esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
In this study, a new modeling approach was developed to predict the fatigue life of concrete using a small dataset. The method consists of two parts: an innovative GRW-DBA sequence data augmentation method and an ANN prediction model based on optimized hyperparameters.  
The high-precision prediction of the model depends on a large amount of training data. Unfortunately, due to the limitation of experimental time and conditions, there is not enough labeled data for the task of fatigue life prediction. As an effective tool to enhance the scale and quality of training data, data augmentation is a reliable method to improve prediction accuracy. Unlike image data, in the application of sequence data, it is difficult to synthesize data and maintain correct labels due to the nature of sequence data such as inter-class correlation and large numerical differences.  
Compared with data augmentation algorithms, such as GAN or classic DBA, the GRW-DBA algorithm proposed in this paper reduces the influence of outliers by grouping calculations, and at the same time, it assigns random weights in the weighted average, which can more efficiently generate data that conform to the distribution of source data. Compared with other algorithms, our algorithm can improve the accuracy of the prediction model in different concrete fatigue life data (including 27 sets and 28 sets of sequence data, respectively). Among them, the R\({}^{2}\) index increased by 10.7% and 4.2% compared with GAN and classic DBA, respectively.  
Due to the data-driven and good nonlinear fitting advantages of the ANN model [49; 50], it is more suitable for modeling with augmented data. Therefore, the ANN prediction model used in this paper has achieved higher prediction accuracy than classical mathematical methods such as the energy method and machine learning methods such as AdaBoost and has improved the R\({}^{2}\) index by 8.2% compared with the suboptimal AdaBoost.  
Concrete fatigue damage is related to factors such as freeze-thaw cycles, shear failure, and tensile failure. One of the limitations of this study is the primary use of freeze-thaw cycle data. In the future, we will test our method on a wider dataset including other variables to improve its robustness. In addition, we will try more data augmentation algorithms, such as literature [51; 52; 53], to further improve the prediction accuracy.
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
###### Abstract  
The positioning task of the Internet of Things (IoT) for outdoor environments requires that the node devices meet the requirements of low power consumption, long endurance, and low cost and that the positioning system can achieve high-precision positioning and wide-area coverage. Considering that traditional IoT positioning technology cannot balance the cost, energy consumption, and positioning performance well, a Wide-Area Positioning System Based on Long Range Mesh (LoRaWAPS), which is a low-cost and low-power outdoor positioning system with multi-anchor wireless mesh networking and multi-dimensional data fusion, is designed in this paper. To meet the need for a positioning system, a low-power consumption and high-reliability LoRa Mesh protocol is proposed. Aiming at the problem that the accuracy of LoRa ranging is easily affected by the non-line-of-sight (NLOS) path propagation of signals, a distance estimation algorithm based on the fusion of time of flight (TOF) and received signal strength indicator (RSSI) multi-sampling data is proposed. Furthermore, a position estimation algorithm is designed to minimize the posteriori RSSI error for multi-anchor cooperative estimation scenarios. Furthermore, the prototype of LoRaWAPS is built and tested in the campus environment. The experimental results show that the proposed system can provide reliable location service with low power and low cost.  
low-power wide-area network; LoRa mesh; outdoor positioning; time of flight; RSSI-based ranging +
Footnote †: journal: Applied
# A Lightweight Method for Detecting Sewer Defects Based on Improved YOLOv5
## 3 Experimental Results and Analysis  
### Data Preparation  
The data used in this study came from the Sewer-ML [26] sewer defect classification dataset published by a Danish laboratory. The dataset contains different defect categories in various pipeline backgrounds, all of which are classified by professional technicians, making it an ideal source of data for studying pipeline defect detection. Since Sewer-ML contains numerous defect categories and does not provide defect location information, it cannot be directly applied to object detection tasks. Therefore, this paper selected 2700 images from Sewer-ML for defect location annotation, including four common drainage pipeline defects in the southern region of China: cracks, stagger, deposition, and root [27]. Figure 7 shows some images of pipeline defects. The position marked by the rectangular box in the figure is the position of the defect. It can be seen that there are multiple defects coexisting in the actual pipeline, which increases the difficulty of detection to a certain extent. Table 1 shows the distribution of each defect label in the dataset of this study.  
Figure 6: G-Bottleneck module with CA mechanism and Ghost-C3 module. (**a**) G-Bottleneck-CA; (**b**) Ghost-C3.  
Figure 7: Example images of sewer defects. (**a**) crack; (**b**) deposition; (**c**) root; and (**d**) stagger, crack.  
When the number of images is small, it may lead to low model accuracy and there is a risk of overfitting. In order to avoid these problems, this paper not only uses the Mosaic data augmentation method, but also uses traditional data augmentation techniques to increase the size of the dataset. Data augmentation techniques are very important for datasets with few images, as they not only increase the size of the dataset but also increase the diversity of input images, making the designed model more adaptable to complex backgrounds and improving its robustness. Traditional data augmentation methods usually include color transformation and geometric transformation methods, where color transformation includes methods such as contrast, hue, and saturation transformation, and geometric transformation includes methods such as random scaling, flipping, and cropping.  
In this paper, four methods, namely, random scaling, random horizontal flipping, random vertical flipping, and color jitter (randomly adjusting the brightness, contrast, saturation, and hue of the image), were used to enhance each image, and the effects of each data augmentation technique are shown in Figure 8. The enhanced images were all scaled to 416 \(\times\) 416 pixels for model training.
## 2 Numerical Simulation of Ytterbium-Based Amplification
### 2.3. Regenerative Amplifier: Seeded Cavity  
We now consider the evolution of an input pulse inside a regenerative amplifier _i.e._, a seeded cavity, for different pump powers. The input is chosen to be a Gaussian pulse centered at 1053 nm with a FWHM bandwidth of 12 nm, well inside the gain bandwidth of both media. Although the initial spectral response of the system is identical, one can expect that the evolution will be now be determined by these significantly different initial conditions. All other parameters are the same as in the previous section (Table 1). Again, the output parameters are taken at the maximum pulse energy.  
It is important to notice that since we are not considering any spectral filtering or hard spectral clip on the input pulse, its spectrum spreads infinitely, leaving a residual finite energy at lower wavelengths. For instance, for the parameters above the intensity at 1034 nm and 1032 nm will be respectively \(\sim\)10\({}^{-3}\) and \(\sim\)\(2\times 10^{-4}\) times that at 1053 nm. Such values can be considered as noise inside the cavity, and from the results of the previous section we can expect their role in the amplification process to become more significant as the pump power increases.  
Figure 5 (left) shows the results for Yb:CaF\({}_{2}\) for pump powers between 46-66 W. Contrary to the previous section we now observe that the output spectrum remains centered around 1049 mm for the entire pump power range. There is also a slight broadening (2.1 to 2.7 nm) towards the lower wavelengths due to the reduced reabsorption for higher population inversions. Besides, seeding the cavity allows reaching a higher output energy in a lower number of roundtrips for the same pump conditions due to the lower percentage of global losses.  
The results for Yb:YAG are presented in Figure 6. The major difference is that there is still an abrupt transition around 26-28 W where the output central wavelength changes from 1049 to 1032 nm, in spite of the injected signal. The reason for this difference between the two materials lies in their  
Figure 5: Simulation of regenerative amplification based in Yb:CaF\({}_{2}\) in injection mode. **Left**: Output intensity _vs_. wavelength for a range of pump power; **Right**: output energy and required number of passes _vs_. pump power.  
emission and absorption cross-sections. Yb:CaF\({}_{2}\) exhibits smoothly-varying curves between 1020 and 1080 nm (Figure 1), such that small changes in the population inversion do not cause an abrupt transition between the lasing wavelengths. On the other hand, the very strong emission peak of Yb:YAG around 1030 nm (\(\sim\)8 \(\times\) greater than that at 1050 nm) becomes dominant once the population inversion is favorable. Although it is possible to achieve mJ-level output energies at 1049 nm, more than 800 passes are required. One option could be the use of additional spectral filtering in the cavity by means of e.g., polarizers, etalons or dichroic mirrors, thereby allowing stronger pumping.
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 2 Taxonomy of Malware in the Smart Home Network
### Attacks Based on the Smart Home Central Hub  
We know that the internet of things made it easier than ever to set up a smart home that allows easy remote control of lights, thermostats doors, and other devices using a smartphone and an application. It also makes monitoring your smart home from anywhere simple. A smart security system is one that is highly customized and is available as full-blown setups or do-it-yourself kits that can include professional monitoring and installation.  
IoT devices within smart homes are vulnerable to a wide range of device attacks. They interact with the internet and the physical world, enabling intelligent interaction between the surroundings and the physical world, but giving rise to cyber-security risks.  
* Threats in a smart home wield negative impacts that exploit security weaknesses in a system [37]. Different active threats, such as man-in-the-middle attack, spoofing attacks, Sybil attack, denial of service and malicious inputs, and passive attacks can affect the internet of things system within the smart home. Since the objective of the system is to allow intruders to access anytime, anywhere, attack vectors or surfaces also become accessible to intruders [38; 39]. Therefore, potential threatsare becoming more likely, wherein an intruder can have access to these devices. Generally, over the wireless network, smart home devices are usually connected, and an attacker can expose private information from the communication channel by eavesdropping. The secure smart home system is a challenging and complex task.
* Software attack: The main source of security vulnerabilities in any network system is considered to be software attacks. Such attacks exploit deployment vulnerabilities in the router through its communication interface. This type of malware includes virus, denial of service, worms, and VPNfilter attack that allows injecting malicious code into the system.
* Denial of service attack is a very common kind of attack used by attackers to disrupt an entire network and the router. The attacker uses multiple series requests to flood the router with message requests using internet control message protocol (ICMP) packets.
* Packet mistreating attack: This type of attack is similar to a denial of service attack. A packet mistreating injects packets with malicious codes to disrupt and confuse networks; data packets appear to mistreat the router, which brings the positive result of the router starting to mistreat harmful packets within the network, but the routing processes can no longer handle the number of packets occurring on the routing table.
* The VPNfilter attack is a common malware attack on routers wherein false information about routing is redirected by compromising the smart home system.
## 6 Conclusions  
We proposed the SOA-enabled HONoC architecture to reduce the total power consumption and mitigate SNR degradation, considering network size scalability without increasing laser source output power. An SOA placement algorithm was developed for efficient laser source power reduction and SNR enhancement. We suggested the worst-crosstalk noise model and presented the attuning method for optimal SOA gain with respect to power consumption and SNR, respectively. We constructed a C-based simulator to evaluate the SOA-enabled HONoC with an associated SOA placement algorithm. Simulation results showed that the worst-case SNRs for the 1 st, 2 nd and 3 rd longest paths are improved by an average of 4.26 dB and power consumption is reduced by 32.17% to 45.49% under various traffic patterns in the \(8\times 8\) network. The performance gaps regarding various SOA spacings were not significant; on the other hand, the improvement achieved in both SNR and power consumption was increased in proportion to the mesh size. Therefore, the proposed SOA-enabled HONoC can be a scalable solution to cope with the performance degradation problem of large-scale HONoCs.  
Formal analysis, J.Y.J.; Investigation, C.-L.L.; Methodology, M.S.K.; Validation, J.Y.J.; Writing--original draft, J.Y.J.; Writing--review & editing, T.H.H.  
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea under Grant NRF-2018R1D1A1B07043585 and by the MOTIE (Ministry of Trade, Industry & Energy) (10080594) and KSRC (Korea Semiconductor Research Consortium) support program for the development of the future semiconductor device.  
The authors declare no conflict of interest.
# Stability and Bioaccessibility of \(\beta\)-Arbutin in Simulated Digestion Conditions
## 2 Materials and Methods
### Quantitative Determination of \(\beta\)-Arbutin  
High-performance liquid chromatography with ultraviolet-visible spectrophotometric detection (HPLC-UV/VIS) was applied for the determination of the content of \(\beta\)-arbutin in vitro bioaccessible fractions. Samples were separated on a Varian ProStar HPLC-UV/VISsystem (Varian Inc., Walnut Creek, CA, USA) equipped with a 250 mm \(\times\) 4.6 mm RP-C18 Gemini column (particle size 5 \(\upmu\)m, pore size 110 A) (Phenomenex, Torrance, CA, USA). The column heater temperature was set at 25 \({}^{\circ}\)C, and a 1 mL/min constant flow rate of the mobile phase was applied. The mobile phase was composed of 0.1% formic acid in ultrapure water (\(v/v\)) (component A) and 0.1% formic acid in acetonitrile (component B). The elution gradient profile was 0 min, 4% B; 5 min, 4% B; 10 min, 7% B; 12 min, 100% B; 16 min 100% B; 18 min, 4% B; 22 min, 4% B. Detection of \(\beta\)-arbutin was carried out at 280 nm. \(\beta\)-arbutin was identified by comparing the retention time with that of a standard, and quantification was performed using an external standard curve. Additionally, the obtained chromatograms were analysed for the presence of hydroquinone as a potential \(\beta\)-arbutin degradation product.
# Exploring ISAC: Information-Theoretic Insights
###### Abstract  
This article reviews results from the literature illustrating the bottlenecks and tradeoffs of integrated sensing and communication (ISAC) through the lens of information theory, thus offering a distinct perspective compared to recent works that focus on signal processing, wireless communications, or other related overviews. Different models and scenarios are considered and compared. For example, scenarios where radar sensing is performed at the communication and radar transmitter (mono-static ISAC) and scenarios where the radar receiver differs from the radar transmitter (called bi-static radar). Similarly, we discuss ISAC bottlenecks and tradeoffs both in slowly-varying environments where the main sensing target is described by a single parameter and accordingly, sensing performance is described by detection error probabilities, as well as in fast-varying environments, where the sensing targets are described by vectors and thus vector-valued performance measures such as average distortions like mean-squared errors are used to determine sensing performances. This overview article further also considers limitations and opportunities in network ISAC environments, such as collaborative or interactive sensing, and the influence of secrecy and privacy requirements on ISAC systems, a line of research that has received growing interest over the last few years. For all these scenarios, we provide and discuss precise models and their limitations and provide either bounds or full characterizations of the fundamental information-theoretic performance limits of these systems. Further extensions as well as important open research directions are also discussed.  
Keywords:integrated sensing and communication; information theory; radar; capacity-distortion tradeoff; state-dependent channels; multi-user communication; secrecy constraints +
Footnote †: journal:
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 3 Ground Microgravity Analogues
### Dry Immersion  
Another widely used ground model for microgravity is dry immersion. First introduced in the early 1970s [47], dry immersion accurately and rapidly reproduces most of the physiological effects of short-term spaceflight. The test subjects lay in supine position on  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Bed Rest** & **ULLS** & **HU** & **Neutral Buoyancy** & **Dry Immersion** \\ \hline
**Microgravity Duration** & Hours to weeks & Hours to weeks & Hours to weeks & Hours & Days to months \\
**Hypogravity** & Yes & No & No & No & No \\
**Biological System** & Humans & Humans & Animals & Animals, humans & Humans \\
**Cost** & Medium & Low & Low & Medium & Medium \\
**Accessibility** & Easy & Medium & Easy & Medium & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of ground microgravity analogues. ULLS, unilateral lower limb suspension; HU, hindlimb unloading.  
Figure 2: Ground analogues of microgravity. Schematic representation of **(a)** bed rest; **(b)** neutral buoyancy (Fg, gravitational force/weight, Fa, Archimedes/buoyancy force); **(c)** dry immersion; **(d)** rodent hindlimb unloading (HU); and **(e)** unilateral lower limb suspension (ULLS). Microgravity analogues reproduce the effects of microgravity on physiological responses, like muscle and bone loss, and head-ward fluids shifts.  
waterproof elastic fabric, immersed up to neck level into a deep bath (Figure 2c). The combination between the water buoyant force and the high elasticity of the fabric reproduces the effects of weightlessness, like a lack of vertical vascular gradient, physical inactivity, and support withdrawal.  
Dry immersion reproduces the same physiological changes as HDT bed rest, but the effects can be seen after a relatively shorter period of exposure. In addition, dry immersion can be used over longer periods of time than neutral buoyancy. These characteristics have allowed scientists to perform experiments in dry immersion from a few hours up to 56 days (Table 2) [48]. Dry immersion has been used to study cardiovascular deconditioning and changes in body fluid and mineral content [49]. It is also especially important in the design of passive countermeasures, i.e., not requiring intensive motor activity of the subjects. Recently, dry immersion has also found applications for terrestrial medicine, like rehabilitation programs for premature children and in sport medicine, and for the treatment of Parkinson disease [48].
## 2 Study Area
### The Carbona Landslide  
The Carbona landslide (Figure 3a,b) is located within the municipality of Vergato (province of Bologna, Italy). It develops on a slope composed of chaotic clay-shales belonging to the Palombini Shale Formation [40]. This landslide has been known since the first half of 1800, and, up to the present day, six activations are reported [41]. The first one occurred during the spring of 1837. The second one occurred in late 1901 and caused serious damages to the main road located at the toe of the slope. The third activation dates 28 September 1937. In this case, the reactivated portion had a length of more than 1000 m and an average width of 500 m. In this case, the landslide caused damages, such as the displacement of some meters of telegraph and electricity poles. Another activation occurred during the night between 27 and 28 November 1965, affecting a 900 m long and 150 m wide area. The last historical event took place in the night between 5 and 6 November 1979. On that occasion, the landslide showed signs of movement in the previous months (May and September, according to the reports). The failed mass had a length of 1500 m and an average width of 1000 m for an area of 300,000 m\({}^{2}\). Rainfall was the cause of the trigger. The landslide damaged a house (declared uninhabitable), forced local authorities to evacuate several families and created swellings and cracks for a stretch of about 100 m along the main road at the toe [41]. The portion that reactivated during the last event during December 2019 develops from an altitude of 308 to 564 m a.s.l. with an average slope of 16.5 degrees. During this last reactivation, the landslide moved as earthflow [19] after the persistent rainfall in mid and late November. Furthermore, In this case, damages occurred to the municipal road that crosses the slope.  
Figure 2: UAV (Unmanned Aerial Vehicle) photograph of Braina earthflow taken after reactivation (**a**). Schematic map and profile of the landslide deposit (**b**).