esa-sceva/satcom-chunk-collection huggingface dataset:
# Hide45: A Method for Optimal Payload Data Hiding in Base45 Encoded Strings
## 5 Proposed Algorithm
### Optimization Method
#### 5.1.3 Constraint Linearization  
Luckily, several common techniques are known (and can be easily found in any of the already cited books) to _linearize_ this family of exponential and binary-related constraints, as we will see. Let us define a new _binary_ variable \(x_{ik}\in\{0,1\}\) that is tightly linked to the different integer values that \(n_{i}\) can take.  
\[x_{ik}=\begin{cases}1&\text{if }n_{i}=k\\ 0&\text{otherwise}\end{cases} \tag{10}\]  
By the definition (10) we know that \(x_{ik}=1\) if and only if \(n_{i}=k\); then, \(x_{ik}=1\) implies that we assign \(k\) bits to binary configuration \(i\).  
For constraint (8) we can derive a limit on the maximum power of 2, because no single \(n_{i}\) can be higher than a certain amount: given that there are a maximum of 25,589 unused Base45 configurations, we have that \(2^{n_{i}}-1\leq 25,589\). This power limit is, in fact, 14: since \(2^{14}-1=16,383<25,589<2^{15}-1=32,767\), we know that \(n_{i}\leq 14\) for all \(i\). This information can be propagated to variables (10) by defining only such variables for values of \(k\in\mathbb{N}\) in the range \(0\leq k\leq 14\).  
These new variables allow us to rewrite constraint (8) in a linear fashion: if \(x_{ik}=1\) we know that \(n_{i}=k\), exactly \(k\) bits are assigned to the \(i\)-th binary configuration, and the number of Base45 configurations required is \(2^{n_{i}}\). But \(n_{i}=k\), and \(k\) is a known integer (a number, not a variable), so we can evaluate it for every term.  
\[\sum_{i=0}^{25,589-1}\sum_{k=0}^{14}\left(2^{k}-1\right)x_{ik}\leq 25,589 \tag{11}\]  
Equation (11) will be used in place of (8) in the linear model. Additionally, the objective function needs to be changed: for the same reason, maximizing Equation (12) written with the \(x_{ik}\) variables  
\[\sum_{i=0}^{25,589-1}\sum_{k=0}^{14}e_{i}kx_{ik} \tag{12}\]  
is equivalent to maximize the objective function (7).  
There is a need for an additional constraint, nevertheless. The value assumed by \(n_{i}\) is _unique_; in other words, we cannot assign both 0 bits and 3 bits to a configuration. Hence, exactly one variable \(x_{ik}\) for every configuration \(i\) can be 1, all the others must be 0. Constraints (13) are then required and will be added to the model.  
\[\sum_{k=0}^{14}x_{ik}=1\qquad\forall\ 0\leq i<25,589 \tag{13}\]
## 4 Experimental Validation
### Kinematic Model Experimental Results  
The first experiment confirmed the end effector's position based on derived from the forward kinematics derived from the DH parameters. The VSL robot prototype was subjected to various poses, and photos were taken of each pose. Using the SOLIDWORKS 2020 program, the different angles for each pose were measured, along with the end effector's position.  
The photos of each pose were inserted into a 2D drawing and measured manually (see Figures 8 and 9). These angles were then inserted into the forward kinematics equations to calculate the end effector position. That calculated values for the end-effector position were then compared with the values obtained from the photos.  
The first experiment results show an average error of 2.90 mm in the x-direction, 2.29 mm in the y-direction, and 2.40 mm in the z-direction, so there is still room for improvement. To achieve better results, more precise assembly and alignment are required. It should also be noted that links 2 and 3 of the prototype were slightly curved. These adjustments can ensure that the system reaches its full potential and achieves an even higher level of accuracy and precision. The plots for the experimental manipulator's end tip position and the calculated position for different poses along the \(X\)-axis, \(Y\)-axis, and  
Figure 8: Measuring the end-effector position from the photos using SOLIDWORKS (X-Z plane).  
Figure 9: Measuring the end-effector position from the photos using SOLIDWORKS (X-Y plane).
## 4 The Proposed AAC-IoT System Model
### Registration and Authentication
#### 4.2.4 DU Authentication  
The authentication process of the DU is based on the neural network that is able to tolerate all the arrived user devices. The neural network is enabled to consider multiple user requests received at a time. The security credentials are transferred in hash format for assuring security. Let \(u_{Q}\) represent the total number of DU authentication requests arrived at time \(t\). The neural network consists of three layers: an input layer, a hidden layer and an output layer. Initially, the requests are received at the input layer, and then security credentials are extracted individually for each DU.  
A set of nodes participates in each layer of the network. The layers and their work process is illustrated below. The construction of an artificial neural network (ANN) for DU authentication is depicted in Figure 6. The nodes in each layer of the ANN connect to the nodes in the next layer. The operational functions that are used in the neural network are the linear function or the activation function.  
* Layer 1: Input Layer This is the first layer of the ANN that receives the DU requests along with the identity, signature, certificate, successful access to policy and rating value. The entity's identity, signature and certificate are in hash values. The input security credentials are independent variables; hence they are to be validated individually.
* Layer 2: Hidden Layers The hidden layer can be constructed in multiple numbers. In this work, four layers are created in a hidden layer. Each hidden layer extracts the particular credential and validates the hash values. In the fourth hidden layer, the credence score for the DU is computed, and then the decision is taken. The credence score is estimated from the total number of counts of the successful access policy \((S_{P})\) and the behavior of the DU  
Figure 5: Workflow of DO authentication.  
that gives a higher rating value or a low rating value \((R_{v})\). The credence score \(CS\) is given as, \[CS= S_{P}+R_{v}\] (1) If the \(CS>CS(T)\), then the DU is legitimate; if the condition fails, then the DU access will be denied as it is identified as illegitimate.
* Layer 3: Output Layer The validated security credentials are involved in decision making in this layer. If all the credentials pass, then the result is positive, and the CA sends a challenge from the PUF.  
The DU receiving a \(C_{k}\) will identify the corresponding \(Re_{k}\), and finally the authentication process of a DU has been successfully completed. After this, based on the given access policies, the meta-data are extracted from the HLF blockchain and original data from cloud in an encrypted format.
## 3 Results
### Regression Relationship between Seed Yield and Physiological Traits  
The regression relationships between seed yield of safflower genotypes and physiological traits showed that water stress index, water potential and water use efficiency had a strong relationship with seed yield and had higher R\({}^{2}\) coefficients under both normal and water deficit stress conditions. Stomatal conductance also showed a strong correlation with seed yield and had a remarkable R\({}^{2}\) only under water deficit stress conditions (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Stress Levels** & **Genotype** & \begin{tabular}{c} **Water Use** \\ **Efficiency** \\ **(kg m\({}^{-3}\))** \\ \end{tabular} & \begin{tabular}{c} **Capitula per** \\ **Plant** \\ \end{tabular} & \begin{tabular}{c} **Seeds in a** \\ **Capitulum** \\ \end{tabular} & \
\begin{tabular}{c} **1000-Seed** \\ **Weight (g)** \\ \end{tabular} &
\begin{tabular}{c} **Seed Yield** \\ **(kg h\({}^{-1}\))** \\ \end{tabular} \\ \hline Non-stressed & Padideh & 0.53 b & 11.3 a & 52.4 b & 31.6 b-e & 3697 cd \\  & Golemehr & 0.55 ab & 8.7 cd & 51.7 b & 31.1 c-e & 4676 ab \\  & Mec.14 & 0.55 ab & 10.7 ab & 51.8 b & 37.4 a & 4026 c \\  & Mec.248 & 0.54 ab & 11.5 a & 61.0 a & 34.1 a-d & 4508 b \\  & Mec.295 & 0.56 a & 9.8 bc & 60.3 a & 35.7 a-c & 4961 a \\  & Parnian & 0.49 c & 5.5 f & 36.3 e & 37.5 a & 1593 g \\ Stressed & Padideh & 0.44 e & 9.0 cd & 41.4 de & 28.1 e & 2939 f \\  & Golemehr & 0.45 de & 8.2 de & 50.9 b & 30.4 e & 3211 ef \\  & Mec.14 & 0.44 de & 7.2 e & 50.0 bc & 29.8 de & 3564 de \\  & Mec.248 & 0.44 de & 9.5 b-d & 43.9 cd & 32.5 e & 3364 de \\  & Mec.295 & 0.47 cd & 9.0 cd & 52.7 b & 34.5 a-d & 3385 de \\  & Parnian & 0.41 f & 4.3 f & 27.1 f & 36.4 ab & 1068 h \\ \hline LSD 5\% & 0.026 & 1.253 & 5.671 & 4.458 & 400.7 \\ \hline \end{tabular} Different letters indicate a significant difference at (\(p<0.05\)) determined by ANOVA with the comparison of means using Tukey’s HSD.  
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c l c c c c c c c c} \hline
**Trait** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** & **(8)** & **(9)** & **(10)** \\ \hline (1) & Crop Water Stress Index & \(-0.91\)** & \(-0.93\)** & \(-0.98\)** & \(-0.98\)** & \(-0.98\)** & \(-0.98\)** & \(-0.60\)** & \(-0.65\)** & \(-0.21\) & \(-0.66\)** \\ (2) & Relative water content & & 0.95** & 0.88** & 0.90 & 0.87 ** & 0.63 * & 0.55 & 0.15 & 0.58 * \\ (3) & Stomatal conductance & & & 0.88 ** & 0.90 ** & 0.87 ** & 0.65 * & 0.55 & 0.15 & 0.59 * \\ (4) & Osmotic adjustment & & & & 0.99 ** & 0.95 ** & 0.46 & 0.55 & 0.32 & 0.55 \\ (5) & Water potential & & & & & 0.95 ** & 0.51 & 0.60 * & 0.28 & 0.58 * \\ (6) & Water use efficiency & & & & & 0.65 * & 0.74 ** & 0.20 & 0.75 ** \\ (7) & Capitula per plant & & & & & & 0.82 ** & \(-0.17\) & 0.86 ** \\ (8) & Seeds in a capitulum & & & & & & & \(-0.13\) & 0.92 ** \\ (9) & 1000-seed weight & & & & & & & \(-0.23\) \\ (10) & Seed yield & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 5: Simple correlation coefficients between traits measured in safflower genotypes in the period 2017–2019.
###### Abstract  
The analysis and follow up of asphalt infrastructure using image processing techniques has received increased attention recently. However, the vast majority of developments have focused only on determining the presence or absence of road damages, forgoing other more pressing concerns. Nonetheless, in order to be useful to road managers and governmental agencies, the information gathered during an inspection procedure must provide actionable insights that go beyond punctual and isolated measurements: the characteristics, type, and extent of the road damages must be effectively and automatically extracted and digitally stored, preferably using inexpensive mobile equipment. In recent years, computer vision acquisition systems have emerged as a promising solution for road damage automated inspection systems when integrated into georeferenced mobile computing devices such as smartphones. However, the artificial intelligence algorithms that power these computer vision acquisition systems have been rather limited owing to the scarcity of large and homogenized road damage datasets. In this work, we aim to contribute in bridging this gap using two strategies. First, we introduce a new and very large asphalt dataset, which incorporates a set of damages not present in previous studies, making it more robust and representative of certain damages such as potholes. This dataset is composed of 18,345 road damage images captured by a mobile phone mounted on a car, with 45,435 instances of road surface damages (linear, lateral, and alligator cracks; potholes; and various types of painting blurs). In order to generate this dataset, we obtained images from several public datasets and augmented it with crowdsourced images, which where manually annotated for further processing. The images were captured under a variety of weather and illumination conditions and a quality-aware data augmentation strategy was employed to filter out samples of poor quality, which helped in improving the performance metrics over the baseline. Second, we trained different object detection models amenable for mobile implementation with an acceptable performance for many applications. We performed an ablation study to assess the effectiveness of the quality-aware data augmentation strategy and compared our results with other recent works, achieving better accuracies (mAP) for all classes and lower inference times (3\(\times\) faster).  
asphalt damage; dataset; deep learning; object detection; asset management 2020 10, 3974; doi:10.3390/app10113974 www.mdpi.com/journal/applsci
## 2 The First and Second Parts of Making the Surgical Guide for TADs
### Part 1: Rationale for Using a Surgical Guide in the Implantation of TADs
#### 2.1.1 Collecting Data, Planning, and Designing in Consideration of the Making of the Surgical Guide
The implantation in the interdental bone will limit the movement of the tooth, mesial and distal. Therefore, to avoid interferences with roots, the TADs are preferred on the mandible's ridge or the mandible's oblique line (Figure 3a, H), especially when en mass retraction of the arch is desirable. The ridge of the mandible between the body and the ramus of the mandible is a favorable site for TAD placement [47]. The tricky part is that the mandible ridge may seem insignificant in women relative to men, and modification may be needed in the surgical guide's design [48; 49]. Since the placement location is a steep bone wall, manual placement will be challenging, so a surgical guide is preferred. The angulated placement of the TADs in this area and the flabby mucosa also necessitates using a surgical guide. Care must be taken during the intraoral scan as the superficial mucous membrane above the ridge must be gently stretched to record this critical area. Lack of recorded data in this area will affect the visibility of the area site, and the surgical guide cannot be planned. Implantation in this area is recommended using contra-angle motorized instruments, as the buccal cheek tissue prevents using a hand screwdriver and will obstruct the surgeon's vision. The buccal musculature, maxillary teeth, and mouth opening will limit the manoeuvrability of the placement procedure. These limitations should be evaluated during a clinical examination. Considering the hindrance of lower lip movements, placement between the four incisors should be avoided as much as possible. TADs in this area can cause painful lip ulcerations with delayed healing. The location of the canine and the first bicuspid (Figure 3a, F) is an area that can fit for the implantation choice. This location is between anterior and posterior dental arch segments and thus will provide an excellent place to resolve both anterior and posterior malocclusion. Depending on the extraction case or non-extraction case, considerations can be different, and placement in the extraction site of the bicuspid and adjacent area is required to avoid proximity to vital structures in the mentalis foramen. In Figure 3a, G location is possible when there is a need to retract the anterior teeth after an extraction case where placement of the ridge of the mandible is difficult or impossible due to the younger age of the patient. Placement at a young age, e.g., under 12, is risky as bone metabolism is still high in this area.
## 5 Discussion  
### The 2014 Ludian Earthquake's Seismogenic Fault and Its Tectonic Implications  
Within the vicinity of the epicenter, the NE-oriented Zhaotong-Ludian fault is primarily recognized for its thrust faulting. However, the focal mechanism of strike-slip and the spatial-temporal pattern of aftershocks, which align predominantly along NW-SE and near E-W directions, imply that the Zhaotong-Ludian fault may not be the only seismogenic fault. The presence of the NW-oriented Baogunao-Xiaohe fault, noted for its left-lateral strike-slip movement, provides an alternative tectonic feature that corresponds more closely with the aftershock distribution and focal mechanism. Particularly within the first 5 h following the main shock, the predominance of aftershocks in the NW-SE direction, coupled with their depth characteristics consistent with a strike-slip fault, suggested that the Baogunao-Xiaohe Fault may have been the seismogenic fault for the Ludian earthquake. Synthesizing the aftershocks' spatial-temporal pattern, the focal mechanism, and the intrinsic characteristics of the regional faults, we inferred that the seismogenic fault responsible for the Ludian earthquake is the NW-oriented Baogunao-Xiaohe fault.  
The distribution of the NW-trending Ludian aftershocks, intersecting the NE-trending Zhaotong-Ludian fault, suggests that the former may be transecting the latter. This intersection could imply that the Zhaotong-Ludian fault, despite its longstanding geological presence, has been intersected and possibly offset by the younger Baogunao-Xiaohe fault, a tectonic feature that may have developed during the Cenozoic era [25]. Furthermore, geological structural maps indicate that the Baogunao-Xiaohe Fault is a minor and less distinct fault located north of the Zhaotong-Ludian Fault. On the south side of the Zhaotong-Ludian Fault lies another small fault, with both minor faults being separated by the Zhaotong-Ludian Fault. Based on these, we assumed that the 2014 Ludian earthquake  
Figure 8: Seismicity–time distribution of the Ludian aftershock catalog. (**a–c**) The earthquake catalogs obtained in this study (CERP), the earthquake catalog by [PERSON] et al. [25] and the earthquake catalog from the China Earthquake Administration (CEA) regional seismic network, respectively. Panel (**d**) shows the seismicity rate calculated from the three earthquake catalogs between 1 August 2014 and 21 August 2014. The seismicity rate is defined as the number of earthquakes occurring per hour in the region.  
may have interconnected the Baogunao-Xiaohe Fault across the north and south sides of the Zhaotong-Ludian Fault.
# Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges
## 2 Review of Probabilistic Model of Extreme Response Scenarios in Long-Span Bridges  
A probabilistic model was derived and fully explained in a previous publication by the authors [33]. The application to the case of free-flow traffic can also be found in [33]. The present study demonstrates how the model can also be applied to the case of congested traffic. Therefore, only the main characteristics of the model are described here as the full description can be obtained in [33].  
When obtaining extreme values of load effects on long-span bridges, the possible vehicle distribution on the bridge deck can vary significantly. To simulate the vehicle distribution pattern on the bridge deck during these extreme scenarios, the bridge deck was divided into cells of a certain length. Given that the number of vehicles on the bridge deck during extreme scenarios is relatively high and that lighter vehicles contribute minimally to the extreme effect values, they were omitted in the modeling process to simplify the simulation and more clearly study the distribution characteristics of heavy vehicles. To further facilitate the simulation, it was assumed that the centroid of each heavy vehicle was located at the center of the nearest bridge deck cell in the same lane. The details of the extreme response scenarios simulation method were already explained in [33]. Moreover, the driving habits of heavy vehicles also significantly impact their distribution, where heavy vehicles tend to cluster in the outer lanes. Therefore, the distribution of heavy vehicles across different lanes should be simulated during the modeling process. Two key factors needed to be considered when modeling the extreme response scenarios on the bridge deck:heavy vehicle weights and positions within the deck. They are described in the following subsections.
## 3 Results and Discussion
### Compressive Strength
#### 3.1.2 Influence of the Air Content of Concrete  
Figure 11 presents the results for the compressive strength at 7, 28 and 56 days of age, for concrete mixes with 50% GGBS produced with different targeted air contents (4.5% \(\pm\) 0.5% for Mix 3, and 6.0% \(\pm\) 0.5% for Mix 5).  
The results show that the concrete mix with higher amount of air (Mix 5, with air content between 5.5% and 5.6%, measured according to SS-EN 12350-7) presents lower compressive strength at all ages when compared to Mix 3 (air content measured between 4.1% and 4.9%). Since the compressive strength of concrete is directly related to its density and an increase in the air voids will reduced the density of the paste, an increase in the air content is expected to lead to a lower compressive strength [4].  
Figure 11: Compressive strength development of concretes with 50% GGBS, k = 0.6, cured at 20 \({}^{\circ}\)C, and with different air contents.  
Figure 10: Compressive strength development for concretes with different addition percentages of GGBS, k = 0.6, targeted air content of 4.5% \(\pm\) 0.5%, cured at 20 \({}^{\circ}\)C.  
According to the literature, a 1% increase in the volume of air pores in concrete results in an average compressive strength loss between 5.5% [4] and 6.0% [7]. Considering the average value of 4.5% for the air content of Mix 3, and 5.6% for Mix 5 (results obtained with the pressure gauge method described in SS-EN 12350-7), a difference of 1.1% is obtained. For this difference, however, the results obtained in compressive strength tests show a drop of between 15% and 17%, for all ages of testing. This value is much higher than the 6.0% mentioned in the literature. The reasons for these results are unclear, but might be related with the accuracy of the measurements of the air content of the concrete qualities. In fact, the results obtained with the AVA show a higher air content for both mixes (in comparison with the targeted values), with Mix 3 presenting an average air content of 5.9% and Mix 5 and average air content of 9.3%. Considering an average decrease of 5.5% in the compressive strength for each 1% of increase in the air content, a total decrease of approximately 19% is obtained for Mix 5, which is closer to the results obtained. These results show that there is a possibility that the results for the air content measured with the AVA are more accurate than the results obtained with the pressure gauge method, at least for these mixes. An air void analysis of the hardened concrete could have been carried out to assess the actual air content.