esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modeling
#### 2.2.4 Performance Evaluation Criteria  
To evaluate the learning ability of the LSTM models, in this study, the loss function was used and the most commonly used MSE method was selected among the loss functions. The mean absolute error (MAE) method was used as a metric to measure the accuracy of LSTMs. The MAE is the average of the absolute value between the predicted value and the actual value. In addition, we used mean absolute percentage error (MAPE) to measure the performance of the LSTM and ARIMA models. The MAPE is used to estimate the prediction accuracy of the proposed LSTM and ARIMA models. The MAPE is one of the typical accuracy metrics for time series forecasts. Since the MAPE varies between 0 and 1, we can judge how good the prediction is irrespective of the scale of the series. The error values of these criteria show higher accuracy as the values approach 0. The MSE, MAE, and MAPE are defined as:  
\[\text{MSE}=\frac{1}{n}\sum_{1}^{n}(y-\hat{y})^{2} \tag{7}\]  
\[\text{MAE}=\frac{1}{n}\sum_{1}^{n}\lvert y-\hat{y}\rvert \tag{8}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{1}^{n}\left\lvert\frac{y-\hat{y}}{y}\right\rvert \tag{9}\]  
where, \(y\) and \(\hat{y}\) are the actual value and the predicted value for the frequency of marine accidents, respectively.
# A Biologically Inspired Self-Organizing Underwater Sensor Network
## 3 Problem Statement and Solution
When a node receives a message packet, the forwarding guide decides if it should be ignored or sent to a certain neighbor. Details are in Section 5, where we prove the message packet can be sent to its destination along the path with the shortest expected wait. With the communication coordinator, the message packet sent by a node contains the following information:  
\[(id_{self},S_{1-neighbor},S_{abandon},c_{self},id_{next},S_{msg})\]  
Figure 4: TDMA framework. Time is divided into a sequence of frames, with each frame further scattered into a set of slots. Figure 3: The two situations that can cause communication collisions. Dots denote sensor nodes, and the concentric circles with the same color indicate the node is broadcasting a message. A node with a check mark indicates it has received a message successfully, while a cross indicates it has failed to receive a message. Subfigure (**a**) exhibits communication collisions between a pair of first-order neighbors. Nodes A and B are a pair of first-order neighbors, and they are speaking simultaneously. In this situation, Nodes A and B fail to receive messages, while Nodes C and E receive messages successfully. Subfigure (**b**) exhibits communication collisions between a pair of second-order neighbors. Nodes A and C are a pair of second-order neighbors that are speaking simultaneously. Node B is the first-order neighbor of both A and B. Node B receives both signals at the same time, so it is unable to parse messages in either of the signals. with \(id_{self}\) indicating the ID of the speaker, and \(S_{1\text{-}neighbor}\) containing the IDs of the speaker's first-order neighbors. \(S_{abandon}\) is a set generated by the collision resolver. On receiving \(S_{abandon}\), a node needs to abandon the occupation of slots in this set except for its identical slot. The variable \(c_{self}\) indicates the cost of the current node to send the message to the base station, whose initial value is \(\infty\) unless the node itself is the base station (then, the value becomes 0), and \(id_{next}\) indicates the ID of the next node that should relay the message packet so that the packet can be sent to the base station jump-by-jump. The initial value of \(id_{next}\) is \(-1\), indicating that the node cannot decide on a successor. \(S_{msg}\) contains the data collected by the sensor that need to be sent to the base station. The message packet a node receives contains the same information. To avoid confusion, in the rest of this paper, we use superscript \({}^{sender}\) to mark the information of the sender, so the received message packet is written as  
\[(id_{self}^{sender},S_{1\text{-}neighbor}^{sender},S_{abandon}^{sender},c_{ self}^{sender},id_{next}^{sender},S_{msg}^{sender}).\]  
For readers to better understand this paper, the definitions of key variables are summarized in Figure 1:0
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
###### Abstract  
The contribution to sea level rise from Patagonia icefields is one of the largest mass losses outside the large ice sheets of Antarctica and Greenland. However, only a few studies have provided large-scale assessments in a spatially detailed way to address the reaction of individual glaciers in Patagonia and hence to better understand and explain the underlying processes. In this work, we use repeat radar interferometric measurements of the German TerraSAR-X-Add-on for Digital Elevation Measurements (TanDEM-X) satellite constellation between 2011/12 and 2016 together with the digital elevation model from the Shuttle Radar Topography Mission (SRTM) in 2000 in order to derive surface elevation and mass changes of the Southern Patagonia Icefield (SPI). Our results reveal a mass loss rate of \(-\)11.84 \(\pm\) 3.3 Gt-a\({}^{-1}\) (corresponding to 0.033 \(\pm\) 0.009 mm\(\cdot\)a\({}^{-1}\) sea level rise) for an area of 12573 km\({}^{2}\) in the period 2000-2015/16. This equals a specific glacier mass balance of \(-\)0.941 \(\pm\) 0.19 m w.e.-a\({}^{-1}\) for the whole SPI. These values are comparable with previous estimates since the 1970s, but a magnitude larger than mass change rates reported since the Little Ice Age. The spatial pattern reveals that not all glaciers respond similarly to changes and that various factors need to be considered in order to explain the observed changes. Our multi-temporal coverage of the southern part of the SPI (south of 50.3\({}^{\circ}\) S) shows that the mean elevation change rates do not vary significantly over time below the equilibrium line. However, we see indications for more positive mass balances due to possible precipitation increase in 2014 and 2015. We conclude that bi-static radar interferometry is a suitable tool to accurately measure glacier volume and mass changes in frequently cloudy regions. We recommend regular repeat TanDEM-X acquisitions to be scheduled for the maximum summer melt extent in order to minimize the effects of radar signal penetration and to increase product quality.  
glacier elevation change; geodectic mass balance; InSAR; Patagonia; SPI 1 Friedrich-Alexander-Universitat Erlangen-Nurnberg, Institut fur Geographie, Wetterkreuz 15, D-91058 Erlangen, Germany; [PERSON]W.M.); [EMAIL_ADDRESS] ([PERSON].)  
11
# A Systematic Mapping Study on the Modernization of Legacy Systems to Microservice Architecture
## 4 Results
### Quality Attributes  
Based on the empirical findings compiled by [PERSON] et al. (2016) and [PERSON] et al. (2017), quality attributes are recognized as first-class elements in distributed applications. Therefore, the objective of RQ5 is to understand which QoS (Quality of Service) attributes are used in a microservice-based application during modernization. The definition used for this application type encompasses any service-oriented application that communicates by exchanging messages via APIs, such as SOAP (Simple Object Access Protocol) and REST (Representational State Transfer). Although the importance of quality attributes in this type of application is recognized, our investigation revealed that 19 (i.e., 44.19%) of the 43 studies in our SMS did not provide evidence of the adoption of any quality attribute in the proposed initiative (i.e., S2, S11, S12, S14, S17, S18, S22, S25, S26, S28, S29, S30, S31, S34, S35, S36, S37, S38, and S41). Figure 5 illustrates the relationship between the quality attributes identified in our investigation and the respective studies.  
A correlation analysis of the studies that provided evidence on the incorporation of quality attributes in the proposed initiatives revealed that the attribute of availability was mentioned in nine studies (i.e., S1, S5, S7, S19, S24, S27, S39, S40, and S43). Our investigation revealed that the attributes of reliability and scalability were the most frequently referenced, occurring in the second and third positions, respectively. The attribute of reliability was identified in six studies (i.e., S20, S21, S32, S33, S39, and S42), while scalability was found in five studies (i.e., S6, S7, S9, S23, and S27). Furthermore, it is notable that several studies indicated the use of multiple quality attributes, which corroborates the initial evidence presented in this section (i.e., the distributed nature of the application following a modernization activity). Notwithstanding the aforementioned evidence, it should be highlighted that only one study (i.e., S7) explicitly stated the inclusion of three quality attributes in its proposed initiative. Finally, ten studies highlighted two attributes, and thirteen, only one quality attribute.  
Figure 4: Evaluation strategies versus maturity levels.  
As outlined in the overview provided by [PERSON] et al. [16], agility is not a quality attribute pertinent to the distributed computing scenario. The use of this term within the context of this study enables us to present two interpretations. First, the term \"agility\" is being used as a method to meet new market demands rapidly, aligning seamlessly with development strategies centered on microservices. Second, the proponents have failed to utilize the term \"agility\" effectively, lacking a proper understanding of its concept.
## 3 Methodology  
### Sample Selection and Data Collection  
Data collection was drawn from container shipping companies, container shipping agencies, and an ocean freight forwarder in Taiwan. The Directors of the National Association of Shipping Companies and Agencies were selected as the survey population. This survey was conducted by mail. A total of 500 questionnaires were sent to employees who had a position of manager or above. Every company received two survey questionnaires on average. The questionnaires were sent to the potential participants on 8 January 2020; 188 valid responses were returned. Follow-up mail was sent after one month and data were increased by 97. In total, 285 questionnaires were collected, with a response rate of 57%.  
Figure 1: Research conceptual model.  
The respondents in the study were high-level executives in container shipping firms, such as vice presidents or higher, managers, directors, and IT operators (see Table 1), ensuring that the respondents had strategic-level supervising experience in fields related to IT security management. Further, most respondents had spent more than 63% of their time working in the container shipping industry, indicating that they had sufficient knowledge to answer the questionnaire.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of Respondents** & **Percentage of Respondents** \\ \hline
**Job title** & & \\ Vice president or higher & 14 & 4.9 \\ Manager & 77 & 27.0 \\ Director & 22 & 25.3 \\ General employees & 45 & 15.8 \\ IT operators & 77 & 27.0 \\ \hline
**Number of employees** & & \\
50 or fewer & 74 & 26.0 \\
51–100 & 37 & 13.0 \\
101–200 & 83 & 29.1 \\
201–300 & 76 & 26.7 \\
300 or more & 15 & 5.3 \\ \hline  & **Work experiences (years)** & \\
5 years or fewer & 17 & 6.0 \\
6–10 & 38 & 13.3 \\
11–15 & 47 & 16.5 \\
16–20 & 90 & 31.6 \\
20 or more & 93 & 32.6 \\ \hline  & **Tenure in current company (years)** & \\
5 or fewer & 31 & 10.9 \\
6–10 & 50 & 17.5 \\
11–15 & 84 & 29.5 \\
20 or more & 120 & 42.1 \\ \hline \multicolumn{3}{c}{**Length of business operations (years)**} \\
5 or fewer & 2 & 0.7 \\
6–10 & 10 & 3.5 \\
11–20 & 33 & 11.6 \\
21–30 & 96 & 33.7 \\
30 or more & 144 & 50.5 \\ \hline \multicolumn{3}{c}{**Ownership pattern**} \\ Local firm & 197 & 69.1 \\ Foreign-owned firm & 40 & 14.0 \\ Foreign local firm & 48 & 16.8 \\ \hline  & **Security training participation (number of training courses attended)** & \\ Never & 65 & 22.8 \\
1 & 95 & 33.3 \\
2 & 57 & 20.0 \\
3 & 43 & 15.1 \\
4 & 11 & 3.9 \\
5 or above & 14 & 4.9 \\ \hline \multicolumn{3}{c}{**Security experience**} \\ Phishing & 136 & 22.1 \\ Malware & 103 & 16.7 \\ Ransomware & 95 & 15.4 \\ Spam & 203 & 32.9 \\ Outdated security & 49 & 7.9 \\ None & 31 & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Profile of respondents.
## 3 Methods and Materials
### Fusion Approach of Deep Features Maps  
Implementing pre-trained CNN models still faces challenges such as satisfactory accuracy, high-cost computers and time consumption [42]. In addition, the features extracted from one of the VGG-16 and GoogLeNet models are insufficient to reach a promising accuracy. Therefore, to solve these challenges, we applied a modern methodology to integrate the features extracted by VGG-16 and GoogLeNet models into the same feature vectors and their diagnosis by the ANN algorithm. Figure 4 describes the basic idea of this approach as follows: First, feed the enhanced cervical cancer data set to the VGG-16 and GoogLeNet models. Second, extract 2048 features from the VGG-16 model and store in vectors of the feature so the data set becomes 25,000 \(\times\) 2048. Third, extract 2048 features from the GoogLeNet model and store in feature vectors so the data set becomes 25,000 \(\times\) 2048. Fourth, integrate vectors of feature for both VGG-16 and GoogLeNet models in new feature vectors to 25,000 \(\times\) 4096 size. Fifth, use the PCA to reduce the features to 25,000 \(\times\) 720 data set size. Sixth, feed the low-dimensional features into the ANN to classify them with better accuracy and speed than CNN models.  
Figure 5 describes the basic architecture of the ANN, consisting of an input layer that has 720 input units according to the number of features sent after high-dimension reduction by the PCA algorithm. Many hidden layers perform complex tasks to solve the required problems. The network is set to 15 hidden layers, which is the best performance for diagnosing WSIs of cervical cancer. The output layer is represented by the activation function that classifies the characteristics of each input image into five classes (types of abnormal cells) of cervical cancer.  
Figure 4: Framework for a feature fusion approach of the VGG-16 and GoogLeNet models for cervical cancer data set diagnosis.
# Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication
## 5 Performance Evaluation
### Benchmark through the Master  
Figures 5 and 6 show the IOPS and energy consumption outcomes of Redis when running the Memtier benchmark. We generate one million SET and GET operations varying  
Figure 4: Write path of master and replica in Redis.  
Figure 3: Comparison of the propagation and processing rates of a write operation.  
the value size from 256 B to 2 KB. In the figures, we compare the performance and energy consumption of Concerto (CNCT) with those when executing all nodes at a high frequency (ALL-H) and at the low frequency (ALL-L). Figure 6 presents the energy consumed by the processor during the execution of the benchmark; this value is calculated by multiplying the power and the total elapsed time for each configuration. For comparison, we plot the energy consumption for two different cases: when a master runs with a single replica (M-R) and with two replicas (M-2R). This is done in an effort to observe the energy consumption trend as the replication factor increases.  
With respect to energy efficiency, ALL-L achieves the most significant savings, reducing the energy consumption by 54% compared to ALL-H. However, it inhibits the performance significantly, decreasing IOPS by 36% and 15% on average for SET and GET operations compared to ALL-H. In contrast, CNCT decreases the energy consumption by 27-36%, but this is achieved while maintaining the performance. CNCT provides throughput identical to that of ALL-H for both SET and GET operations.  
We investigate the effectiveness of Concerto using YCSB benchmarks. The YCSB benchmark suite has six different workloads, and Table 3 summarizes the characteristics of each. We initially load one million data items and perform one million operations for each workload. Figures 7 and 8 show the IOPS and the energy consumption outcomes for the YCSB benchmark. While ALL-L performs worse than ALL-H by up to 25% (20% on average), Concerto achieves a performance similar as ALL-H across all YCSB workloads. This experimental result demonstrates that the performance relaxation in replicas can be hidden to from the user experience, thus enabling considerable energy savings without a noticeable performance loss.  
Figure 5: IOPS with Memtier benchmark.  
Figure 6: Energy consumption with Memtier benchmark.  
With regard to energy consumption, Concerto is inferior to ALL-L because we stick to executing the master node at full speed. Concerto reduces energy consumption by 44% on average compared to ALL-H, whereas ALL-L uses less energy than ALL-H by 67% on average. However, the advantage of ALL-L in terms of energy is largely compensated for by its performance loss, making it less feasible in practice.
## 3 Results
### Proof-of-Concept Demonstration  
To test the feasibility of the proposed smart-farming system, a proof-of-concept demonstration with a single smart-farming unit is demonstrated. Figure 6a shows the experimental setup for the uplink and downlink communications. For downlink communication, a waveform generator (Keysight, 33500B, Santa Rosa, CA, USA) creates a pulse amplitude modulation (PAM) with two levels (2-PAM), also known as on-off-keying (OOK). The signal is combined with DC bias using a bias-T (Mini-circuit, JEBT-4R2 GW, Brooklyn, NY, USA) and applied to a commercial white-light LED (downlink light). An APD near the RGB LED lights (KHL, LED54 Gurp Light 162W, Seoul, Korea) receives the signal and is analyzed on the oscilloscope (Keysight, DSO6104, Santa Rosa, CA, USA). For the uplink communication, the waveform generator (Keysight, 81150A, Santa Rosa, CA, USA) generates the PAM signal, and it is applied to the green LEDs. The blue and red LEDs are connected to a DC power supply (Smart, RDP-303 AU, Carlsbad, CA, USA) for generating the grow lights. An optical camera (Thorlabs, Zelux 1.6 MP CMOS, Newton, NJ, USA) placed near the white-light is used to detect the signal and is analyzed on the camera monitoring PC. Figure 6b shows reflected RGB lights from the vegetable surface for the uplink communication.  
Figure 7 presents the optical camera image at the distance of 0 cm, 25 cm,50 cm, and 85 cm away from the center. At 0 cm (center-aligned), most leaves are covered by the grow light unit, where less reliable communication performance is expected. At 25 cm and 85 cm, almost half of the vegetables are exposed due to the LOS blockage and being outside the camera's field of view (FOV), respectively. The camera can capture the majority of the reflected light at 50 cm.  
Figure 5: (**a**) Optical channel loss per unit area for uplink with blockage from grow light fixture; (**b**) its contour plot.
# Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors
## 3 Methods  
### Architecture  
To construct a prototype system to detect users' travel modes, we developed an Android application that automatically collected user's moving data by smartphones. The architecture of the system is demonstrated in Figure 1.  
As shown in Figure 1, the travel mode detection system mainly consists of two parts: the mobile terminal and server terminal.  
(1) Mobile terminal  
We developed an Android application to collect four different sensors data, which includes accelerometer data, gyroscope data, magnetometer data, and barometer data at the mobile terminal. Through the mobile phone Android application, users can collect and upload their own mobile phone sensor data.  
(2) Server terminal  
The web server can monitor and query the sensor data uploaded by the user in real time, and data processing algorithms are built into the server, including data segmentation and CNN models. Firstly, the time series data are separated according to a specific time window before being used to construct a detection model. Then, the datasets are used as the input of convolutional neural network (CNN) for travel mode (walk, bike, bus, car, metro) recognition.
## 3 Materials and Methods
### Autoregressive Moving Average (ARMA)  
The Box-Jenkins model was invented by [PERSON] and [PERSON] in 1960; this forecasting method with this model performed better than the others because it uses the time series rule. This model uses stationary time series regression and does not weigh seasonal elements [58]. The Box-Jenkins model has three components: autoregressive (AR), moving average (MA), and integrated (I). Combinations of the three form the ARMA and ARIMA models. These models are built to identify the presence of heteroscedastic elements. If any are present, then the volatility measurement is continued using the GARCH model [59].  
ARIMA is a forecasting method used to measure variables in time series. It is often referred to as the Box-Jenkins method, with various combinations of autoregressive and moving averages that can be used to obtain time-series projections. This model tends to ignore independent variables in making estimates or forecasts. It takes the value of past data training to produce useful forecasts. This method is best used to determine the historical importance of data, including food price volatility [50]. Analysis of the volatility of food prices can also be carried out using the vector autoregressive (VAR) method, which can measure total net volatility, including the impulse response function to variables that affect the volatility value. This method can measure many variables, but it has the weakness that the bias toward the dominant variable is less clear [60].
###### Abstract  
Accurate tumor segmentation is important for aided diagnosis using breast ultrasound. Interactive segmentation methods can obtain highly accurate results by continuously optimizing the segmentation result via user interactions. However, traditional interactive segmentation methods usually require a large number of interactions to make the result meet the requirements due to the performance limitations of the underlying model. With greater ability in extracting image information, convolutional neural network (CNN)-based interactive segmentation methods have been shown to effectively reduce the number of user interactions. In this paper, we proposed a one-stage interactive segmentation framework (interactive segmentation using weighted distance transform, WDTISeg) for breast ultrasound image using weighted distance transform and shape-aware compound loss. First, we used a pre-trained CNN to attain an initial automatic segmentation, based on which the user provided interaction points of mis-segmented areas. Then, we combined Euclidean distance transform and geodesic distance transform to convert interaction points into weighted distance maps to transfer segmentation guidance information to the model. The same CNN accepted the input image, the initial segmentation, and weighted distance maps as a concatenation input and provided a refined result, without another additional segmentation network. In addition, a shape-aware compound loss function using prior knowledge was designed to reduce the number of user interactions. In the testing phase on 200 cases, our method achieved a dice of 82.86 \(\pm\) 16.22 (%) for automatic segmentation task and a dice of 94.45 \(\pm\) 3.26 (%) for interactive segmentation task after 8 interactions. The results of comparative experiments proved that our method could obtain higher accuracy with fewer simple interactions than other interactive segmentation methods.  
interactive image segmentation; breast ultrasound; weighted distance transform; prior knowledge +
Footnote †: journal: Applied Sciences