esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Eye Movement Predictive Models for Reading Comprehension by Reading Conditions and Text Types
A model that predicts inferential comprehension included scanpath length and AFD, significantly accounting for 56% of the variance (\(F(3,143)=63.34\), \(p<0.001\)). Age (\(\beta=0.61\), \(t(143)=9.60\), \(p<0.001\)) and scanpath length (\(\beta=0.15\), \(t(143)=2.27\), \(p=0.025\)) exhibited a positive and significant impact on inferential comprehension, while AFD (\(\beta=-0.12\), \(t(143)=-1.89\), \(p=0.061\)) had a negative and non-significant impact. The predictive models for literal and inferential comprehension according to reading conditions and text types are presented in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **Dependent Variables** & **Predictors** & \(\beta\) & **Std. \(\beta\)** & \(t\) & \(p\) & \(R^{2}\) & **Adj. \(R^{2}\)** \\ \cline{2-9} \multirow{2}{*}{R\_F} & Literal comprehension & Age & 0.20 & 0.47 & 6.39 & \(<\)0.001 & 0.22 & 0.21 \\ \cline{2-9}  & Inferential comprehension & Age & 0.02 & 0.50 & 7.13 & \(<\)0.001 & 0.38 & 0.37 \\  & & TFD & \(-0.21\) & \(-0.22\) & \(-3.21\) & 0.002 & & \\ \hline \multirow{4}{*}{R\_NF} & Literal comprehension & Age & 0.02 & 0.53 & 7.32 & \(<\)0.001 & 0.38 & 0.37 \\  & & AFD & \(-0.21\) & \(-0.19\) & \(-2.63\) & 0.010 & & \\ \cline{2-9}  & & Scanpath length & 0.12 & 0.12 & 1.73 & 0.086 & & \\ \cline{2-9}  & & Inferential comprehension & Age & 0.03 & 0.61 & 9.50 & \(<\)0.001 & 0.47 & 0.47 \\  & & TFD & \(-0.20\) & \(-0.21\) & \(-3.24\) & 0.002 & & \\ \hline \multirow{4}{*}{AR\_F} & Literal comprehension & Age & 0.02 & 0.46 & 6.13 & \(<\)0.001 & 0.24 & 0.22 \\ \cline{2-9}  & & TFD & 0.16 & 0.21 & 2.73 & 0.007 & & \\ \cline{2-9}  & & Age & 0.02 & 0.39 & 5.08 & \(<\)0.001 & 0.27 & 0.25 \\ \cline{2-9}  & & Isernath length & 0.13 & 0.15 & 1.81 & 0.073 & & \\ \cline{2-9}  & & AFD & \(-0.12\) & \(-0.14\) & \(-1.76\) & 0.081 & & \\ \hline \multirow{4}{*}{AR\_NF} & Literal comprehension & Age & 0.02 & 0.55 & 7.62 & \(<\)0.001 & 0.46 & 0.45 \\  & & Scanpath length & 0.10 & 0.11 & 1.42 & 0.159 & & \\ \cline{1-1} \cline{2-9}  & & TFD & 0.13 & 0.14 & 2.06 & 0.041 & & \\ \cline{1-1} \cline{2-9}  & & AFD & \(-0.13\) & \(-0.13\) & \(-1.72\) & 0.088 & & \\ \cline{1-1} \cline{2-9}  & & Age & 0.03 & 0.61 & 9.60 & \(<\)0.001 & 0.57 & 0.56 \\ \cline{1-1} \cline{2-9}  & & Isernath length & 0.14 & 0.15 & 2.27 & 0.025 & & \\ \cline{1-1} \cline{2-9}  & & AFD & \(-0.12\) & \(-0.12\) & \(-1.89\) & 0.061 & & \\ \hline \end{tabular}
\end{table} 
Table 3: Eye movement predictive models for reading comprehension in each reading condition and text type. R: reading-only; AR: audio-assisted reading; F: fiction; NF: non-fiction; TFD: total fixation duration; AFD: average fixation duration.
# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 3 The Proposed Deep Crack Segmentation Approach
### Segmentation-Based Variant U-Net Models
#### 3.4.4 Utilized U-Net-Based Models  
* **Deep ResU-Net model:** In this paper, a nine-level architecture of deep ResU-Net is utilized along with two different loss functions for pixel-by-pixel crack detection. All levels are built with residual blocks comprising two \(3\times 3\) convolution blocks and an identity mapping connecting both the input and output of the block.
* **ResU-Net++ model:** The original architecture of ResU-Net++ is utilized along with two different loss functions for pixel-by-pixel crack detection with the filter numbers [16; 32; 64; 128; 256]. The filter number was selected based on experiments.
* **U2-Net model:** Moreover, the original architecture of \(U^{2}\)-Net is utilized along with two different loss functions for pixel-by-pixel crack detection.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 5 Testbed Deployment and Experimentations
### Evolution of the Number of Packets in the Network  
Figure 13 depicts the evolution of the number of packets in the network, that is the total number of packets that nodes are buffering for transmissions.  
Figure 12: Average percentage of sent data packet per node.  
Figure 13: Evolution of the number of packets in the network.  
We compare two values of \(\alpha\): 0 and 40. In the INFR scenario, the number of packets in the network oscillates very much; as the packets generation rate is low, their total number rapidly decreases in the network (maximum number is equal to 4 packets distributed over all nodes). The plot with \(\alpha=40\) reaches higher amplitudes and higher width than the plot with \(\alpha=0\) in all scenarios. The difference in amplitude is due to the network congestion and to the duty cycle. However, the number of packets is the network oscillates less in MED_N_ADAP scenario and it reaches its maximum values before starting to decrease. This means, as highlighted above, that some packets are more blocked in this scenario than in MED_ADAP scenario. Also, higher values of \(\alpha\) increase the packets sojourn time in the network leading to higher end-to-end delays; which is shown by the larger width of the plots for \(\alpha=40\).
## 2 _Introduction_
However, most of the research in the literature has focused on increasing the strength of beams or columns in the joint area; the location of the plastic hinge in the retrofitted joint has not been considered, even though the occurrence of a plastic hinge in the beam-column connection is the most critical threat to the functioning of the joint when the structure is subjected to catastrophic excitations. Hence, implementing an appropriate retrofitting method is required to prevent plastic hinge formation in the beam-column joint area. Therefore, the main aim of this research is proposing a new retrofitting scheme for beam-column connections in the RC frame using CFRP to relocate and shift plastic hinge from near the face of the column to the further length of the beam. Then, the joint is protected against applied excitations and able to function properly to provide structural safety and integrity. For this purpose, in the present study, the newly proposed retrofitting scheme has been applied to the RC joint (beam-column connection), and experimental and numerical investigations have been carried out. Accordingly, two full-scale RC interior joints from a moment-resisting frame with moderate ductility have been cast and experimentally tested, and the results were used to validate the numerical solutions and to evaluate the performance of the CFRP flange-bonded retrofitting scheme. Furthermore, a parametric study has been conducted to find the optimal thicknesses of the CFRP sheets for relocating the plastic hinges from near the face of the column to further along length of the beam.
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 2 Background Review and Related Works  
### LoRa Low Power Consumption Network  
LoRa means \"remote\" and is extended by the LoRa Union remote wireless communication system. This system is designed for equipment powered by batteries with long life spans that are critical to energy consumption [15,16]. The uplink uses a significant amount of data to send the measurement data, while the downlink is primarily used for acknowledgment in many applications [17].
## 3 Approximate Inverse Kinematics Solution for Imposed End-Effector State
### Calculation of Arc Segments' Endpoints and Orientations
The estimated vector \(\mathbf{p_{N\mathrm{e}2}}^{\mathrm{T}}\) lies in the first segment coordinate system. Getting the \(x\)-\(y\) coordinates requires applying Equations (22)-(44) similarly for the second section. The estimated second segment's endpoint and its endpoint's orientation vector exist in the first segment coordinate system (Figure 12). Translation of the second robot segment endpoint \(P_{2}{}^{\mathrm{T}}\), arc center point \(C_{2}{}^{\mathrm{T}}\), and orientation vector \(\mathbf{s_{orien2}}^{\mathrm{T}}\) back to the second segment coordination system requires their rotation according to the first segment counter-clockwise quaternion rotation matrix in Equation (41). After that, \(P_{2}{}^{\mathrm{T}}\), \(C_{2}{}^{\mathrm{T}}\) and \(\mathbf{s_{orienN2}}^{\mathrm{T}}\) add to the first segment endpoint \(P_{1}\). The second robot arc-like segment's position and orientation are evaluated in the second segment coordinate system (Figure 13). Figure 12: Second segment endpoint \(P_{2}{}^{\mathrm{T}}\) and orientation vector \(\mathbf{s_{orienN2}}^{\mathrm{T}}\) in the first segment’s coordinate system. Figure 13: Second segment endpoint \(P_{2}\) and orientation vector \(\mathbf{s_{orienN2}}\) in second segment’s coordination system. These second segment calculations apply similarly to the third robot segment. The only difference in this situation is that, in the beginning, vector \(\mathbf{p}_{\text{ec2}}\) needs to reduce vector \(\mathbf{p}_{\text{ec3}}\) to get \(\mathbf{p}_{\text{ec23}}\) (\(\mathbf{p}_{\text{sec23}}\)), which starts from point \(O=(0,0,0)\). Then, \(\mathbf{p}_{\text{sec23}}\) needs to be rotated clockwise with the first segment quaternion rotation matrix \(\mathbf{R}_{\text{alt1}}\) and then with the second segment quaternion rotation matrix \(\mathbf{R}_{\text{alt2}}\). For this purpose, similar calculations using Equations (22)-(44) are necessary. The transposition of the third robot segment endpoint \(P_{3}\text{T}\), arc center point \(C_{3}\text{T}\), and orientation vector \(\mathbf{s}_{\text{orien3}}\text{T}\) (Figure 14) back to the third segment coordination system is then necessary. These points need to be rotated first with the second segment counter-clockwise quaternion rotation matrix and then with the first segment counter-clockwise quaternion rotation matrix. The third segment endpoint \(P_{3}\), arc center \(C_{3}\) point, and orientation vector \(\mathbf{s}_{\text{orienN3}}\) add up to the second segment endpoint \(P_{2}\). The third robot segment or robot end-effector position and orientation endpoint vector lie in the third segment coordinate system, as shown in \[\overset{\wedge}{\mathbf{s}}_{\text{orien1}}=\mathbf{R}_{1}p_{\text{r1}}. \tag{44}\]0
## 6 Experimental Results and Discussion
### Performance of Methods with Different Number of in-Context Triples  
Previous experiments have demonstrated that in-context triples retrieved by the proposed method can enhance the reasoning of LLMs. In this experiment, we discuss the performance when introducing different numbers of in-context triples. The experiments are conducted on the validation set of FB15k-237. The numbers of in-context triples are set to 1, 3, 5, 7, and 9, respectively. The results are depicted in Figure 4.  
Figure 4 demonstrates the MRR (Figure 3(a)), Hits@1 (Figure 3(b)), and Hits@10 (Figure 3(c)) of the reasoning results when varying the number of in-context triples. It can be observed that as the number of in-context triples increases from 1 to 5, the reasoning performance gradually improves. However, as the number continues to grow beyond 5, there is a slight decline in the reasoning results. This phenomenon may be attributed to the fact that when the number of in-context triples becomes larger, more noise may be introduced into the reasoning and consequently deteriorate the results. Taking the query triple in Figure 1 as an example, when the number of in-context triples are set to 5,  
Figure 4: Performance of methods with different number of in-context triples, including (**a**) MRR, (**b**) Hits@1, and (**c**) Hits@10.  
Figure 3: Performance on entities with different frequencies, including (**a**) MRR, (**b**) Hits@1, and (**c**) Hits@10.  
the extracted in-context triples are ([PERSON], works at, University of Colorado Boulder), (University of Colorado Boulder, is located in, Colorado), ([PERSON], was born in, Palo Alto), (Palo Alto, is located in, California), ([PERSON], graduated from, Massachusetts Institute of Technology), these triples are enough to provide necessary clues for reasoning. If the number is set to 9, four more triples ([PERSON], has won prize, King Faisal International Prize), ([PERSON], has friend, [PERSON]), ([PERSON], nationality, Germany), ([PERSON], graduated from, Heidelberg University) are extracted, and it is obvious that they are not helpful for reasoning. In summary, the optimal performance is achieved when the number of in-context triples is set to 5.
# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 5 Conclusions and Future Works  
In this article, we carried out a comprehensive survey focusing on time-sensitive applications implemented in fog computing architectures. The aim of our literature review was to get the state of the art and identify how the concept of real time is being approached by the scientific community and which applications are being studied and implemented.  
For this, we propose and execute a research protocol in order to answer two questions: How many articles propose the use of fog computing architecture and a real-time solution? and How do the authors conceptualize real-time or time constraint when their proposals are based on fog architecture? We searched the articles in the Scopus and Web of Science databases, which returned 1730 articles, of which 95 answered our research questions.  
We performed an analysis of the selected articles and obtained the number of publications made by year and country, as well as by type of publication (journal or conference) and publishers. In addition, we observed a pattern in the way the concept of real-time was presented by the authors, which allowed us to classify the articles into five categories, which are: \"Fog Computing Concept\" (38 articles), \"Faster Response\" (20 articles),\"Low Latency\" (13 articles), \"Data Streaming Application\" (8 articles), and \"Time, Delay or Latency Constraint\" (16 articles). Additionally, we check the type of application implemented in the proposals. The three most used applications were: Health (18 articles), Smart Vehicular (17 articles), and Smart City (13 articles).  
As a future work, we intend to design and model an environment with a fog computing configuration so that it presents near real-time response time. In other words, we intend that the model allows the processing of the data to be carried out immediately after its arrival and that the environment presents a best effort so that the response to the processing is within acceptable times and/or delays.  
Figure 7: Tag cloud from the keywords used in the survey of the publications.  
**Author Contributions:** Conceptualization, E.G.; methodology, E.G.; validation, E.G., M.D. and C.D.R.; formal analysis, E.G., M.D. and C.D.R.; data curation, E.G., F.C.; writing--original draft preparation, E.G., M.D., C.D.R.; writing--review and editing, M.D., C.D.R., F.C., P.P., visualization, M.D., C.D.R., F.C., P.P., supervision, M.D.; co-supervision, P.P.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES)-Finance Code 001.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 2 Related Work
### Recent Trends  
The recent literature indicates a growing interest in QoS-aware solutions that utilize machine learning techniques. Machine learning is recognized as an effective approach that enhances performance through training, observation, and analysis of historical data, enabling predictive capabilities regarding future outcomes based on accumulated data. A systematic review by [PERSON] [35] offers insights into various performance parameters. The key QoS parameters discussed in this context include scalability, throughput, energy consumption, latency, packet loss ratio, packet error ratio, reliability, availability, maintainability, bandwidth, jitter, cost, bit error rate, periodicity, priority, deadlines, confidentiality, integrity, safety, and security.  
Ensuring robust QoS is crucial in the realm of next-generation 6 G-enabled devices. Effective QoS protocols are essential for optimizing network performance and enhancing user experience quality. The authors of [48] proposed a reinforcement-learning-based multi-objective optimization method for addressing QoS in edge-enabled WSNs and the IoT, focusing on simultaneously optimizing objectives such as energy consumption, latency, throughput, and coverage to achieve Pareto-optimal solutions.  
_The key challenges_ associated with this topic encompass the feasibility of QoS requirements, inadequate formulations, oversimplification, the homogenization of diverse scenarios, dynamic network conditions, unstable routing paths, and substantial overloads.  
_The noteworthy solutions_ to tackle these issues involve traffic classification, prioritization, centralized computations, leveraging fog and edge resources, hierarchical organization, clustering, applying machine learning techniques, and implementing multi-path routing strategies. The subsequent section will elaborate on the objectives of our recent investigation into QoS routing in WSNs.
# Object-Environment Fusion of Visual System for Automatic Pear Picking
## 2 Materials and Methods
### Object Perception  
#### 2.2.1 Instance Segmentation  
In this section, instance segmentation is applied to identify all pears in an RGB image, which generates the confidence, bounding box, and masks for each instance, as shown in Figure 3. We aim to calculate the 3D space of the pear as accurately as possible. In this case, instance segmentation provides more detailed information, since each bounding box only includes the pixels of the object, whereas other bounding boxes additionally contain the pixels of other objects.  
As shown in Figure 4, instance segmentation is affected by occlusion and illumination, which result in failure to identify the object. To select the instance segmentation network, we compared some models used to identify pears: Mask R-CNN [14], YOLOv5 [37], and YOLOv8 [38]. The detailed experiments are presented in Section 3.2. Moreover, we employed Mask R-CNN with a backbone of ResNet-50 [22] and Swin Transformer [24] to analyze their performance. ResNet-50 is a convolutional neural network, and Swin  
Figure 1: The overall workflow of the object–environment fusion visual system.  
Figure 2: Object localization under different feedback signals.  
Transformer is notable for its use of self-attention to process data. In contrast, Swin Transformer has better performance with contextual information communication, but it brings high computation costs.
## 3 Data Processing
### Web Interface  
Output results are displayed into a private web interface, which has been implemented with OpenSource technologies compliant with OGC. The interface shows the processing results in a simple and handy way through a GIS, which is implemented with open source tools (MapServer and OpenLayers) that adopt geographic standards (WMS and WFS). Users can visualize the information extracted from every single product or review the acquisition data of future products (see Figure 9). Different interfaces have been designed for element filtering that allow the users to customize the information displayed in the web GIS and included in the reports  
Figure 8: Examples of two types of reports that can be generated.  
Figure 9: Web GIS support for SIMONS with single products and future products planning.
## 4 Discussion
These receptors are activated by binding certain nucleotides that are released from injured neurons [76], and then activated in microglia [77]. Various pro-inflammatory mediators are generated by the activated microglia such as cyclooxygenase-2 (COX-2), tumor necrosis factor (TNF-a), and interleukin 6 (IL-6) [78], leading to neuronal damage, disability of tissue renovate and neuronal lesions [79]. A previous study reported that over concentration of A\(\beta\) in the brain tissue leads to the over expression of P2Y12R and P2X7R in the brain tissue [16]. Another apoptotic pathway of Cis is the inhibition of XIAP. XIAP belongs to the IAP family [80]. Reference [81] stated that XIAP has a powerful neuroprotective effect by reducing cerebral apoptosis. Our results showed that Cis administration diminished cerebral XIAP concentration with a concurrent up-regulation of caspase-3 which activates apoptotic cell death through a pathway of signal transduction Figure 7. The down-regulation of XIAP inhibits the phosphorylation of the tropomyosin linked kinase-B (TrkB) receptor; one of the most important membrane receptors of BDNF [82] and this could explain the significant reduction of BDNF expression in Cis treated rats. Added to that, [PERSON] stated that too much accumulation of A\(\beta\) reduces the BDNF expression [83]. The reduction of BDNF inhibits Bcl-2 which is an important factor inhibiting apoptosis. Thus, caspase-3 is augmented and the intrinsic pathway of apoptosis is initiated in the brain tissue [84]. From what has been previously mentioned, using anti-apoptotic agent such as _Ginkgo_ is a logic approach to protect against cis induced cerebral apoptosis. _Ginkgo_ has a powerful protective effect against the induced neuronal apoptosis which may be developed due to many sets of conditions, such as oxidative stress [85], and aggregation of A\(\beta\)[86]. Our data reported that co-administration of _Ginkgo_ and Cis reduced APP causes a consequent reduction of A\(\beta\) accumulation in the brain tissue [87; 88]. Our obtained results showed a significant reduction of P2Y12R, P2X7R and up-regulation of XIAP in the Ginkgo+Cis group. These changes were followed by a significant increase of BDNF. The overexpression of BDNF is in accordance with [89] who reported that _Ginkgo_ protects against acrylamide induced brain toxicity. The increased BDNF expression promotes the neuronal regeneration [89], inhibits many inflammatory mediators, up-regulates anti-apoptotic proteins such as Bcl-2 [90], thus blocking apoptotic markers such as caspase-3 [91].
## 6 Discussion
### UE Provision  
ICN supports UE provision by assigning a unique name to support the data transmission and service delivery under heterogeneous access technologies using the connectionless communication model. In our ENRS, to provide differential low-latency name resolution, some extra parameters are needed, for example, latency requirement parameter \(T_{d}\). The UE should be able to configure these parameters in the name registration and name resolution requests. What is more, the service-related parameters may transmission cross-layer from the application layer to the network layer. Thus, specific inter-layer parameter mapping and adaptation mechanisms should be proposed to improve the Quality of Service(QoS). To solve the cross-layer incongruity issue on the current Internet architecture, the interoperability with IP-based infrastructure and ICN-based infrastructure also should be considered.
## 4 Simulation Results
To further investigate the effectiveness of the controller, an additional comparative simulation which removes the fault-tolerant mechanism is carried out. The tracking response of the controller is shown in Figure 7. It seems that the displacement tracking is also achieved without the help of the fault-tolerant mechanism. However, from Figure 8, we can obtain that the tracking performance is degraded during \(2\leq t\leq 3\) and even beyond the constraint bounds when \(4\leq t\leq 5\). This is because the controller cannot properly adjust its output when the actuator fault occurs. Figure 3: Curves of the geometric transformation. Figure 4: Curves of the displacement tracking. Figure 5: Curves of the tracking error. Figure 6: The commanded controller and actual controller.