can be written as:  
\[P(O_{1:T},S_{1:T}\mid\theta,z_{1:T})=\pi_{i}(z_{1})b_{S_{1}}(O_{1}\mid z_{1}) \prod_{t=1}^{T-1}a_{ij}(z_{t})b_{S_{t}}(O_{t+1}\mid z_{t+1}). \tag{6}\]  
Note, the following details:  
1. \(S_{t}\) is an element of the set of \(n\) latent states.
2. \(\pi(zi)=P(S=i|z_{1})\), denotes the probability of state \(i\) at time \(t=1\) with covariate \(z_{1}\)
3. \(a_{ij}(z_{t})=P(S_{t+1}=j|S_{t}=i,z_{t})\), provides the probability of a transition from state \(i\) to state \(j\) with covariate \(z_{t}\).
4. \(b_{S_{t}}\) is a vector of observation densities \(b_{j}^{k}(z_{t})=P(O_{t}^{k}|S_{t}=j,zt)\), namely, the conditional densities of observations \(O_{t}^{k}\) associated with latent class/state \(j\) and covariates \(z_{t},j=1,\ldots,n;k=1,\ldots,m\).
5. Specific analytic steps to model daily changes in ozone and aerosol levels by HMMs are as follows: 1. Compare the days when the ozone state changes with respect to the observed daily aerosol levels. 2. Compare the trends across the three geographical regions (i.e., Regions 1, 2 and 3).  
Figure 10 part (B) shows our paradigm for HMM modeling which entails adding covariates (e.g., aerosol levels over time).  
For modeling ozone, with and without aerosol as a covariate, we use function depmax to create an S4 object of class depmix, which needs the fit function to fit and to optimise the parameters. The output of the fit function comprises a transition probability matrix, mean intercept and standard deviation table. The probabilities are the estimated probabilities of transitions between states [18].
## 3 Results
### Integrated System Performance: Gesture Control and Obstacle Avoidance
#### 3.4.2 AI-Deck Streaming and Remote Navigation  
The AI-Deck's video streaming feature allowed for navigation beyond line of sight using both greyscale and color modes, enhancing user control and environmental awareness. While the color mode (Figure 9(a)) provided better scene interpretation, the greyscale mode (Figure 9(b)) offered better contrast, which could be beneficial in certain lighting conditions.  
The limited field of view (FOV) of the AI-Deck stream meant that users could only see directly in front of the drone, making it difficult to gauge surroundings beyond the camera's perspective. Combined with the low, 324 \(\times\) 324 pixel resolution, this limitation made it challenging to discern fine details and detect distant obstacles during navigation. These constraints posed difficulties when maneuvering through complex environments. However, the obstacle avoidance system compensated for this limitation by detecting nearby obstacles outside the camera's view and preventing collisions. This combination of AI-Deck streaming and autonomous obstacle avoidance allowed for a more reliable navigation experience, even with the limitations of the video feed.
## 5 Conclusions
#### Future Work  
In the ongoing research, this communication system will be applied to full-sized cars and the impact of influences such as distance, angle, absolute and relative velocity, weather, daytime, other interfering light sources, etc. will be analyzed to see how the system can be optimized in this regard. With this information a specific channel model for this optical communication channel can be defined to develop an appropriate security protocol that allows fast, reliable, and secure identity verification for establishing a connection in another wireless channel that can then be used in applications such as platooning or similar.  
Conceptualization, M.P.; methodology, M.P.; software, M.P.; validation, M.P. and G.O.; writing--original draft preparation, M.P.; writing--review and editing, M.P. and G.O.; supervision, G.O.; project administration, G.O.; funding acquisition, G.O. All authors have read and agreed to the published version of the manuscript.  
This project has been co-financed by the European Union using financial means of the European Regional Development Fund (EFRE). Further information to IWB/EFRE is available at [[http://www.efre.gv.at](http://www.efre.gv.at)]([http://www.efre.gv.at](http://www.efre.gv.at)) (accessed on 2 March 2021).  
This paper and all corresponding results, developments and achievements are a result of the Research Group for Networks and Mobility (NEMO).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ADASAdvanced Driver Assistance Systems  
BERBit Error Rate  
CMOSComplementary Metal-Oxide-Semiconductor  
DSRDigital Single-Iens Reflex Camera  
FPSFrames Per Second  
GPIOGeneral-Purpose Input Output  
OCCOptical Camera Communication  
OWCOptical Wireless Communication
## 5 Experiment and Results
### Evaluation and Analysis
#### 5.2.2 Using Only Range-Doppler Data as Input  
Similar to 3D point cloud data, we combine eight frames of range-Doppler data. For consolidation, we use two methods, i.e., adding a new dimension to keep the number of channels unchanged and increasing the number of channels while keeping the dimension unchanged. As mentioned above, different networks are used due to different data formats. The former uses a three-dimensional single-channel convolution network, while the latter uses a two-dimensional multi-channel convolution network. Their network structures are similar. After training the two networks with 100 epochs, respectively, we draw the curves of verification accuracy and loss into Figures 13 and 14, find out the maximum value of accuracy and the minimum value of loss, respectively, and make Table 3.  
As can be seen from the above figures and table, 3D CNN has a slight advantage over 2D CNN in terms of accuracy and loss, with an accuracy of 0.42% higher and a loss of 0.005 lower. Moreover, by observing the two curves, we find that 3D CNN has obvious advantages in convergence speed, which also means that 3D CNN needs fewer hardware resources on the basis of training the same amount of data. By printing the model structures of the two networks, we find that the parameters of 3D CNN that need to be trained are far less than those of 2D CNN. The 3D CNN needs to train 131,622 parameters, while 2D CNN needs to train 279,076 parameters, which is more than twice as few as the latter. In order to compare the verification accuracy of each action in the 3D CNN network, we draw the confusion matrix, as shown in Figure 15. In this network, we find that high-knee lifting is easy to predict while circling is relatively difficult to identify. The spectrograms of other activities are similar.  
Figure 14: Two loss curves of using only range–Doppler data as input.  
Figure 13: Two accuracy curves of using only Range–Doppler data as input.
## 4 Materials and Methods
### dsRNA Synthesis
#### 4.6.2 Vector Construction and Expression  
1. Construction of transformed _E. coli_ expressing dsRNA  
PCR products obtained in the previous steps were excised and cloned into the plasmid vector L4440 (Wuhan Miaolino Biotechnology Co., Ltd., Wuhan, China), between the _Not_I and _XbaI_ restriction sites. Successful cloning was verified through PCR and sequencing. Plasmids containing the correct insert were extracted and transformed into _E. coli_ strain HT115 (DE3) (Shanghai Weidi Biotechnology Co., Ltd., Shanghai, China). Positive clones were incubated at 37 \({}^{\circ}\)C until the mid-exponential phase (OD600 = 0.4). To activate the T7 promoter for RNA transcription, IPTG (isopropyl-\(\beta\)-D-1-thiogalactopyranoside) was added to a final concentration of 0.8 mM and then incubated for an additional 4 h under the same conditions. Each bacterial culture (100 mL) was transferred into a 50-mL Falcon tube and centrifuged at 4000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C.  
Isolation of dsRNA using conventional method  
Cells were harvested via centrifugation at 5000\(\times\)\(g\) and 4 \({}^{\circ}\)C for 10 min. DsRNA was isolated from 1 mL of the cell suspension with the UNIQ-10 Column Trizol Total RNA Isolation Kit (Sangon Biotech, Shanghai, China) according to the manufacturer's protocol. The extracted RNA was compared with the dsRNA not induced by IPTG to determine whether IPTG had been successfully induced. Its integrity was checked on 1% agarose gels, and quantification was performed by spectrophotometry with a NANO DROP 2000 (Thermo Scientific, Pittsburgh, PA, USA). The reactions were allowed to proceed overnight at 42 \({}^{\circ}\)C, followed by both the RNase and DNase digestion and purification steps to obtain the dsRNA. The dsRNA was spectrophotometrically quantified before injection  
Based on the L4440 vector, two expression vectors, L4440-AST and L4440-JHAMT, corresponding to _DaAST_ and _DaJHAMT_, were constructed. The plasmids were digested by the restriction endonuclease _Not_I and _Xbu_I. The gel electrophoresis results indicated that there were two bands; one is about 390 bp from L4440-AST, and the other 570 bp from L4440-JHAMT. After HT115 harbouring the plasmids, L4440-AST or L4440-JHAMT was induced by IPTG; we extracted the total RNA (containing dsAST or dsJHAMT) from the engineered bacteria (Figure S1). The gel electrophoresis results showed that the remainder was the band of dsAST and dsJHAMT (Figure S1).
## 4 Discussion
### Security  
In the healthcare area, digital records are replacing paper-based ones, easing medical practice and allowing continual access to patient health information [72; 73]. Nonetheless, because patient information is extremely valuable, digitalisation has also made this industry a major victim of external and internal assaults around the world [72]. Clearly, this issue became expanded with the COVID-19 pandemic as more employees transitioned away from office work and toward entirely remote or hybrid work, thereby making the healthcare system even more exposed to external threats. Our survey suggests users are greatly unaware of the aforementioned risks as they perceive the AI systems they use in their routine practice to be safe and comply with GDPR. Moreover, they seem unsure about whose responsibility it is when a data leakage occurs. We propose the following recommendations for improving security:  
* _Device encryption_. All devices that employees use to access corporate data should be completely encrypted, and sensitive data stored and transferred in an encrypted way [74].
* _Security testing_. Effort, time, and money should be invested in cyber-security. Hiring a professional organisation to conduct security audits is an excellent way to test data security, as these reveal security weaknesses [75], especially in remote work setups. These professionals, as independent organisations, can verify that AI systems comply with the utmost security standards.
* _Restrict and control access/modification/delete/storage data and device use access_. Our survey showed a third of respondents did not have user hierarchies and permission settings set up in their organisations, increasing the risk of data breaches or mishandling. Employees should only have access to data that is absolutely necessary for them to accomplish their jobs [76].
* _Employee training_. Training all members on why security is important and how they can contribute can not only decrease risks but also improve reaction times when breaches occur [77]. Frequent training sessions and up-to-date policy information can encourage employees to put these guidelines into practice. For example, an easy and common employee mistake is to write down their passwords on a sticky note.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 3 Morphological Characteristics of the Vascular Network  
Morphological and functional abnormalities of the tumor vasculature are crucial for the enhanced permeability and retention (EPR) phenomenon, facilitating the targeted delivery of nanomedicines. The extracellular matrix (ECM) acts as a structural scaffold for cellular repair and consists of a random and disorganized complex of collagen fibers, glycosaminoglycans (GAGs), solutes, proteins, and residues [72,73]. It regulates cell behavior and tissue homeostasis by providing biochemical signals and determines the diffusion and convection forces within the vascular wall for molecular movement, which are influenced by the degree of collagen organization. Enzymes such as collagenase restore mobility by enhancing the diffusion of molecules, while the covalent binding of GAGs to proteins such as collagen alters the viscosity of the environment and fluid diffusion directions [72]. Cancer and stromal cells secrete angiogenic and growth factors, as well as cytokines, stimulating the formation of blood vessels with abnormal architecture (Figure 2) [74,75].  
Figure 2: Comparative imaging of the vascular endothelium of normal and cancerous tissue. The vascular system of the normal tissue is well-organized and differentiated, in contrast to cancerous tissue, which is a complex of abnormal and branched blood vessels. In normal tissue, the endothelial cells are organized in a monolayer that adheres to the homogeneous basement membrane, which in turn is covered with mature pericytes. In cancerous tissue, the vascular system appears with abnormal structure and function. The basement membrane is heterogeneous, the immature pericytes detach, and the endothelial cells are irregularly stacked, resulting in the creation of interspaces. These gaps allow for the enhanced permeability of macromolecules and their retention within the tumor. The abnormal tumor vasculature also creates a hypoxic and acidic microenvironment with high interstitial fluid pressure (IFP) (created with BioRender.com).
## 5 Discussion  
### Analysis of the Quality of the Solution  
Based on the experimental results in Section 4.5, the PCC\({}^{2}\)LS algorithm consistently matches or outperforms compared algorithms on common UDG instances. For smaller graphs, these algorithms also yield competitive solutions, diminishing the algorithm's advantage. However, on larger graphs, the algorithms demonstrate more pronounced benefits. To illustrate statistical findings effectively, we compare the best solutions obtained by six heuristic algorithms (Greedy, ACO, ACO + PCS, GRASP, VDN, and MSLS) with those from PCC\({}^{2}\)LS on identical examples.  
Figure 2 depicts the vertical axis as the difference between the best solution found by the comparison algorithm and that by the PCC\({}^{2}\)LS algorithm, labeled as \(BEST\) (comparison algorithm) - \(BEST\) (PCC\({}^{2}\)LS). The horizontal axis represents the instance number, where BEST (comparison algorithm) indicates the best value achieved by the comparison algorithm for each instance. A positive value on the vertical axis indicates that PCC\({}^{2}\)LS has found a superior solution. A value of 0 indicates that both algorithms found the same best solution. Conversely, a negative value indicates that the comparison algorithm performed better.  
From Figures 2 and 3, it can be observed that the PCC\({}^{2}\)LS algorithm shows better performance in general, and the best and average solutions obtained by this algorithm outperform those obtained by other comparative algorithms. This is mainly due to the fact that three simplification rules are used to simplify the original graph in the initial solution construction stage, while the local optimum is handled in the local search stage by a perturbation strategy, a vertex penalization strategy, and a series of strategies to avoid the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multirow{2}{*}{**LB**} & \multirow{2}{*}{**Greedy**} & \multicolumn{3}{c}{**ACO**} & \multicolumn{3}{c}{**ACO + PCS**} & \multicolumn{3}{c}{**GRASP**} & \multicolumn{3}{c}{**VDNS**} & \multicolumn{3}{c}{**MSLS**} & \multicolumn{3}{c}{**PCC\({}^{1}\)LS**} \\ \cline{3-19}  & & & \multicolumn{1}{c}{**Min**} & **Avg** & **Min** & **Avg** & **Min** & **Avg** & **Tim** & **300 s** & **30 s** & **Min** & **Avg** & **Tim** & **Min** & **Avg** & **Tim** \\ \hline
400.80.60 & 12 & 48 & 20 & 21.6 & 19 & 21.2 & 19 & 19.8 & 7.1 & _18_  
cyclic problem. Therefore, the PCC\({}^{2}\)LS algorithm has a significant advantage in minimizing the quality of the solution.  
Figure 3: The comparison of ‘_Average_’ values of greedy, ACO, ACO + PCS, GRASP, VDNS, MSLS, and PCC\({}^{2}\)LS on common UDG instances.  
Figure 2: The comparison of ‘_’BEST_’ values of greedy, ACO, ACO + PCS, GRASP, VDNS, MSLS, and PCC\({}^{2}\)LS on common UDG instances.
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 3 Model Development
### Mathematical Model  
#### 3.2.1 Objective 1: Maximize Service Distribution Equity  
In this study, service distribution equity is reflected by the service distribution Gini coefficient (SDGC), which serves as a quantitative indicator for assessing the service distribution equity among different socioeconomic groups ( socioeconomic groups are defined in Section 4.2.1). The Gini coefficient is a relative index initially defined by Gini [65] to measure the degree of unequal distribution. At present, the Gini coefficient has been widely used to measure the equity of the distribution of public resources, such as medical services [66], education [22], water resources [67], etc. The Gini coefficient ranges between 0 and 1, with values closer to 0 indicating better equity. And a Gini coefficient below 0.2 signifies optimal equity. By introducing the Gini coefficient calculation formula [13], the objective of maximizing SDGC in period \(t\) can be expressed as follows:  
\[min\ f_{1}=1-\sum_{k=1}^{n}\big{(}X_{k}^{t}-X_{k-1}^{t}\big{)}\big{(}Y_{k}^{t} +Y_{k-1}^{t}\big{)} \tag{1}\]
# New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation
## 5 Conclusions  
Produce can be affected by the quality of the irrigation water and, therefore, affect the people that consume it. Therefore, monitoring water quality is important to ensure food safety. In this paper, we have proposed an architecture and communication protocol for an irrigation water quality monitoring system for precision agriculture. It is based on groups of sensor nodes that monitor the water quality of the main canal and of biosorption auxiliary canals to determine if the water needs to be treated. Simulations were performed to determine the amount of data stored on the SD card of the nodes, the consumed bandwidth, and the energy consumption of the system. The results show that the proposed system is able to transmit the necessary information and alerts with a low bandwidth generation. However, the scheduled data forwarding of the central node of the last auxiliary canal had to be modified to two transmissions instead of one to ensure the fair access policy of LoRa. Lastly, the system is able to sustain energy consumption even on winter days with fewer hours of light and adverse weather conditions. Therefore, when considering data transmissions for wireless networks using LoRa communications, the addition of data pre-processing techniques would be beneficial to reduce energy consumption and ensure that the fair access policy is not violated.  
For future work, the proposed protocol will be adapted to include the monitoring of the field area and tests will be performed on different vegetated environments. Furthermore, data pre-processing techniques will be implemented to improve the performance of the system.  
Conceptualization, J.M.J. and L.P.; methodology, J.M.J., L.P., and L.G.; formal analysis, J.M.J. and L.P.; investigation, J.M.J., L.P., and L.G.; resources, J.L. and P.V.M.; writing--original draft preparation, J.M.J., L.P., and L.G.; writing--review and editing, L.G.; supervision, J.L.; project administration, J.L. and P.L.; funding acquisition, J.L., P.V.M., and P.L. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR. This work has also been partially funded by the Universitat Politecnica de Valencia through the post-doctoral PAID-10-20 program and by Conselleria de Educacion, Cultura y Deporte with the Subvenciones para la contratacion de personal investigador en fase postdoctoral, grant number APOSTD/2019/04.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy constraints.  
The authors declare no conflict of interest.
## 3 DL Models for Ocean Studies
### Mlp  
The Multilayer Perceptron (MLP) represents one of the most foundational neural network designs. It typically consists of an input layer, one or more hidden layers, and an output layer, with each neuron in a given layer fully connected to every neuron in the next. During training, the model adjusts its learnable weights to minimize prediction errors, while nonlinear activation functions such as Rectified Linear Unit (ReLU) enable the MLP to capture complex, nonlinear patterns in the input data. In ocean-related research, MLPs are well suited for analyzing tabular data, modeling relationships among environmental variables (e.g., temperature, salinity, pH), and making predictions based on sensor inputs [81]. Their relatively simple structure also ensures computational efficiency and ease of implementation.
## 4 Sampling of Precipitation Conditions for the Constellation Simulation
### Constellation Design Considerations

Table 5: Orbital sampling needed to separate three temperatures by 1 K, corresponding to the regions in Table 4. For each constellation configuration, the required number of total events to be collected by the constellation is presented and the corresponding orbit duration (days). S = number of constellation satellites, \(\phi\) is the orbit inclination in degrees, Alt = constellation altitude in km. The satellite temporal spacing \(\Delta t\) between the satellites is fixed at 2 min. The row shaded in gray refers to the baseline simulation used in Table 4.  
475-km altitude was used, this number would roughly double (Table 5), to approximately 6 months. Another consideration is the operation period. The simulations sampled IMERG precipitation spread across different months of the year in order to represent seasonality, but actual operations may occur in specific months of the year where precipitation is more or less frequent. The inclined orbits will capture the variability in the diurnal cycle (not examined in this study). Using the GPM satellite as an example (65-degree inclination), a 60-degree constellation inclination would sample a full diurnal cycle approximately every two months. This provides an additional consideration, independent from the sufficiency calculations above.