esa-sceva/satcom-chunk-collection huggingface dataset:
# PECSA: Practical Edge Computing Service Architecture Applicable to Adaptive IoT-Based Applications
## 4 Materials and Methods
### Three Basic Service Types and the Associated Cost Evaluation Schemes
#### 4.2.3 Minimum Price Evaluation with a Given Time  
In certain situations, users want to enjoy services at the lowest price within a given time. Let us denote the given time by \(T_{G}\). To successfully proceed with the service process, \(T_{G}\) must be no less than \(\text{min}\hat{S}_{T}\) computed by the Formula (13) above. Combining all the aforementioned models, the minimum price estimation problem within a given time can be further formulated with the following linear program:  
\[\begin{split}\text{min}S_{P}^{*}&=\sum_{i=1}^{F}(p_{s_ {i}}+\gamma+\frac{l\cdot pc_{i}}{cp_{i}})\cdot da_{i}+\sum_{i=F+1}^{m}(p_{s_{i} }+\delta\\ &\quad+\frac{l\cdot pc_{i}}{cp_{i}})\cdot da_{i}+\epsilon\end{split} \tag{25}\]  
s.t.  
\[\begin{split}\begin{cases}\sum_{i=1}^{m}\{t_{s}\cdot cp_{i} \cdot da_{i}+da_{i}/v_{i}+ca_{i}/cp_{i}\}\leq T_{G},\\ \sum_{i=1}^{m}da_{i}=tv_{t},\\ \rho_{i}ca_{i}\leq E_{i}^{max}(1\leq i\leq m),\\ da_{i}\geq 0(1\leq i\leq m)\end{cases}\end{split} \tag{26}\]  
This multivariate linear optimization problem can be solved by any solver for linear programming problems. The service estimation and resource allocation algorithm is presented in Algorithm 2. All services are divided into three types: Type 1, services completed within the shortest time; Type 2, services completed with the lowest price; and Type 3, services completed with minimum price estimation within a given time. Since the number of edge nodes used for completing a service is unpredictable, to complete the service by using a minimum amount of equipment, we set the initial value of \(m\) as 1 and the step length as 1 in Algorithm 2. These settings can be changed according to concrete scenarios, e.g., we can use dichotomy to quickly search for an appropriate value of \(m\). Trust management can effectively distinguish malicious nodes from normal nodes through the observation of historical behavior. The IoT nodes with low trust values are removed from the routing table; that is to say, the IoT device without security functions is not used as a transmission relay node for IoT applications with security requirements, such as encryption and digital signature. Furthermore, both the total service time \(S_{T}\) and the total service price \(S_{P}\) include the costs of security operations (refer to Formulas (12), (24) and (25)). Thus, trust management can help users obtain information security services at a lower price and less delay.
## 2 Circuit Implementation  
### Top Architecture  
The block diagram of the proposed low-noise and low-power readout IC for two-electrode ECG recording is shown in Figure 3. The proposed IC comprises a CMCP stage, amplifying stage, and sub-blocks in a single chip.  
Figure 3: Block diagram of the proposed readout IC.  
The CMCP stage comprises switched capacitors, CMCP control logic, and a window comparator. The amplification stage comprises a floating high-pass filter (HPF), chopper-stabilized CFIA, and QRS peak detector. The sub-blocks include the current/voltage reference, timing generator, 2 MHz oscillator, and serial peripheral interface (SPI).  
In addition, since the CMRR characteristics are degraded because of electrode impedance mismatch, additional CMRR enhancement is required. Finally, additional measurements are needed by fabricating a small module for portable long-term recording.  
In the CMCP stage, the input CMI is subtracted using switched capacitors, CMCP control logic, and a window comparator. The window comparator compares the hysteresis voltage range at V\({}_{\text{COMP\_H}}\) and V\({}_{\text{COMP\_L}}\) to maintain the common-mode range between V\({}_{\text{COMP\_H}}\) and V\({}_{\text{COMP\_L}}\).  
CFIA amplifies the ECG signal with a high-input impedance characteristic between other IA structures [19; 20; 21; 22]. CFIA uses a chopper stabilization technique to reduce low-frequency noise. The QRS peak detector detects the QRS peak of the ECG signal.  
The internal operation parameters, including clock timing, register setting, CMCP control capacitor value, CMCP operation frequency, and chopper frequency, are programmable using the SPI.
## 1 Introduction
They proposed a method to predict the remaining bearing capacity of the bridge based on the distribution position of section strain and the changes in the neutral axis of the section. Their work provides a theoretical basis for the detection of flexural stiffness and evaluation of the bearing capacity of pre-stressed concrete beams. Prefabricated assembly technology has many advantages in bridge construction, such as specialized construction, environmental friendliness, shortened construction period, cost reduction, and improved safety and quality. Currently, the length of prefabricated beams has increased from 10 m, 13 m, 16 m, 20 m to 50 m, becoming a development trend in bridge construction both domestically and internationally. The 50 m T-beam is the largest span assembly prefabricated beam in highway bridges [19], with a universal applicability in the testing methods of pre-stressed bridges. Although the 50 m T-beam is common in highway bridge engineering, there is limited related literature, making it a bottleneck in the development of prefabricated highway bridges. Therefore, this study conducted a full-scale destructive test on a 50 m span T-beam bridge and characterized the relationship between load and deflection at different load levels. Additionally, the equivalent stiffness and short-term stiffness of the T-beam under different load levels were calculated based on formulas in the specifications, and a segmented stiffness inversion method was proposed using system identification theory to analyze the degradation law of stiffness of each segment section of a newly constructed prefabricated concrete T-beam bridge under sustained loads until failure. The segmented stiffness-based system identification inversion method proposed in this study can more accurately describe the failure test process of each section of the T-beam, providing valuable reference for future load tests and assisting in the evaluation of the load-bearing capacity of such bridges.
## 1 Introduction
### Related Works
#### 1.1.5 Machine-Learning-Based Approaches  
Recently, a new category emerged, adding machine learning modules to the SLAM, VO, and SFM pipelines. Some of the most prominent approaches are DynaSLAM, SVR-Net, VOLDOR, DROID-SLAM, SDF-SLAM, CNN-SLAM, CodeSLAM, DeepFactors, MonoRec, and CNN-SVO. CNN-SLAM [PERSON] et al. (2018) was one of the first systems to incorporate CNN-predicted depth maps into monocular SLAM, overcoming the scale ambiguity issues. It also performed joint semantic segmentation and 3D reconstruction, pioneering multitask learning. DynaSLAM [PERSON] et al. (2018) was one of the first attempts to detect and remove dynamic objects from the mapping process using a CNN for segmentation and a multiview geometry approach enabling more robust tracking and mapping in dynamic environments. CodeSLAM [PERSON] et al. (2018) incorporated an encoder-decoder CNN for scene geometry into a compact latent code conditioned on image intensities retaining only nonredundant information for joint geometry and motion optimization. The CNN-SVO [PERSON] et al. (2018) incorporated CNN depth predictions to initialize the depth filters in SVO, reducing uncertainty and improving mapping. DeepFactors [PERSON] et al. (2018) was built over the basis of CodeSLAM to formulate dense monocular SLAM as a factor graph optimization combining the learned depth priors, the reprojection error, and the photometric error for robust performance. VOLDOR [PERSON] et al. (2018) integrated a CNN into its visual odometry pipeline using log-logistic depth residuals and probabilistic inference, eliminating the need for feature extraction or RANSAC, enabling real-time performance. The DROID-SLAM [PERSON] et al. (2018) integrated a recurrent neural network to iteratively update camera poses and estimate depth maps through differentiable bundle adjustment. MonoRec [PERSON] et al. (2018) addressed the alternative to incorporate mask prediction and depth prediction modulesto enable high-quality monocular reconstruction in dynamic scenes. SDF-SLAM [36] combined classic sparse feature extraction with a CNN for dense depth prediction and semantic segmentation enabling semantic 3D reconstruction while retaining real-time performance. SVR-Net [37] integrated a Support Vector Regression network to estimate 3D keypoint locations, enabling robust tracking in challenging conditions using online learning and graph optimization for map refinement. In summary, machine-learning-based methods progressively incorporated deep learning into sparse indirect SLAM systems to improve the robustness and handle the dynamics, achieving dense reconstruction enabling end-to-end learning. The key innovations included using CNNs for segmentation, depth prediction, semantic segmentation, compact scene encoding, and uncertainty modeling.
# LoRa Architecture for V2X Communication: An Experimental Evaluation with Vehicles on the Move
## 6 Outdoor Testing and Analysis of the Prototypes
### Preliminary Handoff Testing with and without Kalman Filter
The number of data packets fluctuates by 48.75% (20 vs. 32) for 15 kmph and 21.28% (17 vs. 21) for 25 kmph. It is to note that, for 50 kmph, the fluctuation is 22.5% which is considerably lower than that of 15 kmph. So, it is evident that the fluctuation of the number of data packets received by RSU 1 and RSU 2 is not due to the higher speed of the car rather it is due to the fluctuation of the RSSI readings. As presented in Section 4, the Kalman filter is introduced in the algorithm and this preliminary test is repeated with the same setup. Figure 11 shows the number of data packets received by each RSUs with the Kalman filter in the algorithm. This shows that the handoffs between the two RSUs improved significantly due to the stabilization of the RSSI values. Figure 10: The number of data packets received by each RSU in preliminary testing without Kalman filter. Figure 9: Testing setup of the track with one vehicle and three RSU. At 15 kmph, fluctuation of the number of data packets improved by 48.75% and for 50 kmph, it improved by 22.5%. Even though the handoffs improved, the Kalman filter increased the computational burden. With the Kalman filter in the algorithm, it takes an interval of 2.75 s before the next packet is sent. Without the Kalman filter, it would take an interval of 2.59 s to run the algorithm once and send a data packet to the algorithm defined destination. This trade-off needs to be considered to achieve smoother handoffs and ensuring improved reliability. All the data packets that are sent from OBU are received by RSU 1 or 2 with no packet loss for both the tests- without and with Kalman filter. So, a packet delivery ratio of 100% is obtained in the preliminary tests. These results suggest that the proposed architecture would perform reliably with smooth handoffs under practical scenarios also. Further testing is needed under practical scenarios where the reliability might be affected by other vehicles, pedestrians, and buildings causing loss of direct line of sight.
## 3 Results and Discussion
### Volatile Organic Compounds
Although the relative abundance of bubritz acid in ryazhenka was low, previously, this acid, along with hexanoic acid, was determined as one of the most flavor-producing components for the fermented baked milk (a yazhenka-like product) [47]. The acidic flavor of kefir and amasi can be attributed to acetic acid (14%) and 3-hydroxypropionic acid (17%), respectively. The main alcoholic flavor of kefir can be attributed to 3-methyl-1-butanol (36%) and ethanol (16%). Both of these VOC are well known flavor-forming compounds in kefir. While ethanol is produced by kefir grains as a result of sugar fermentation by yeasts, 3-methyl-1-butanol is formed from isoleucine through deamination and decarboxylation reactions [48]. Previously, it was shown that the VOC profiles of various kefir beverages obtained from different milk (e.g., goat and cow) using kefir grains with different microbial composition can vary greatly [49; 50]. Figure 1: Summary of volatile organic compounds determined in kefir, ryazhenka, amasi and manehwu: (**A**) Area-proportional Euler diagram (the numbers represent the powers of corresponding sets); (**B**) Chemical classes of compounds; (**C**) Flavor classes of compounds. Nevertheless, compounds such as 3-methyl-1-butanol, ethanol, acetic acid, hexanoic acid, octanoic acid, decanoic acid and benzoic acid were determined as common kefirs' VOC, while the profile of ketones, aldehydes, esters and other alcohols varied greatly depending on the origin of the kefir grains. In ryazhenka, the presence of furfuryl alcohol (possessing a distinctive burnt caramel flavor) as well as the abundance of ketones, can be attributed to the baking process that initiates the Maillard reaction in milk [51]. It should be noted that, except for furfuryl alcohol, there were no other products of the Maillard reaction (i.e., 2,5-dimethyl-4-hydroxy-3(2H)-furanone and 2-methyl-3-(methyldithio)furan) with a caramel flavor detected in ryazhenka, in contrast to the ryazhenka-like product [47]. This can be explained by the low volatility of such compounds, and the necessity for more sophisticated experimental procedures to enhance their extraction yield [47]. While for ryazhenka the fruity flavor can be explained by the abundance of ketones, for the mahewu, this flavor is attributed to esters. In the group of esters, one should note the dominant content of acetic acid pentyl ester (pentyl acetate, 20%), which, along with esters of butyric and isobutyric acids, pelargonic acid ethyl ester (ethyl nonanoate) and caproic acid allyl ether (prop-2-enyl hexanoate), gives the product a pleasant, fresh, banana-resembling fruity aroma. The presence of esters in mahewu is most probably related to the metabolic activity of yeasts. Although for mahewu yeasts are a non-starter culture, they are always present in the final product; in mahewu, an acceptable yeast concentration was previously reported as \(10^{6}\) CFU\(\cdot\)mL\({}^{-1}\)[8]. Moreover, water absorption by the corn meal decreases water activity in the medium, which, in turn, promotes esterification reactions [52].
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 4 Methodology
### Performance Metrics  
The following performance metrics were analyzed:  
* Network Load: Total total volume of transmitted data (in bytes/sec), including both useful application data and routing protocol overhead. \[\text{Network Load (bps) = Total number of bytes transmitted/Time period}\] (1)
* Throughput: Total amount of successfully received useful data (excluding control overhead) per unit time. \[\text{Throughput = Total number of successfully delivered bytes/Time period}\] (2)
* Delay: Average time taken for packets to reach the destination.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Simulation Tool & OMNeT++ 6.0.1 with INET-4.5.0 \\ Network Type & MANET \\ Simulation Area & 1000 \(\times\) 1000 units \\ Node Densities & 20, 80, 200, 500 nodes \\ Connectivity Probability & 0.2 (each node has a 20\% chance of connecting) \\ Mobility Model & Random Waypoint Mobility Model \\ Packet Size & 1024 bytes \\ Packet Arrival Rate & 1.0 packets per second \\ Total Packets per Simulation & 100 packets \\ Routing Protocols & OLSR, DSDV, AODV, DSR, AOMDV, TORA, ZRP, GRP \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Network Details.  
Delay = (Sum of individual packet delays)/Total number of successfully delivered packets  
\[\bullet\] Energy Consumption: Total energy consumed during the simulation.  
Energy Consumption = \(\sum\) (Power consumed by each network component \(\times\) Time component was active)  
\[\bullet\] Jitter: Variation in packet delay.  
\[\text{Jitter}=\Sigma\ \left|\text{D(i}+1)-\text{D(i)}\right|/(\text{N}-1) \tag{5}\]  
where,  
D(i) is the one-way delay (or latency) of the i-th packet.  
D(i + 1) is the one-way delay of the (i + 1)-th packet.  
N is the total number of packets.  
\[\bullet\] Packet Loss Rate: Ratio of lost packets to total packets sent.  
\[\text{Packet Loss Rate}=\text{(Number of Lost Packets/Total Packets Sent)}\times 100\% \tag{6}\]  
\[\bullet\] Packet Delivery Ratio (PDR): Ratio of successfully delivered packets to total packets sent.  
\[\text{PDR}=\text{(Number of Packets Delivered/Number of Packets Sent)}\times 100\% \tag{7}\]
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
###### Abstract  
Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average \(F_{1}\) score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.  
electrocardiogram (ECG); arrhythmia; deep learning; convolutional neural network (CNN); inter-patient paradigm; systematic review +
Footnote †: journal: Medical Imaging
## 2 Unified Modeling of LEO Satellite Constellation
### System Principle  
The LEO communication satellite constellation refers to a constellation system distributed at an orbital altitude of 200 km to 2000 km [14]. It is usually divided into three parts: space segment, ground segment and user segment, as shown in Figure 1. Among them, the space segment refers to all satellites in the constellation. The ground segment usually consists of ground stations, system control centers and integrated ground-based networks responsible for satellite monitoring and control in the space segment as well as for the management of the space network. The user segment refers to all types of user terminals, including mobile terminals, ship terminals, vehicle-mounted terminals, airborne terminals, etc.  
Figure 1: Schematic of an LEO communication satellite constellation.  
The communication functions of the entire constellation system are realized in three main types of links, namely, feeder links, intersatellite links (ISL) and user links. Among them, the feeder link and the user link belong to satellite-ground links, with the difference that the feeder link connects the satellite and the ground station and the user link connects the satellite and user terminals. ISL refers to a communication link between any satellite in the constellation and a neighboring satellite in the same orbital plane or a neighboring satellite in an adjacent orbital plane. In order to cover the three links and realize satellite multidomain simulation, constellation system simulation and communication link simulation, key modeling objects are identified in Table 1.
# A Simhash-Based Integrative Features Extraction Algorithm for Malware Detection
## 2 Related Work  
In recent years, many researchers have proposed a variety of solutions. For instance, some detection methods are based on content signatures [12; 13], which compare each sample with known malware signatures. However, the signature-based warning mechanism cannot solve the metamorphic or unidentified instances of malware [14]. [PERSON] [15] proposed a malware classification algorithm that uses static features called MCSC (Malware Classification using Simhash and CNN), which convert the disassembled malware codes into gray images based on simhash and then identifies their families by a convolutional neural network. In addition, Idrees [16] combines permissions and intents, and extracts API calls to detect malware [17]. These methods all belong to static analysis, which means that it is the process of detecting malware without executing. Thus, it cannot reflect the behavior of malware very well, and cannot easily detect malicious code that is packed and obfuscated.  
In addition to the above methods, observing dynamic behaviors and features is also usually used to detect malware [18]. For example, a sandbox is used to monitor the sample in real time and dynamically analyze the behavior by extracting the IP address, system calls, memory usage, returned values and times between consecutive system calls [19]. In addition, [PERSON] [20] proposed a method for determining whether dynamic analysis should be suspended, based on network behavior, to collect malware communications efficiently and exhaustively. While dynamic analysis can offer a more comprehensive view of malware detection, the cost of building the environment and of the manual endeavors, in the process of investigation, is high. More importantly, dynamic analysis is often more complex than static analysis and requires more resources to reduce the number of false positives reported, and usually suffers from low detection rates due to lack of adequate training [21].  
Therefore, we propose a method to integrate static and dynamic features to obtain new features. The remainder of this paper is organized as follows. In Section 3, we present our materials and method. Experiments and comparisons are reported in Section 4. Finally, we conclude our work in Section 5.
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Hydrogen Diffusion at Temperatures between 900 and 1400 \({}^{\circ}\)C  
The aim of these investigations was the determination of the temperature correlation of the diffusivity of hydrogen in Zircaloy-4 in the high-temperature \(\beta\) phase. In the literature, two different correlations are given for this zirconium phase [16; 17] determined for different temperature ranges.