esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Experiments and Numerical Results
### Numerical Results  
#### 5.2.1 Uniform Traffic Load  
The experiment is designed to consider task arrivals with a uniform distribution, which is interpreted as normal traffic for daily cases in a regular manner, as shown in Table 4. First, this case examines the objective values trend with the number of tasks as the control variable. Figure 8 displays the result; the objective value increases along with the number of tasks. Compared with the curves of _FF_, _SP_, _OI_, and _UP_ to demonstrate how high the performance than of the proposed _FF_ algorithm when the number of tasks is higher than 700. The reason is that the dropping penalty is considered due to the number of tasks that is beyond the system capacity.  
Method _CP_ exhibits the worst performance. The decreasing trends in this method are lower than those mentioned above or the _FF_ method for tasks. In brief, better resource allocation and scheduling strategies are jointly considered for determining the level of task significance through a near-optimization-based solution with _SP_, _OI_, and _UP_ for maximizing objective values. The _SP_ is the mostly near-optimal strategy with the highest number of feasible solutions, and the minimum solution gap (3.74% \(\sim\) 12.19%).
## 4 Results and Discussion
### Experiment of 2D-3D Registration
#### 4.1.5 Time Efficiency Analysis Experiment  
Figure 9 shows the time efficiency of the proposed method. The runtime of the algorithm is largely affected by the computer configuration. In this experiment, the computer was configured with a Windows 10 64-bit operating system on an Intel Core i9-9980 XE 3.0 GHz CPU, with 64 GB of RAM and an NVIDIA GeForce RTX(tm) 3090 graphics card.  
Figure 9a shows the time performance of the feature matching and pose estimation methods under the constraint of inverse depth coordinates. The results show that the proposed method has advantages in Trial 1 in terms of time performance compared with the results of Trial 4. In Trial 5, the GMS method was used, which reduces the matching step time, but its feature point location accuracy is low. Figure 8b compares the time performance of different feature extraction methods and shows that the SuperPoint method took the shortest time.
## 3 Site and Data Set Description  
Two flood cases are used in this experiment. The first flood occurred on DOY (Day Of Year):201, 2021, in Zhengzhou City, Henan province, China. The observation station was named ZHNZ and is located in Zhengzhou (34.52\({}^{\circ}\)N, 113.10\({}^{\circ}\)E). This station was established and is managed by the Infrastructure of National Earthquake Data Center, China. The antenna is set on an observation pier, and the height of the pier is approximately 3 m. Both the GPS and GLONASS dual-frequency signal can be received, and the sampling rate of data set is 30 s. The receiver is a TRIMBLE NETR8 geodetic dual-frequency receiver with a TRM59900.00 antenna. It should be pointed out that there is no surrounding picture at present because of the confidentiality of the observation environment. However, the data set from the Infrastructure of National Earthquake Data Center, China, can be applied.  
The second flood occurred on DOY:203, 2021 in Xinxiang city, Henan province, China. The observation station was established and is managed by the [PERSON] team from Wuhan university. The receiver is located at 35.02\({}^{\circ}\)N, 114.55\({}^{\circ}\)E around a field of corn, and the height of the antenna is approximately 2 m. Both the GPS and BDS single-frequency signal can be received, and the sampling rate of the data sets is 1 s. Considering that the double frequencies carrier phase and pseudo-range observation should be used to calculate the MP, this flood can only analyze the influence of floods on the CNR, but cannot analyze the MP. The station is equipped with a low-cost u-blox geodetic single-frequency receiver with a ANN-MB antenna. The surrounding environment of the station is presented as Figure 1.  
Both of these two floods are induced by a continuous accumulation of rainfall. The rainfall information is presented in Figure 2. In addition, the latitude and longitude for these two floods is presented in Figure 3. Considering that the data set collected on eight and seven days before the flood should be used to analyze the GLONASS and BDS MEO satellite, the rainfall information of these eight days is given in Figure 2. The blue bar denotes the rainfall in Zhengzhou, and the red bar denotes the rainfall in Xinxiang. It should be pointed out that although the rainfall on DOY:203 is weaker than that of on DOY:201 in Xinxiang, the flood occurred on DOY:203 because of the continuous rainfall and overflow of the river in previous days. Relevant information can be found by searching the website: [[https://q-weather.info/weather](https://q-weather.info/weather)]([https://q-weather.info/weather](https://q-weather.info/weather)) (accessed on 20 July 2021).  
Figure 1: Surrounding environment of the observation station, located in Xinxiang City, Henan Province, China.  
Figure 2: Accumulative rainfall from DOY:193, 2021 to DOY:203, 2021 in Zhengzhou and Xinxiang, China. The blue and red bars denote Zhengzhou and Xinxiang, respectively.
###### Abstract  
Quality parameters are always of major importance in fruit sensory perception and they are influenced by the agriculture and environmental strategies of water-use efficiency that in the last few years are being developed due to water scarcity. Blueberry fruit quality depends on several factors, such as variety, water availability or cultivation system. The purpose of this work was to determine the effect of deficit irrigation and cultivation systems on the quality of three blueberry cultivars along two consecutive seasons. Three blueberry varieties ('Rocio', V2 and V3) were subjected to two irrigation regimes (100% and 80% crop evapotranspiration) and grown under two cultivation systems (open field and plastic tunnels). Their quality attributes (color, sugars, acidity, firmness, size and weight) were evaluated over two consecutive seasons (2011-2012). The application of deficit irrigation did not significantly affect the quality parameters, except for an increase in firmness. The cultivation system used influenced the quality attributes of blueberries cultivated under plastic tunnels in both seasons, increasing the soluble solids content and decreasing the titratable acidity. All the studied quality parameters were significantly affected by genetic factors. The V2 cultivar was the most influenced by the cultivation system. In conclusion, the use of deficit irrigation and plastic tunnels in blueberry cultivation is a good alternative to obtain fruits with enhanced quality features.  
deficit irrigation; plastic tunnels; blueberry; food quality; crop evapotranspiration; water stress; color +
Footnote †: journal: Applied Sciences
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Software Solutions
#### 3.2.3 E-Parking  
Once the information has been got, the most common way to present such information is via software solutions over the Internet, either through a mobile application or a web browser. In these solutions, users can interact with different deployed parking services.  
An example of this interaction is reserving parking spaces, as mentioned in [15]. The system allocates a parking space and the user decides whether it is appropriate to continue with the reservation. Likewise, in [46], a similar system is described, where the user is in charge of selecting the parking lot to reserve from a list of available spaces. Moreover, in other solutions these parking slots can be reserved with days of anticipation or if the user requests it, they can keep changing as long as a better one is found based on the user requirements as shown in [80].  
In other cases, e.g., in [48], the user was allowed to decide whether he/she wants a manual or automatic allocation of the parking spaces through a mobile application. This approach has the particularity of verifying user's identity when arriving and leaving the parking space to provide a certain level of security to the system.  
All the papers reviewed within this scope have been marked according to the sub category where they fall based on the solution type that has been implemented. A summary of such classification is listed in Table 4.  
The following table has four different columns; each of them represents a type of Software Solution that was identified from the review. Selected columns were: Information Management, Prediction, E-Parking, and None. No column shows specification details on the type of solution built. Then, only the papers that described a particular type of solution were considered and marked with a black bullet \"\(\bullet\)\" under the corresponding column; otherwise, an empty space was left.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Information Management** & **Prediction** & **E-Parking** & **None** \\ \hline  
\hline \end{tabular}
\end{table}
Table 4: Papers according to software solution types.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Information Management** & **Prediction** & **E-Parking** & **None** \\ \hline  
\hline \end{tabular}
\end{table}
Table 4: _Cont._The results shown in Table 4 point out that most of smart parking software solutions among the scientific community are designed and proposed for information management. However, E-Parking software solutions are gaining adherents, which is probably because this type of solutions provide services besides information. Anyhow, software solutions are vital to properly handle and disseminate information collected and provide services to final users.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
###### Abstract  
In wireless multi-hop networks, instead of using the traditional store-and-forward method, the relay nodes can exploit the network coding idea to encode and transmit the packets in the distributed coding-aware routing (DCAR) mechanisms, which can decrease the transmission number and achieve higher throughput. However, depending on the primary coding conditions of DCAR, the DCAR-type schemes may not only detect more coding opportunities, but also lead to an imbalanced distribution of the network load. Especially, they are not energy efficient in more complex scenarios, such as wireless ad-hoc networks. In this paper, to solve these shortcomings, we propose a constrained coding-aware routing (CCAR) mechanism with the following benefits: (1) by the constrained coding conditions, the proposed mechanism can detect good coding opportunities and assure a higher decoding probability; (2) we propose a tailored \"routing + coding\" discovery process, which is more lightweight and suitable for the CCAR scheme; and (3) by evaluating the length of the output queue, we can estimate the states of coding nodes to improve the efficient coding benefit. To those ends, we implement the CCAR scheme in different topologies with the ns-2 simulation tool. The simulation results show that a higher effective coding benefit ratio can be achieved by the constrained coding conditions and new coding benefit function. Moreover, the CCAR scheme has significant advantages regarding throughput, average end-to-end delay, and energy consumption.  
network coding; coding-aware routing; coding conditions; coding benefit; wireless ad-hoc networks +
Footnote †: journal: Sensors
# The ConTAD Project: Detection of Multi-Container Loss for Safety of Navigation +
## 3 Current Container Tracking Technology and Maritime Monitoring Systems  
A central part for the digitalization of the supply chain is knowing the location of cargo at any time. CTDs enable standard freight containers to send position updates multiple times a day. Therefore, more and more containers are equipped with such devices [7]. Their basic functions are tracking a GNSS, sending regular position updates over a mobile network and monitoring the container with shock and acceleration sensors. Often, an additional radio interface for the connection to additional sensors, e.g., via Bluetooth Low Energy (BLE), is also available. Modern and flexible tracking solutions for shipping containers are the focus of some research projects [8; 9; 10].  
Some container tracking use cases have special requirements. For example, in aviation one requirement is to turn off GSM modems during the flight. The tracking device \"SocraFlite\" already automatically detects if it is loaded on an airplane and is certified for air transport by EASA [11].  
This paper also considers a special use case for container tracking during sea transport. While it is commonly accepted that during sea transport the position of the freight containers is equivalent to the ship's AIS position, a solution should be developed for the event that a container is lost overboard. Certain technologies and concepts are already available. A key element in the concept for e-Navigation by the International Maritime Organization (IMO) is maritime machine-type communication (MTC). This includes the already mentioned AIS as well as the extended functionality of very high frequency (VHF) Data Exchange System (VDES). An overview on frequencies for MTC is given in [12]. Before the approach of ConTAD is introduced in Section 4, a few other ideas to track or localize lost containers are presented.  
A tracking device utilizing radar reflections and their detection was proposed at ICMET [13]. Some advantages are the simplicity and the low cost of this solution. The disadvantage is that a radar reflection does not include any information on the object it is reflected from. It is useful to avoid a collision with the floating, stranded, or sunk container; however, during night or in restricted visibility, the navigator cannot tell if the object is a container or something else. Therefore, it still requires a search for the container if the  
Figure 1: Description of the container loss accident phase model and comparison of the MSC Zoe accident (yellow) with the goals in ConTAD (green).  
precise position of the container loss is unknown. Also, the passive radar reflection does not inform anyone in the supply chain about the loss of containers.  
Another solution exploiting AIS was proposed by [PERSON] [14]. He suggested a floating device, connected with a cable to the container. The device can transmit its position to ships nearby via AIS and therefore has a GPS position sensor, too. Its sole purpose is to warn other ships of floating containers and does not fulfill any of the tracking needs for a digital supply chain.
## 3 Methodology
### Metrics accuracy:  
the proportion of correctly predicted quantity, in positive and negative examples, to the total quantity can be expressed by the formula:  
\[accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{10}\]  
Figure 5: Disease image.  
precision_: based on the predicted results, the proportion of correctly predicted samples with positive examples is:  
\[precision=\frac{TP}{TP+FP} \tag{11}\]  
recall: based on actual samples, the proportion of correctly predicted positive samples to the total of actual positive samples.  
\[recall=\frac{TP}{TP+FN} \tag{12}\]  
F-score is a type of evaluation metric that harmonizes precision and recall:  
\[F\text{-}score=(1+\alpha^{2})\frac{P\times R}{\alpha^{2}\times P+R} \tag{13}\]  
when \(\alpha\) = 1 h is an F1 score, which is the harmonic mean of precision and recall (both are equally important). When \(\alpha\) = 0.5, it is an F0.5-score (precision is more important than recall). When \(\alpha\) = 2, it is an F2-score (recall is more important than precision).  
For classification problems, examples can be divided into four scenarios based on the combination of their true category and the learner's predicted category: True Positive, False Positive, True Negative, and False Negative. _TP_, _FP_, _TN_, and _FN_ represent the corresponding number of examples, respectively. The confusion matrix of the classification results is shown in Table 1.  
Intersection over Union (_IoU_) is a standard for evaluating the accuracy of detecting corresponding objects in a specific dataset. Any task that obtains a bounding box in the output can be measured using _IoU_.  
\[IoU=\frac{|A\cap B|}{|A\cup B|} \tag{14}\]  
This article uses _Dice Loss_ as the final loss function.  
_Dice Loss_ is named after the _Dice_ coefficient, a metric used to assess the similarity between two samples. The larger the value, the more similar the samples are. The mathematical formula for the _Dice_ coefficient is as follows:  
\[Dice=\frac{2|A\cap B|}{|A|+|B|} \tag{15}\]  
_Dice Loss_ can be calculated as follows:  
\[DiceLoss=1-Dice=1-\frac{2|A\cap B|}{|A|+|B|} \tag{16}\]  
\(A\) and \(B\) represent the predicted binary image and the ground truth binary image, respectively.
# High-Speed Dynamic Projection Mapping onto Human Arm with Realistic Skin Deformation +
## 3 Proposed Projection Mapping System
### High-Speed Motion Tracking  
As mentioned in Section 2, it is difficult to reconstruct skin deformation with high accuracy and speed using marker-less methods. However, fast and accurate methods are required for applications such as prototyping and medical assistance. Although it may increase the burden in setup and cause some discomfort to the user, marker-based methods are more suitable than their marker-less counterparts to track joint poses and reconstruct the arm surface using a parametric deformable surface model, such as SMPL [38]. For instance, the system in [16] used marker-based tracking to obtain accurate arm poses.  
The SMPL model represents a realistic human body using 10-dimensional body shape parameter \(\mathbf{\beta}\) and 72-dimensional pose parameter \(\mathbf{\theta}\)[38]. As the user's body shape does not vary in a short time, \(\mathbf{\beta}\) can be determined in advance using marker-less methods, such as those in [39; 40; 41; 42; 43]. In contrast, the user's pose changes rapidly and notably contributes to skin deformation. We consider the forearm (i.e., the region between the elbow and wrist) for the proposed system. According to the definition of blend skinning, each vertex of the mesh is transformed by the weight of its neighboring joints. Hence, only the poses of the wrist and elbow are required to represent the forearm.  
Commercial marker-based motion tracking systems, such as OptiTrack (NaturalPoint, Corvallis, OR, USA) [47] and Vicon (Oxford Metrics, Yarnton, UK) [48], can offer robust pose information of rigid bodies with a very short latency. In the proposed system, wrist pose \(\mathbf{P}_{\textit{wrist}}\) and elbow pose \(\mathbf{P}_{\textit{elbow}}\) are obtained from a motion tracking system. Joint pose \(\mathbf{P}\) is a six-dimensional vector that includes the joint position, \(\mathbf{J}\in\mathbb{R}^{3}\), and joint orientation, \(\mathbf{\gamma}\in\mathbb{R}^{3}\).  
Figure 1: System configuration and method pipeline.
## 2 Study Area and Methods
### Data Sources  
#### 2.2.1 Remote Sensing Data  
The MODIS data were derived from the GPP (MOD17A2, NASA, Washington, D.C., USA) and ET (MOD16A2, NASA, Washington, D.C., USA) terrestrial grade 4 products. The GPP product is calculated based on the photosynthetically active radiation utilization efficiency model, and the ET product is calculated using the improved MOD16 algorithm. The accuracy of these data has been compared and verified with flux site data in many regions worldwide, and these data have been widely used in global and regional studies [27; 28; 29].
# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 4 Experiments
### Data Preprocessing and Experimental Settings  
We define a record as overdue repayment behavior if the overdue day or overdue amount in a repayment record is not equal to zero. The label for overdue repayment is set as positive; otherwise, it is set as negative. To define the target of the training process, we first sort the data by unique identification and a repayment timestamp. The target of the individual with the last repayment behavior being overdue is set to positive; otherwise, it is set as negative. As a result, the number of positive individuals is 15,608, accounting for 18.3% of the total, while negative samples account for 81.7%.  
The sequence of all the behavior labels except the last label is regarded as the dynamic features and fed into the multi-BiLSTM layer. The window length of the label sequence is the maximum of each individual's record length, and padding is added at the end of the sequence to make up the required length. We operate a feature derivation and selection approach on the records, except the last record, for each individual to obtain static features as the input of the DNN layer. In feature derivation, we design five major classes as described in Section 3.1 with 14, 7, 20, 3 and 10 subindexes, respectively; thus, the total amount of newly derived features is \(\mathcal{N}_{d}=14\times 7\times 20\times 3\times 10=58,800\). After feature selection, we retain 42 of the most influential features in the DNN layer.  
We choose TensorFlow ([[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/)))--a deep learning tool based on Python--as the deep learning framework for our experiment. We randomly select 80% of the data as the training set, 10% as the validation set and 10% as the test set. The parameters of our AD-MBLSTM model are listed in Table 1.
# Fermentation Strategies for Production of Pharmaceutical Ternenoids in Engineered Yeast
## 5 Factors Affecting Fermentation Process of Pharmaceutical Terpenoids
### pH  
In general, as an acidophilic organism, _S. cerevisiae_ grows better under acidic conditions, in the pH range from 4 to 6, depending on the temperature, the presence of oxygen, and the strain [98]. The pH range between 5 and 6 has been mainly employed for terpenoid production (Table 3). In the study by [75], it was shown that fermentation time was reduced in batch cultivation at pH 6, while total terpenoid productivity decreased. Moreover, precipitation of solid particles, containing insoluble crystals and good quantity of triterpenonds on the inner wall of bioreactor was observed. The solubility of many terpenoids, especially hydrophobic triterpenoids, is pH-dependent, being higher at alkaline pH values, decreasing at lower pH values [99]. In another study, alkaline medium with pH ranging from 7 to 8 enhanced farnesol accumulation by _S. cerevisiae_[100]. It was also reported that the optimum pH for the wild type _S. cerevisiae_, and mutant strain in isopentenyl diphosphate isomerase activity (one of the important rate-limiting enzymes in terpenoid production), was 7.5 [101]. Alkaline pH might enhance the synthesis and solubility of terpenoids in the cultivation medium, but growth and biomass accumulation might be low, not allowing high cell density cultivation as desirable in industrial terpenoids production.