esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Based on experiments and theoretical analysis, this paper studies the rheological properties of high-temperature and high-pressure drilling fluids and draws the following conclusions:  
1. The rheological properties of oil-based drilling fluid used in three wells in an oilfield in Xinjiang were measured via conducting experiments, and the variation law of drilling fluid under high temperature and high pressure was analyzed. When the temperature is lower than 100 \({}^{\circ}\)C, the shear stress decreases faster, and when the temperature is higher than 100 \({}^{\circ}\)C, the shear stress decreases gradually. As the pressure increases, the shear stress increases gradually. When the pressure is lower than 80 MPa, the shear stress increases more slowly, and when the pressure is higher than 80 MPa, the shear stress increases faster. 2. This paper presents a method for directly predicting the readings of a six-speed viscometer and then optimizing the rheological model. The model reduces errors caused by prioritizing rheological models in traditional prediction methods. At the same time, in view of the fact that different drilling fluids conform to different regression functions, this paper adopts a parameter-free method for regression prediction, which expands the scope of the application of the model. 3. The error between the shear stress and the measured value of different methods at certain shear rates was compared, and the results show that the model established in this paper had the best prediction effect and the smallest model error. This paper improves the prediction effect of oil-based drilling fluid rheology and provides theoretical support for accurate drilling hydraulic calculation. Conceptualization, Y.Y. and H.F.; methodology, Y.Y. and Y.L. ; software, Y.Y. ; validation, Y.Y. ; data curation, Y.Y. ; writing--original draft preparation, Y.Y. All authors have read and agreed to the published version of the manuscript. Sinopec Shengli Petroleum Engineering Co., Ltd., Drilling Technology Research Institute; 10200001-22-ZC0613-0044. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Section Length, m** & **Inner Diameter, mm** & **Outer Diameter, mm** \\ \hline Drill collar & 9.4 & 57.2 & 158.8 \\ Bit & 0.3 & — & 241.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**The Method Established in This Article,** & **Traditional Method,** & **PWD,** \\
**g/cm\({}^{3}\)** & **g/cm\({}^{3}\)** & **g/cm\({}^{3}\)** \\ \hline ECD & 2.19 & 2.15 & 2.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Calculation results of circulating pressure at 6880 m of Well H.  
**Institutional Review Board Statement:** Not applicable. **Data Availability Statement:** Data are available on request due to restrictions, e.g., privacy or ethics. **Conflicts of Interest:** The authors declare no conflict of interest. **Appendix A**  
**Table A1. ** Rheological experimental data of the Tianan \(1\) well.
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 2 State of the Art
### The Need for Middleware and Open Interfaces  
Middleware is the cornerstone of any IoT system as it is crucial for efficient machine to machine communication insofar as it masks the heterogeneity of devices, communications and networks. Middleware provides a set of enablers such as standardised Application Programming Interfaces (API), protocols and infrastructure services to support the swift and accurate development of services and distributed applications of Future Internet services.  
In light of their importance, many research projects have focused on IoT middleware, the latter being studied, compared and classified by different authors [33; 34]. In most cases, WSN middleware is implemented as middleware embedded in the node. A list of WSN middleware, software/OS and WSN programming languages is available at [35]. Each middleware solution focuses on different challenges posed by IoT such as data volume management, device management, interoperability, context awareness and many more. A middleware solution that addresses all the challenges posed by IoT has yet to be designed.  
The current trend in middleware development is the use of a Service Oriented Architecture (SOA) services oriented approach and the implementation of Web standards (SOAP and RESTful) [35; 36]. By extending the Web paradigm to devices, the latter can become a natural component in the construction of any IoT application and facilitate the integration of device services into any business system that is based on SOA. IoT applications can thus become independent of technology and programming language. This will help drive the IoT applications development market, Open APIs being a key component in its establishment.  
WSO\({}_{2}\)[37], PubNub [38] and FIWARE [39] are clear examples of this new type of platform. Specifically, FIWARE applies the concepts of the SENSEI project [40] and of IoT-A architecture [41]. Reference [1] describes the evolution of _Farm Management Systems_ from monolithic systems to open systems capable to operate over the Internet. This paper shows a broad range of possible scenarios in the precision farming domain, where FIWARE technology can be applied, and it also offers a general description of FIWARE technology. However, it does not describe in detail how to use the FIWARE components to develop precision farming applications, or the performance and scalability of the resulting applications. Reference [42] complements and expands the contents of reference [1]. This paper shows a deployment of the FIWARE technology that represents a concrete example of the use of FIWARE in precision agriculture: a greenhouse. This paper presents the system components at a very high level of abstraction without entering into the details of application development. In addition, this paper is mainly focused in the cloud aspects of the farm management systems.
## 4 Morphology of the Blastocyst and Quality Assessment  
The post insemination of fertilized oocyte after Day 5 is known as a blastocyst, and its structure is shown in Figure 3. A blastocyst is characterized by the formation of a fluid-filled cavity (blastocole) that occupies the center of the embryo, which is surrounded by a single layer of cells (the trophectoderm (TE)) which will form most extraembryonic tissues such as the placenta [33]. Additionally, a small protuberance of cells known as embryoblast or inner cell mass (ICM, from which the fetal tissues develop, may also be visible. The TE and ICM morphological structure differentiation was primarily agreed amongst embryologist to represent the progression of the developed blastocyst [25; 34].  
Generally, the blastocyst expansion can be developed into many different shapes and patterns towards the sizes of expansion, ICM cells and TE development [35]. Occasionally, the features such as the zona pellucida (ZP) thickness [36] and blastocole [35] were mostly being considered in grading the quality of blastocyst. Specifically, the embryos with blastocolel cavity formation were considered as blastocysts; otherwise, embryos were regarded as non\(-\)blastocysts (undeveloped blastocysts) [28]. Currently, most of the embryo grading systems for assessing the viability of IVF embryos are very subjective and fully rely on visual inspection of morphological characteristics of the embryos, i.e., the appearance of embryo by qualitative evaluation [10; 34]. In extension, the key morphological features of relevance to embryo viability have been highlighted as [10]:  
1. Cell number and degree of symmetry: if all cells are similar in size and an appropriate number of cells are present, this indicates that the embryo has a good chance of being viable.
2. Fragmentation of cells: a low proportion of embryo volume composed of cell fragments is an indicator of high viability, while an embryo containing many fragmented cells is considered to have reduced potential.
3. Characteristics of the zona pellucida (ZP): embryos with a thinner ZP and higher variation in ZP thickness have a greater likelihood of producing a pregnancy.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Study Participants and Data Acquisition  
The data acquisition process is performed with non-intrusive equipment based on the use of a mobile device that incorporates different sensors, including an accelerometer, magnetometer, and gyroscope sensors. During the data acquisition, some failures may occur, and the missing samples were detected (Section 2.4). The data acquired includes the performance of different activities, including walking, running, standing, walking upstairs, and walking downstairs [22]. The different activities were performed and labeled by 25 individuals aged between 20 and 60 years old with different lifestyles and health states.  
In general, the dataset is composed by 2000 captures of 5 s for each activity that corresponds to around 2.78 h of captures, representing 169.44 h of captures related to each activity. Thus, this dataset is composed by 13.9 h of captures shared by different individuals. The data were acquired using an Android application installed in a mobile device to record the mobile sensors data while performing the activities. All the participants kept the mobile phone in the front pocket of their pants while performing activities. The mobile device used is the BQ Aquaris 5.7 smartphone with a Quad Core CPU and 16 GB of internal memory [23]. Next, the data were used for the implementation of different techniques for data classification (Section 2.7). The mobile devices have different constraints related to the low memory, battery, and power processing, which may cause different failures [6; 24]. After the acquisition, the original dataset without the application of the data imputation technique is available in [25], and the dataset with the application of the data imputation technique is available in [26].
## 5 Results
### Results for Regular Images  
We initially illustrate the results for the mean absolute error as the result of the logistic regression and subsequent prediction efforts in Figure 4.  
We immediately observe that employing all EEG channels at all measurement points (All) results in a fairly low MAE value on average. Additionally, we only note little differences amongst subjects as indicated by the illustrated standard deviation. A closer inspection reveals that the introduced error is slightly higher for the prediction of the user ratings (QoE) than for the set level (QoS). An attempt at reducing the number of required EEG electrodes to the two outside or center ones (e.g., to reduce the complexity of future devices and/or systems employing this approach) results in an increase of the error and prediction outcome variability between subjects. Following this initial observation, the effect is amplified for the subject-dependent QoE in comparison to the QoS. Interestingly, a reduction to either left or right electrode pairs (Left, Right) yields smaller increases of the error, with the left electrodes resulting in the smallest increase overall. These increases, however, remain relatively low in their impact. Attempts at further complexity reduction to individual electrodes does increase the error more significantly, commonly to over one half of the user ratings or QoS levels. Approaches that would employ a single electrode in the utilized placement configuration, thus, would result in more deviation from the real values.  
Shifting the view to the \(R^{2}\) scores for the employed logistic model and prediction outcomes, we present our high-level results in Figure 5.  
Following the trend visible for the MAE values, we initially observe that the application of all electrodes results in the highest attainable \(R^{2}\) scores, on average close to one. Inverse to the MAE, the \(R^{2}\) scores subsequently are higher for the QoS prediction, but only trai  
Figure 4: Mean Absolute Errors (MAE) for the regular image (AR) condition averages and standard deviations for subject ratings (QoE) and impairment level (QoS) prediction performance analysis of individual subjects.  
Figure 5: \(R^{2}\) scores for the regular image (AR) condition averages and standard deviations for subject ratings (QoE) and impairment level (QoS) prediction performance analysis of individual subjects.  
QoE scores. Again, we notice that an attempt in the system complexity reduction results in a decrease of prediction performance, both on the general average and variability amongst participating subjects. A further reduction of individual electrodes decreases the model performance even further.
## 3 Metadata Model for Social Media Data  
One of the main problems to solve when working with social media data is to adopt a standardized data model for representing and integrating data coming from different sources. To this end, [PERSON] et al. [10] proposed a unified metadata model for representing different data extracted from social media platforms. According to this model, each social media item (e.g., post, photo, or video) is described by a JSON document [21] organized into two sections. The first section, called _basic_, includes the main descriptive fields available in all major social media platforms (source, item ID, date and time, location coordinates, user information). The second section, called _extra_, contains specific fields that depend on each source. For example, Listing 1 and 2 show the metadata related to a tweet and a photo posted on Flickr. The basic section is the same for the two documents, instead the extra section contains specific information for tweets (e.g., retweet and retweet count) and for Flickr photos (e.g., a list of tags and the photo quality).  
```
{ \"BASIC\":{ \"SOURCE\":\"Titter\", \"ID\":\"1234567890123\", \"DATETIME\":\"2021-02-20722:19:35.021\", \"LOCATION\":{\"LNG\":-0.1259,\"LAT\":51.5623}, \"USER\":{\"USERID\":\"0123456789\", \"USERNAME\":\"mpropato\"}, \"ETTRA\":{ \"nieDipyOfScreenName\":\"djdma\", \"inReplyToUserId\":987654321, \"inReplyToStatusId\":9565757346292993, \"text\":\"ddjdma perfect soundl\", \"hashtags\":[ \"sfestival\", \"music\"], \"retweets\":10, \"isRetweet\":true) }
```  
Listing 1: Metadata of a tweet.  
```
{ \"BASIC\":{ \"SOURCE\":\"Flickr\", \"ID\":\"43146791176\", \"DATETIME\":\"2020-11-09T15:21:112.000\", \"LOCATION\":{\"LNG\":12.567772,\"LAT\":41.89256}, \"USERID\":\"087654321801\", \"USERNAME\":\"samjack\"}, \"ETTRA\":{ \"title\":\"The Colosseum is amazing\", \"description\":\"The Colosseum in Rome is really amazing\" \"tags\":[{\"count\":1,\"value\":\"trip\"},{\"count\":2,\"value\":\"rome\"}], \"dateTaken\":\"Oct 10, 2020 15:21:12 AM\", \"accuracy\":15} }
```  
Listing 2: Metadata of a Flickr photo.
## 3 Results  
### Volatile Compounds in Essential Oils and Recovery Oils  
The detailed volatile profiles of essential oils (EOs) and recovery oils (ROs) derived from the hydrolate of lemon cattip for each year of investigation are presented in Table 1, while the range of volatile compounds, along with the regression coefficients for temperature (K\({}_{\rm t}\)) and precipitation (K\({}_{\rm p}\)), are provided in Table 2.  
The essential oil samples from three successive years showed the dominance of nerol (47.5-52.3%) and geraniol (25.2-27.9%), followed by geraniol (7.0-8.9%) and neral (5.9-6.7%). The composition of the recovery oil from hydrolates was similar to that of essential oils, with the same dominant compounds: nerol (43.5-54.3%), geraniol (29.4-32.3%), gerani (2.9-8.4%), and neral (3.8-8.8%). In both the essential and recovery oils of Lemon catnip (Table 1), the terpene alcohols need and geraniol consistently accounted for the majority of the total volatile content, jointly comprising over 75% of the essential oil profile across all the studied years. The geraniol content in the recovery oils was slightly higher than that in the essential oils in 2020 and 2022 (32.3% and 32.6%, respectively), suggesting enhanced extraction into the aqueous phase due to its partial water solubility. Neral and geraniol, the _cis_ and _trans_ isomers of citral, displayed moderate abundance, with essential oil levels between 5.9-6.7% and 7.0-8.9%, respectively, whereas in recovery oils, geraniol fluctuated markedly (2.9-8.4%), and neral peaked at 8.8% in 2020. Several mono-oxygenated compounds were uniquely or more prominently present in the recovery oil, such as 6-methyl-5-hepten-2-one (2.1% in 2020), iso-isopulegol (0.3% in 2020), and 2,6,6-trimethyl-2-cyclohexene-1-methanol (0.7% in 2020), indicating their preferential partitioning into The 1,8-cineole, \(\alpha\)-pinene oxide, and \(\beta\)-pinene oxide, although detected at trace levels (<0.3%), were primarily observed in the recovery oil, likely due to their hydrophilic tendencies. Conversely,
## 4 Experiment
### Presentation of the Experiment Environment  
Montoldre experimental farm is a large plot of agricultural land dedicated to the experimentation of agricultural techniques, especially fertilizer spreading techniques. The area shown in the satellite view in Figure 6 is composed of a dozen fields (in red) maintained by a handful of agricultural vehicles.  
The application considered in this first experiment consisted in collecting environmental data (humidity and temperature) measured by sensors. This experiment was run in the early stages of ASAWoO development. Even though the whole middleware was not complete at that time, the opportunistic communication module was operational. Hardened sensors that can be exposed to bad weather conditions were also not ready to use for this experiment. Consequently, we used tiny single-board computers (RaspberryPis) equipped with Wi-Fi antennas that produce data exactly as the sensors would have done. As these devices are not hardened, they were placed in farm buildings, as shown in Figure 7. These experimental conditions may appear as degraded, but are not in reality because the communication behavior of sensors was reproduced as accurately as possible. This experiment allowed us to validate the RESTful opportunistic communication support presented in Section 3.  
Three instances of ASAWoO runtime were deployed on Raspberry Pis equipped with 5 dB Wi-Fi antennas. The first one, identified as RASP00, acted as a base station located in IRSTEA office at the limit of Wi-Fi communication range from the tractors' warehouse. The two other RaspberryPis (RASP01 and RASP02) were equipped with an external battery pack and a GPS module. The kits were  
Figure 5: Avatar runtime environment.  
embedded on two tractors. GPS antennas were placed on the tractors' roofs and the battery power cords were plugged into the tractors' cigarette lighters to power up/on the Raspberry and recharge. Two other self-powered RaspberryPis with smaller range acting as sensors were deployed in farm buildings located on tractors paths. Locations of the five nodes are shown on the map in Figure 7. The embedded system deployed on sensors was restricted to ASAWOO communication module since the whole middleware stack was not required. During this experiment, the sensors produced data every 30 min.  
During the four months of experimentation, over 30 trips were monitored. Mobility traces of RASP01 and RASP02 (i.e., tractors) were stored in log files, as well as the contacts and exchanges  
Figure 6: Montoldr experiment farm.  
Figure 7: Node locations and their approximate communication ranges.  
between tractors and the base station). Logs were compiled and sent to the base station when entering its radio range. A ssh tunnel between the base station at IRSTEA and our lab was established to access the logs, update the code if necessary, and remotely monitor the experiment throughout its progress. The following subsection presents the experiment outcomes, focusing on a few trips involving both tractors or longer activity periods.
## 5 Conclusions  
This study investigated the deodorization effect of ozone oxidation on tuna polypeptide to determine optimal conditions. Ozonation efficiently deodorized tuna peptide powder. The optimal wet ozonation parameters for tuna peptides were as follows: ozone concentration of 99.1 mg/L, reaction time of 40 min, and temperature of 50 \({}^{\circ}\)C, yielding a sensory score of 2.2. This process maintained the protein composition and nutritional value of tuna peptide powder. Low-temperature stability tests revealed that the sensory score of ozonated tuna peptide powder in cosmetics remained stable, unaltered by temperature changes. These findings offer theoretical and practical insights for ozone deodorization in seafood processing, particularly in cosmetic applications. Unlike masking, adsorption, alkaline, or microbial-fermutation deodorization, ozone oxidation eliminates fishy aldehydes rapidly without introducing foreign volatiles, does not generate solid or liquid waste, and preserves both nitrogen content and sensory stability. These features make it particularly attractive for cosmetic formulations. However, applying ozone requires consideration of its chemical properties, treatment depth, safety, and equipment to ensure effective and safe deodorization. Comprehensive pre-implementation testing is essential to validate efficacy and preclude potential issues.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/md23050191/s1](https://www.mdpi.com/article/10.3390/md23050191/s1)]([https://www.mdpi.com/article/10.3390/md23050191/s1](https://www.mdpi.com/article/10.3390/md23050191/s1)), Figures S1-S16: GC-MS results of samples before and after treatment, and the results for different compounds before and after treatment; Table S1: Peak areas of selected compounds in tuna peptide powder before and after ozone treatment.  
Conceptualization, H.Z. and S.G.; methodology, S.H.; software, H.Z.; validation, D.L., F.X. and W.Y.; formal analysis, H.Z.; investigation, X.C.; resources, Y.Z.; data curation, K.Z.; writing--original draft preparation, H.Z.; writing--review and editing, S.G.; visualization, X.C.; supervision, Y.Z.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2024 Guangdong Province Special Funding for Marine Economy Development (GDNRC [2024]49); the Scientific Research Project of Guangdong Provincial Department of Education (2023 KTSCX132); the 2021 Guangdong Provincial Universities Characteristic Innovation Project (2021 KTSCX114); and the University-level Scientific Research Project of Foshan University of Science and Technology (CGZ0400166). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.  
Not applicable.  
The original data presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.