esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Numerical Studies on the Structural Performance of a Double Composite Wall
## 1 Introduction
Regarding the shear design of the DCW, as the CIP concrete accounts for greater than 60% of the cross-sectional area of the DCW, in practice the entire section is considered to be CIP concrete to derive the design results on the safe side in a simple manner. Moreover, the shear contribution of the bolts used for assembling the PC panels is not considered in the estimation of shear strength. That is, only the shear contribution of the CIP concrete (\(V_{c,CIP}\)) is taken into account in the shear design of the DCW. However, in this case excessively conservative design results can be obtained because the characteristics of the PC panel to which concrete with a compressive strength greater than 40 MPa is applied and the shear contribution of the bolts are not reflected. In this study, experiments and numerical analysis were performed to clearly identify the flexural and shear performances of DCW, which have not been investigated in detail  
Figure 2: Construction process of a real-scale double composite wall (DCW) system: (**a**) assembling mold; (**b**) placing reinforcements; (**c**) casting precast concrete; (**d**) assembling precast panels; (**e**) installing precast panels; (**f**) casting in-filled concrete; (**g**) after construction. elsewhere before. To this end, one flexural specimen and three shear specimens were fabricated, and the presence of bolts for assembling the PC panels was set as the main variable in the shear test. To analyze the flexural and shear behaviors of the DCW in detail, strain gauges were attached to the concrete surface, reinforcing bars, and bolts, and the slip between the PC panel and the CIP concrete was measured. Note that strain gages with a gage length of 60 mm were used for concrete, and those with a gage length of 5 mm were applied to the reinforcing bars and bolts. In addition, a non-linear flexural analysis and a finite element analysis were conducted, and the analysis and test results were then compared in detail.
## 4 Discussion
Plate fixation techniques were demonstrated to have a higher fixation stability than that of a nail fixation technique for the treatment of bone fractures [36; 37]. The use of locking screws for the plate fixations can allow the forces of the fractured bones to be shared. However, locking screws need to cut into bone and create a stress riser effect [38]. The above idea can also be found in the present study. It was shown that plate fixation techniques, which use locking screws to fix the fractured bones, achieve a better fixation stability compared to a nail fixation technique. However, a nail fixation technique, which does not place screw holes in the clavicle, shows lower bone stress and can reduce the risk of a peri-implant fracture compared to plate fixation techniques. Stress in the plate and nail fixations was shown to be affected by various parameters, such as the implant geometry, implant size, and implant material [16; 23; 28]. In the present study, all plate fixations revealed a different implant stress. Thus, the implant stress of the TEN was higher than that of the CAS and ACP but was lower than that of the SCP. As a reason for this result, the variation in the stress of the clavicle plates occurs from the different plate fixation locations and plate geometries. Thus, to reduce the stress in plate fixation devices, a clavicle plate system inserted into the anterior site of the clavicle bone, or a multi-plane fixation (a spiral plate), was suggested. In addition, increasing the number of locking screws for a clavicle plate system can reduce the implant stress and might also decrease the risk of an implant failure. The present study has a few limitations that need to be noted. First, the ideal linear elastic material models were applied to the fixation devices and bones during the finite element simulation. Relative comparisons between the different fixation techniques were suggested. Second, all muscles were modeled using tension-only springs and were considered passive elements. These assumptions might not be suitable for modeling the real behavior of the muscles. Third, the cross-sectional features and number of locking screws for the CAS were different compared to those of the SCP and ACP. The comparison between them might have a certain bias. Fourth, a load of 200 N was applied to the distal humerus of the right arm. This loading cannot represent the real loading of the human arm. Fortunately, forces generated by arm motion or activity can be considered in the finite element simulation if the magnitude and orientation of them can be defined. Fifth, a middle-third clavicle fracture with a normal bone quality was considered in this study. The study findings may not apply to all types of clavicle fractures and different bone quality. Fortunately, this methodology can be applied to various specimens to draw the treatment suggestion for general population with clavicle fractures. Finally, both the simplified single-clavicle models and the complete human upper-body skeleton models were derived from a commercial model. Thus, it is a solid model and can not control any CT settings or parameters.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 5 Empirical Results
### Empirical GNSS Error Analysis
Thus, one can empirically examine multipath effects in isolation from ionospheric effects. Figure 15 shows the results of this study based on all six data capture campaigns. Note that biases for all components are much smaller. It appears that for the test route chosen, non-zero-mean horizontal errors in the enhanced code phase positions were almost entirely driven by ionospheric modeling errors, and not by persistent effects of multipath due to the permanent structures along the test route. This is broadly consistent with the analyses presented earlier in this paper on position-domain biases due to ionospheric and multipath errors. However, it does appear that a bias due to multipath remained in the vertical direction over the first 400 m, even when ionospheric errors were removed. Apparently, the arrangement of buildings over this segment caused non-line-of-sight effects that did not average away. Mercifully, horizontal errors, which appear to be close to zero-mean over the six campaigns, matter most for high-accuracy digital mapping, since obstacle avoidance and vehicle coordination are largely 2-D problems, and since multiple vehicles can straightforwardly agree on a particular feature's relative vertical position from an inferred road surface. Figure 15: Errors in double-differenced pseudorange-based position estimates with respect to ground truth in the east, north, and up directions. Different colors distinguish data from six different campaigns. Dashed reference lines are drawn at \(\pm\)50 cm. The solid black lines show the mean positioning error over the six campaigns. The error standard deviation in the east and north directions is \(\sim\)0.9 m over the first 200 m along the path and \(\sim\)0.4 m over the rest. In the up direction, the standard deviation is \(\sim\)1.9 m over the first 400 m and \(\sim\)1 m over the rest. Based on Figure 15, one can conclude that multi-session averaging with a sufficiently accurate ionospheric model, such as the Fast PPP model, yields sub-50-cm global referencing accuracy for digital maps in the horizontal plane with code-phase-based GNSS, even in the presence of persistent multipath.
## 2 The Proposed Approach
### DL Ontology for AM Machine Selection  
A DL ontology for AM machine selection was created in Protege [61]. The DL ontology takes the alternative AM machines and attributes as inputs and the corresponding attribute values as outputs. Thus, the automatic acquisition of attribute values can be realized. In this paper, six AM processes (_EBM, SLM, LENS, DMLS, SLS, DMD_) and seven attributes (_Capability, Strength, Build Time, Density, Part Cost, Complexity, Hardness_) are used to describe the developed DL ontology. Fifteen top-level concepts are depicted in Figure 1. These concepts are all atomic concepts.  
To represent the data and knowledge for automatic generation of AM machine attribute values, an object relation named _hasAttribute_; seven data relations named _hasBuildTimeValue_, _hasCapabilityValue_, _hasComplexityValue_, _hasDensityValue_, _hasHardnessValue_, _hasPartCostValue_, and _hasStrengthValue_; and 13 individuals named _dmd_, _dmls_, _ebm_, _lens_, _slm_, _sls_, _buildtime_, _capability_, _complexity_, _density_, _hardness_, _partcost_, and _strength_ are also created in the DL ontology. Based on the created entities, an ontological view of automatic generation of AM machine attribute values is depicted in Figure 2.  
Figure 1: Graphical representation of fifteen top-level concepts in the DL ontology.
# Recent Advances in Nanoparticle-Mediated Treatment of Inflammatory Bowel Diseases
## 6 Site-Specific Drug Delivery and Nanoparticle Aggregation  
Nanoparticle-based targeted drug delivery and therapeutic efficacy were assessed in vivo in a murine model of colitis [24]. The authors also identified the mitigating effect of the nanocompounds using a clinical scoring system, the value of colon weight/length ratio and enzymatic activity measurements of myeloperoxidase.  
The phosphodiesterase-4 inhibitor, rolipram, was incorporated within PLGA-nanoparticles and given via the oral administration route (single daily dose for 5 consecutive days). The authors noticed a significant reduction in the clinical activity score and MPO activity following oral administration of rolipram-loaded nanoparticles or rolipram alone. While the latter was associated with greater side effects, rolipram-loaded NPs demonstrated to be efficient in reducing systemic drug absorption.  
Particulate systems made of polymers are used for targeted drug delivery in the treatment of IBD. These novel carriers are efficient in achieving preferential uptake into inflamed tissues. Some of their benefits include the ability of the drug to reach the intended site of action in higher concentrations, thus reducing side effects and enhancing therapeutic efficacy, and the sustained release that extends the effect of the drug as it resides at the site of inflammation for longer periods of time.
## 2 NB-IoT System Detailed Design and Implementation
### NPDSCH Receiver Chain Implementation
#### 2.2.11 Channel Decoder  
Forward error correction (FEC) is one of the techniques applied to detect and correct errors in the received data without the need for retransmissions. The convolution encoding with Viterbi decoding is considered a powerful method for the FEC, where the data are convoluted and then transmitted into a noisy channel. This process involves an insertion of redundant data, which helps in detecting and correcting errors due to those noisy channels. The Viterbi algorithm is the most powerful method for decoding the convoluted data in terms of bit error rates and efficiency, as it tracks down the most likely sequences that the encoder has gone through, and uses this information to deduce the original message [35].  
A \((k,n,m)\) convolutional encoder has \(m\) stages, \(k\) bits entering the shift register at each time instant and \(n\) output bits, which are formed using the following generator sequences [10]:  
\[G_{0}=[1011011],\quad G_{0}=1+2+8+16=133\quad\text{(Octal)} \tag{34}\]  
\[G_{1}=[1111001],\quad G_{1}=1+8+16+32+64=171\quad\text{(Octal)} \tag{35}\]  
\[G_{2}=[1110101],\quad G_{2}=1+4+16+32+64=165\quad\text{(Octal)} \tag{36}\]  
The encoder rate \(R\) is equal to \(k/n\).  
The Viterbi decoder applies a tree search procedure to optimally detect the received data sequence. It calculates a measure of similarity between the received signal, entering each state at time and all the trellis paths, and removes all candidates that are not possible based on the maximum likelihood (ML) choice. Each received sequence goes through a number of computations starting with branch metric computation, where the Hamming distance is calculated and is then followed by path metric computation, where errors are accumulated and stored to be used in the decision making. At the end of the received sequence, a trace back procedure starts from the path with the least number of errors [35].  
The decoding of the convolutional codes is done by applying the modified circular Viterbi algorithm, which performs several iterations of the Viterbi algorithm until a tail biting pattern is found. The decoding steps can be listed as follows [35]:  
* Start the Viterbi algorithm with initial metrics set to zero.
* Compute the survival path based on the path with the least accumulated metric.
* If this path is a tail biting path, decoding stops and the survival path is decoded.
* If the survival path is not a tail biting path, metrics are updated with final metrics of the previous iteration.
* Apply the Viterbi algorithm on the same message, until a new survivor path is obtained.
* If the new survivor path is a tail biting path, the algorithms stops.
* If maximum number of iterations is reached, the algorithm stops, and the best survival path is decoded.  
The decoded sequence is stored in a last in first out (LIFO) memory to be passed in order after the tail biting condition is checked correctly.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Link Layer
#### 5.2.3 Data Transmission, Aggregation, and Segmentation  
Downlink communications are generally executed through broadcast messages from the GW. Conversely, uplink communications are unicast and follow a multi-hop route. The _staggered wakeup pattern_ fits here perfectly with the approach of data aggregation in WSN. Thus, nodes attach their own data to that received from their children and all the information is jointly sent to the next hop (i.e., parent). If the total amount of data aggregated by an STA exceeds the maximum payload supported by the hardware, it is split into segments sent consecutively. The amount of data aggregated by an STA (from itself and from its children) is called _packet_. If this packet is split into different parts, each one of these parts is called _segment_. In case both terms can be indistinctly used, the current article will use _packet_.  
A selective ACK mechanism has been developed, so that, before the end of the allocated time slot, the receiver explicitly lists which segments in a stream coming from the same child are acknowledged. Upper layers are therefore responsible for making the sender retransmit only the missing segments in successive transmission windows.
# Polymer Models of Chromatin Imaging Data in Single Cells
## 4 Discussion  
In this work, we discussed the application of polymer physics models to investigate the mechanisms that establish the complex 3D structure of the genome as observed by recent single-cell imaging data [15]. We focused on two main, distinct physical processes that are supported by growing experimental evidence, i.e., loop-extrusion and phase-separation. In the first mechanism, an active motor (e.g., Cohesin or Condensin) extrudes DNA loops between specific anchor sites (envisaged as CTCF binding sites with opposite orientation) in an out-of-equilibrium process; in the second, chromatin contacts between distal genomic sites are established by diffusing molecular agents (such as transcription factors) or direct DNA interactions that, sustained by the thermal bath, spontaneously bridge their cognate sites.  
By using as benchmark super-resolution microscopy data available for a 2 Mb wide chromosome region in human fibroblast cells, we showed that both mechanisms can quantitatively recapitulate single-cell imaged conformations, indicating that they can coexist in shaping chromosome folding [28]. By allowing a deeper understanding of the mechanisms driving the organization of the genome, models from polymer physics can be used for real-world applications in real experimental contexts [48]. For example, validated polymer models can be efficiently employed to impute missing values or reduce noise effects in large imaging datasets. Additionally, and importantly, they can be employed to predict in silico the structural effects of disease-associated mutations, linked, for instance, to congenital disorders [11; 12] or cancer [13; 49].  
Overall, those studies show that novel data from microscopy can be complemented with quantitative models from physics to understand the mechanisms and function of  
Figure 4: Statistical significance of the RMSD analysis: (**a**) the RMSD distribution of the experiment-model best-matches (for each type of model) is statistically distinguishable from a control made of random pairs of imaged conformations; (**b**) less than 5% of the entries of the experiment-model best-match distributions are within the first 10% of the control. Adapted from [28].  
Figure 3: Example of experiment-model best-matching 3D structures, along with their corresponding single-molecule distance maps, as identified by the RMSD method. Adapted from [28].  
genome structure, paving the way for important and useful applications, such as the prediction of genomic perturbations on chromosome 3D architecture.  
Conceptualization, M.C., A.M.C., S.B., A.E., M.N., T.M. and F.V.; writing--original draft preparation, M.C.; writing--review and editing, M.C. and M.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks
## 1 Introduction
Studies suggest that hybrid models achieve higher accuracies than those of standalone algorithms, making them a promising method for improving phishing detection effectiveness. In summary, this study makes the following contributions:  
* Improvement in the accuracy and adaptability to evolving phishing behaviors by utilizing graph convolutional networks (GCNs) for feature extraction and combining them with SVMs. * The introduction of an innovative feature selection process using Manhattan similarity and the neighborhood random walk method, ensuring that the model can dynamically capture the relationships between features. * The use of the hinge loss function alongside similarity metrics to enhance the model's classification performance, improving its ability to discriminate between real and phishing sites. The rest of this paper is organized as follows. Section 2 examines recent phishing detection strategies, concentrating on a hybrid deep learning-based model. Section 3 describes the suggested model's functioning premise. Section 4 provides the evaluation dataset, experimental setup, model performance metrics, experiment outcomes, and discussions. Section 5 summarizes the findings and discusses future work.
# Integrated Adaptive Steering Stability Control for Ground Vehicle with Actuator Saturations
## 4 Simulation Verification
### Scenario 2: J-Turn Front Wheel Input with Sinusoidal Vertical Road Surface  
To further demonstrate the effectiveness of the proposed controller on steering stability under more severe circumstances, another scenario involving J-turn front wheel input and a sinusoidal vertical road surface was used as an additional simulation environment. The J-turn manoeuvre represents a type of sharp turning operation; the corresponding front wheel steering angle input is provided in Figure 12. The sinusoidal vertical road input is set as \(z_{rl}=0.01 sin(\pi t)\) for the left wheel and \(z_{rr}=0.01 cos(\pi t)\) for the right wheel. The controller parameters are kept identical to those in Section 4.1.  
The simulation results are shown in Figures 13-21. Figures 13-17 show the sprung mass dynamics and the corresponding active suspension control inputs, while Figures 18 and 19 show the the later motion dynamics. A detailed elaboration is not provided here due to the similarity between this scenario and the first scenario described in Section 4.1.  
Figure 11: State of the anti-windup compensator.  
Figure 12: Front wheel input for J-turn simulation.  
Figure 10: Yaw motion control input.  
Figure 16: Vehicle roll angular acceleration.  
Figure 14: Vehicle body vertical acceleration.  
Figure 13: Vehicle body vertical displacement.  
Figure 15: Vehicle roll angle.  
The yaw motion control input \(M\) together with the state of the anti-windup compensator \(\zeta\) are shown in Figures 20 and 21. Similarly, it can be seen that by feeding back \(\zeta\) the controller saturation can be alleviated, thereby reducing the control impact from actuator saturation.  
Figure 19: Vehicle yaw rate.  
Figure 17: Active suspension input.  
In short, the proposed controller can provide lower vehicle body vertical and roll angle acceleration, thus improving vehicle ride comfort, and simultaneously guaranteeing alleviation of yaw rate tracking performance degradation caused by actuator saturation.  
It should be noted that the yaw rate is taken as the controlled state and the sideslip angle is not considered in the proposed controller. On the one hand, the zero dynamics of the sideslip angle is proven to be stable by Equation (18); on the other hand, from [38], we know that a small lateral velocity and sideslip angle can be maintained under the condition that yaw rate is controlled to track its reference value. Furthermore, the road condition is not considered in this paper; for practical implementation, the reference yaw rate can be replaced by  
\[\gamma_{r}=\left\{\begin{array}{ll}\frac{v/L}{1+K\pi}\delta_{f}&if\left| \frac{v/L}{1+K\pi}\delta_{f}\right|<\mu g\\ \frac{\mu g}{v}\text{sgn}(\frac{v/L}{1+K\pi}\delta_{f})&otherwise\end{array} \right.. \tag{19}\]