esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Field Testing
### Test Set-Up  
A test site consisting of nine concrete pavement slabs was constructed at Tongji University. The thickness of the nine slabs was all 25 cm. Figure 5a depicts the length and width of the nine slabs. As shown in Figure 5b, the thickness was 15 cm of the cement stabilized base. The subgrade was made of compacted soil and was more than 40 cm thick. The grade of the concrete was C45. Its flexural strength was 4.9 MPa (28 day). The concrete was produced and provided by a commercial company (Shanghai Chengjian Group).  
Figure 5: Field test set-up: (**a**) Dimension of the nine concrete pavement slabs; (**b**) Sectional view of the test site; (**c**) Scenarios of the joints; (**d**) Cross section of rabbit joint; (**e**) Vibration sensing system; (**f**) Maintain the sensors in the right position.  
There were eleven scenarios set for the field testing, as shown in Figure 5c. Note that some scenarios (e.g., J4 and J6) were located at the same joint but the LTE was assessed from different directions, so they were considered as two different scenarios.  
Three kinds of joints (dummy joint, rabbet joint, and dowel bars) were constructed in the field testing. The cross-section of the rabbet joint is shown in Figure 5d. The dowel bars were 50 cm in length, 2.5 cm in diameter, and arranged with a forty-centimeters gap. The dummy joint was 10 cm in depth and 0.3 cm in width.  
A vibration sensing system was developed based on DOVS in field testing. Monitoring units were embedded into the concrete pavement slabs to measure their vibrations. There were ten monitoring units arranged along a four-meter-long joint while five monitoring units along a two-meter-long joint. The monitoring unit's diameter was 30 cm.  
As shown in Figure 5e,f, the monitoring units were fixed on the base before concrete placing to maintain the optical sensors in the right place. During concrete placing, some concrete was firstly placed around the optical sensors to provide support to keep the optical sensors in right place.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 5 Towards Algorithm Development: Propagation Concepts, Path Loss and Communication Graph
### Propagation Environment and Channels  
The environment has a significant impact on the propagation models because physical aspects, such as the existence of buildings and their heights, the terrain and the level of vegetation, impact propagation. There are six types of propagation environments: (1) free space, (2) rural, (3) suburban, (4) urban, (5) dense-urban and (6) high-dense urban. In our paper, we only approach rural and free space situations, as they are more specific to isolated areas.  
Moreover, from the propagation channels available in the literature, we extracted and adapted the following classification to our use cases:  
* Aerial and terrestrial channels
* Air-to-ground channel (A2G) Propagation channel for communication between aerial vehicles and ground nodes, when aerial vehicles transmit to ground devices.
* Ground-to-ground channel (G2G) Propagation channel for communication between ground devices.
* Ground-to-air channel (G2A) Propagation channel for communication between aerial vehicles and ground nodes, when ground nodes transmit to aerial vehicles.
* Air-to-air channel (A2A) Propagation channel for the communication between aerial vehicles. It is worth mentioning that air-to-ground channel characteristics are also reflected in the ground-to-air channel features [62].
* Water channels  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Description** & [57] & [58] & [59] \\ \hline f (MHz) & Frequency & 900 & 2400 & 868 \\ \(P_{tx}\) (dBm) & Maximum Transmitter power & 20 & 14 & 14 \\ S\({}_{rx}\) (dBm) & Sensitivity & \(-106\) & \(-98\) & \(-121\) \\ \(LB\) (dB) & Final Link Budget & 106 & 92 & 115 \\ DR & Data Rate & 115 kbps & 72 Mbps & 37.5 kbps \\  & **Technology** & Long Range & Wi-Fi & LoRa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of transceivers involved in the simulations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **868 MHz** & **433 MHz** \\ \hline Maximum allowed TX power & 14 dBm & 10 dBm \\ Antenna’s dimension & L & 2\(\times\) L \\ Congested & no & yes \\ Packet reception stability [60] & yes & no \\ Penetration capability [60] & no & yes \\  & Better at the edge of the & Better close to the GW \\ Overall performances [61] & coverage area & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between LoRa on 868 MHz and LoRa on 433 MHz.  
* Ship-to-shore channel (S2S) Propagation channel for communication between vessels on sea and devices placed onshore.
* Over-water channel (OW) Propagation channel for communication between ships on sea, rivers or lakes.
* Air (UAV)-to-ship channel (A2S) [63] Propagation channel for communication between ships on water or ports and aerial vehicles.  
The following sections describe the effects of the considered channels by large-scale models, that is, from the point of view of path loss and the propagation environment [64].
## 2 Experiment and Measurements  
### Experiment Setup  
A milling machine (Awea Mechatronics, Hsinchu County, Taiwan, Type: A + 1020) was utilized to demonstrate the effectiveness of work-piece surface roughness prediction and cutting parameter optimization in this research. The Inconel 718 that has been used as the component material of gas turbine engine or rocket engine in the aerospace engineering field was selected as the work-piece material in the experiment. According to the material properties of work-piece, the TiAlN-PVD-coated carbide grade cutter (Kennametal Incorporation, Pittsburgh PA, USA, type: KC725M) was utilized in the milling machine. The specifications of the cutter are shown in Table 1.  
As shown in Figure 1, two tri-axial accelerometers (PCB Piezotronics, Depew, NY, USA, type: 629A31) were stuck on the spindle and the rise, respectively to measure the vibration acceleration synchronously during the milling process. The vibration signals were discretized and recorded through the data acquisition module (National Instruments Corporation, Austin, TX, USA, type: NI9234) with the sampling frequency of 25.6 kHz. The slot milling process produced five straight routes on each block of Inconel 718 work-piece. The surface roughness measurement system (Kosaka Laboratory Limited, Tokyo, Japan, type: SE3500K) was utilized to detect the Ra value of each milling route on the work-piece. The current signals of the spindle were acquired synchronously through the Servo Guide system (Fanuc Corporation, Yamanashi Prefecture, Japan) in the spindle drive module with the sampling frequency of 2 kHz.  
According to the parameter range suggested by the cutter manufacturing company, the cutting parameters were set in this experiment. Additionally, the accumulated volume removal of each cutter and the accumulated operation time of each cutter were estimated to simply represent the factor of tool wearing. All the cutting parameters as well as the associated accumulated volume removal and the accumulated operation time per cutter are shown in Table 2. The measured signals, the corresponding Ra values and the cutting parameters utilized in this research article are available at the website [16].  
\begin{table}
\begin{tabular}{c c} \hline \hline Diameter (mm) & 16 \\ Blade length (mm) & 11 \\ Cutter length (mm) & 90 \\ Number of blades & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of cutter.
###### Abstract  
Hub-and-Spoke (H&S) network modeling is a form of transport topology optimization in which network joins are connected through intermediate hub nodes. The Short Sea Shipping (SSS) problem aims to efficiently disperse passenger flows involving multiple vessel routes and intermediary hubs through which passengers are transferred to their final destination. The problem contains elements of the Hub-and-Spoke and Travelling Salesman, with different levels of passenger flows among islands, making it more demanding than the typical H&S one, as the hub selection within nodes and the shortest routes among islands are internal optimization goals. This work introduces a multi-objective tri-level optimization algorithm for the General Network of Short Sea Shipping (GNSSS) problem to reduce travel distances and transportation costs while improving travel quality and user satisfaction, mainly by minimizing passenger hours spent on board. The analysis is performed at three levels of decisions: (a) the hub node assignment, (b) the island-to-line assignment, and (c) the island service sequence within each line. Due to the magnitude and complexity of the problem, a genetic algorithm is employed for the implementation. The algorithm performance has been tested and evaluated through several real and simulated case studies of different sizes and operational scenarios. The results indicate that the algorithm provides rational solutions in accordance with the desired sub-objectives. The multi-objective consideration leads to solutions that are quite scattered in the solution space, indicating the necessity of employing formal optimization methods. Typical Pareto diagrams present non-dominated solutions varying at a range of 30 percent in terms of the total distance traveled and more than 50 percent in relation to the cumulative passenger hours. Evaluation results further indicate satisfactory algorithm performance in terms of result stability (repeatability) and computational time requirements. In conclusion, the work provides a tool for assisting network operation and transport planning decisions by shipping companies in the directions of cost reduction and traveler service upgrade. In addition, the model can be adapted to other applications in transportation and in the supply chain.  
Hub-and-Spoke network; marine transportation; Short Sea Shipping: optimization; genetic algorithms +
Footnote †: journal: 2023, 16, 379. [[https://doi.org/10.3390/a16080379](https://doi.org/10.3390/a16080379)]([https://doi.org/10.3390/a16080379](https://doi.org/10.3390/a16080379))
## 5 Traffic Flow Prediction Algorithm Based on Data Completion Strategy  
The missing data of the original traffic flow tensor can be recovered by the Algorithm 1 proposed in the previous Section. To distinguish the original traffic flow tensor from the tensor after completion, we denote completed tensor as \(\chi_{t}^{d,n}\). We predict the future traffic flow based on the completed tensor. When we predict the future traffic flow, the data of time slices that close to the forecast point play a greater role, while the data of time slices that far away from the forecast point are less effective. Therefore, we introduce the tensor window \(W(T,s)=\left\{\chi_{T-s+1}^{d,n},\ldots,\chi_{T}^{d,n}\right\}\), which localizes the tensor into smaller time slices sequence with size \(s\) at time \(T\). As shown in Figure 4, the traffic flow prediction problem can be defined. Given the data in \(W_{\{D-1\}}(T,s)\) of previous \(D-1\) days and the data in \(W_{D}(T-1,s)\) of the \(D\) day, we need to predict data at time \(T\) of the \(D\) day (\(\chi_{T}^{D,n}\)), where \(n=1,2,\ldots,N-1,N\).  
This problem can be solved by using data completion strategy, which means regarding data at forecast point as the missing data. As Algorithm 3 shows, we use data completion strategy to predict the future traffic flow. Firstly, we extract the data in \(W_{\{D-1\}}(T,s)\) and \(W_{D}(T-1,s)\) as historical data. Secondly, we recover the missing data of the historical data. Thirdly, the united tensor is composed by the historical data that recover the missing data and the prospective data that need to be predicted. Fourthly, the united tensor is processed by Algorithm 1.  
Figure 4: Traffic flow prediction.
## 3 Hardware Description
### Secondary Node
For the prototype testing, the frame rate was selected to comply with a 240 \(\mu\)s bit period. The selected value gives a raw symbol rate of about 8 kbps and a data rate of about 4 kbps, because of the Manchester encoding. The frame is made up of 8 ON/OFF transmission periods to allow synchronization, a 2 bit start word, 1 byte of data and 1 byte of CRC, where the data byte contains the node ID. Therefore the transmission of a frame lasts for 6.24 ms. Using the ID described, a maximum of 256 nodes are tagged. However in a final production design, the ID node should be flash-programed. Therefore, the ID could be increased up to the duration value power pulses (typically above 20 ms). Finally an adapted antenna has been added. We have chosen a pig tail antenna (see Figure 5) which allows the design of the board to be thinner than by using a standard monopole. Because future experiment expectations will involve mammals, we wanted the secondary node to be as least uncomfortable as possible for the animals, without reducing range transmission capabilities. As shown in Figure 5, both the harvesting and transmission modules are located in the same board.
## 5 Experiments and Results
### Preprocessing and Data Cleaning  
The Barcelona Bike Sharing dataset is a noisy dataset with some anomalies and thus preprocessing is necessary to remove noise. In particular, analysing the stations' status over time, it is possible to notice irregularities among the total number of slots for certain stations. In fact, high oscillations were present in the dataset, having the station a variable amount of available slots, ranging from 0 to a maximum value of 59. Moreover, some information is missing because not all the stations were able to log their status every two minutes. Hence, the following points were addressed by our preprocessing step:  
* Removal of data associated to total number of slots for a specific station equal to 0 or 1. Such situations can possibly be associated with the system not working properly or maintenance operations ongoing during the time the data was collected.
* Removal of some bike stations from the dataset due to them being logged rarely. In particular, we removed stations that were not present in at least \(f\%\) of the available timestamps, being \(f\) an input parameter for our framework. We refer to such stations as infrequent stations.
* Removal of some bike sharing stations due to high fluctuation of total number of slots. Specifically, we analysed the variance of the total number of slots for each station and filtered out stations whose variance was higher than a specific threshold denominated as _varianceThreshold_. We refer to such stations as unstable stations.  
As representative examples of unstable stations, Figure 4a,b show the total number of slots of station 8 and station 29 over the entire time span available, respectively. The total number of slots oscillates between 0 and more than 30 confirming the instability of the information associated with those two representative unstable stations. Unstable stations must be removed in order to extract robust patterns not affected by noisy input data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**StationId** & **Timestamp** & **Used Slots** & **Free Slots** \\ \hline
280 & 2008-08-24 21:44:00 & 8 & 19 \\
223 & 2008-09-25 04:52:00 & 5 & 22 \\
108 & 2008-06-16 17:20:00 & 3 & 24 \\
67 & 2008-06-26 10:24:00 & 5 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Random sample of stations’ status extracted from the dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**StationId** & **Longitude** & **Latitude** & **Name** \\ \hline
1 & 2.180019 & 41.397978 & Gran Via Cortes Catalanes \\
2 & 2.176414 & 41.394381 & Placa de Tetuan \\
3 & 2.181164 & 41.393750 & Ali Bei \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example of stations’ information.  
After the removal of the data points associated with a total number of slots equal to 0 and 1, we analyse the impacts of the \(f\) and _varianceThreshold_ parameters on the cleaning operations. Hence, hereafter we discuss the effects of every parameter on the dataset and the final outcome on the prediction and rebalancing phase, starting from the cleaning operations.
# A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
## 5 Performance Evaluation
### Discussion and Overhead Analysis  
When a cache miss occurs, its performance penalty includes the extra time to access DRAM-based structures. However, DRAM has a higher access speed (e.g., about 20 GB/s) than NAND flash memory because the read and write time of a 16 KB page in NAND flash memory [25; 26] are about 80-90 us and 340-900 us. Therefore, the performance penalty of a DRAM cache miss could cost about 100 ns, but a DRAM cache hit can avoid the access time of a 16 KB page in NAND flash memory and save about 80-900 us. Even if the cache hit ratio is only 1%, the time saved can be N * 0.01 * 80 us = N * 800 ns, but the performance penalty of 99% cache misses could be N * 0.99 * 100 ns = N * 99 ns, where N represents the access count to the cache and 80 us represents the read time of a 16 KB page in NAND flash memory. In fact, the actual cache hit ratio of the proposed method under different cache sizes (i.e., 4, 8, 16, 32 and 64 MB) for the eight workload traces is from 2.21% to 73.22%. As a result, the benefit of the cache hits is greater than that of the cache misses. That is why the proposed method and the previous typical methods (such as LRU, ARC, and VBBMS) have to improve the cache hit ratio.  
We used the Python language and the embedded NVIDIA Jetson TX2 board to implement the proposed RAA and RAA-Delay. Based on the specification in Table 1, NVIDIA Jetson TX2 has enough ability to support the Python environment required about 2 GB main memory and act as a cache accelerator to assist SSDs of large enterprises. According to the results in Table 4, we observed that the average computation time by RAA or RAA-Delay is reasonable and short (e.g., about 0.3 s) when compared to the computation interval. We also observed that RAA-Delay can extend the computation interval by about 2 times to 34 times so that the computation overhead caused by the association analysis can be reduced. As a result, the proposed method based on request association analysis is practical and efficient.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Workload** & **RAA when Compared with** & **RAA-Delay when Compared with** \\  & **LRU, ARC, and VBBMS** & **LRU, ARC, and VBBMS** \\ \hline MSR-usr\_0 & 42.25\%/28.77\%/35.27\% & 28.76\%/17.09\%/22.75\% \\ Systor’17-LUN\_0 & 60.45\%/27.68\%/40.94\% & 55.80\%/24.29\%/37.04\% \\ Systor’17-LUN\_1 & 39.99\%/17.99\%/27.85\% & 34.60\%/13.62\%/23.01\% \\ Systor’17-LUN\_2 & 29.46\%/21.65\%/24.20\% & 27.98\%/20.29\%/22.81\% \\ Systor’17-LUN\_3 & 43.07\%/18.52\%/29.26\% & 33.75\%/10.89\%/20.288\% \\ Systor’17-LUN\_4 & 28.68\%/14.73\%/21.39\% & 24.38\%/11.05\%/17.41\% \\ Systor’17-LUN\_6 & 33.18\%/16.15\%/24.82\% & 30.16\%/13.59\%/22.01\% \\ Financial & 3.58\%/2\%/4.6\% & 3.48\%/1.91\%/4.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Improvement of the average hit ratio.
## 3 Results
### Caries Detection  
Two deep convolutional neural networks (CNNs), Resnet18 and Restext50, were applied by [PERSON] et al. to detect caries lesions in near-infrared light transillumination (NILT) images [10]. In this study, 226 extracted permanent human teeth (113 premolars and 113 molars) were employed. According to their results, the two models performed similarly in predicting the caries on tooth segments of the NILT images. The area under the curve (AUC), sensitivity and the specificity were evaluated with results of 0.74, 0.59, and 0.76, respectively.  
A deep learning model was employed by [PERSON] et al. for the automated detection and localization of dental lesions in 217 near-infrared transillumination images of upper and lower molars and premolars. Here, 185 images were used to train the network and 32 images were used to validate it. The results concluded an area under curve (AUC) of 85.6% for proximal lesions and an AUC of 83.6% for occlusal lesions [26].  
A total of 3000 periapical radiographies were employed by [PERSON] et al. to detect dental caries [13]. From the total dataset, 25.9% of the images were maxillary premolars, 25.6% were maxillary molars, 24.1% were mandibular premolars and 24.4% were mandibular molars. The authors implemented deep CNN algorithm weight factors. A pre-trained GoogleNet Inception v3 CNN network was used for preprocessing and the datasets were trained using transfer learning. For detecting caries,this study obtained an accuracy of 89%, 88% and 82% in premolar, molar and premolar-molar, respectively, and for AUC, values of 0.917, 0.89, and 0.845 were obtained for premolar, molar and premolar-molar, respectively.  
Caries from given socioeconomic and dietary factors were analyzed by [PERSON] et al. employing an ANN to determine the state of health [27]. An ANN designed with seven layers, four dense layers and three dropout layers, was used in this study. A total of 9812 subjects were employed, 70% of them were used for training and the remaining 30% for testing. The results obtained an accuracy of approximately 0.69 and an AUC of 0.75.  
A total of 3000 bitewings images were employed by [PERSON] et al. to detect dental caries with a deep fully convolutional neural network. The results of this study were a recall of 0.805, a precision of 0.615 and a F1-score of 0.7 [22].  
A total of 251 radiovisiography images were employed by [PERSON] et al. to detect caries with a convolutional neural network, which achieved an accuracy of 0.875 [29].  
A back-propagation neural network with a database of 105 intra-oral images was employed by [PERSON] et al. to detect caries. This architecture achieved an accuracy of 0.971 and a precision recall curve (PRC) area of 0.987 [31].
# A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
###### Abstract  
Secure transmission of medical images and medical data is essential in healthcare systems, both in telemedicine and AI approaches. The compromise of images and medical data could affect patient privacy and the accuracy of diagnosis. Digital watermarking embeds medical images into a non-significant image before transmission to ensure visual security. However, it is vulnerable to white-box attacks because the embedded medical image can be extracted by an attacker that knows the system's operation and does not ensure the authenticity of image transmission. A visually secure image encryption scheme for secure fingerprint-based authenticated transmission has been proposed to solve the above issues. The proposed scheme embeds the encrypted medical image, the encrypted physician's fingerprint, and the patient health record (EHR) into a non-significant image to ensure integrity, authenticity, and confidentiality during the medical image and medical data transmission. A chaotic encryption algorithm based on a permutation key has been used to encrypt the medical image and fingerprint feature vector. A hybrid asymmetric cryptography scheme based on Elliptic Curve Cryptography (ECC) and AES has been implemented to protect the permutation key. Simulations and comparative analysis show that the proposed scheme achieves higher visual security of the encrypted image and higher medical image reconstruction quality than other secure image encryption approaches.  
image security; visual security; image encryption; medical image; fingerprint; discrete wavelet transform +
Footnote †: journal: Medical Image and Image and Image and Image Processing  
**Citation:**: [PERSON]; [PERSON]; [PERSON] A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission. _Appl. Sci._**2023**, 13, 6099. [[https://doi.org/10.3390/app13106099](https://doi.org/10.3390/app13106099)]([https://doi.org/10.3390/app13106099](https://doi.org/10.3390/app13106099))  
10.390/app13106099