esa-sceva/satcom-chunk-collection huggingface dataset:
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation  
The work presented in this paper makes use of the Service oriented architecture (SOA) approach by generating service interfaces to increase the interoperability in heterogeneous SOA environments. SOA is a well-known and adopted architectural style based on the use of services as the main mean to exchange information [22]. It has been proposed to provide syntactic interoperability and interoperability across heterogeneous systems [23]. It is built on top of the network layer; hence, processed information and data are easily managed through services [24]. SOA has been selected as a reference architecture for this work due to its principles, such as encapsulation, composition, reuse, loose coupling, late binding, and maintainability. The proposed service interface translation has been designed following the aforementioned SOA principles. For this paper, microservices are considered a specialization of SOA [25]. Therefore, the presented approach may be also adopted in this variant or other SOA variants.  
In SOA environments, systems communicate through services. Successful service communication is critical to achieving system interoperability. A mismatch can occur at different service levels: application protocol, encoding, semantics, and notation.  
The first step to solve service interoperability issues is to define and identify the service mismatches. Service metadata are defined in the service contract; consequently, an accurate definition of the service contract is crucial for the identification of mismatches. The information defined in the service contract is required not only for the identification of mismatches but also for the autonomous generation of interface code.
## 3 Results and Discussion
### Analysis of Evaporation Characteristics at Different Filling Ratios
The average BOG generation rate over the 300-s simulation was calculated for each filling ratio and is presented in Table 5. As the filling ratio increases, the average BOG generation rate shows a clear upward trend, increasing from approximately 0.0000865 kg/s at 50% filling to 0.0003001 kg/s at 95% filling. This quantitative analysis confirms the accelerated BOG production at elevated filling rates observed in Figure 17. This trend can be attributed to several factors: first, at higher filling rates, the contact area between the liquid phase and the tank wall increases, facilitating greater heat absorption from the surroundings. Second, the enhanced heat transfer at the gas-liquid interface under high filling rates further contributes to the accelerated evaporation of the liquid phase. These combined effects result in a higher BOG generation rate at elevated filling rates. Figure 16: The temperature evolution curves of the vapor (a) and liquid (b) phases. Figure 17: The difference in BOG generated under different filling rates.
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 4 Effective \"Green\" Technologies
### CRISPR/Cas9 Gene Drive  
Another emerging technology that could prove to be indispensable in mosquito control is CRISPR/Cas9 gene drive. The CRISPR/Cas9 system has received considerable attention over the past decade not only because of its significant role in bacterial and archaeal immunity, but also because of its broad application as a highly specific gene editing tool [194]. This system utilizes a noncoding guide RNA (gRNA) that allows the Cas9 endonuclease to cleave dsDNA at a designated site. The cleavage is repaired in vivo by non-homologous end-joining or homology-directed repair [195]. From an applied perspective, the CRISPR/Cas9 system can be used to generate mosquitoes that harbor heterologous sequences or deleterious mutations that affect the filial generation in which the targeted function is expressed, or even to potentially disrupt the pathogen lifecycle in biological vectors. For example, sex determination in _Aedes aegypti_ is regulated by the M factor, a dominant male-determining factor harbored in the M locus of the Y chromosome. [PERSON] et al. [196] showed that an M-locus gene, Nix, functions as the M factor in _Aedes aegypti_. CRISPR/Cas9 knockout of Nix resulted in genetic females with almost complete male genitalia, demonstrating that the technique could be used to convert female mosquitoes into essentially harmless phenotypic males that are incapable of breeding.  
The disruption of functions required for female mosquito development will likely be the hallmark of the gene drive technology. This is highlighted in studies where three genes (AGAP005958, AGAP011377, AGAP007280) that confer female-sterility phenotypes were disrupted with CRISPR/Cas9 constructs; the transmission to progeny rates ranged from 91.4-99.6% [197]. Genetic systems that distort sex ratios with a bias for males via RNA-guided shredding of the X-chromosome during spermatogenesis are also quite promising [198; 199; 200]. More current cage studies have been described, and the results lend support to the efficacy of this emerging technology in controlling vector mosquito populations [201; 202; 203; 204; 205]. In addition, commentaries on the prospect of eradicating malaria using CRISPR/Cas9 gene drive [206; 207] and regulatory and policy considerations [208] are the subjects of scrutiny and debate.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 2 Related Work
### Delay Tolerant Networking (DTN) Routing Protocols
#### 2.1.3 Spray and Wait (SAW)  
With the intention of reducing the resource consumption for the Epidemic protocol, [PERSON] et al. [15] proposed the Spray and Wait routing protocol to control the replication of messages in the network. Spray and Wait (SAW) routing is a combination of the distribution speed of Epidemic and the simplicity of Direct Delivery protocols [16]. Similar to the Epidemic protocol, the SAW protocol copies messages without any knowledge of the network topology but it limits the number of copies of the message.  
This routing protocol consists of two phases:  
**Spray phase**: The source node after generating a new message, sprays L message copies to L intermediate nodes it encounters. When the copies are received by intermediate nodes these go into the second phase.  
**Wait phase**: The intermediate nodes carry all the received messages in their buffer and wait until hopefully one of the nodes will encounter the final destination and deliver the data to it directly.  
There are two types of Spray and Wait [17]:  
* **Source Spray and Wait**: In this method, the source node spreads all L-1 copies to the first L-1 encountered distinct nodes (each encountered node gets a copy; and the source itself retains a copy), and all nodes with a copy are in the Wait phase until they meet the destination and release the copy.
* **Binary Spray and Wait**: In Binary type, the source node generates L copies of the original message and whenever it meets a node with no copies, it transfers half of the copies (L/2) to the node and keeps the remaining copies (L/2). The source and relay node with (L > 1) copies continue this process until a single copy remains, which is delivered to the destination directly.
# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 4 Experimental Results
### Classification Results  
Tables 1 and 2 show the obtained classification accuracies and F1-scores, respectively. As previously mentioned, four types of shallow local features (SIFT, GIST, LBP, and HOG) are designed to describe image patches from different perspectives. We divide the dataset as 80% for training and 20% for testing. As the first integration strategy, we can see that the classification accuracy of the shallow feature integration is higher than that of each single feature. Furthermore, from the experiments we observe that the classification accuracies keep increasing from Conv1 to Conv5, because the features of shallow layers of deep CNN models are typically basic and shared, preventing the differentiation of the information in X-ray images. Therefore, we disregard the integration of Conv1 to Conv4 with our proposed handcrafted features. The results of Conv5 features, which are integrated with either each single feature descriptor or all the conventional features, are superior to those obtained in the first integration strategy. Regarding the performance of the supervised classifiers at the last stage of our approach, ELM works best among all the single and integrated features, followed by AdaBoost. Figure 6 summarizes the classification results achieved by the seven supervised classifiers.
## 3 Results
### Calibration Models  
The PLS and PCA methods calculated concentration values well in the models. This was verified when the data points in the calculated vs. actual concentration plot formed a line exactly 45 degrees from both axes Figures 2 and 3). The PCA calibration absorbance models had good RMSEC correlation coefficients with R\({}^{2}\) values of 0.98 (Ag, Cu, Pb) and 0.99 (Cd, Zn) (Figure 2a-e). The Ag model had the highest RMSEC value of 2.43, while the lowest was observed in Zn models with a value of 1.48.  
The PLS calibration absorbance models had R\({}^{2}\) values of 0.95 (Ag), 0.98 (Cu) and 1 (Cd, Pb, Zn) (Figure 3). There were differences in the R\({}^{2}\) values for Ag, Cu, Pb and Zn between the PCA models (Figure 2) and PLS models (Figure 3). The only similarity was in the models for Cu, Figures 1c and 2c. Slightly lower RMSEC values were observed in the PLS as compared to PCA models. In these models, the highest RMSEC was also observed in the Ag model with a value of 3.42. The lowest was obtained in Pb models with a value of 0.55. Models for Pb had a higher prediction ability, while Ag had the lowest. Generally, all the methods had good prediction abilities as the RMSEC were mostly below 2.50, irrespective of the metal ion.  
In the presented results, the model correction was based on calibration values (Figures 2 and 3). Correction, cross-correction and ignored standards were only used in the pre-processing of the data for the method corrections. Each model in Figures 2 and 3 used 10 standards for data validation in the pre-processing for the presented calibration models.  
Data normalization in the models based on the mean centering technique determined the number of principal components used in the PLS and PCR models as shown in Table 2. This resulted in each generated model (Figures 2 and 3) using different principal component factors (Table 2) to increase the prediction of variables.  
Figure 2: Showing the PCA calibration models for caption (a) Ag: (b) Cd; (c) Cu: (d) Pb; and (e) Zn.  
Figure 3: Showing the PLS calibration models for caption (a) Ag (b) Cd; (c) Cu; (d) Pb; and (e) Zn.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 2 Materials and Methods
### Data
#### 2.1.4 Aerial Photo Mosaics  
Norgeibilder.no [36] is a cooperation between Norwegian institutions providing an overview of aerial orthophoto mosaics over Norway. The aerial ortho-mosaics each have their individual meta dataset and specifications, and it is, therefore, not a homogenous database with equal quality, resolution, coverage, etc. or predefined acquisition plans. At best, there are yearly mosaics available for recent years. The general resolution of the aerial data is in the range of 10 cm to 1 m, and the main types are aerial photos in visible wavelength (red-green-blue). Publicly, there is only the date available but no specific time information or acquisition time period. It is, therefore, not possible to compare this dataset with the tidal charts without access to single aerial images or to use it operationally in an easy way. It is only by comparison of different aerial mosaics over the same region that one can roughly estimate high, middle, or low tide from different years. Because of its high resolution, this database is still a good source of ground-truth data with regard to some tidal zone types and the presence of vegetation or algae [37]. However, the water line is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nr.** & **Date/Time** & **Satellite** & **Path** & **Direction** \\ \hline
1 & 1 August 2019; 16:16 & S1A & 131 & ASC \\
2 & 2 August 2019; 16:07 & S1B & 058 & ASC \\
3 & 3 August 2019; 05:28 & S1B & 066 & DES \\
4 & 3 August 2019; 16:00 & S1A & 160 & ASC \\
5 & 4 August 2019; 05:20 & S1A & 168 & DES \\
6 & 5 August 2019; 05:12 & S1B & 095 & DES \\
7 & 7 August 2019; 16:15 & S1B & 131 & ASC \\
8 & 8 August 2019; 16:07 & S1A & 058 & ASC \\
9 & 9 August 2019; 05:29 & S1A & 066 & DES \\
10 & 9 August 2019; 15:59 & S1B & 160 & ASC \\
11 & 10 August 2019; 05:20 & S1B & 168 & DES \\
12 & 11 August 2019; 05:12 & S1A & 095 & DES \\ \hline \hline \end{tabular}
\end{table}
Table 1: S1 paths over the Hansjordnesbukta tidal gauge station in Tromso during a 12 day S1 cycle period 1–12 August 2019. Paths in gray were eliminated due to the cutoff incidence angle at 33.8\({}^{\circ}\).  
generally difficult to see and extract exactly, especially in shallow waters. In this paper, such aerial mosaics are only used for presentation and comparison purposes.
## 7 Conclusions and Future Developments
A comparative analysis has been provided for each covered topic, focusing on architectural aspects, data analysis, and communication technologies, as well as characteristic aspects of each topic and bringing out strengths and limitations of analyzed solutions. In this way, the features, functionalities, and real-world applications of IoT in the framework of smart manufacturing have been outlined. In the future, to optimize smart manufacturing processes, the goal is to improve the interactions between IoT systems and ML by enhancing different aspects: data integration by developing models for data acquisition from various sources (IoT sensors, ERP, production management systems); improvement of the quality of information for ML; development of new standards and protocols that facilitate interoperability between IoT systems and devices; and improvement of predictive models through the use of advanced ML techniques. Considering security aspects, a starting point of the research will be to develop encryption protocols and secure access strategies to better ensure privacy and data security. Taking into account that the implementation of IoT and ML methodologies needs an in-depth knowledge of several aspects, companies will need to train their staff by investing time and capital to have skilled operators. With continuous technological progress, the enhancement of processing software, and the adoption of high-performance ML algorithms, it will be possible to process an ever-increasing amount of data at a higher speed, obtaining a more precise and rapid response to input data. In conclusion, in the near future, the IoT paradigm will be the central cornerstone on which all companies will revolve. Conceptualization, P.V., G.R., and R.D.F. ; methodology, P.V., R.V., and R.D.F. ; validation, R.V., D.C., and R.D.F. ; formal analysis, G.R., C.D.-V.-S., D.C., and R.D.F. ; investigation, P.V., C.D.-V.-S., D.C., and R.D.F. ; resources, P.V., G.R., R.V., and R.D.F. ; data curation, G.R. and R.D.F. ; writing--original draft preparation, P.V., G.R., and R.D.F. ; writing--review and editing, P.V., C.D.-V.-S., D.C., and R.V. ; visualization, G.R., C.D.-V.-S., and R.D.F. ; supervision, P.V, D.C., and R.V. ; funding acquisition, P.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data are available upon request. The authors declare no conflicts of interest.
# Improving Congestion Control of TCP for Constrained IoT Networks
## 4 Preliminary Performance Evaluation
### Issues to be Addressed
#### 4.2.3 Limit on the Number of Retransmissions  
The implementation of uIP TCP in Cooja sets the maximum number of retransmissions to 8 by default. We note that this limit is much lower than that of Linux. For example, in Linux 2.6+, the default value of tcp_retries2 which limits the number of retransmissions is 15. When the 15 th retransmission fails, the connection is deemed broken and the application at the upper layer is notified of the broken connection. With the default limit of 8, we observed many occurrences of delivery failures and even connection failures, particularly when ContikiMAC was used. Hence, for our simulation, we extended the limit to 15. Figure 3b shows that a TCP segment was successfully delivered after 10 retransmissions.
## 1 Introduction  
Nowadays, at the international level [1], statistics show that by the end of 2025, there will be over 20,000,000 operational drones/UAVs, with an estimated value of transactions in this field of over USD 45.8 billion. Within these types of UAVs, the ones with applications relating to social security represent over 60%; consequently, drones are expected to have a high market potential. The crisis of personnel working in agriculture as well as the development of many large farms that cover thousands of hectares are pushing farmers and agricultural associations to use UAVs with increased autonomy for surveillance and culture monitoring. The development of highly advanced video-cameras as well as artificial intelligence is also a factor that helps in the development of UAVs.  
One of the most convenient methods to extend the autonomy of electrically propelled UAVs is to install photovoltaic cells on the wings and/or fuselage and to use the electrical power generated by these cells to charge the batteries. There have been numerous attempts to install photovoltaic cells on UAV wings [2; 3; 4; 5]. The main issues that have been encountered in all of these attempts are generated by the efficiency and the reliability of the photovoltaic cells. The attempt to use large-sized photovoltaic cells was hindered by wing vibrations of the UAVs, which led to silica wafer cracking and sometimes the disruption of soldered connections. Moreover, due to the frequency at which UAVs manoeuvre, the solar irradiation is not equally dispersed on the cell surface, leading to a less efficient conversion. Another disrupting factor was that the weight of the solar cell matrix, the material used to connect them, and the resin used to glue them on the wings increased significantly the weight of the UAV, thus decreasing the weight of the usable payloads.  
The approach presented here was completely different and was oriented in two main directions:  
* To develop an ultra-light UAV wing;
* To develop photovoltaic cells that are suitable to be embedded into the structure of the wing.  
These two efforts are interconnected by specific requirements such as the mechano-climatic loads that are specific for fixed wings.  
The present paper is structured into two main parts: A part consisting of an introduction as well as discussion of the design and fabrication processes, and the second part presents the tests and the prototype validation, conclusions, and perspectives. A short video is also included in the Supporting Materials that documents the flight of the prototype.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 6 Prototype Evaluation
### Smart Home Scenario  
Another domain, in which we evaluated BPMNE4 IoT, is Smart Home. To keep the presented example simple, we focus on the sub-processes for controlling and monitoring the temperature and brightness of the smart office based on the BPMNE4 IoT framework. Figure 17 shows the smart office, which is equipped with four different types of IoT sensors (cf. Table 4) and three different types of IoT actuators (cf. Table 5). In total, six sensors and three actuators are involved in the two sub-processes for controlling and monitoring the temperature and brightness.  
Figure 16: IoT-driven smart factory process in execution.  
Figure 17: Overview of the sensors and actuators in the smart office scenario.  
In addition to the sensors installed in the smart office, the scenario comprises actuators to influence the physical environment (cf. Table 5).  
The sub-process for controlling the brightness in the smart office is illustrated by Figure 18. This example shall demonstrate how such a controlling process can be modeled with BPMNE4 IoT. The process is started as soon as the brightness exceeds 1000 Lux. In this case, the window blinds 1 and 2 are closed, controlling the respective actuators based on the IoT actuator artifact. The associated tasks are successfully completed as soon as the conditions specified by the IoT catch artifacts are met, i.e., position sensor _\"PS01 == 512\"_ and position sensor _\"PS02 == 512\"_. While the window blinds are closed, the wind speed is continuously checked to determine whether it exceeds 20 km/h. If the latter applies, the corresponding blind motors are stopped using an IoT end event. Then, the process is aborted.  
The sub-process for controlling the temperature in the smart office is illustrated by Figure 19. The process starts upon the detection of any movement. Afterwards, the temperature is queried using a smart IoT sensor. If the temperature is lower than 16 \({}^{\circ}\)C, the heating level is set to 100%. The heating level is set to 25% if the temperature lies between 16 \({}^{\circ}\)C and 22 \({}^{\circ}\)C. The heating level is set to 0% if the temperature exceeds 22 \({}^{\circ}\)C. Finally, the process ends.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IoT Sensor** & **Quantity** & **Short Label** & **Color** \\ \hline Brightness sensors & 1 & BN & Green \\ Temperature sensor & 1 & TS & Orange \\ Motion detector & 1 & MD & Red \\ Position Sensor & 2 & PS & Cyan \\ Wind Sensor & 2 & WS & Blue \\ \hline \hline \end{tabular}
\end{table}
Table 4: IoT sensors used in Smart Office scenario with short label and color.  
Figure 18: Process to control the brightness in a smart office.