esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Analysis
### Throughput  
Let \(S\) be the normalized throughput of a ZigBee device. The ratio of average time occupied by the successful transmission information to the interval between two consecutive transmissions is known as normalized throughput. The channel is sensed as busy, if there is at least one ZigBee device sending packets. Let \(P_{tr_{j}}\) be the probability that there is at least one transmission during the \(j\)-th time transmission. Again, there are \(N_{j}\) ZigBee devices attached to the coordinator during \(j\)-th time transmission, and \(\phi\) is the probability that a ZigBee device attempts carrier sensing for the first time for transmission. Hence,  
\[P_{tr_{j}}=(1-(1-\phi)^{N_{j}})(1-\alpha) \tag{28}\]  
Therefore, the normalized throughput of a ZigBee device during the \(j\)-th time transmission is:  
\[S_{j}=\frac{P_{tr_{j}}P_{S_{j}}L_{D}}{(1-P_{tr_{j}})\sigma+P_{tr_{j}}P_{S_{j}} T_{s}+P_{tr_{j}}(1-P_{S_{j}})T_{c}} \tag{29}\]  
where \(T_{s}\) is the average time for successful transmission and \(T_{c}\) is the average time that the channel is occupied due to collision. Let \(L_{D}\) be the time of the payload and \(\sigma\) be the duration for a unit backoff slot. Since acknowledgment is transmitted separately in the notice period in our proposed superframe, the durations of these \(T_{s}\) and \(T_{c}\) are equal and are calculated as:  
\[T_{s}=T_{c}=T_{cca}+\sigma+T_{pkt} \tag{30}\]  
where \(T_{cca}\), \(T_{pkt}\) and \(\sigma\) are the time duration for performing CCA, for transmitting the data and the duration of one backoff slot, respectively. Hence, the average normalized throughput of a ZigBee device is:  
\[S=\frac{\sum\limits_{j=0}^{r}S_{j}}{r} \tag{31}\]
## 3 Materials and Methods
### Multi-Temporal InSAR Processing  
Ground deformation in Bolvadin is analyzed using the PSI technique. Instead of analyzing the whole image, only persistent scatterers (PS) points which are stable over time are analyzed. The main processing steps of PSI consist of interferogram generation, multi-temporal persistent scatterers (PS) processing and removal of atmospheric phase screen (APS) [14]. The processing of each main block is carried out as suggested in [21]. Single master interferograms are generated using Sentinel Application Platform (SNAP, version 6.0) [38]. Initially, data acquired before March 2015 are corrected for Elevation Antenna Pattern (EAP) to obtain appropriate interferograms. EAP used for Sentinel-1 data upgraded to correct the EAP phase in addition to the EAP gain in March 2015. It is reported that this change may cause range varying phase offset while generating interferogram with products acquired before and after [39]. After proper sub-swath and burst selection, all Sentinel-1 slave images are coregistered to a master image with Sentinel-1 Back Geocoding operator using orbit files [40] and digital elevation  
Figure 3: Baseline plot for Sentinel 1 data. Red star represents the master image of all the pairs. (**a**) Asc. track 160, master image date is 5 May, 2017. (**b**) Desc. track 65, master image date is 2 July, 2017.  
model (DEM). Then, interferograms are formed and the topographic phase component is removed. Shuttle Radar Topography Mission (SRTM) [41] with 1 arc-second (\(\sim\)30 m pixel size) DEM is used for the coregistration and for topographic phase removal from the interferometric phase. VV polarization bands are processed since co-polarized bands achieve higher coherency.  
The stack of interferograms is passed to the next stage for time series analysis. The Stanford Method for Persistent Scatterers (StaMPS, version 4.1-beta) software package [13; 42] is used for selection of PS points within the stack of interferograms and for multi-temporal processing of them. Candidate PS points are initially selected by using the amplitude dispersion index originally proposed by [43] with a relatively higher threshold. Then, spatially-correlated terms are estimated using phase analysis and subtracted from the interferometric phase to obtain more stable PS points that not only depend on signal intensity but also phase stability. In the last stage, the Toolbox for Reducing Atmospheric InSAR Noise (TRAIN) [44] is used to remove atmospheric phase screen. The ERA-I global weather model is used to compute tropospheric phase effects caused by temperature, pressure and humidity variations. Lastly, tropospheric phase delays for each acquisition date are removed from the interferometric phase to obtain the final deformation signal.
## 4 Result Analysis
### Analysis of Annotation Efficiency for Different Image Complexities  
The automatic annotation method developed in this study effectively addresses the challenge of extensive manual labeling typically required after classification adjustments or image modifications during or following model training. This approach enhances efficiency through two primary strategies:  
1. Leveraging Existing Annotation Results: By fully utilizing previously verified annotations as a foundation.
2. Intelligent Adaptation: By generating variations of annotated data through data augmentation based on existing labels.  
However, it is important to note that the current data augmentation process in this study is limited to rotation and translation of quadrilateral bounding boxes compatible with the YOLO format. This constraint limits the applicability of the method to other annotation types, such as polygon annotations or semantic segmentation masks. In scenarios involving diverse annotation formats or more complex image characteristics, such as varying resolutions, irregular object shapes, dense object distributions, or high-noise  
Figure 8: Normalized confusion matrix of the trained model.  
backgrounds, the current approach may encounter limitations. Consequently, future work will aim to extend augmentation strategies to support additional annotation formats and enhance robustness when processing more challenging image conditions.
## 6 Conclusions  
In this paper, we investigated the application of meta-learning in 6G wireless communication systems and evaluated the performance of two prominent meta-learning algorithms, model-agnostic meta-learning (MAML) and Contextual Variable Meta-Learning (CAVIA), across three communication scenarios: IoT, optical wireless communication systems (OWCSs), and molecular communication systems (MCSs). Our findings indicate that meta-learning algorithms significantly outperform conventional learning methods, particularly in environments with limited training data.  
In the IoT scenario with a binary fading channel and 16-QAM modulation, conventional learning methods failed to achieve error rates below 0.25, whereas both MAML and CAVIA reached optimal solutions, with MAML outperforming CAVIA by 52%. Similarly, in the OWCS setting, meta-learning algorithms demonstrated superior performance even with minimal pilot symbols, with MAML achieving a 9% performance improvement over CAVIA. For MCSs, conventional methods could not achieve error rates below 0.25, while meta-learning algorithms showed significant promise.  
Our results also reveal that, while conventional machine learning can match the performance of meta-learning when ample training data are available, meta-learning excels in rapidly adapting to new tasks with minimal data, making it ideal for dynamic and data-constrained environments.  
The significance of this research lies in demonstrating the potential of meta-learning to enhance the efficiency and robustness of 6G communication systems. By enabling quick adaptation and efficient learning in diverse and challenging communication environments, meta-learning algorithms pave the way for the development of more intelligent, adaptive, and resilient wireless networks. This work contributes to the ongoing efforts to meet the growing demands of future communication technologies and supports the advancement of smart, connected societies.  
Conceptualization, A.Z. and M.A.O.; methodology, A.Z. and M.A.O.; software, T.-A.D.L.; validation, A.Z. and M.Z.A.; formal analysis, A.Z. and M.A.O.; investigation, A.Z., M.A.O. and M.Z.A.; writing--original draft preparation, A.Z.; writing--review and editing, A.Z. and M.Z.A.; supervision, M.A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 5 Case Study
### Parameter Settings of the BPNN Model, the SVM Model and the LSTMNN Model  
When the BPNN model, the SVM model and the LSTMNN model were used to forecast the wind speed series in the cases, a series of parameter settings for the models are shown in Table 3. In addition to the parameters specified in the table, the default values given by the corresponding software toolbox were used for other parameters. The wind speed data should be normalized before being input into the forecasting model. The normalization function used in this paper is the mapminmax function, which normalizes the wind speed series to \([-1,1]\).
## 6 Performance Analysis
### Results and Discussion  
In this section, the results of our performance testing are given and analyzed.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Send Rate for GetBallot (tps)** & **Send Rate for CastVote (tps)** & **Number of Transactions** \\ \hline _Experiment I_: Transaction send rates and its impact & & & \\ on scheme throughput and latency & 50, 100, 150, 200, 250, 300 & 5, 25, 50, 75, 100 & 1000 \\ \hline _Experiment II_: Varying numbers of transactions & & & \\ on scheme throughput and latency & 50, 100, 150, 200, 250, 300 & 5, 25, 50, 75, 100 & 1000, 2000, 10,000, 20,000 \\ \hline _Experiment III_: Transaction send rates impact & & & \\ on error rate & 50, 100, 150, 200, 250, 300 & 5, 25, 50, 75, 100 & 1000, 2000, 10,000, 20,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for Performance Evaluation (Author’s own processing).
## 3 Results
### Evaluation of Pathogenic Inhibition by Standard VOCs  
We decided to evaluate different volume concentrations of 2,5-dimethyl-pyrazine and acetoin independently and mixed, and we assessed them in relation to _K. cowanii_ Cp1 to determine whether these chemical compounds are essential to colonization competition and the reduction in soft rot symptoms caused by _P. aroidearum_ SM2 in serrano chili. In the results (Figure 12), 2,5-dimethyl-pyrazine at a volume of 20 \(\upmu\)L achieved the complete inhibition of soft rot symptoms in treatments T6 and T11, while lower volumes (T4 and T5) yielded similar results to treatment T3, that is, reduced soft rot symptoms compared with the positive control (T2). When acetoin was tested, at all volume concentrations (T7, T8, and T9), the soft rot symptoms were less prevalent than those present in treatment T3, but when both VOCs were mixed at a volume of 5 \(\upmu\)L each (T11), a complete inhibition of soft rot symptoms was observed. Interestingly, when _P. aroidearum_ SM2 was tested with acetoin alone at a volume concentration of 20 \(\upmu\)L (T12), the soft rot symptoms were similar to those seen in the positive control (T2). Negative controls did not show any perturbation (T1, T13, and T14). Additionally, bactericidal effects were observed on both bacterial strains when using 2,5-dimethyl-pyrazine at a volume concentration of 20 \(\upmu\)L, with a zone of inhibition area of 2 mm. For acetoin, no effects were observed for any volume concentration tested. These results support the hypothesis that during competitive colonization, _K. cowanii_ Cp1 synthetizes a mixture of VOCs to yield a competitive advantage.  
Figure 12: Evaluation of pathogenic inhibition by standard VOCs. In a total volume of 100 \(\upmu\)L of sterile water, we resuspended both bacterial strains (10\({}^{8}\) CFU/mL) for treatment without VOCs (T3) or with VOCs in addition to 5, 10, and 20 \(\upmu\)L of 2,5-dimethyl-pyrazine (T4, T5, and T6) or acetoin (T7, T8, and T9) and a mixture of both VOCs at a volume concentration of 5 \(\upmu\)L each (T10). T11 and T12 treatments only used _P. aroidearum_ SM2 combined with 20 \(\upmu\)L of 2,5-dimethyl-pyrazine or 20 \(\upmu\)L of acetoin, respectively. The controls used sterile water (T1), _P. aroidearum_ SM2 (T2), 2,5-dimethyl-pyrazine (T13), and acetoin (T14).
## 4 Results and Discussion
### Modeling of SVAR for Experimental Data
#### 4.5.4 Effect of Day of the Year on the Energy of SVAR  
Figure 15 shows the energy of cooling produced by the SVAR system for the 21 st of every month during the year for the standard-designed model. The maximum energy obtained for 21 December is 7.50 kWh while the minimum is 7.23 kWh obtained for 21 June.
# The Need for Machine-Processable Agreements in Health Data Management +
###### Abstract  
Data processing agreements in health data management are laid out by organisations in monolithic \"Terms and Conditions\" documents written in natural legal language. These top-down policies usually protect the interest of the service providers, rather than the data owners. They are coarse-grained and do not allow for more than a few opt-in or opt-out options for individuals to express their consent on personal data processing, and these options often do not transfer to software as they were intended to. In this paper, we study the problem of health data sharing and we advocate the need for individuals to describe their personal contract of data usage in a formal, machine-processable language. We develop an application for sharing patient genomic information and test results, and use interactions with patients and clinicians in order to identify the particular peculiarities a privacy/policy/consent language should offer in this complicated domain. We present how Semantic Web technologies can have a central role in this approach by providing the formal tools and features required in such a language. We present our ongoing approach to construct an ontology-based framework and a policy language that allows patients and clinicians to express fine-grained consent, preferences or suggestions on sharing medical information. Our language offers unique features such as multi-party ownership of data or data sharing dependencies. We evaluate the landscape of policy languages from different areas, and show how they are lacking major requirements needed in health data management. In addition to enabling patients, our approach helps organisations increase technological capabilities, abide by legal requirements, and save resources.  
data sharing; consent; privacy policies; privacy languages; genomic data; genomic medicine; health data management +
Footnote †: This paper is an extended version of G.K.’s invited talk abstract published in the Proceedings of the Second International Workshop on Semantic Web Meets Health Data Management (SWH 2019), Auckland, New Zealand, 26 October 2019.
# Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves
###### Abstract
Moreover, the increment of RPAS datasets is additionally broadening the potential application of such an approach [52; 53]. Although these systems were initially implemented to acquire data in extensive outdoor environments, there are now many recent studies that describe their applicability in indoor spaces [54; 55], even in dark caves [56]; in this last case, proposed solutions are mainly employed for the survey of small portions of the natural cave [57] and not for extensive surveys. The main advantage of the use of passive sensors is that they do not require elaborate data acquisition planning, and data processing is also relatively easy. The resolution of the generated 3D point clouds strictly depends on: the image resolution, the camera characteristics, and the acquisition distance from the studied object, namely by the Ground Sample Distance (\(GSD\)), which can be calculated as follows:  
\[GSD=\frac{h_{g}\cdot\rho}{c_{k}}, \tag{1}\]  
where \(h_{g}\) is the flying height above ground (or the acquisition distance from the object), \(c_{k}\) is the focal length of the camera and \(\rho\) is the image's pixel size [58]. Even if the density of point clouds can be compared to that of the TLS, it may be affected by a certain level of noise in particular with low light conditions, light variations, and reflective or uniform surfaces. The recent evolution of SfM applications on caves seems to be encouraging for the identification of a methodology that could be used for a massive 3D mapping of caves. To date, only a minimal number of caves are represented in the third dimension and they are often equipped with ladders and walkways, for tourism or archaeological purposes, to ease their access [59; 60]. In this paper, we investigated the best compromise between the accuracy and feasibility of different techniques and to define a general methodology to carry out a 3D survey of natural and complex caves. The proposed methodology should be easily replicable and ensure sufficient precision, such as to be able to populate a possible 3D cadastral map of the caves or carry out virtual tours, even for difficult to access environments. In fact, this type of application requires a user-friendly methodology that refers to simple concepts and allows a high diffusion of the technique. The paper is organized as follows: we made an accurate survey of a test site, i.e., a road tunnel segment to compare the proposed techniques with the reference data and assess their accuracy. According to the test site results, we developed and proposed a methodology for the survey of natural caves based on the SfM approach. The proposed survey technique is tested in the Gazzano cave. The final discussion presents an analysis of the different 3D survey methodologies and the best solution for the 3D natural caves survey, showing the general validity of the SfM approach.
## 3 Improved Q-Learning Algorithm for Better Management of Channel Utilization in UWCNs
### Reward Function (\(R_{f}\))  
The success of an activity performed in a certain condition is measured by the reward function. Successful data transfer, collision avoidance, and energy efficiency should all be balanced:  
* Energy consumption (\(EC\)): The energy used in a node to forward data packets at the transmission energy level. Nodes can be running at different energy levels, which impacts the achievable range and level of reliability. Transmission energy allows nodes to increase the coverage area of the communication and to conserve more energy by reducing the transmission power when interacting with other nearby nodes. This flexibility is particularly important in power-limited platforms such as UWSNs, where one has to be very careful when using both energy resources and communication subroutines.
* Collision occurrence (\(CO\)): Energy transmission: A node is also capable of varying the energy required for transmission over another node probably as a function of distance or the state of the network. Higher energy levels make the transmission more successful over longer distances but consume more energy. On the other hand, a reduced energy level is adopted every time nodes are nearby, or in cases where power usage is to be minimized.
* Successful transmission (\(ST\)): The simultaneous adjustment of transmission energy (E) and the selection of a retransmission strategy (R) makes the system much more dynamic. For example, a node can decide to boost the transmitted power level for urgent data frames for them to reach the intended destination intact, and decide on a retransmission technique for frames that were presumably not received at the intended destination due to interferences or collisions. These enable flexibility in the action space and lead to better energy management and overall network performance. Thus, R(s, a) = W1 \(\times\)\(ST-\)W2 \(\times\)\(EC-\)W3 \(\times\)\(CO\) may be the reward function, with W1, W2, and W3 being weights signifying the relative significance of each element.
## 3 Anaerobic Digestion of Food Waste
### Key Parameters
#### 3.2.6 Metal and Heavy Metals Traces  
Together with nutrients (C, H, O, N), light metals (Na, K, Meg, Ca), and heavy metals (Cr, Co, Zn, Cu) are necessary for anaerobic bacteria to develop enzyme synthesis and activity maintenance [158; 159], although an excess of metal concentration can lead to AD inhibition.  
In FW, concentration of heavy metals is almost insufficient [46; 160] while light metals such as Na, K, and Ca are, generally, found at high concentration levels [47; 84; 161], as can be observed in Table 1, which summarizes metal concentrations in FW reported by several researchers.  
Heavy metals are supposed to cause inhibition due to a disruption of the enzyme function and structure of microorganisms [133]. Regarding light metals, Na, for example, creates an optimal environment for methanogens in 350-400 mg/L [133] and concentration, respectively. K was capable of enhancing methanogenesis in mesophilic and thermophilic conditions. Ca, on its own, has a controversial effect: Some researchers discovered that no inhibition was found when the Ca concentration was really high, specifically up to 7000 mg/L [162], others found out that the optimum Ca concentration range is 150-300 mg/L [163], or with a toxicity threshold even lower, up to 200 mg/L [164].
## 3 Results  
### Rv0934 (PstS-1) and Rv1886c (Ag85B) Gene Identification  
The primers were designed based on the Rv0934 and Rv1886c genes of _M. tuberculosis_, as described previously, and were used to amplify and compare the same genes of M. microti using PCR. In Figure 1C,D, the PCR amplicons were detected for both strains that corresponded to the appropriate molecular weight (MW) of 1125 bp for Rv0934 and 978 bp for Rv1886c in _M. tuberculosis_ (Figures 1C and 1D, lane 1) and _M. microti_ (Figures 1C and 1D, lane 2). The sequencing and analysis of the Rv0934 gene from _M. microti_ revealed a 99.9% identity to the sequence reported for _M. tuberculosis_, with a base change from T-to-C at nucleotide 1 055 bp, resulting in an Ala-to-Val mutation in the protein sequence (Figure S1A). For the Rv1886c gene, we found a 100% identity between the two strains (Figure S1B). These results confirmed that the Rv0934 and Rv1886c genes of M. microti are homologous to the previously described genes of _M. tuberculosis_.  
Figure 1: Plasmid design and construction of (**A**) pMV261-Rv0934 for PstS-1 and (**B**) pMV261-Rv1886c for Ag85B. (**C,D**) PCR amplification of Rv0934 and Rv1886c, respectively, from _M. tuberculosis_ (lane 1) and _M. microti_ (lane 2). (**E,F**) PCR amplification of Rv0934 and Rv1886c from plasmid isolated from _E. coli_ colonies after transformation with pMV261-Rv0934 and pMV261-Rv1886c, respectively (lane 3); controls (lanes 1 and 2, respectively). (**G,H**) PCR amplification of Rv0934 and Rv1886c from plasmid isolated from _M. microti_ colonies transformed with pMV261-Rv0934 and pMV261-Rv1886c, respectively (lane 3); controls (lanes 1 and 2). Black arrows indicate the expected amplicon size.