esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 3 Self-Drilling Anchor Bolt Anchorage Mechanism Simulation Study
### Uplift Simulation of Self-Drilling Anchor Bolts  
#### 3.3.1 Initial Stress Equilibrium  
Before simulating the uplift test, an initial equilibrium process must be carried out on the geotechnical strip to establish the initial stress and displacement fields and to eliminate any node displacements and node velocities generated during this process. Figures 9 and 10, respectively, show the initial stress contour and the initial displacement contour.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Anchor System Material Parameters** & \multicolumn{3}{c}{**Soil Material Parameters**} \\ \hline unit weight/(kg/m\({}^{3}\)) & 7.18 & Grouting cohesion/N-m\({}^{-1}\) & 4.73 \(\times\) 10\({}^{4}\) & unit weight/kN-m\({}^{-3}\) & 20.7 \\ Elastic modulus/Pa & \(2.1\times 10^{11}\) & Shear coupling spring & 23 & Bulk modulus/MPa & 1915 \\  & & internal friction angle/\({}^{\circ}\) & 23 & Bulk modulus/MPa & 885 \\ Poisson’s ratio & 0.3 & Y-axis moment of inertia/m\({}^{-4}\) & 3.21 \(\times\) 10\({}^{-9}\) & shear modulus/MPa & 885 \\ Cross sectional area of the rod & 632 & Z-axis moment of inertia/m\({}^{-4}\) & 3.21 \(\times\) 10\({}^{-9}\) & Poisson’s ratio & 0.3 \\  & & polar moment of inertia/m\({}^{-4}\) & 6.43 \(\times\) 10\({}^{-9}\) & cohesion/Pa & 24.2 \\ Shear coupling spring & \(1.818\times 10^{6}\) & yield strength/kN & 272 &
\begin{tabular}{c} internal friction angle/\({}^{\circ}\) \\ \end{tabular} & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for the anchor system and geotechnical materials.  
Figure 9: Initial displacement cloud.
## 5 Conclusions and Future Work  
In this study, we presented a novel model based on DL for detecting DDoS attacks on SDN networks.  
The goal of this research was to recognize and classify DDoS attacks using a CNN + BiLSTM hybrid deep neural network model. The following are the proposed elements for the proposed system (CNN + BiLSTM): (i) dataset acquisition, (ii) preprocessing, (iii) feature selection, and (iv) classification. We trained and evaluated the proposed model using the newly available CICDDoS2019 dataset. The collection includes the first most relevant and recent DDoS categories of attacks. Numerous further tests were performed using the data set. In the provided data collection, feature selection was used to choose just the most relevant features by ranking and choosing the top-ranking features. Finally, a CNN + BiLSTM hybrid model was used to predict DDoS attacks: _normal_, _or attack_. In comparison to current well-known traditional ML approaches, the assessment of our model revealed that the suggested system generates the best evaluation metrics in terms of recall, precision, F-score, and accuracy. When contrasted to the baseline techniques, the research findings are encouraging in terms of increased accuracy (94.52%), precision (94.74%), recall (92.04%), and f-score (93.44%).
# A Brief Review on Micro-Implants and Their Use in Orthodontics and Dentofacial Orthopaedics
## 3 Results
### Use in Orthodontic Clinical Practice and Dento-Facial Orthopedics
The maxillary expansion enlarges the nasal cavity and increases the air quantity that passes through the nasal pathway. In adult patients, the side effects are unwanted teeth movements. Micro-implant assisted rapid maxillary expansion (MARME) could reduce even eliminate the dental side effects. MARME produces skeletal effects that enable a larger mid-palatal suture separation and a larger increase of the nasal cavity volume. The maxillary skeletal expander (MSE) is a special MARME appliance of four mini-screws inserted in the posterior palate with bi-cortical engagements in the palatal and nasal cortical bone. MSE expands the superior and posterior areas of the nasal cavity [62]. The pterygopalatine suture can be split without surgery, by using midfacial skeletal expanders [63]. The device used had a jackscrew unit with four parallel holes for the micro-implant insertion, with two soft supporting arms on each side which are soldered to the molar bands for increasing stability. The jackscrew was seated on the hard palate between the zygomatic buttress bones. After the treatment, the mean palatal suture opening angle was 0.57\({}^{\circ}\). There was no significant difference between males and females regarding the palatal suture opening pattern and 84% of cases had openings between the medial and lateral pterygoid plates on both right and left sides. Micro-implants are indicated as anchorage elements in cases of periodontal patients who need orthodontic treatment [64]. For the advantages like a simple surgical procedure, low cost, immediate loading, the placement's possibilities the OMIs are indicated for molar intrusion, molar up-righting, and other minor tooth movements [54]. Rupture of the intermaximillary suture with a micro-implant-supported screw was experimentally achieved with an expansion force of 86 N. A high tensile stress concentration was exerted and opened the fused intermaximillary suture [65]. Experimentally on dogs, the bi-cortical micro-implants with two anchorage units demonstrated their efficiency for the difficult movement that is the posterior teeth protraction, in the mandible [66].
# Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography
## 4 Lattice-Based Cryptography
All operations performed are on a lattice, and the security of the cryptographic scheme is based on the hardness of the computational lattice problems. The first digital signature protocol based on lattices was introduced in 1997, with ideas comparable to those of the GGH cryptographic scheme [39]. In 2003, another digital signature using the NTRU lattice, NTRUSIGN [46], was proposed, and other proposals were additionally introduced, such as the Ring-LWE signature scheme [47] and the BLISS signature scheme [48]. During the NIST Post-Quantum Cryptography Standardization Process [37], various cryptographic protocols were submitted for evaluation, and three of the four potential candidates for post-quantum cryptography were lattice-based digital signature schemes. CRYSTALS-Dilithium, SPHINCS+ and Falcon are cryptographic schemes that stood out for their efficiency, compactness, speed, flexibility and strong security guarantees against both classical and quantum attacks.
## 4 Discussion
Table 11 shows that among all approaches, the standard stacking approach had the worst performances on all datasets. According to these results, our novel stacking method achieved the highest F1-scores across all datasets in all four experiments. Among all approaches, standard stacking performed the worst, whereas novel stacking performed the best. This improvement confirmed the effectiveness of our novel approach. As shown in Table 13, the proposed approach outperformed the state-of-the-art in both HatEval and Davidson datasets. Our approach has a lower performance than the state-of-the-art only on the ZeerakW dataset. This may be attributed to the combination of the two features word2 vec and USE employed here. Indeed, [PERSON] and [PERSON] [6] observed that using character N-grams on this dataset outperformed other features. They also noted that the other features had an adverse effect on the performance. [PERSON] [38] stated that in comparison with N-gram features, which eliminate the concept of location in a text (apart from immediate surrounding terms for bi/tri-grams), CNN and word embeddings can utilize a sequence of tokens by concatenating token embeddings into a matrix. In fact, [PERSON] et al. [18], achieved a higher F1-score than our approach by using this combination. In addition, combining these features with the LR (which performed the best, as explained previously) improves the performance. This observation is consistent with our findings on the ZeerakW dataset, where the proposed approach's result was the only one that was lower than the state-of-the-art. It should be noted that no specific combination of classifiers or a specific single classifier can deliver the highest performance for all types of datasets. Each dataset has different characteristics such as the context of the tweet [14], the targets of the hate speech, the classes used for categorizing the tweets, the distribution of the classes, and the overall dataset size. All these unique characteristics of the datasets indicate that specially tailored systems with architecture and features best suited for a particular dataset may not generalize well and adapt to the dynamic hate speech domain. Overall, the proposed approach can improve performance by overcoming the inadequacies of the base classifiers while considering the dataset's appropriate features. The results presented here show that the proposed architecture with the (SVM, LR, XGB) combination as Base-Level classifiers improves the performance of the base classifiers, and in general, outperformed most of the state-of-the-art.
## 2 Results
### In Vitro Assays  
The three pure compounds **1**, **2** and **4** were submitted to in vitro assays with the TRPA1 receptor. For comparison, sinigrin **5**, which is the reference standard for GLSs, was also tested in the same conditions. Allylisothiocyanate **6** (AITC) is conventionally used as the reference agonist for TRPA1  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**C** & **Assignment** & **\(\mathbf{\delta_{C}}\) (ppm)** &
\begin{tabular}{c} \(\mathbf{\delta_{H}}\) (ppm, Moletepilicity, \\ **(j in Hz))** \\ \end{tabular} \\ \hline \hline \multicolumn{3}{l}{Aliphatic moiety} & & \\
1 & C & 165.68 & \\
2 & CH & 34.03 & 2.97, m (6.90) \\
3 & CH\({}_{3}\) & 21.33 \({}^{*}\) & 1.298 \({}^{*}\), d (6.90) \\
4 & CH\({}_{3}\) & 22.45 \({}^{*}\) & 1.296 \({}^{*}\), d (6.90) \\ Glucose moiety & & & 4.89, d (9.75) \\
1\({}^{\prime}\) & CH & 62.78–86.91 & \\
2\({}^{\prime}\) & CH & & 3.24–4.20 dd \\
3\({}^{\prime}\) & CH & & \\
5\({}^{\prime}\) & CH & & \\
6\({}^{\prime}\) & CH\({}_{2}\) & & \\ \hline \hline \end{tabular} \({}^{*}\)Chemical shift after resolution enhancement; these attributions can be interchanged.  
\end{table}
Table 1: \({}^{1}\)H and \({}^{13}\)C data for glucoputranjivin **1** in MeOH-d4 at 25 \({}^{\circ}\)C.  
and was used to establish the relative efficacy for each tested compound. Antagonist/desensitizing behavior was also measured. The results of in vitro assays of isolated compounds are shown in Table 2.  
TRPA1-human embryonic kidney (HEK)-293 cells exhibited a sharp increase in intracellular Ca\({}^{2+}\) upon application of the compounds examined--that is, **2** and **4**--with efficacies of 108.8 and 98.0, respectively, and EC\({}_{50}\) values \(<\) 6 uM (Figure 2, panel a). Both **2** and **4** at 100 uM showed no activity (no elevation of internal Ca\({}^{2+}\)) on not-transfected HEK cells (with an activity less than 1%).  
Five minutes of preincubation of TRPA1-HEK-293 cells with ITCs **2** and **4** followed, and then incubation was continued with AITC, causing the inhibition of TRPA1 response to this agonist with IC\({}_{50}\) values of 10.8 and 12.1 uM, respectively (Figure 2, panel b). Then, upon activation, calcium enters the cell and stimulates a series of calcium-dependent processes that ultimately lead to the desensitization of the TRPA1 channel. Upon desensitization, the TRPA1 channel enters a refractory period in which it can no longer respond to agonist (AITC) stimulation. The EC\({}_{50}\) and IC\({}_{50}\) values were comparable, of the same order of magnitude. This implies that ITCs **2** and **4** desensitize the receptor to the next AITC action leading to a paradoxical inhibitory effect of these compounds.
## 6 Investigating Several RL-Based Routing Methods
### [PERSON] et al. in [58] have suggested the Q-learning and grid-based (QGrid) routing method for VANETs. This method works on both levels, macroscopic and microscopic. At the macroscopic level, the network is partitioned into smaller grids. Then, QGrid explores the most suitable next grid towards the destination direction. Unlike other grid-based methods, this approach does not select a fixed grid head node for each grid. It chooses the relay vehicle in each grid using the strategy provided at the microscopic level. When selecting the optimal next grid, QGrid uses a Q-learning algorithm. In this process, a virtual agent is considered in the network. Also, the state space contains all grids in the network. As a result, QGrid decreases the state space and accelerates the convergence rate. Also, the set of actions indicates the transmission of a message to the neighboring grid. The learning structure of QGrid is displayed in Figure 13. In this learning model, the historical traffic data is obtained from GPS and Q-table is created based on this data. Q-value illustrates the possibilities of a vehicle to enter the next grids. In this scheme, the agent creates Q-table using an off-line manner. Thus, this table is fixed throughout the simulation. This table is loaded into the memory of each vehicle before deploying the network. At the microscopic level, the transmitter node should select a relay vehicle at the optimum grid. The relay selection process is based on two different strategies: (1) In the greedy forwarding methodology, the neighboring vehicle closest to the destination is selected as the relay node. (2) In the Markov prediction method, the relay vehicle is selected by the two-order Markov chain with regard to the possibility of moving toward the optimal grid.  
Figure 12: Learning structure in PFQ-AODV.
# Wind Lidar and Radiosonde Measurements of Low-Level Jets in Coastal Areas of the German Bight
## 3 Study Sites and Data Base  
The data sets for this study were recorded using ground-based pulsed Doppler wind lidar systems of the types WindCube WLS8-8, WindCube v1, and WindCube v2, developed and manufactured by the former French company Leosphere (now Vaisala). The pulsed laser infers the horizontal wind speed from so-called line-of-sight (LOS) or radial wind speeds \(V_{r}\) using a four-Doppler-beam-swing (DBS) scan [54; 55]. An internal optical switch steers the laser beam at four azimuth angles in successive intervals of \(90^{\circ}\) (i.e., \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\)) to form a conical scan with a fixed elevation angle with respect to the horizontal. The LOS or radial speed is then inferred within a range gate of 40 m from the Doppler shift of the resulting return signal due to atmospheric particle scattering with respect to the original signal. The pulsed system together with the internal algorithm enables the simultaneous reconstruction of the mean wind speed and wind direction from several heights or range gates. The measurement quality is evaluated according to a threshold based on the carrier-to-noise ratio (CNR), which is dependent upon atmospheric conditions, such as the aerosol backscatter, turbulence, humidity, and precipitation. Values with a CNR of less than 22 dB are discarded. Furthermore, events with rain periods are discarded. Only lidar data with availability of 80% for each 10 min value are used.  
The wind lidar systems were installed consecutively at different locations of the German Bight (Figure 1), the WindCube v1/v2 at FINO1, the Alpha-Ventus offshore wind park, and St. Peter-Ording, the Windcube WLS8-8 at Heligoland and Norderney. Only data averaged over 10 min were used for this study.  
The WindCube WLS8-8 system obtained data on wind speed and wind direction at 25 altitudes (40 m to 500 m, with a vertical resolution of 10 m up to 100 m, 20 m up to 400 m, and 50 m up to 500 m) for the locations Heligoland and Norderney.  
The WindCube v1/v2 were operated at different sites: a WindCube v2 (measurement altitudes from 71 m to 241 m with a vertical resolution of 20 m) at the FINO1 research platform; a WindCube v1 (measurement altitudes from 81 m, 91 m, 102 m, 119 m, 139 m, 159 m, 179 m, 199 m, 249 m, 279 m, 319 m) at the substation of the Alpha-Ventus wind farm; a WindCube v2 (measurement altitudes from 81 m, 91 m, 102 m, 119 m, 139 m, 159 m, 179 m, 199 m, 249 m, 279 m, 300 m) at St. Peter Ording. All lidar systems were installed and monitored under remote surveillance by the company UL International GmbH, Germany. An overview of the operation times at the different locations is provided in Table 1.
## 5 Discussion  
### Differences Observed among the Four Tested Configurations  
One of the most significant findings of the performed tests is the comparison of the four configurations. Although the generated magnetic field should be uniform no matter the polarity of the coil, as some of our inductive coils are composed by a high number of layers, and they are manually crafted, it is necessary to evaluate this effect.  
The first difference was observed during the calibration tests, in which two prototypes have shown different WF when the configuration is changed. In P1, the WF was higher with the first configuration. Meanwhile, for P3, the lowest PF was measured in the fourth configuration. Since the test is performed three times, and in all the cases, the results were the same, the observed change in WF due to the different configuration parameters must be considered in future prototypes. More tests needs to be done to find the exact WK, since we measure every 10 kHz to have more information about this effect. Notwithstanding the aforementioned (about the WF), the PK is found at the same frequency among the sampled ones. As with the WF, more data is needed to evaluate that the different configuration patterns might modify the PK.  
The other parameter, which was modified due to the different configurations, is the Vout. We are going to evaluate the changes in Vout of P1 with configurations 2 to 4, P2 in all the configurations, and P3 in configurations 1 to 3. The other two configurations are not considered since a difference in the WF is detected in the excluded configurations. We can affirm that the fact of change the configurations has caused a change in the Vout, which is not explained by the changes in the WF since all the comparisons are made with the same configuration. According to P2 and P3, the Vout values in configuration 1 and 3 show an almost equivalent trend. The Vout of the first configuration is a bit higher in all of the samples than in the third configuration, an average a 0.17 V higher. The second and fourth configurations tend to have lower values than the previous ones.  
This effect has been found in other publications related to inductive coils [29].  
Figure 15: The graph presents a multi-range analysis for the four configurations where they are drawn by columns. Each column contains non-significant values between them. The different letters show that the values are statistically significant. In each column the vertical bar on it represents the deviation.
###### Abstract
All the mentioned consequences can pose problems in a time when AI applications are moving on the edge, and models are expected to work with very limited computing and memory resources. As pointed out by researchers in Reference [9], the reason why AI models are still confined to offline data processing is that their weights and topology are often too large to fit into the available resources onboard Earth Observation satellites. At the same time, inference of DL systems is computationally intensive, and this can be a problem in a low-power-budget environment. New networks need to be engineered taking into account the different computation restrictions related to memory usage, training, and inference time cost. On-board data processing in spaceborne Earth Observation systems is gaining relevance, and methods for different Remote Sensing applications are being developed [9; 10; 11; 12; 13]. This trend is substantially accelerated by the recent joint effort of multiple Deep Learning research studies of providing new implementations of efficient network architectures that limit the overall number of parameters while achieving state-of-the-art performances. These networks [14; 15; 16] are built out of custom-designed operation modules that fulfill this task. A careful reorganization of existing architectures and introduction of efficient modules can solve the previously described problems and accelerate the transformation of AI-driven systems from offline processing tools to powerful dynamic edge applications. Motivated by these considerations, in this paper, we propose an efficient implementation of the HAFNet model called HAFNetE that exceeds state-of-the-art, fusion-based building extraction performances while, at the same time, affording a 92% reduction from the original number  
Figure 1: Harmful information in input data. (**a**) RGB patch containing discriminative information. (**b**) DSM patch containing incorrect information. (**c**) Ground truth map. (**d**) Segmentation result. The Att-MFBlock re-weights the RGB and the DSM input so that RGB information is highlighted and the damaged DSM information is suppressed. of network parameters. This substantial cut in requirements makes it possible to directly deploy the model as an on-board spaceborne urban mapping system.