esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 7 Conclusions  
This paper presents a secure proxy re-encryption protocol for FANETs that can be used to securely transmit medical information in off-road emergency situations. The proposed protocol utilizes cryptographic techniques, including proxy re-encryption, to protect sensitive user information and enables authorized participants to access the encrypted medical records without revealing the original encryption key. The use of proxy re-encryption allows for secure transmission of medical information even when the original encryption key is not known by the recipient.  
The use of drones and smartphones in emergency communication networks can significantly improve the response time and accuracy of emergency medical services. The analysis results show  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Message Size** & **Re-Encryption Process** & **Decryption** \\
**(Bytes)** & **(Seconds)** & **(Seconds)** \\ \hline
100 & 0.015946626663208008 & 0.0 \\
500 & 0.016944408416748047 & 0.0 \\
1000 & 0.01795029640197754 & 0.0 \\
2000 & 0.020200729370117188 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Processing time for each message size.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{Hardware} & CPU & 11 th Gen Intel(R) CoreTM \\  & i5-1155G7 @ 2.50 GHz \\ \cline{2-3}  & Memory & 16G \\ \cline{2-3}  & Disk & 1 TB \\ \hline System type & 64-bit operating system, \(\times\)64-based processor \\ \hline OS & Windows 11 Home \\ \hline Language & Python 3.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environments.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Pseudorandom Function f** & **AES 128 bits** \\ \hline Group parameter & cyclic group \\ Modulus p & 2048 bits \\ Order q & 256 bits \\ Generator \(g\) & 2048 bits \\ Crypto library & PyCryptodome \\ Time measurement function & time.time() \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cryptographic parameters and libraries.  
that the proposed protocol is resistant to CCA, collusion attacks, and forgery attacks and effective in protecting user privacy and ensuring the secure transmission of medical information.  
Our proposed protocol did not use a pairing function with severe computational overhead, but because it used an exponential operation, it required a time complexity of \(O(L^{2}+kL)\). The proposed protocol can be further improved by incorporating more advanced cryptographic techniques such as lightweight encryption or hybrid encryption technologies and optimizing the communication network.  
This study was supported by research funds from Honam University, 2023.  
Not applicable.  
The author declares no conflict of interest.
## 2 Experimental Setup and Experimental Procedure  
Research was conducted on a test rig used to simulate the influence of internal accidental explosions on explosion-venting systems (EVS) in accordance with the National Standard GOST R 56289-2014. The test rig includes: an explosion chamber; a flammable gas-supply and mixing system; a remote gas-ignition system; an excessive-pressure measuring, registering, and processing system; and a high-frame-rate video-recording system. The explosion test chamber is capable of withstanding an excessive explosion pressure of up to 100 kPa and has a relief valve that opens when excessive pressure reaches 20 kPa. The chamber's volume is 8 m\({}^{3}\) and is cubic in shape. The opening in the chamber where an EVS is installed is 1700 \(\times\) 1500 mm. Inside the chamber, there is a flammable gas-supply system, a fan for gas-air mixture homogenization, an automatic ignition device, and a chamber-ventilation fan to extract the remaining combustion products when tests are completed.  
The gas-supply system includes a gas cylinder, a gas meter with a tolerance of \(\pm\) 3%, and a gas-mixing system that provides a stoichiometric mixture throughout the entire volume of the chamber. The mixing system includes a spark-resistant fan installed inside the explosion chamber with an airflow capacity of 3000 m\({}^{3}\)/hour minimum and a remote-control system.  
The pressure-measuring and registering system includes: static excessive-pressure ports; a source of direct current for pressure sensors; an analog-digital converter; and a PC with relevant software capable of providing pressure time-law graphs and saving the obtained data for further processing.  
The filming system contains a high-frame-rate camera with a frame rate of 1000 frames per second and a resolution of 1280 \(\times\) 720 pixels, as well as software for frame-by-frame analysis.  
The tests were conducted under an outside air temperature of +18 \({}^{\circ}\)C. The explosion chamber was supplied with an amount of flammable gas needed to create a stoichiometric mixture. Then, the mixing system inside the chamber was switched on. The mixing system was on for at least one minute in order to provide an even stoichiometric mixture throughout the entire volume of the chamber. The mixing system was then switched off, and the gas mixture was ignited.  
During the test, the following parameters were registered: the change in excessive pressure in time inside the explosion chamber was recorded through excessive-pressure sensors; the process of EVS discharging from the blast-relief opening was recorded by registering the outside distance between the EVS and the blast-relief opening. The moment of EVS opening is the time from the mixture ignition until the instant when the distance between the EVS and the relief opening is at least 1 cm.  
The test results were evaluated with regard to excessive EVS opening pressure (pressure values of similar experiments should not vary by more than 20%).
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 5 Results and Discussion
### Packet Delivery Ratio  
The packet delivery ratio (PDR) is the ratio of the number of packets received by the destination node to the number of packets sent by the source node. In Figure 9, PDR increases for D2D routes \(k_{1}\), \(k_{2}\), and \(k_{3}\) as the PU OFF time increases. When the PU OFF time increases from 50 to 250 s, the PDR of: (a) route \(k_{2}\) increases from 0.891 (89.1%) to 0.929 (92.2%); (b) route \(k_{3}\) increases from 0.853 (85.3%) to 0.915 (91.5%); and (c) route \(k_{4}\) increases from the lowest at 0.844 (84.4%) to 0.912 (91.2%).  
Route \(k_{2}\) achieves a better PDR compared to routes \(k_{3}\) and \(k_{4}\) since it has a lower number of hops and PU activities. For routes \(k_{3}\) and \(k_{4}\), their PDRs are very close to each other and their gap reduces as the PU OFF time increases. This is because both routes have the same number of hops; however, route \(k_{3}\) has a lesser presence of PUs, explaining why it is a preferred route over route \(k_{4}\).
# Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection
## 5 Conclusions  
In this paper, the LHPMA, a pattern matching algorithm based on CPU and GPU cooperation for packet inspection is proposed, which is an enhanced version of our HPMA to improve efficiency in the case of various payload lengths in input traffic. This work achieves the goal by automatically categorizing and allocating the incoming packets to the corresponding process, in which the categorizing depends on the pre-determined payload length bound to reduce the payload length diversity. We present the experiments, which consists of the GPU efficiency affected by the payload sets with different lengths, the LHPMA performance affected by different payload length bounds and the performance comparison with other methods. The results demonstrate that the LHPMA can outperform the HPMA and the CPU and GPU individual processing methods. Since the LHPMA is an initial design, future work involves analyzing the input traffic with different payload length distributions and processing overhead to have a more advanced design of the LHPMA method.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview
### Command and Data Management System
#### 3.5.1 Onboard Computer (OBC) Hardware  
The OBC hardware is based on the SKITH interface, using the EFR32 FG12 for both data processing and communication with other modules. Since the EFR32 FG12 microcontroller is powerful enough for most applications, it will not only deliver the communication interface, but will also serve as a controller, handling sensors, actuators and memories. It implies a 40 MHz Cortex-M4 CPU with 1 MB flash memory and 256 kB RAM. A property that makes the EFR32 FG12 perfectly suitable for a small satellite application is its low power consumption with a maximum power of just 36.3 mW while sending and 27.7 mW when receiving data over the 2.4 GHz interface. Additionally, the OBC is equipped with an external SPI 128 MByte Flash and 1 MBbyte MRAM, which have already been subjected to total-dose radiation tests.  
Figure 13 depicts a PCB, carrying two OBC modules. The layout is identical for both modules, but mirrored due to thermal reasons. Since the PCB dimension is given by the mechanical structure, this layout offers an area for additional components.  
Figure 12: Swappable ADCS box accommodating control electronics and actuators.
# A Novel Overcurrent Suppression Strategy during Reclosing Process of MMC-HVDC
## 4 Simulation  
### The Test System  
A three-terminal MMC-HVDC grid, shown in Figure 4, is modeled based on PSCAD/EMTDC simulation platform. The system adopts the symmetric monopole configuration with a rated DC voltage of \(\pm 20\) kV. All the converters are connected by bipolar transmission lines which make use of a frequency-dependent distributed parameter model, and a hybrid DCCB and a FCL are placed at the terminal of each line. MMC1 and MMC3 control the active power, while MMC2 controls the DC voltage. The detailed parameters of the test system are shown in Table 1. Other simulation parameters are set as follows: the protection time \(\Delta t_{c}\) is 5 ms; the deionization time \(\Delta t_{d}\) is 150 ms; the threshold of SMs overcurrent protection \(I_{th}\) is 2 kA; the CLI on each line is 15 mH. Moreover, the converter will be blocked after the fault current declines to 10 A.  
\begin{table}
\begin{tabular}{c c} \hline
**Quantity** & **Value** \\ \hline AC line voltage & 10 kV \\ Fundamental frequency & 50 Hz \\ Number of SMs per arm & 20 \\ SM capacitor & 6000 \(\mu\)F \\ Arm reactor & 15 mH \\ Arm resistance & 0.1 \(\Omega\) \\ Transformer leakage reactance & 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: The parameters of the test system.
## 3 Preliminaries
### Feature Extraction  
Most feature vectors are either global vectors, such as a global color histogram, or local vectors such as SIFT descriptors [28; 29] and SURF descriptors [6; 7]. The first model generates an extreme compressed feature vector for each image. Such a model can effectively identify global similarities, e.g., how many colors two images share. The second model searches the image to identify the interest key points invariant to scale and orientation. A feature descriptor is generated for each key point. Compared with global-feature based image retrieval, local-feature based image retrieval characteristically acquires more accurate results than the globally based equivalent but requires comparatively complex metrics relating to distance. In this paper, we will focus on SURF local features model, which has the advantage of identifying local similarities, e.g., scenes and objects. PCA transform is employed to achieve distance metric efficiency [22].  
Specifically, the SURF algorithm [6; 7] is a novel scale and rotation-invariant detector and descriptor. SURF approximates or even outperforms a previously proposed SIFT algorithm [28; 29], which is patented, with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. Thus, the method posited here improves feature extraction speed. SURF extracts the feature vectors of the provided image as follows. First, SURF selects several interest points at distinctive locations in the image, such as corners, blobs, and T-junctions. Such points are selected in such a way that enables the detector to find the same physical interest points under different viewing conditions. Next, the neighborhood of every interest point is represented by a feature vector. This descriptor has to be distinctive and robust to noise, detection displacements, and geometric and photometric deformations. The descriptor vectors are matched between different images. Matching is based on a distance between the vectors, e.g., the Euclidean distance or cosine similarity. Figure 1 illustrates the interest points of Lena image and their counterparts in the same image after rotation.  
Formally, given image \(Img\), we use the SURF algorithm to generate its feature vectors \(F=\{v_{1},v_{2},...,v_{k}\}\), where \(k\) is the number of interest points in the provided image. Note that different images may differ in the number of descriptors \(k\). Then, we utilize the PCA transform [22] to facilitate a lowering of the dimensionality of SURF vectors so that the performance of the matching time can improve with ineffectual of discriminative power. Recall that the relationship between lowering dimension and the precision of image searches is analysed further in Section 6.
## 4 Method  
### Evaluation and Datasets  
The evaluation of the framework performance is conducted using the PoseTrack 2018 dataset [5], which is a widely used benchmark for human pose estimation and articulated pose tracking in videos. The dataset consists of 1337 video sequences, with 593 videos for training, 74 videos for validation, and 375 videos for testing. The dataset includes three different challenges: (1) multi-person pose estimation in single frames, (2) multi-person pose estimation in videos, and (3) multi-person pose tracking in real-world scenarios. The evaluation metrics used for challenges (1) and (2) are mean Average Precision (mAP), while challenge (3) is evaluated using the MOTA metric [31]. More detailed information can be found in the original paper [5].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ours** & **ID** & **Boxes** & **Landmarks** & **Boxes + Landmarks** \\ \hline (MOTA) 57.8 & 59.6 & 62.9 & 68.6 & 74.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Upper bounds of analytical framework.  
The modern PoseTrack framework generally follows a similar pipeline [7; 16; 26]. First, a human detector is applied to a still image (I) to generate bounding boxes (B) encompassing the individuals in the image. Then, a pose estimator is employed on the image region defined by the bounding boxes (B) to predict the keypoints (K) representing the body joints. Finally, a tracker module is built upon the bounding boxes (B) and keypoints (K), incorporating correlation features between pairs of consecutive frames to assign a unique track ID to each individual.  
For the purpose of simplifying the performance analysis, we simplify the framework by replacing the complex flow-based pose tracking algorithm with a more straightforward greedy matching algorithm that uses bounding box overlap as the cost criterion. This simplified framework, which consists of a Residual Network-50 (ResNet-50) backbone [32] for image feature extraction, a Region-based Fully Convolutional Networks (R-FCN) [33] detector without joint propagation for detecting human key point coordinates in images, and a bounding box overlap similarity metric, will be referred to as the analytical framework for the sake of explanation.
# Constructing a Microstrip Phase Shifter with Low Phase Error Using a Y-Resonator
## 3 Analysis and Design
### Design Steps  
The design flow chart is shown in Figure 7: (1) Draw the corresponding odd-mode and even-mode circuits based on the structure of the proposed filter phase shifter; (2) Using your knowledge of microwave theory, write the input impedances corresponding to an odd-mode circuit and an even-mode circuit, noted as \(Z_{ino}\) and \(Z_{ine}\); (3) Find the S-parameters from the reflection and transmission coefficients with respect to the odd/even input impedance with the ABCD matrix; (4) Write the reflection coefficient \(S_{11}\), transmission coefficient \(S_{21}\), and insertion phase of the network based on the conversion relationship between the ABCD matrix and the scattering parameter; (5) The insertion phases of the main and reference networks are operated as a difference to derive the phase shift value \(\Delta\Phi\); (6) First-order differentiation of the insertion phase such that the slopes of the insertion phases of the two networks are the same in the operating frequency band; (7) Determine component parameter values using \(|S_{11}|=0\), \(|S_{21}|=1\), and steps (5) and (6); (8) Based on the parameters of the selected dielectric substrate (substrate thickness, dielectric constant, and loss angle tangent), the calculated circuit parameters (characteristic impedance and electrical length) are converted to actual physical dimensions using closed-form equations; (9) The physical dimensions of the phase shifter are slightly adjusted using simulation software. With this design step, the designed \(90^{\circ}\), \(135^{\circ}\), \(180^{\circ}\), and \(225^{\circ}\) equivalent circuit parameters, as well as physical dimensions, can be obtained, as shown in Table 1.  
\begin{table}
\begin{tabular}{|c c|} \hline \hline
**Phase Shifter (*)** & **Equivalent Circuit Parameters (*\(\backslash\)ohm)** & **Physical Dimensions (mm)** \\ \hline \multirow{2}{*}{90} & \(Z_{0}=50\); \(Z_{1}=51.2\); \(Z_{2}=92.4\); \(\theta_{1}=84\); \(\theta_{2}=76.2\); K = 0.482; \(\theta=226.1\) & L1 = 12.8; L2 = 1
# A Novel Evolution Strategy of Level Set Method for the Segmentation of Overlapping Cervical Cells
## 3 Experiments  
### Image Datasets  
To evaluate the performance, we used the ISBI-2014 dataset [71], which is provided as the first cervical cytology image segmentation challenge. This dataset was provided in the competition stage and divided into 45 synthetic training images (train-45 dataset) and 90 synthetic test images (test-90 dataset). The images in the datasets were synthesized from the 16 EDF images, each of which was generated by applying the discrete wavelet transform method [72] on the according 16 FOV image volumes that were acquired under an Olympus BX40 microscope system. The boundaries of the nuclei and cytoplasm in these 16 EDF images were manually annotated by an experienced cytotechnologist. The organizer divided the 16 annotated EDF images into 4 training EDF images and 12 test EDF images from which the 45 synthetic training and 90 synthetic test images (512 \(\times\) 512 pixels, gray-scaled) were constructed separately. Specifically, after the manual selection of 12 isolated cells in the 4 training EDF images and 41 ones in the 12 test EDF images, an automatic synthesis procedure was applied to construct synthetic training and test image datasets with the following regulations: (a) the image patches, which contain the isolated cells, were clipped from the EDF images and then pasted into one 512 \(\times\) 512 synthetic image after applying random rigid geometric transformation and random linear brightness transformation. (b) The background of the synthetic image consisted of the pixels randomly selected from EDF images. (c) The number of the patches (cells) pasted into each synthetic image varied from 2 to 10, with (d) the overlapping ratio ranges [0,0.1], [0.1,0.2], [0.2,0.3], [0.3,0.4] and [0.4,0.5] between pairs of cells. The datasets contained a total of 810 cells with annotated cellular component pairs (nuclei and cytoplasm), including 270 pairs for the train-45 dataset and 540 pairs for test-90 dataset.
# Sequential Polar Decoding with Cost Metric Threshold
## 2 Preliminaries
### Successive Cancellation Techniques  
Successive Cancellation (SC) treats the decoding process as a tree traversal, with each node representing a bit decision.  
It decodes the \(i\)-th bit by maximizing the likelihood function \(W_{n}^{(i)}(\mathbf{y}_{1}^{N},\hat{\mathbf{u}}_{1}^{i-1}|u_{i})\), with \(\mathbf{y}_{1}^{N}=(y_{1},y_{2},\ldots,y_{N})\) representing the received log-likelihoods vector (LLR) from the channel.  
\[\hat{u}_{i}=\left\{\begin{array}{ll}u_{i}\in\{0,1\}W_{n}^{(i)}(\mathbf{y}_{1 }^{N},\hat{\mathbf{u}}_{1}^{i-1}|u_{i}),\text{ if }i\in\mathcal{I},\\ 0,\text{ otherwise.}\end{array}\right. \tag{1}\]  
The Successive Cancellation List (SCL) decoding method takes a different approach compared to SC. Instead of directly estimating the message vector, SCL creates a list of potential candidates and determines [10] the correct one in the final step. To identify the path closest to the source word in terms of probability, additional Cyclic Redundancy Check (CRC) bits are used [11]. These CRC bits are appended to the end of the source word and retained during decoding. This method is known as CRC-aided SCL (CA-SCL) and significantly enhances the performance and path selection accuracy.  
The Successive Cancellation Stack (SCS) algorithm [12] is an optimization of the SCL decoder, designed to reduce the decoding complexity (memory and computational) with basic list size and stack size (\(D\)) parameters.  
Operating on the principle of successive cancellation, the SCS decoder recursively processes the received data to decode the original message. It employs a stack-based approach to efficiently manage the decoding process, maintaining a stack of partially decoded bits to optimize the computation. By iteratively traversing this stack and cancelling out unreliable bits, this decoder achieves a low-complexity decoding process.
# Forecasting the Number of Road Accidents in Polish Provinces Using Trend Models
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Province/Model** & **Model** & **ME** & **MPE** & **The Sum of the Squares** & **MSE** & **MAPE (\%)** & **MAE (\%)** \\ \hline Lower Silesia & Polynomial of 2 nd degree & 6201.42 & 6201.42 & 6,965,812,277.61 & 38,698,957.10 & 208.40 & 208.40 \\ \hline Kuyavia–Pomerania & Linear & 2046.27 & 2046.27 & 770,270,675.15 & 4,279,281.53 & 92.75 & 92.75 \\ \hline Lublin Province & Linear & 1316.42 & 1316.42 & 319,951,089.84 & 1,777,506.05 & 79.68 & 79.68 \\ \hline Lubusz Province & Exponential & 75.75 & 111.95 & 3,708,472.78 & 20,602.63 & 6.29 & 10.97 \\ \hline Lodz Province & Exponential & 1175.17 & 1175.17 & 262,350,703.45 & 1,457,503.91 & 47.01 & 47.01 \\ \hline Lesser Poland & Linear & 778.52 & 778.59 & 129,389,638.72 & 718,831.33 & 29.95 & 29.95 \\ \hline Masovia & Exponential & 4894.72 & 4894.72 & 4,479,240,358.37 & 24,884,668.66 & 97.47 & 97.47 \\ \hline Opole Province & Exponential & 502.58 & 502.58 & 47,967,460.50 & 266,485.89 & 60.72 & 60.72 \\ \hline Subcarpathia & Linear & 56.11 & 156.75 & 7,374,357.20 & 40,968.65 & 5.31 & 10.83 \\ \hline Podlasie Province & Exponential & 198.30 & 206.68 & 11,259,059.55 & 62,550.33 & 20.66 & 21.26 \\ \hline Pomerania & Exponential & 1937.33 & 1937.33 & 701,204,745.03 & 3,895,581.92 & 95.63 & 95.63 \\ \hline Silesia & Exponential & 2575.68 & 2575.68 & 1,245,839,964.63 & 6,921,333.14 & 58.25 & 58.25 \\ \hline Holy Cross & Linear & 591.69 & 591.69 & 66,739,299.40 & 370,773.89 & 55.47 & 55.47 \\ \hline Warmia–Masuria & Linear & 1171.74 & 1171.74 & 253,066,000.67 & 1,405,922.23 & 85.00 & 85.00 \\ \hline Greater Poland & Exponential & 699.20 & 707.79 & 110,227,747.58 & 612,376.38 & 22.44 & 22.90 \\ \hline West Pomerania & Exponential & 1157.77 & 1157.77 & 252,984,847.29 & 1,405,471.37 & 74.04 & 74.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of errors for monthly data.