esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Aim of the Present Study  
From the literature analysis presented above, several traceability systems have been developed and are commercially available. However, significant gaps in the literature were identified; the short range of application of these systems, which cover only a few stages of the supply chain, the extremely limited interoperability among the different systems, and the closed structure not allowing for open source use. In this study, a state-of-the-art IoT-based system is proposed, named \"AgroTRACE\", aiming at providing an integrated solution for traceability and agro-logistics of fresh fruits and vegetables from farm to fork and back to the farm by also tracing waste to be used as input for crop production. Furthermore, AgroTRACE is an open source system, composed of a web platform available for all the participants of the food supply chain. In addition, a user-friendly Android application has been developed for consumers encompassing all the basic functions in relation to the on-the-fly scanning and retrieving of all the important aspects of the perishable products. The system, which is implemented within the AgroTRACE project [60], supports both the internal and external traceability throughout the supply chain.  
Table 1 presents a comparison of the proposed system with the aforementioned traceability systems currently available on the market. It can be inferred that the proposed modular structure is based on open and transparent interoperability standards that allow for faster monitoring of the system's operation in product's tracking processes. As a consequence, the quality of the fresh produce is guaranteed via a reliable traceability system, which starts from the farm level up to the consumer and back to the farm, in contrast with the limited range of the supply chain of the other available systems. Moreover, AgroTRACE incorporates all of best data transfer technologies used by other systems, namely RFID, LoRaWAN network for inter-device communication, and beacons technology along with the corresponding Bluetooth Low Energy (BLE) communication protocols. The innovation of AgroTRACE is further reinforced by covering also the part of waste management and recycling, incorporating circular economy practices to the traceability system. To this end, the proposed solution fully supports traceability of biowaste by providing documentation on supply chain partners' Corporate Social Responsibility and supporting industrial symbiosis. The latter refers to the process where the \"waste\" of a production process is an input of another unit. To sum up, the proposed holistic approach is the first that uses an open architecture, deals with waste management, covers the entire length of the supply chain, combines a variety of IoT technologies for real time acquisition of information and decision making and considerably facilitates interoperability.
# Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters
## 1 Introduction
Therefore, we investigate the validity and the effectiveness of different setups using adaptive filters to improve climate forecasts. In this paper, we employ the output of the Eta model at 40-km resolution and compare it against the NCEP reanalysis at 38-km resolution. The reanalysis dataset was remapped onto the Eta grid in order to enable comparisons. The chosen filter-updating technique refers to the RLS (Recursive Least-Squares) algorithm [24]. Different grid cells are grouped depending on the climate forecast variables during the forecast period. For data partitioning and clustering, we use the K-means algorithm [25; 26]. This unsupervised learning algorithm is used to automatically choose the number of filters employed for forecast improvement in the geographical domain. Seminal work was introduced in [27], and we extend it here by comprising more climate forecast variables, presenting an improved adaptive filter algorithm, and using additional evaluation metrics. The paper is organized as follows. Section 2 presents some details on the climate forecast data used in this work, more specifically on its volumetric characteristic and the volume discretization (grid), and also on the geographic region of interest. Section 3 shows how the adaptive filters are employed to reduce the climate forecast error. Following, Section 4 describes the proposed methodology, which comprises the data grid adjustment approach between Eta RCM and NCEP data sets, the clustering technique applied to the climate time series, and the evaluation of the clustering quality that is used to decide the number of filters to employ in a given region. The methodology also comprises a brief presentation of the RLS algorithm used for adaptive filtering and how to evaluate the performance of the proposed climate forecast error reduction approach. Section 5 presents the experimental setup and numerical results, and discusses the performance of the proposed method for the reduction of RCM forecast deviations. Section 6 ends with the conclusions.
## 2 Evaluation of Pavement Crack Damage by Deep Learning
### Outline of Convolutional Neural Network
#### 2.1.4 ResNet  
In this study, we apply ResNet [30], which is a CNN network model. Since the breakthrough of the CNN in the field of image recognition, its accuracy has been improved by adding deeper layers. However, the deepening of the layers caused a degradation problem. The degradation problem is a phenomenon in which the improvement in training errors in a deep-layered model reaches its peak earlier than in a shallow-layered model. A model with deeper layers should have the same or better training errors than a model with shallower layers, but the accuracy is not easy to be increased and deteriorates rapidly as the depth of the network increases. The difference between ResNet and conventional CNNs is that ResNet learns the residual function with reference to the input of the layers. A part of the general network and ResNet is shown in Figure 3. Consider the case where the function we want to train is (\(x\)). In ResNet, in two consecutive convolutional layers, the input \(x\) is skipped and connected to the output two layers away. In this case, the difference between the input (\(x\)) and the input \(x\) is given by Equation (4), and we proceed with the learning based on a modification of this Equation (5).  
\[F(x):=H(x)-x \tag{4}\]  
\[H(x):=F(x)+x \tag{5}\]This replaces the problem of estimating the optimal function \(H\) with the problem of estimating the optimal residual function \(F\). The shortcut connection acts as a detour to combine the input values of the layers with the output of the network in front of the activation function, and since the shortcut connection transmits the input information as it is, the gradient also transmits in the back propagation. Therefore, the gradient remains without the risk of being too small or too large. A variety of forms of ResNet have been proposed, such as ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, among others, which are different in the number of layers and the number of parameters that can be learned. ResNet is so powerful that there are several examples of its use in pavement crack detection, as shown in [31; 32]. In this study, ResNet50 is used in consideration of the balance between accuracy and computation time.
# Effects of Melatonin Treatment of Postharvest Pear Fruit on Aromatic Volatile Biosynthesis
## 2 Results  
### Effects of MT on Volatile Production during Postharvest Ripening  
There was no difference in the aroma maps of 'Korla' IAT and at PRC (Figure S1a); however, substances were much richer at PRC than IAT for 'Abbe Fetel' (Figure S1b). MT inhibited ethylene production in the pears, and it was lower in 'Korla' than in 'Abbe Fetel' (Figure 1). In 'Korla', there was no significant ethylene release peak, but after the 4 th day, the ethylene content in the fruits began to gradually increase, so we designated the fourth day as PRC.  
Figure 1: Effect of melatonin on ethylene production during storage. CK: negative control; MT: 100 \(\mu\)M melatonin; data are means of 5 replicates \(\pm\) SD.  
Aldehydes were the principal aroma components in 'Korla' pear (Figure 2), representing 81.24% and 78.26% in the CK and MT treatment, respectively, following treatment; after storage at 25 \({}^{\circ}\)C for three days, the proportion of aldehydes in the MT treatment remained similar (80.11%), but had decreased to 70.70% in the CK. Proportion of esters was higher in the MT treatment than in the CK IAT, and the proportion of alcohol esters in 'Abbe Fetel' was greater than that in 'Korla'. In 'Abbe Fetel', the proportion of aldehydes decreased at PRC, while that of esters increased. The proportion of esters was higher IAT in the MT treatment, while the proportion of alcohols at PRC was greater than in the CK.  
Hexanal was the dominant volatile recorded in both pear varieties (Table 1). Following treatment, hexanal content of 'Korla' IAT was lower in the MT treatment than in the CK; there was no treatment difference in 'Abbe Fetel'. At PRC, content of hexanal in MT treated 'Korla' pear was greater than in the CK, while in 'Abbe Fetel' it remained lower than in the CK. The trend in content of (_E_)-hex-2-enal was similar to that of hexanal. MT increased ethyl ester content in 'Korla'. IAT, MT had inhibited almost all volatile substances, but by the respiratory jump phase at PRC, content of propyl acetate, and hexyl ester had increased (Table 1).
## 3 System Model
### VNF Resources Allocation Process  
In the resource allocation, the controller receives a slice request with specific service requirements and slices the substrate network to accommodate the slice request [39]. This process can be modeled as the slice deployment for VNFs and virtual links, considering network features and service requirements.  
Figure 1 illustrates an example of the slice deployment with a group of NSIs. The model consists of a service-specific virtual network layer and substrate network resource layer. According to the actual network architecture, the substrate transport network is divided into three parts: access layer network composed of edge DCs, aggregation layer network composed of metro DCs, and core layer network composed of central DCs [42]. Each substrate node is implemented as standalone boxes based on dedicated hardware running in the cloud environment or general-purpose commodity servers [43]. As shown in the figure, substrate nodes A, B, C, D, and E accommodate amounts of VNU1s, VNU2s, and VNU3s respectively. For the logical network layer, multi-tenancy E2E slices are considered. For instance, as shown in Figure 1, NSI4 for auto driving consists of three communication service instances and communication service instances consist of different VNUs and virtual links. VNUs can be implemented as a set of VNFs running on general x86 hardware in DCs, while each logical slice corresponds to an abstraction of a subset of physical substrate network resources tailored to meet the specific customer QoS/resilience requirements [14]. The slice controller achieves the resource allocation by mapping NS requests into substrate network resources (e.g., BBUs, GWs, and AMFs) to implement specific network functions.
## 2 Status and Literature Review
### Domestic Licensing Status  
The information required for the current domestic licensing process can be divided into architectural drawings and documents. Construction drawings should provide information on the quadrature, layout, parking plan, elevation (two or more), floor and roof plan, cross sections (longitudinal and transverse cross sections), vertical motion details, partial details (main structure, etc.), windows, and construction equipment to be provided. The structural design should provide information on the structural plan, elevation, cross section (for a building that will be subjected to structural safety verification), interior finish, and firefighting equipment [13]. Scanned files of documents and drawings are uploaded through SEUMTER, a governmental E-Submission platform. Decision on grant of permission is notified after a review process that takes 2-4 weeks. It consists of a licensing process that requires re-examination through the SEUMTER after supplementary work if the permit is not obtained. In order to support BIM-based licensing, guidelines have been framed for the preparation of BIM design documents [14]. The guidelines specify the types and standards of drawing required in the licensing process and provide information on how to create drawings; they also include examples of drawings based on a BIM model, and they range from general floor plans to three-dimensional floor plans and cross-sectional views. In addition, the draft delivery management standard, submission data file creation standard (recommended), step-by-step creation model creation process (Level of Detail/BIM Information Level stage standard), design information for and standard specifications of structural objects, etc., are specified. However, the design guidelines are for Autodesk Revit and Graphisoft ArchiCAD software, and it is difficult to use them for other BIM creation tools such as CATIA, Bentley, and Tekla. To overcome this limitation, research should focus on the creation of licensed drawings based on IFC, which is not limited to what can be accessed on all BIM creation tools.
# State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM
## 2 Materials and Methods
### Causal Convolution
#### 2.2.3 Battery SOH Prediction Framework  
A framework for predicting the State of Health (SOH) of lithium-ion batteries is constructed, as illustrated in Figure 7. First, the publicly available lithium-ion battery dataset from the University of Maryland is preprocessed to extract key information such as voltage, current, and time. Gaussian filtering is then applied to the raw incremental capacity (IC) data to obtain a smoothed IC curve. Subsequently, IC features are extracted, including the peak value of the IC curve, peak position, area to the left of the peak, area to the right of the peak, and the total area under the IC curve, which are used as health indicators. [PERSON]'s correlation analysis is performed to evaluate the correlations between these features and the battery SOH. The processed data is then fed into a time-series model using a sliding time window to form the training dataset. Temporal Convolutional Network (TCN) is employed for data mining; the training data is first normalized and passed through dilated causal convolutions, followed by pooling layers to reduce computational complexity and enhance the model's focus on critical features. The data then enters the TCN residual blocks, where skip connections help mitigate the vanishing gradient problem. Finally, Relevance Vector Machine (RVM) regression is used for the SOH prediction. The test dataset is input into the trained prediction model to validate the model's estimation performance, and the predicted SOH is output. Table 3 presents the specific structural parameters of the TCN-RVM model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of convolution kernels & 64 \\ Convolution kernel size & 5 \\ Number of layers & 2 \\ Dropout rate & 0.05 \\ Optimizer & Adam \\ Training epoch & 100 \\ Learning rate & 0.01 \\ Nuclear type & RBF \\ Kernel coefficient & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the TCN-RVM model.
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## 1 Introduction
Compared to the single user model, the multi-user models for searchable encryption have become more practical in actual cloud storage environments because they allow more than one authorized users to share data files that are outsourced to the cloud. [PERSON] et al. proposed a multiple user searchable encryption scheme based on the general access structure [9] that allows any user to add encrypted data to the outsourced database and any authorized user to retrieve and decrypt cipher-texts. [PERSON] and [PERSON] proposed a searchable encryption scheme based on mixed structure, which could provide accurate keyword retrieval and fine-grained access control on encrypt data [10]. However, the scheme makes it possible for the server to obtain keyword information, which is not regarded as a secure practice. In this paper, we propose a fuzzy keyword search scheme for the scenarios of multiple servers and multiple users that has the property of preserving security and privacy of data. Our contribution in this work can be summarized as follows:  
* The proposed multiple server searchable encryption scheme can provide the functionality of cipher-text retrieval for multiple servers. Compared to the single server model, our method can offer the capability of processing big data while securing data in the cloud. * The proposed scheme provides more flexible control of user access rights on cipher-text files. Access rights of different users on the same file are not required to be the same. Moreover, the scheme supports dynamic updating of user access rights to provide better security over shared data in the cloud. * The proposed scheme can reduce the complexity of Min-Hash by eliminating the construction of the fuzzy keyword set by performing fuzzy keyword search using Min-Hash fingerprints. The scheme utilizes Jaccard similarity to order the results of retrieval, which not only reduces the amount of calculation for similarity, but also lowers the space overhead to achieve high efficiency of retrieval. * Through constructing an efficient fuzzy keyword index tree, the proposed scheme uses the Min-Hash to generate the fingerprint index for the keywords without having to set the index storage space and define dictionary library in advance, which greatly reduces the complexity of search and saves a lot of storage space. The rest of this paper is organized as follows. Section 2 introduces some related work on searchable encryption. Section 3 describes the proposed scheme, which includes the system model, the design goal, and a detailed description of the scheme. Section 4 contains the security analysis and provides the experiment results of the proposed scheme. Finally, Section 5 concludes the paper.
_Applied_  
_sciences_  
_Article_  
**Preliminary Study of the Use of Root with Sedline(r) EEG Monitoring for Assessment of Anesthesia Depth in 6 Horses**  
**[PERSON] 1,*, [PERSON] 1(r), [PERSON] 2 and [PERSON] 3**  
1 Department of Large Animals Diseases with Clinic, Faculty of Veterinary Medicine, Warsaw University of Life Sciences, Nowouvsynowska 100, 02-797 Warsaw, Poland;  
[EMAIL_ADDRESS]  
2 National Geriatrics, Rheumatology and Rehabilitation Institute, Spartanska 1, 02-637 Warszawa, Poland;  
[EMAIL_ADDRESS]  
3 Emeritus Professor of Anesthesiology and Pain Management, Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14850, USA; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +48-608-709-346 check for updates  
Received: 2 January 2020; Accepted: 3 February 2020; Published: 5 February 2020  
**Featured Application: The use of EEG during equine anesthesia can be a new complementary method for clinical use in order to achieve more precise anesthesia depth assessment due to brain awareness measurement, visible in real time during surgery. Can it be used to assure the desired management of potential pain from surgical stimulation? Purpose of the study: The purpose of this study is to determine whether the Root with Sedline EEG monitor could be adapted to use during equine anesthesia and surgery for commonly diagnosed conditions. This was indicated prior to in-depth studies comparing this technology to other parameters currently used during various anesthetic protocols in both minor- and major-risk surgeries.**  
**Abstract: Management of equine anesthesia monitoring is still a challenge. Careful monitoring to provide guidelines for anesthesia depth assessment currently relies upon eye signs, cardiopulmonary responses, and the level of muscle relaxation. Electroencephalography, as a non-invasive brain activity monitor, may be used to complement the routinely monitored physiologic parameters. Six horses, undergoing various surgical procedures and anesthesia protocols, were monitored with the use of a Root with Sedline EEG monitor and a routine monitor of life parameters. The life parameters were compared to the changes on the EEG density spectral array observed live during anesthesia. During all procedures the level of awareness was monitored using the EEG, with higher frequency and power of waves indicating a higher level of awareness. It was evident from this that there were variations according to the type of procedure and the anesthetic protocol. Cerebral activity was elevated during painful moments of the surgery and recovery, requiring adjustments in anesthetic concentrations. Evaluation of changes in the spectral edge frequency (SEF) could show the periods when the patient is stabilized. EEG monitoring has the potential to be used in clinical anestisiology of horses. It was shown that this system may be used in horses under general anesthesia but is currently less effective in a standing horse for diagnostic or minor procedures.**  
**Keywords: electroencephalography; equine anesthesia; anesthesia monitoring; density spectral array**
## 2 Experimental Design
### Flow Visualization  
To gain insight into the physical phenomena in the test cup, the velocity field is measured using Particle Image Velocimetry (PIV). The experimental setup for flow visualization is shown in Figure 5. To visualize the flow in the test fluid, a small amount of nylon tracer particles with a diameter of 20 \(\upmu\)m and a specific gravity of 1.02 are introduced. Planar measurement of the velocity field is performed using a PIV system consisting of a CW Nd:YAG laser of 8 W power and a high-speed CMOS camera (1280 \(\times\) 1024 pixels with 8-bit) operated by a pulse controller. The CMOS camera is positioned at a distance of 550 mm from the test cup. The frame rate of the camera is set to 130 frames per second with an exposure time of 7.6 ms. It is important to note that the camera lens has a focal length of 75 mm and an f-number of 2.8, while the thickness of the light sheet used for visualization is approximately 1 mm [23; 24].  
Figure 5: Experimental setup for flow visualization.  
To continuously monitor the temperature in the test cup, three thermocouples are strategically placed at different locations. The test cup is immersed in a heating cup during these temperature measurements. The thermocouples are specifically positioned along the longitudinal axis of the test cup, with one in the center and the other two near the wall. This arrangement allows comprehensive collection of temperature data from various points in the test fluid.  
To ensure accuracy, these experiments are repeated three times, and the average values obtained from these repetitions are used to create a non-uniform temperature field for each liquid. The heating process is facilitated by a heating unit located under the heating water.  
Throughout the experiments, the temperature readings of all three thermocouples are recorded simultaneously. The thermocouples are placed at a depth of 20 mm in the test fluid.