esa-sceva/satcom-chunk-collection huggingface dataset:
# Quadcopters in Smart Agriculture: Applications and Modelling
## 5 Challenges and Future Directions
### Consumption of Resources
#### 5.1.2 Future Directions  
Future research could focus on developing advanced battery technology that can be mounted on quadcopters or using renewable energy sources, such as solar power, to prolong flight duration [147]. However, in the case of integrating solar cells, they should be lightweight and positioned properly to avoid negatively impacting the quadcopter's weight or functionality. For example, the authors in [148] proposed an efficient solar-powered quadcopter system, incorporating a small battery, which extended the quadcopter's endurance by 48.7 times compared to using the battery alone. This innovative design did not compromise the quadcopter's functionality, demonstrating its potential for extended missions in agriculture. Such technology is ideal for long-duration tasks like mapping vast areas or collecting data from large fields in a significantly reduced time.  
Alternatively, users could consider solar charging stations where quadcopters can stop, recharge, and resume operations. For example, the authors in [149] proposed a design for solar-powered mobile charging stations equipped with a battery selection system to control the charging process and optimize battery replacement. In this system, quadcopters can autonomously land, swap their batteries, and quickly resume their missions. This approach has significant potential in agricultural applications, where charging stations can be placed across large fields. This is particularly valuable for missions such as the precision mapping of large farms, the real-time monitoring of crop health, and managing irrigation.  
Additionally, path optimization algorithms can be used to accomplish the quadcopter's mission in less time and with lower energy consumption [150]. For example, the authors in [151] conducted a study using a quadcopter to identify stressed regions in a field. An optimal path for spraying was then determined using a specific path optimization algorithm. This approach helps manage irrigation by ensuring efficient spraying in less time, and it can also be applied to other tasks such as chemical spraying, crop monitoring, and planting.  
Furthermore, multiple UAVs can collaborate to perform the same agricultural tasks more quickly and efficiently [152]. For example, the authors in [153] demonstrated that a swarm of UAVs is more efficient than a single UAV for mapping large agricultural fields. The UAVs, each equipped with different sensors, coordinated to map large areas in parallel. This approach illustrates how multiple UAVs can rapidly cover vast fields, and can be similarly applied to other agricultural tasks.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 4 System Model  
### Entity  
The system model of our proposed secure deduplication protocol is described in Figure 1. The entities in the system model are as follows:  
* Client: A client is a person who has ownership by uploading data to cloud storage. Since the data were deduplicated and stored, only the initial client's data are stored in the storage. The client refers to both the initial and subsequent uploaders that have ownership.
* Cryptographic server (CS): The CS acts as an intermediary between the client and cloud storage. The CS configures the access control list (\(ACL\)) with the hash value received from the client. The \(ACL\) manages data information stored in the cloud storage and client information that owns it. The CS controls the client's data access rights based on the \(ACL\). If data need to be stored in cloud storage, CS encrypts the data and sends them to cloud storage.
* Cloud storage: Cloud storage stores data from clients. Cloud storage generates a group key for the data in which the storage request is made to manage the dynamic ownership update. The key is generated independently of a key shared in the previous owner group. The data in which the storage request occurs are re-encrypted with the generated key and stored in the storage. It is assumed that cloud storage is unreliable.
## 2 Research Questions and Hypotheses  
To investigate the interaction between human and machine, test times were in focus during this research. As the investigation focuses on the influence of display parameters and devices on test times, two RQs and Hs were formed. The RQs are the following:  
* RQ1: Are test times influenced by various display parameters and devices? Is there an interaction between them?
* RQ2: Which combination of these factors results in either the smallest or the largest test times? The authors try to recognize the best and the worst combinations.  
After the RQs were formed, the same number of Hs was also formed. To make complete testing hypotheses, first, the null hypotheses are phrased, then the alternative hypotheses are phrased. These are the following:  
**Hypothesis 1 (H1).**_No significant influences on completion times exist due to various display parameters and devices, opposite to significant influences on completion times exist due to various display parameters and devices._  
**Hypothesis 2 (H2).**_The smallest and the largest completion times are not significantly influenced by these factors, opposite to the smallest and the largest completion times are significantly influenced by these factors._
###### Abstract
Despite these challenges, the potential to use this waste heat remains significant, especially since coolers are widely used to manage the high temperatures generated during processing. Coolant not only dissipates heat, but it also contributes to environmental concerns by becoming a source of waste. Current systems often discard the coolant after a period of use, neglecting the energy recovery potential inherent in the fluid. However, sustainable production does not only mean saving energy and natural resources but also using processes and systems that do not pollute. Therefore, sustainable production costs must take into account energy consumption, waste management, environmental impacts, and occupational health and safety. In this context, in recent years, concepts such as clean production, environmentally friendly production, and green production have been introduced in many production sectors to support sustainable production systems [12]. This study aims to address these gaps by developing an original integrated system that recovers waste heat from coolant during the machining process. By implementing advanced control technologies and incorporating a heat pump, the system effectively captures low-temperature heat and reuses it for beneficial uses such as water heating and shop floor environments. The proposed system provides a comprehensive solution to the long-neglected problem of waste heat recovery in machining by increasing energy efficiency, reducing coolant consumption, and extending tool life. It also makes significant contributions to sustainable manufacturing by reducing negative environmental impacts. This study was conducted on metal drilling, an important machining method. When the system is widespread among other machining methods, it will turn into a great potential benefit that has been neglected so far. Therefore, considering the improvement in machining quality and the reduction in cutting-tool and cutting-fluid usage, it will contribute significantly to productivity and sustainability. In this paper, the design and integration of the developed coolant heat recovery system will be explained first, followed by the presentation of the experimental setup and parameters, the evaluation of the system's performance under different cutting parameters, and finally a discussion on energy recovery and system efficiency.
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 3 Results
### Maize_4  
Figure 4 presents the results of the evaluation of the entire test dataset using the metrics MAAPE and NRMSE. Regarding the observed prediction performance, method C (conventional) clearly outperforms method A (augmented) by 57.65% in terms of MAAPE and 53.41% in terms of NRMSE. However, when focusing the analysis on the top 20% of the test dataset (Figures MAAPE_20 and NRMS_20), it becomes apparent that method A exhibits a greater performance in comparison to method C, showing a reduction of 39.65% in MAAPE_20 and 37.24% in NRMSE-20 when compared with MAAPE and NRMSE, respectively. For additional details, refer to Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Method** & **Metric** & **Average** & **SE** \\ \hline Maize\_3 & A & MAAPE & 0.2873 & 0.0039 \\ Maize\_3 & A & MAAPE\_20 & 0.0818 & 0.0018 \\ Maize\_3 & A & NRMSE & 1.8485 & 0.0057 \\ Maize\_3 & A & NRMSE\_20 & 1.4239 & 0.0040 \\ Maize\_3 & C & MAAPE & 0.1241 & 0.0014 \\ Maize\_3 & C & MAAPE\_20 & 0.1314 & 0.0012 \\ Maize\_3 & C & NRMSE & 0.8648 & 0.0030 \\ Maize\_3 & C & NRMSE\_20 & 2.3428 & 0.0177 \\ \hline \end{tabular}
\end{table}
Table 4: Prediction accuracy results for dataset Maize_3 using conventional (C) and augmented (A) methods, with metrics MAAPE and NRMSE for the total testing and MAAPE_20 and NRMSE_20 for the best 20% testing.  
Figure 4: Prediction accuracy performance results for the Maize_4 dataset using conventional (C) and augmented (A) methods in terms of mean arctangent absolute percentage error (MAAPE), mean arctangent absolute percentage error for the best 20% cultivars (MAAPE_20), normalizing the mean square error (NRMSE), and normalizing the mean square error for the best 20% cultivars (NRMSE_20).
## 3 Length and Orientation  
The locations and volume of CMLs that were discussed in Section 2 characterize CMLs collectively. Individual CML attributes present a significant attenuation measurement factor as well. These characteristics are the orientation and length of the CML. The understanding of all three factors allows for 2-D modeling of CMLs, as this paper is concerned with. It should be mentioned that if one were to examine the 3-D modeling of CMLs, the CMLs' height variability would be a factor as well.  
The study presented here regards CMLs in the state of Israel belonging to a single cellular provider, Cellcom (see Appendix A for further details). All CMLs are operating in the same frequency range, the K-band.  
Results show that in any type of region studied, the CMLs' orientation takes on any angle with equal probability. This means the direction of CMLs is distributed uniformly and is not at all correlated with the type of population density. Figure 3 portrays this conclusion. Moreover, the orientation is found to be statistically independent of the other factors studied here, the CML's length and density.  
[PERSON] [12] studied the distribution of CMLs as well. To do so, a map of Israel was partitioned into four parts based on latitude (as Israel stretches from latitude 29.5\({}^{\circ}\) N to 33.29\({}^{\circ}\) N). These four parts were used as sub-regions to study CMLs. However, these four regions were heterogeneous in their environmental types. Here, by isolating sub-regions of homogeneous environment types and then characterizing CML statistics by such types, we suggest a contribution to [PERSON]'s work on statistical modeling of CMLs in Israel.  
The study of CMLs' lengths yielded much different results than the orientations. Unlike orientations, CML lengths are distributed non-uniformly. CML lengths are distributed exponentially. Moreover, the lengths' statistical characteristics depend on the type of environment. Figure 4 illustrates both findings.  
The sample-means specified in Figure 4 are used for the fitting of an exponential distribution. For an exponential random variable, e.g., \(T\sim Exp(1/\theta)\), the probability density function is:  
\[f_{T}(t|\theta)=\left\{\begin{array}{ll}\frac{1}{\theta}e^{-\frac{t}{\theta} }&t\geq 0\\ 0&t<0\end{array}\right., \tag{1}\]  
Figure 4: Lengths distributions for various environments. (**a**) all Israel, (**b**) top of northern Israel as rural, (**c**) Hasharon as suburban, (**d**) Tel Aviv as urban [6].  
Figure 3: The distribution of CML angles. (**a**) all Israel, (**b**) top of northern Israel as rural, (**c**) Hasharon as suburban, (**d**) Tel Aviv as urban [6].  
where \(\theta=E[T]\). This presents a direct tie between the sample mean of the CMLs' length and the exponential fitting. Table 2 presents a data summary for CMLs in Israel used in this study.
## 4 Experiment
### Parameter Sensitivity  
\(\ell_{2,1}\)HNMFD has three parameters, \(\lambda\), \(\mu\) and \(\gamma\). We investigated their influence on \(\ell_{2,1}\)HNMFD's performance by varying one parameter at a time while fixing the other two. For each specific setting, we run \(\ell_{2,1}\)HNMFD 10 times and record the average performance.  
We plot the performance of \(\ell_{2,1}\)HNMFD with respect to \(\lambda\) in Figure 1a. Parameter \(\lambda\) measures the importance of the graph embedding regularization terms of \(\ell_{2,1}\)HNMFD. A too small \(\lambda\) may cause graph regularization so weak that the local geometrical information of data cannot be effectively characterize, while too big \(\lambda\) may cause a trivial solution. \(\ell_{2,1}\)HNMFD shows superior performance when \(\lambda\) equals 0.01, 0.001 and 0.1 for YALE, ORL and UMIST, respectively.  
We plot the performance of \(\ell_{2,1}\)HNMFD with respect to \(\mu\) in Figure 1b. Parameter \(\mu\) controls the orthogonality of the learned representation. When \(\mu\) is too small, the orthogonal constraint will be too weak, and \(\ell_{2,1}\)HNMFD may be ill-defined. When \(\mu\) is too large, the constraint may dominate the objective function of \(\ell_{2,1}\)HNMFD, and the learned representation will be too sparse, which is also unfaithful to the real-world situation. We can observe that \(\ell_{2,1}\)HNMFD is able to achieve encouraging performance when \(\mu\) equals 0.001, 0.001 and 0.1 for YALE, ORL and UMIST respectively.  
We plot the performance of \(\ell_{2,1}\)HNMFD with respect to \(\gamma\) in Figure 1c. Parameter \(\gamma\) controls the degree of sparsity of the encoding matrix. Sparsity constraints that are too weak or too heavy will be bad for the learned representation. We find that \(\ell_{2,1}\)HNMFD consistently outperforms the best baseline methods on the three datasets when \(\gamma=1\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Kmeans** & **NMF** & **NCut** & **GNMF** & **Ours** \\ \hline YALE & 43.58 \(\pm\) 2.42 & 45.00 \(\pm\) 2.71 & 45.91 \(\pm\) 2.15 & 46.08 \(\pm\) 2.12 & 46.88 \(\pm\) 2.11 \\ ORL & 70.93 \(\pm\) 1.69 & 73.36 \(\pm\) 1.46 & 75.13 \(\pm\) 1.50 & 75.52 \(\pm\) 1.93 & 76.09 \(\pm\) 0.95 \\ UMIST & 60.08 \(\pm\) 1.65 & 60.32 \(\pm\) 0.85 & 62.11 \(\pm\) 1.76 & 63.53 \(\pm\) 1.27 & 66.13 \(\pm\) 1.26 \\ COIL20 & 74.32 \(\pm\) 2.00 & 72.65 \(\pm\) 1.21 & 78.40 \(\pm\) 1.57 & 86.92 \(\pm\) 2.79 & 89.90 \(\pm\) 1.79 \\ PIE & 53.55 \(\pm\) 1.02 & 78.68 \(\pm\) 109 & 81.87 \(\pm\) 1.63 & 89.07 \(\pm\) 0.82 & 91.27 \(\pm\) 2.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Normalized Mutual Information on the 5 datasets (%).  
Figure 1: Influence of different parameter settings on the performance of \(\ell_{2,1}\)HNMFD in 3 datasets: (**a**) varying \(\lambda\) while fixing \(\mu\) and \(\gamma\); (**b**) varying \(\mu\) while fixing \(\lambda\) and \(\gamma\); and (**c**) varying \(\gamma\) while fixing \(\lambda\) and \(\mu\).
# A Deep Learning-Driven Black-Box Benchmark Generation Method via Exploratory Landscape Analysis
###### Abstract  
In the context of algorithm selection, the careful design of benchmark functions and problem instances plays a pivotal role in evaluating the performance of optimization methods. Traditional benchmark functions have been criticized for their limited resemblance to real-world problems and insufficient coverage of the problem space. Exploratory landscape analysis (ELA) offers a systematic framework for characterizing objective functions, based on quantitative landscape features. This study proposes a method for generating benchmark functions tailored to single-objective continuous optimization problems with boundary constraints using predefined ELA feature vectors to guide their construction. The process begins with the creation of random decision variables and corresponding objective values, which are iteratively adjusted using the covariance matrix adaptation evolution strategy (CMA-ES) to ensure alignment with a target ELA feature vector within a specified tolerance. Once the feature criteria are met, the resulting topological map point is used to train a neural network to produce a surrogate function that retains the desired landscape characteristics. To validate the proposed approach, functions from the well-known Black Box Optimization Benchmark (BBOB) suite are replicated, and novel functions are generated with unique ELA feature combinations not found in the original suite. The experiment results demonstrate that the synthesized landscapes closely resemble their BBOB counterparts and preserve the consistency of the algorithm rankings, thereby supporting the effectiveness of the proposed approach.  
algorithm selection problem; exploratory landscape analysis; benchmarking functions; black-box optimization; neural networks Article
## 2 Materials and Methods
### Antibacterial Activity Assessment  
The antibacterial efficacies of the ethanolic extract of _C. villosa_, _P. lentiiscus_, _R. officinalis_, _E. multiiflora_, and _P. latifolia_ were tested against microorganisms, including Gram-positive bacteria (_Staphylococcus aureus_ ATCC 29213; _Listeria monocytogenes_ ATCC 7644) and Gram-negative bacteria (_Escherichia coli_ ATCC 8739; _Salmonella typhimurium_ NCTC 6017). Bacterial Species were generously provided by the Biotechnology Center in Borj Cedria Technopole, Tunisia.  
The antibacterial efficiency of tested ethanolic extracts was qualitatively and quantitatively evaluated.  
The disc diffusion method was employed to assess the antibacterial potential of the extracts that were evaluated [29]. Fifteen \(\mu\)L of the EEs (Concentrations = MIC) were added to the sterile filter paper discs (6 mm diameter). Then, these discs were placed on the agar that had been previously inoculated with the selected bacteria. Gentamicin (10 \(\mu\)g/disc) was used as a positive control. Negative control corresponded to a disc without a sample (disc contains ethanol to determine the solvent activity). The culture plates were incubated for 24 h at room temperature (37 \({}^{\circ}\)C). The qualitative antibacterial activity of the tested extracts was determined by measuring the diameter of the inhibited growth zone (with the diameter of the 6 mm disc). Three repetitions were carried out for these experiments.  
Secondly, the determination of the minimum inhibitory concentrations (MIC) and the minimum bactericidal concentrations (MBC) of tested ethanolic extracts presented the quantitative antibacterial activity and were estimated using the broth dilution method as described by [PERSON] et al. [30]. Different concentrations of ethanolic plant extracts, ranging from 1.56 to 50 mg/mL, were prepared using the serial dilution technique, including a growth control and a sterility control.  
After incubation at 37 \({}^{\circ}\)C for 24 h, Petri plates were inoculated with 10 \(\mu\)L of each dilution and incubated overnight at 37 \({}^{\circ}\)C. The growth of tested isolates (MIC) was determined by the lowest number of colonies formed in each bacterium at 37 \({}^{\circ}\)C overnight. The MBC refers to the minimum concentration of the extract at which isolates were killed completely. Experiments were performed at least in triplicates.
# Machine Learning Techniques for Non-Terrestrial Networks
## 6 Machine Learning Techniques in NTN  
The adoption of ML enables improvements in NTN performance. Each ML technique should be carefully evaluated based on input parameters and the functionality to be supported in a given NTE, thus exploiting the data availability. Proposed ML techniques are provided in the following, grouped per services or functions available in the layers of the system protocol stack.
# A Biologically Inspired Sound Localisation System Using a Silicon Cochlea Pair
## 3 Experiment and Evaluation  
### Experimental Setup  
In the experiment, the binaural data were collected in a reverberant environment, as shown in Figure 7A. In the setup, the sound source incidence angle ranging from \(-90^{\circ}\) to \(90^{\circ}\) was divided into 13 locations with a \(15^{\circ}\) step. Two microphones were placed 0.4 m apart from each other on the floor. A speaker was placed 0.96 m away from the centre of the two microphones. The corresponding maximum ITD of this setup is around 1.17 ms (0.4 m/343 m/s). It is within the range of the 70-channel CAR-FAC delay line, 6.5 ms as explained in Figure 4. The setup is thus suitable for investigating the system. We used ten isolated spoken digits (zero to nine) from five speakers in the AusTalk [59; 60] database as the sound source, and the spoken digits were played at all 13 locations. A PC connecting to the two microphones recorded the speech to create a binaural signal dataset. Additionally, we augmented the dataset by adding different band-limited noises with different Signal-to-noise ratio (SNRs) (between 15 dBFS and 25 dBFS) and inverting the signal values in the time domain. More details about audio data augmentation can be found in Reference [61]. Through data augmentation, the dataset was enlarged to 11,704 samples.  
Figure 7B shows an example of the onset detection in the system. The logarithmic input sound energy change \(\Delta E(t)\), threshold \(\Delta Eth\), and the time window step are highly signal- and environment-dependent. In this experiment, we found that the threshold \(\Delta Eth\) 3 and _step_ 125 ms in (3) are appropriate for most of the data and thus tend to provide optimal performance. For binaural signals, a separated onset time is detected, and the earliest of the two is used as the onset time. The onset correlogram then is generated 90 ms after the onset, i.e., \(\Delta t\) = 90 ms.