esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Multi-Objective (DYMO) Auction-Based Task Allocation
### Bidding Rules
#### 3.5.3 Bid Based on Distance  
A robot \(R_{i}\) located at position \(p_{i}\) will bid on task \(T_{j}\) located at position \(p_{j}\) by computing the minimum distance between either its current location \(p_{i}\) or the position \(p_{k}\) of any unaccomplished task to the new task location \(p_{j}\) as described in Equation (4).  
This rule will guarantee that the synergy between tasks is considered to minimize the traveled distance. Synergy is a term used to describe the relationship between tasks; the tasks have a positive synergy if the total cost of executing all of them by one robot is less than if a different robot performs each task. We consider the spatial synergy in this work. If the total traveled distance for visiting two tasks using one robot is less than the total traveled distance if different robots visit each task, then these tasks have positive synergy, and vice versa; see Figure 2.  
The proposed DYMO-Auction algorithm considers tasks' spatial synergy. This ensures that if a robot wins a task \(T\), then it is more likely to win other tasks that are close to it. Therefore, a robot  
Figure 2: Synergy example: the optimal assignment is to assign \(T_{3}\) to robot \(R_{1}\) and \(T_{4}\) to robot \(R_{2}\) because they have negative synergy. Because \(T_{1}\) and \(T_{2}\) have positive synergy, they must be performed by one robot, \(R_{2}\).  
\(R_{i}\) located at position \(p_{i}\) bids on a task \(T_{j}\) located at position \(p_{j}\) by the minimum of (a) the distance from its location (\(p_{i}\)) and the task's location (\(p_{j}\)); (b) the minimum distance between the task's location (\(p_{j}\)) and any of the robot's assigned but uncompleted tasks' location (\(p_{k}\)), where the robot's assigned tasks are recorded in a special list denoted by \(l^{\prime}_{i}\). For example, in Figure 2 if the task \(T_{1}\) is assigned to the robot \(R_{2}\), following that, the robot \(R_{2}\) bids on the next task \(T_{2}\) by the distance between \(T_{1}\) and \(T_{2}\). Hence, the traveled distance cost that a robot will bid based on is given by Equation (4).  
\[d_{ij}=MIN(|p_{i}-p_{j}|,|p_{k}-p_{j}|)\;\forall\;T_{k}\;\in\;l^{\prime}_{i}. \tag{4}\]
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 6 GitHub Actions CI/CD Pipelines
### GitHub Actions Workflows
#### 6.3.1 Deployment Workflow (deploy-multi-infra.yml)  
The deployment pipeline orchestrates the complete process of building; approximating how much time it would take to complete; packaging; and deploying the application alongside provisioning its required Azure infrastructure, as shown in Figure 4.  
At a high level, this workflow involves the following stages.  
- Checking out the Repository: The workflow begins by fetching the latest code from the repository. This ensures both the application code and infrastructure as code (Bicep templates) are available to the pipeline.  
- Azure Login: Authentication consumes the secret. This step establishes a secure CII session against the Azure subscription, scoped to the permissions of the service principal.  
- Resource Group Creation or Validation: The pipeline ensures the target Azure Resource Group exists (or creates it if absent).  
- Container Image Building and Pushing: One of the distinguishing features of this pipeline is that it integrates application deployment with infrastructure provisioning. The container image is built from source. It is then pushed to Azure Container Registry (ACR), ensuring that the deployed container app always references a reproducible artifact.  
- Bicep Deployment: This step deploys the infrastructure described in Bicep, injecting environment-specific parameters. The result is a fully provisioned Azure environment, including Virtual Networks, Container Apps, PostgreSQL Flexible Server, ManagedIdentities, Azure AI Search, Private Endpoints, DNS Zones, and ACR integration. By integrating the build, push, and deploy stages, the workflow achieves application-centric infrastructure deployments, aligning with best practices for cloud-native development.  
The deployment pipeline is implemented via a GitHub Actions workflow that automates the provisioning of both the infrastructure and containerized application. It supports dynamic targeting of ACR and infrastructure components across environments (dev, QA, prod). The full workflow definition (deploy-multi-infra.yml) is included in Appendix H.
## 2 The Proposed SAR ADC  
The structure of the proposed N-bit SAR ADC is shown in Figure 1. It consists of a positive-conversion capacitor array, a negative-conversion capacitor array, a DAC driver circuit, sampling switches, a comparator, and SAR logic. Each conversion capacitor array is composed of a main array, a subarray, a unit array, and a bridged switch.  
The overall sequence of the N-bit SAR ADC is shown in Figure 2. The clock signal (CLK) and the reset signal (Reset) are provided externally to enable the whole ADC to function properly. The SAR ADC operates in three phases: the sampling phase, the comparison phase, and the shift register phase. In the sampling phase, the Reset goes through a CMOS inverter to produce the sample signal (Sample). In the comparison phase, theCLK and Sample enter the comparator to generate each comparison signal (CLK_comp), after which the results of the comparison (OUTP and OUTN) are output. In the shift register phase, the Reset is used as a start signal in the first bit-slice circuit of the SAR logic, while Valid is generated by the comparison results (OUTP and OUTN), causing the SAR logic to output comparison results (Pi and Ni), and at the same time, it outputs Q signals for shifting and latching.
# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 4 Results
### Computation Cost  
We also compared the real-time performance of several semantic-based dynamic SLAM methods. The specific tracking times and experimental equipment are listed in Table 7. Despite our lower computational power, our method maintains an average tracking speed of 20 FPS on the TUM-RGBD datasets. Considering the real-time requirements, we employed HFNet and YOLOv8, deploying them with TensorRT for performance optimization. Tasks were allocated to two CUDA streams for parallel processing, ensuring no conflicts between semantic information retrieval and feature point extraction. While subsequent geometric processing might be complex and potentially lengthen processing times, in most scenarios without complex rotations, directly utilizing HFNet-generated descriptors ensures that overall processing times remain manageable.
# Modeling Photovoltaic Potential for Bus Shelters on a City-Scale: A Case Study in Lisbon
###### Abstract  
The 2030 Agenda for Sustainable Development set 17 Sustainable Development Goals (SDGs). These include ensuring access to affordable, reliable, sustainable and modern energy for all (SGD7) and making cities and human settlements inclusive, safe, resilient and sustainable (SGD11). Thus, across the globe, major cities are moving in the smart city direction, by, for example, incorporating photovoltaics (PV), electric buses and sensors to improve public transportation. We study the concept of integrated PV bus stop shelters for the city of Lisbon. We identified the suitable locations for these, with respect to solar exposure, by using a Geographic Information System (GIS) solar radiation map. Then, using proxies to describe tourist and commuter demand, we determined that 54% of all current city bus stop shelters have the potential to receive PV-based solutions. Promoting innovative solutions such as this one will support smart mobility and urban sustainability while increasing quality of life, the ultimate goal of the Smart Cities movement.  
solar potential; photovoltaic; smart city; public space; GIS +
Footnote †: journal: Applied Sciences
# Intent to Adopt Location Sharing for Logging Safety Applications
## 3 Results
### Participant Background Information  
Demographic and employment results are summarized in Table 2. Our sample predominately comprised of loggers who were fifty or more years of age (55.4%) with over twenty years of logging experience (74.4%). Specific logger occupations varied, but the majority of respondents selected more than one job, including that of owner (39.4%), followed closely by loggers employed solely as equipment operators (30.3%). Most surveyed loggers worked on ground systems, including mechanized, partially mechanized, or a combination of both (63.1%); whereas only about one-quarter worked on cable systems (26.4%).  
In general, participants did not have previous exposure to LS devices on logging operations (Table 3), having the least experience with emergency receivers (e.g., SPOT, Garmin inReach). Seventy-two percent (72.4%) of surveyed loggers owned a smartphone (\(n=279\)), and of those that did, most (96.4%) reported carrying their phone with them while working on logging operations (\(n=201\)), with 52.7% \"always\" carrying their phone, 30.3% \"often\" carrying it, and only 13.5% \"rarely\" with it.  
Loggers strongly agreed that finding new ways to improve safety on logging operations is important (67.8% chose \"very important\" and 19.2% chose \"somewhat important\"), with only 1.8% of respondents marking it as unimportant (0.7% selected \"very unimportant\" and 1.1% selected \"somewhat unimportant\") (\(n=276\)). Using the Sign and Wilcoxon Sign-Rank tests with an alpha of 0.05, we rejected the null hypothesis that median response scores were less than or equal to 3 (\"neutral\"), indicating that loggers did perceive a significant importance to finding new safety solutions. \(p\)-values were less than 0.001 for both tests, with reported median (Sign) and pseudo-median (Wilcoxon Sign-Rank) values of 5 (\"very important\") and 5.0, respectively.
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
## 4 Results
### Evaluation against Rival Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Size** & \multicolumn{3}{c}{**Cost**} \\ \cline{3-5}  & & **LS [10]** & **HGVNS [23]** & **GRASP** \\ \hline \multicolumn{5}{c}{**Uniform Distribution**} \\ \hline \multirow{5}{*}{\(\alpha=1\)} & 10 & 286.82 & 289.82 & 285.69 \\  & 20 & 365.38 & 368.5 & 364.54 \\  & 50 & 550.38 & 559.20 & 554.94 \\  & 75 & 645.55 & 624.32 & 647.18 \\  & 100 & 729.40 & 698.42 & 731.10 \\  & Avg. & 515.51 & 508.05 & 515.16 \\ \hline \multirow{5}{*}{\(\alpha=2\)} & 10 & 231.29 & 233.20 & 230.75 \\  & 20 & 293.59 & 293.60 & 312.82 \\  & 50 & 428.63 & 420.80 & 478.31 \\  & 75 & 495.90 & 490.40 & 557.15 \\  & 100 & 572.53 & 553.43 & 632.30 \\  & Avg. & 404.39 & 398.29 & 437.65 \\ \hline \multicolumn{5}{c}{**Single Center**} \\ \hline \multirow{5}{*}{\(\alpha=1\)} & 10 & 379.29 & 364.90 & 399.55 \\  & 20 & 529.15 & 553.53 & 553.79 \\  & 50 & 763.28 & 784.32 & 779.33 \\  & 75 & 1017.04 & 978.32 & 1042.00 \\  & 100 & 1203.42 & 1193.95 & 1238.28 \\  & Avg. & 778.44 & 775.00 & 797.32 \\ \hline \multirow{5}{*}{\(\alpha=2\)} & 10 & 278.22 & 291.36 & 287.13 \\  & 20 & 384.87 & 364.00 & 416.47 \\ \cline{1-1}  & 50 & 554.58 & 593.50 & 617.43 \\ \cline{1-1}  & 75 & 741.38 & 754.40 & 861.21 \\ \cline{1-1}  & 100 & 891.28 & 900.10 & 995.72 \\ \cline{1-1}  & Avg. & 570.07 & 580.67 & 626.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the models obtained by the LS [10], a TSP-D version of the HGVNS [23], and the proposed GRASP HCLS variant, showing the average solution cost in _secs_.
## 2 Results and Discussion
### Phenolic Content and Antioxidant Capacity  
Phenolic compounds containing one or more hydroxyl groups (such as flavonoids, gallic acid, tannins, and hydroxybenzoic acid) are able to chelate metals, decompose hydrogen peroxide, and have the capacity to act as antioxidants. The total phenolic content, (0.7 mg GAE g\({}^{-1}\), where GAE is Gallic Acid Equivalent, see Table 1) fell between previously reported values in other cyanobacteria (0.54 mg GAE g\({}^{-1}\) extract measured in _Merismopedia_ sp. and 2.45 mg GAE g\({}^{-1}\) extract found in _Synechocystis salina_) [29; 30]. The phenolic content was reported to be slightly positively correlated with the DPPH (1,1-diphenyl-2-picrylhydrazil) inhibition assay and the ABTS (2,2-azino-bis-3-ethylbenzothazoline-6-sulphonic acid) antioxidant test [29]. The antioxidant capacity in these assays was high when compared to isolates of _Arthospira platensis_, _Lithothanmium calcareum_, _Synechocystis salina_, _Cyamboium_ sp., _Tychoonema_ sp., and similar to _Nodosilinea nodalosa_[30; 31]. Overall, the data suggest that _Halinricronema_ sp. has good antioxidant potential (Table 1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ASSAY** & **Mean \(\pm\) SD** \\ \hline Total Phenolic Compounds & \\ (mg GAE g\({}^{-1}\) extract DW) & 0.7 \(\pm\) 0.1 \\ DPPH Inhibition & \\ (PI\%) & 63 \(\pm\) 5 \\ ABTS assay & 52 \(\pm\) 4 \\ (PI\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total phenolic concentration and antioxidant profile. The data are the mean of three independent experiments, and the standard deviation (SD) is reported. PI is the Percentage of Inhibition.  
Figure 2: Biomass formation of cyanobacteria planktonic and biofilm fractions detected through chlorophyll measurements. _Halomicronema_ sp was grown in BA+ medium for 7 days under 16 h light and 8 h dark; temperature was constant at 23 \({}^{\circ}\)C for all the conditions tested. The strain was also tested in the presence of different chemicals: A = CTR the control condition, B = NaCl 340 mM, C = NaCl 680 mM, D = NaCl 1 M, E = MgSO4 (50 mM), F = MgCl2 (250 mM), G = EDTA (250 \(\mu\)M), H = CaCl2 (500 mM), I = Glucose 110 mM (2%), J = KH2 PO4 (125 mM), K = CaCl2 (250 mM), L = EDTA (2.5 mM), M = Na-acetate (50 mM), N = Na-citrate (50 mM), O = (NH4)2 HPO4 (100 mM), P = (NH4)HSO4 (100 mM), Q = KH2 PO4 (50 mM). The data represent the mean and the standard deviation of three independent biological replicates. A Student’s _t_-test was performed to compare the effects of the different chemicals. The effects were all significant, except for NaCl 340 mM (* was used for _p_-values < 0.05 and ** for _p_-values < 0.01; ns: not significant).  
EPS content was measured previously [32]. The results showed that soluble EPS ranged, depending on the growing conditions, between 47 and 188 mg L\({}^{-1}\), while for the bound EPS, the range was 38-211 mg L\({}^{-1}\). Similarly, the protein fraction of the EPS reached 2.4-12.4 mg L\({}^{-1}\) within the soluble EPS and could go up to 72-100 mg L\({}^{-1}\) within the bound EPS [32].
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation Communication Technologies
#### 2.2.3 Dedicated Short-Range Communications  
Dedicated Short-Range Communications (DSRC) is a wireless communication technology designed specifically for vehicle-to-anywhere (V2X) communication. It is a technology used in fee collection applications. It is one of the best examples that can be given about collecting fees with the Automatic Pass System (APS), which has been used in our country since 1999. In future ITS studies, vehicle-to-vehicle (V2V) will play an important role in vehicle-to-infrastructure (V2I) communication [24]. The data collection systems are shown in Figure 6.  
Figure 5: ITS communication systems [21].  
Figure 6: Data collection systems.
## Appendix C
### Error Localization and Correction. Computation Stage
\begin{array}{l}X^{\prime}{}^{(1)}_{3,4m_{1}}=\left|4\cdot 7+4\cdot 6 \right|_{5}=2_{(10)}=10_{(2)},\ X^{\prime}{}^{(1)}_{3,4m_{2}}=\left|2\cdot 7+1 \cdot 6\right|_{7}=6_{(10)}=110_{(2)},\\ X^{\prime}{}^{(1)}_{3,4m_{5}}=\left|9\cdot 7+9\cdot 6\right|_{11}=7_{(10)}=111_{(2 )},\ X^{\prime}{}^{(1)}_{3,4m_{6}}=\left|9\cdot 7+12\cdot 6\right|_{13}=5_{(10)}=1 01_{(2)}.\end{array} \right.\]  
\[\left. \begin{array}{l}X^{\prime}{}^{(1)}_{5,6m_{1}}=\left|3\cdot 1+1\cdot 2 \right|_{5}=0_{(10)}=0_{(2)},\ X^{\prime}{}^{(1)}_{5,6m_{2}}=\left|1\cdot 1+3 \cdot 2\right|_{7}=0_{(10)}=0_{(2)},\\ X^{\prime}{}^{(1)}_{5,6m_{3}}=\left|6\cdot 1+2\cdot 2\right|_{8}=2_{(10)}=10_{(2 )},\ X^{\prime}{}^{(1)}_{5,6m_{4}}=\left|6\cdot 1+3\cdot 2\right|_{9}=3_{(10)}=1 1_{(2)}.\end{array} \right.\]  
_Step 3. We calculate the missing remainders for each modular projection (Equation (19)). The modular projection \(X^{\prime}_{1,2}\):_  
\[\left|X^{\prime}_{1,2}\right|_{m_{3}}=\left|5+5\right|_{8}=2_{(10)}=10_{(2)}, \ \left|X^{\prime}_{1,2}\right|_{m_{4}}=\left|0+1\right|_{9}=1_{(10)}=1_{(2)},\]  
\[\left|X^{\prime}_{1,2}\right|_{m_{5}}=\left|1+9\right|_{11}=10_{(10)}=1010_{(2 )},\ \left|X^{\prime}_{1,2}\right|_{m_{6}}=\left|6+4\right|_{13}=10_{(10)}=1010_{(2 )}.\]  
_The modular projection \(X^{\prime}_{3,4}\):_  
\[\left|X^{\prime}_{3,4}\right|_{m_{1}}=\left|2+3\right|_{5}=0_{(10)}=0_{(2)}, \ \left|X^{\prime}_{3,4}\right|_{m_{2}}=\left|6+2\right|_{7}=1_{(10)}=1_{(2)},\]  
\[\left|X^{\prime}_{3,4}\right|_{m_{5}}=\left|7+8\right|_{11}=4_{(10)}=100_{(2)},\ \left|X^{\prime}_{3,4}\right|_{m_{6}}=\left|5+10\right|_{13}=2_{(10)}=10_{(2)}.\]  
_The modular projection \(X^{\prime}_{5,6}\):_  
\[\left|X^{\prime}_{5,6}\right|_{m_{1}}=\left|0+2\right|_{5}=2_{(10)}=10_{(2)},\ \left|X^{\prime}_{5,6}\right|_{m_{2}}=\left|0+4\right|_{7}=4_{(10)}=100_{(2)},\]  
\[\left|X^{\prime}_{5,6}\right|_{m_{3}}=\left|2+1\right|_{8}=3_{(10)}=11_{(2)},\ \left|X^{\prime}_{5,6}\right|_{m_{4}}=\left|3+1\right|_{9}=4_{(10)}=100_{(2)}.\]  
_Step 4. We calculate the Hamming distances between each modular projection in the RRNS and the distorted number (Equation (8)):_  
\[h\left(X^{\prime},X^{\prime}_{1,2}\right)=h\left( \begin{array}{c}\left(0,3,7, 6,1,2\right)\\ \left(0,3,2,1,10,10\right)\end{array} \right)=4,\]  
\[h\left(X^{\prime},X^{\prime}_{3,4}\right)=h\left( \begin{array}{c}\left(0,3,7,6,1,2\right)\\ \left(0,1,7,6,4,2\right)\end{array} \right)=2,\]  
\[h\left(X^{\prime},X^{\prime}_{5,6}\right)=h\left( \begin{array}{c}\left(0,3,7,6,1,2\right)\\ \left(2,4,3,4,1,2\right)\end{array} \right)=4.\]  
_Step 5. We choose a correct projection (See, Table A6).__Both correctness conditions (9) are satisfied for the modular projection \(X_{3,4}^{\prime}\) only. Therefore, the corrected number is \(X=X_{3,4}^{\prime}=15=(0,\,1,\,7,\,6,\,4,\,2)\), and the proposed error correction method works properly. End of example._
# Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network
## 2 Methods
### YOLO_v2  
The YOLO_v2 object detector is a one-stage detection network, which runs a CNN model (Figure 1) on an input image to produce feature images from a feature extraction layer. Then, these feature images are input into the YOLO_v2 detection layer and the classification and anchor box of the detection object are obtained. Therefore, the selection of the feature extractor model would be an interesting focus for object detection. It should be noted that the detailed feature extractor contains the convolution, pooling, and ReLU layers [18].  
Furthermore, anchor boxes (Figure 2) were adopted to detect classes of objects in the image. Anchor boxes are a set of predefined bounding boxes of certain heights and widths. These boxes (defined based on the object sizes of the samples) capture the scale and aspect ratio of the specific object classes to be detected. The use of anchor boxes significantly improves the efficiency of object detection [37], making real-time detection possible.  
Figure 1: The YOLO_v2 architecture.  
For the detection layer, the YOLO_v2 predicted the location of the anchor box by the following four steps (Figure 2):  
Step 1: Obtained feature images from the feature extractor;  
Step 2: The predefined anchor boxes were tiled across the image;  
Step 3: To generate the final object detections, tiled anchor boxes that belonged to the background class were removed;  
Step 4: The location error (the distance between refined and predefined anchor box) was gradually reduced by minimizing the loss function. As shown in Figure 2, the upper left coordinates of the predefined anchor box were \((x_{1},y_{1})\), and the refined anchor box was \((x_{2},y_{2})\). During training, the precise location of the anchor box was obtained by minimizing the loss function (squared error loss, \(SEL\)):  
\[SEL=(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2} \tag{1}\]  
Finally, the most suitable anchor box (crack location) and the highest classification score (crack or background) were obtained. As a result, the YOLO_v2 predicted the class probability (i.e., how precisely the model found the object) and the crack location (i.e., using anchor box) for each image.
# Numerical and Experimental Investigation of Recycled Brick Coarse Aggregate Concrete
## 3 Results and Discussion
### The Impact of RCA Replacement Rate  
The representative stress-strain curves were determined by the elastic phase, the stress-strain peak, and the curve descent section. The four characteristic indexes of compressive strength (\(\sigma\)), peak strain (\(\epsilon\)), elastic modulus (\(E\)), and the ratio of ultimate strain to peak strain (\(\epsilon_{\text{u}}/\epsilon\)) were selected to determine the concrete constitutive curve. The constitutive curve includes three significant parameters: the maximum stress value, the straight line in the elastic stage, and the endpoint of the falling section of the curve. The ultimate strain corresponds to 0.85 times the maximum stress after the peak. The elastic modulus is the secant modulus between the origin and 0.4 times the maximum stress [29].  
According to the stress-strain relationship curve obtained by numerical simulation, the compressive strength (\(\sigma\)), peak strain (\(\epsilon\)), elastic modulus (\(E\)), and ultimate strain ratio to peak strain (\(\epsilon_{\text{u}}/\epsilon\)) were studied under different RBCA substitution rates. The variation trend of compressive strength negatively correlates with the RBCA replacement rate, as shown in Figure 13a. The compressive strength of RBAC concrete with a 100% replacement rate is 28% lower than that of ordinary concrete. This reduction in strength can be attributed to the interface and the old mortar that initially existed in the RBCA. According to the research results on the compressive strength of RBCA concrete with different contents in this paper and in the previous literature [22], the elastic modulus of RB-0 is observed to be higher than that of all RBCA specimens, as shown in Figure 13b. On the other hand, the elastic modulus of RBCA concrete is 19% lower than that of ordinary concrete when the replacement rate of RBCA is 100%, which is mainly related to the low stiffness of RBCA and the interface transition zone according to the change results of the elastic modulus of RBCA concrete with different substitution rates in both this paper and in the literature [30,42,43]. Figure 13c shows that the peak strain increases when the replacement rate of recycled aggregate increases according to the evolution law of peak strain of RBCA concrete with different content, consistent with the findings in this paper and in the literature [21,30]. However, the ratio of the limit strain to peak strain decreases with increased recycled aggregate content, as shown in Figure 13d. This phenomenon indicates that ductility increases with the recycled aggregate replacement rate.