esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 MATLAB Simulations  
MATLAB simulations are presented to verify the effectiveness of the proposed approaches. The robot's speed is 2 m/s. The sample duration is set as \(dt=5\) s, and \(k_{p}=1000\) is utilized in the reactive collision evasion control. One uses \(\delta=2\) s as the constant associated with the maneuver of an elevator.  
Recall that the robot is near-collision at the moment when the distance between the robot and any _detectedPoint_ is shorter than the safety margin \(r(k)\). In (3), one sets \(r_{0}=2\), and \(\mu=0.01\). In MATLAB simulations, a true collision happens at the moment when the distance between the robot and any _detectedPoint_ is shorter than 1 m, which is shorter than the safety margin \(r(k)\). A MATLAB simulation ends when a true collision happens.  
As the maximum sensing range of the robot, one sets \(r_{m}=20\) m. One further simulates the measurement noise in the robot's local range sensors. In Algorithm 1, every _detectedPoint_\(\in\)_PointSet_ is perturbed by adding a Gaussian noise with mean 0 and standard deviation 0.1. In this way, one simulates the case where the robot's local range sensors have some measurement noise.  
In practice, the robot's motion is perturbed due to environmental effects, such as wheel slippage. Therefore, instead of (2), the robot's motion model is set as  
\[\mathbf{q}(k+1)=\mathbf{q}(k)+\mathbf{v}(k)*dt+\mathbf{n}(k), \tag{13}\]  
where \(\mathbf{n}(k)\) denotes the process noise at sample-step \(k\). It is assumed that \(\mathbf{n}(k)\) has a Gaussian distribution with mean 0 and standard deviation 0.1. By setting \(\mathbf{n}(k)\), one simulates the process error in the robot's motion command.  
One considers the case where the robot moves from one floor \(\alpha\) to another floor \(\beta\). Recall that the robot can utilize elevators to move from one floor to another floor using the vision-based manipulating approach in [7; 8].  
The robot's initial position is \((200,150)\) at \(\alpha\) floor. At \(\alpha\) floor, we have multiple objects, which do not move. We have an elevator which connects \((370,120)\) of \(\beta\) floor with \((370,120)\) of \(\alpha\) floor. The goal of the robot is \((350,470)\) at \(\beta\) floor. At \(\beta\) floor, we have multiple objects.  
The designated path is built using the building Voronoi graph (blue line segments in the following figures). Then, the robot moves along the designated path while utilizing reactive collision-avoidance control in Section 5 if necessary.  
We run 20 Monte-Carlo (MC) simulations to verify the robustness of the proposed approaches. The robot reaches its goal without collision in all MC simulations.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 3 LSTM-CRF Model with the Integrity Algorithm
### Integrity Algorithm
#### 3.3.3 Algorithm
```
1:Input:\(\mathbf{L}\mathbf{S}_{\text{tag}}\), \(\mathbf{R}_{\text{tag}}\), \(i\)
2:Output:\(\mathbf{L}\)
3:Take the result of attribute tagging as an example
4:for (\(i=0\); \(i<\mathbf{R}_{\text{tag}}\),length; \(i++\))
5: set \(\mathbf{R}_{\text{tag\_begin}}=0\), \(\mathbf{R}_{\text{tag\_end}}=0\)
6: if (\(\mathbf{R}_{\text{tag}}\) [\(i\)] = O)
7: R\({}_{\text{tag\_begin}}=i\);
8: while (\(\mathbf{R}_{\text{tag}}\) [\(i\)]! = O)
9:\(i++\); R\({}_{\text{tag\_end}}=i\);
10: flag = true;
11: for ls \(\in\mathbf{L}\mathbf{S}_{\text{tag}}\) [\(\mathbf{R}_{\text{tag\_begin}}\): \(\mathbf{R}_{\text{tag\_end}}\)]
12: if ls! = O or ls! = \(\mathbf{R}_{\text{tag}}\) [\(i\)]
13: flag = false;
14: if (flag)
15: LS\({}_{\text{tag}}\) [\(\mathbf{R}_{\text{tag\_begin}}\): \(\mathbf{R}_{\text{tag\_end}}\)] = \(\mathbf{R}_{\text{tag}}\) [\(\mathbf{R}_{\text{tag\_begin}}\): \(\mathbf{R}_{\text{tag\_end}}\)]
16: L\(\leftarrow\) LS\({}_{\text{tag}}\)
17: return L
```  
**Algorithm 1** LSTM-CRF model with integrity algorithm
## 2 Method
### Life Cycle Cost Calculation Method  
The procedure and calculation methodology for LCC follow the standard DS/EN 15459-1:2017 [9]. Overall, a calculation model consists of boundary conditions and building-related model inputs. Boundary conditions include the calculation period and macro-economic factors for the calculation, i.e., interest rate and price development. The model input can be split into different categories, depending on the cost type, e.g., energy supply, acquisition, management, cleaning, component, recurring and non-recurring costs. Income from rent, incentives, loans, or grants can also be integrated into an LCC calculation. Commonly evaluated outputs of LCC are global cost (GC) or net present value (NPV), depending on the objective of the calculation. Global costs are calculated using Equation (1), shown with the same notation as given in standard [9]. The main difference between GC and NPV is that GC includes emission costs by definition. Those, however, are often excluded, and therefore the NPV can be considered the same as GC. Carbon costs are excluded from the analysis of this study as the monetary value of carbon costs and their future development is still quite uncertain, which applies to the consistency and comparability as well [28].  
\[GC=CO_{INIT}+\sum_{j}\left[\sum_{i=1}^{TC}\left(CO_{a(i)}(j)*\left(1+ RAT_{xx(i)}(j)\right)+CO_{CO_{2}(i)}(j)\right)*D_{f(i)}+CO_{fin(TSL)}(j)- VAL_{ft_{TC}}t(j)\right] \tag{1}\]  
where:  
* \(CO_{INIT}\)--initial cost;
* \(CO_{a}\)--annual cost;
* \(CO_{CO_{2}}\)--emission cost;
* \(CO_{fin(TSL)}\)--disposal cost;
* \(VAL_{ft}\)--residual value;
* \(D\)_f_--discount factor;
* \(t_{TC}\)--calculation period;
* \(RAT_{xx(i)}\) price evolution of parameter \(i\).  
To a great extent, the model inputs are dependent on the aim, scope, and purpose of the performed study. Since this study adopts a baseline from previous research of the authors of [18], the model inputs are pre-determined. The selected baseline model accounts exclusively for costs related to the energy renovation of the selected package and building operation. That includes the cost for the purchase of building components and associated energy supply systems, and their installation, maintenance, replacement, and operation (supplied energy). LCC calculations are performed using the free software LCCByg (version 3.2.14) [30]. The considered model output, NPV, is used to determine the sensitivity and correlation of the variated input parameters.
## 2 Islanding Detection Methods
### Computational Intelligent Based Methods
#### 2.6.4 Fuzzy Logic Method (FL)  
The problem was solved computationally using fuzzy logic, which used a rule base. A fuzzy inference system is the name given to the model created utilizing the rule bases. FL has proposed employing decision tree transformation for islanding detection, which enhances the fuzzy system by integrating fuzzy membership functions and rule basis [45]. Therefore, in terms of islanding detection, this strategy produces effective and encouraging results. On the other hand, because of the maximum and minimum combinations, fuzzy classifiers are greatly influenced by noisy input.
# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
###### Abstract  
In this paper, we present the latest research results on the analysis of images taken during the condition assessment of solar cells and solar power plants. We aimed to summarize the most recent articles for 2024 and 2025. The annual volume of solar panels produced is expected to increase in the future. As imaging condition assessment technologies develop, the convolutional neural network models must follow this trend. In the field of real-time detection, CNN models will play an extremely important role because the faster any potential faults are identified, the quicker the response time during manufacturing and PV plant inspections. As part of CNN implementation in large PV power plants, IR and RGB imaging modes are very useful to detect failure sources. While IR imaging is useful in detecting heating from faults within PV panels or from nearby wiring, RGB imaging can detect mechanical defects such as broken glass planes, discolorations, and delamination. The implementation of these thus provides a higher chance of detecting solar panel damage and PV farms' performance degradation or possible failure, resulting in a reduction in power generation interruptions. This will also allow faster and more efficient intervention and decision-making by operators in case of problems.  
convolutional neural network (CNN); electroluminescence (EL); inspection; neural network; photovoltaic (PV); thermography +
Footnote †: journal: Applied sciences
# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Domain Robustness
Similar conclusions as in the previous experiment could be derived. The comparison methods produced several false positive predictions, whereas the proposed method could accurately predict the target area. The quantitative and qualitative results indicated that valuable structural information such as a global shape or location in common across multiple domains can be learned using the proposed method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Setting** & **Method** & **IOU (\(\uparrow\))** & **DSC (\(\uparrow\))** & **ACD (\(\downarrow\))** & **ASD (\(\downarrow\))** \\ \hline \multirow{4}{*}{_site2\(\rightarrow\)_others} & U-Net & 0.766 \(\pm\) 0.005 & 0.863 \(\pm\) 0.004 & 0.747 \(\pm\) 0.033 & 0.579 \(\pm\) 0.013 \\  & ACNN & 0.763 \(\pm\) 0.006 & 0.861 \(\pm\) 0.004 & 0.773 \(\pm\) 0.054 & 0.591 \(\pm\) 0.018 \\  & SRM & 0.758 \(\pm\) 0.014 & 0.856 \(\pm\) 0.012 & 0.655 \(\pm\) 0.038 & 0.567 \(\pm\) 0.017 \\  & Proposed & 0.768 \(\pm\) 0.004 & 0.864 \(\pm\) 0.003 & 0.654 \(\pm\) 0.002 & 0.558 \(\pm\) 0.006 \\ \hline \multirow{4}{*}{_site3\(\rightarrow\)_others} & U-Net & 0.747 \(\pm\) 0.010 & 0.853 \(\pm\) 0.007 & 0.685 \(\pm\) 0.028 & 0.610 \(\pm\) 0.016 \\  & ACNN & 0.741 \(\pm\) 0.010 & 0.849 \(\pm\) 0.007 & 0.697 \(\pm\) 0.024 & 0.626 \(\pm\) 0.018 \\  & SRM & 0.709 \(\pm\) 0.020 & 0.824 \(\pm\) 0.015 & 0.765 \(\pm\) 0.059 & 0.667 \(\pm\) 0.035 \\  & Proposed & 0.740 \(\pm\) 0.012 & 0.847 \(\pm\) 0.009 & 0.686 \(\pm\) 0.029 & 0.615 \(\pm\) 0.018 \\ \hline \multirow{4}{*}{_site4\(\rightarrow\)_others} & U-Net & 0.766 \(\pm\) 0.004 & 0.864 \(\pm\) 0.003 & 0.784 \(\pm\) 0.050 & 0.583 \(\pm\) 0.016 \\  & ACNN & 0.766 \(\pm\) 0.002 & 0.864 \(\pm\) 0.001 & 0.780 \(\pm\) 0.033 & 0.585 \(\pm\) 0.012 \\  & SRM & 0.767 \(\pm\) 0.003 & 0.864 \(\pm\) 0.001 & 0.775 \(\pm\) 0.034 & 0.582 \(\pm\) 0.010 \\  & Proposed & 0.767 \(\pm\) 0.006 & 0.864 \(\pm\) 0.006 & 0.660 \(\pm\) 0.037 & 0.556 \(\pm\) 0.012 \\ \hline \multirow{4}{*}{Average} & U-Net & **0.760** & **0.860** & 0.739 & 0.591 \\  & ACNN & 0.757 & 0.858 & 0.750 & 0.601 \\ \cline{1-1}  & SRM & 0.745 & 0.848 & 0.732 & 0.605 \\ \cline{1-1}  & Proposed & 0.758 & 0.858 & **0.667** & **0.576** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the domain robustness in the spinal cord segmentation task. The best result on averaging over three settings is shown in boldface. Figure 7: Segmentation results on the spinal cord segmentation task obtained from the models trained using the (**a**) _site2_, (**b**) _site3_, and (**c**) _site4_ datasets, respectively. The blue color represents the segmentation results, and the ground-truth is in red.
## 3 Methodology
### MILP Model for Forward Area Replenishment Problem with Buffer Area  
A MILP model has been developed to address the specific challenges presented in this paper. This model is designed to optimize decision-making by capturing the complex dynamics and constraints of the problem. The model is structured around a key policy that prioritizes order picking from the buffer area whenever the required spare part is available there. By giving precedence to buffer stock, we aim to decrease the inventory in the buffer area, reducing congestion and improving operational flow. If the buffer stock is insufficient, the remaining quantity is picked from the forward area, ensuring that the forward area is replenished only when necessary. This policy-driven approach has demonstrated clear benefits in improving warehouse efficiency by reducing unnecessary movements in warehouse and streamlining the replenishment process. In developing the mathematical model, certain assumptions related to warehouse operations were made to reflect realistic business practices. These assumptions include the operational characteristics and warehouse policies that are critical to this study, as outlined below. 1. Each spare part has a specific batch size for replenishment. 2. Due to the involvement of a limited number of staff members in the internal replenishment process, the number of replenishments that can be accomplished daily is limited. 3. The stock needed to cover the demand is available in the warehouse in the forward, reserve, or buffer areas. 4. A dedicated rack with a capacity of a specific batch size is allocated in the forward area for each part. 5. A complete cage is transferred from the reserve area to the forward area for replenishment purposes, i.e., all transferred cages are full. 6. If there is any surplus beyond the forward area's capacity, the remaining stock must be located in the buffer area, which is adjacent to the shelf. 7. If a cage is transferred from the reserve area while some parts exist in the associated shelf in the forward area, this partially filled cage in the forward area is transferred to the buffer area, and the new full cage is located in the forward area. 8. In cases where the order picker exclusively visits the forward area and encounters insufficient stock, they proceed to the reserve area to fulfill the order and perform direct picking. The mathematical model explained in this paper will undergo comprehensive examination alongside the notations employed for delineating the internal replenishment problem. The methodologies for optimization utilized to address the issue will be explained. The notations that will be used in the mathematical model are given in Abbreviations. The framework aims to efficiently ascertain the inventory levels of shelves and enhance product positioning. For this objective, the mathematical expression of the framework and the resolution methods are elaborated thoroughly. The MILP model details are given below.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 4 Case Studies, Results, and Discussion
### Unbalanced Load (Scenario II)  
In this scenario, the load connected at the end of the distribution network is assumed to be an unbalanced inductive load. Other parameters are kept the same as scenario I. Table 4 summarizes the details of all the parameters. In addition to the 10 types of faults, the 3 LG fault is also considered in this case to show how similar it is to the 3L fault.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Query Phase
#### 5.4.1 Creating Composite Key  
As shown in Algorithm 1, Hyperledger Fabric provides a specific API for constructing composite keys from different attributes: \(createCompositeKey(objectType,attributes)\). This method aims to combine the given attributes with the object type to form a composite key, which can then be used to access historical and latest state information.  
```
0: a set of attributes \(A=\{a_{1},\ldots,a_{s}\}(1\leq s)\)
0:\(k\)
1:\(k\leftarrow\textit{createCompositeKey}(\textit{String},A)\)
2:return\(k\)
```  
**Algorithm 1** Create composite key  
During the process of uploading medical data records for storage, for each record \(r_{i}(1\leq i\leq t)\) in the given collection of medical data records \(R=\{r_{1},r_{2},\ldots,r_{t}\}(t\geq 1)\), check if there are multiple values corresponding to one attribute. For example, in the \"diagnosis\" attribute of an EMR, there may be multiple symptoms, such as \"hypertension\" and \"hyperglycemia\". If this situation exists, and the attribute corresponds to \(n\) values, then split it into \(n\) separate records. Then, individually create a composite key for each record, with its corresponding \(N_{EMR_{\textit{hash}_{i}}}\) as the value for that key. This results in the EMR having \(n\) keys associated with it. As shown in Algorithm 2, we generate a key-value pair for each record and then submit all key-value pairs corresponding to these records to the blockchain for storage.  
```
0: a set of records \(R=\{r_{1},r_{2},\ldots,r_{t}\}(t\geq 1)\)
0: a set of key-value pairs \(KVP\)
1: Declare an empty set \(KVP\)
2:// \(r_{i}=\{N_{EMR_{\textit{hash}_{i}}},A_{i}\}\), where \(A_{i}\) is a set of attributes with elements that can be used as a query condition
3:for all\(r_{i}\in R\)do
4: Compute \(k_{i}\) based on Algorithm 1
5:\(kvp_{i}=(k_{i},N_{EMR_{\textit{hash}_{i}}})\)
6: Add \(kvp_{i}\) into \(KVP\)
7:endfor
8:return\(KVP\)
```  
**Algorithm 2** Generate key-value pairs
## 2 Description of the Facilities
### Anechoic Wind Tunnel FL-17 CARDC  
The aeroacoustic wind tunnel FL-17 CARDC (Mianyang, China), shown in Figure 3, is a closed-circuit open-jet wind tunnel; its circuit length is 368 m. In the test hall, the nozzle has an exit area of 5.5 m (width) \(\times\) 4 m (height). With the 12.5 MW power supply, a maximum wind speed of \(V\) = 100 m s\({}^{-1}\) is achieved in the open test section. The open-jet test section is surrounded by a full anechoic test hall of 28 m \(\times\) 27 m \(\times\) 20 m; the cut-off (lowest) frequency of the test hall is \(f_{0}\) = 100 Hz. The centerline of the jet is 8 m high above the ground, and the distance from the nozzle outlet to the collector is 14 m.  
Figure 2: Landing gear noise tests in AC-2 TsAGI: (**a**) photo of a small-scale model installed on the horizontal plate; (**b**) scheme of the experiment (top view); (**c**) scheme of the experiment (front view); (**d**) photo of microphones installed in AC-2 TsAGI.  
For acoustic measurements, a linear microphone array was used that was located at the sideline from the landing gear model (Figure 4).  
Figure 4: Landing gear noise tests in FL-17 CARDC: (**a**) photo of the linear microphone array in the anechoic test hall; (**b**) scheme of the experiment (top view); (**c**) scheme of the experiment (front view); (**d**) photo of a microphone on the holder.  
Figure 3: General scheme of FL-17 CARDC.  
The microphones were installed on horizontal holders directed toward the landing gear model, with a length of 0.23 m. The microphone array comprised 10 free-field microphones 1/2\" Bruel & Kjaer 4189 with preamplifiers Bruel & Kjaer 2671 (frequency range 20 Hz-20 kHz, dynamic range 16.5-134 dB, sensitivity 50 mV Pa\({}^{-1}\)) were used. Before the tests, the microphones were calibrated with a pistonphone Bruel & Kjaer 4228. The microphone coordinates comprised polar angles \(\varphi\) = 20\({}^{\circ}\) and 40\({}^{\circ}\) (Figure 4c) and azimuthal angles \(\theta\) = 50\({}^{\circ}\), 70\({}^{\circ}\), 90\({}^{\circ}\), 110\({}^{\circ}\), and 130\({}^{\circ}\) (Figure 4b). It can be seen that these positions correspond to microphone coordinates in AC-2 TsAGI, with the exception of azimuthal angle \(\theta\) = 130\({}^{\circ}\), which was absent in AC-2 TsAGI tests due to the proximity of the microphone to the anechoic wedges in AC-2 TsAGI for this azimuthal angle.  
In addition to the linear microphone array, acoustic tests in FL-17 CARDC also used a 135-microphone beamforming array (Figure 5a), located at the sideline but on the opposite side from the landing gear model. A general view of the test hall of FL-17 CARDC with an installed landing gear model and the measurement systems is shown in Figure 5b-d.  
The scheme of the landing gear noise measurements in FL-17 CARDC has also been described previously [21; 22], providing some test results for large-scale landing gear models.
# Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies
## 2 Materials and Methods
### Signal Attenuation  
The sections of 5 GPs that provide the needed weather protection to the (5G) radios, are typically referred to as 'radomes'. Low-attenuation and low-permittivity materials are an especial family of polymers that could be employed for the radomes. Radomes mainly protect against moisture and ultraviolet (UV) radiation (e.g., the reference standard UL 746C), as illustrated in Figure 6. The selected material samples' attenuation was measured by using a split-post dielectric resonator (SPDR) at 2.45 GHz (QWED, Warsaw, Poland) with a Microwave Frequency Q-Meter (QWED, Warsaw, Poland). The sample size was 60 mm \(\times\) 60 mm (thickness 2.5-3.0 mm). The measurements were made at a constant signal frequency of 2.45 GHz and sample-specific thickness was measured to determine attenuation. An especial polymer blend (PREPERM, Premix, Finland) was selected as a candidate material for these details of the pole.  
To account for environmental ageing during the anticipated pole operation, a series of samples were conditioned in a UV-chamber. The chamber had UVA-340 fluorescence lamps (Q-Lab, Farnworth, Bolton, UK) with a peak intensity at 340 nm. Each sample set involved five test samples and a set of samples was removed from the chamber and measured at pre-set time intervals. Accelerated aging cycles of 0, 432 h, 864 h and 1728 h (0, 18, 36, 72 days) were analyzed. Due to the durability requirements of sustainable long-term application, black and white paints on the outer surface of the radome materials were surveyed in addition to non-painted samples. The temperature at the chamber varied between 22 \({}^{\circ}\)C and 36 \({}^{\circ}\)C.
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 4 Discussion
In urbanized areas or areas close to industrial agglomerations, standard corrections do not give good results. The changes in composition of the atmosphere (content of moisture, dust, and other pollutants) may require different corrections. Therefore, it becomes necessary to determine a reference surface that enables a reflectance correction. This can be any surface with spectral properties constant in time. In this case, a fragment of the dam crest served as the reference surface. The base reflectance was the first reference surface reflectance in a series of measurements. The changes in the reflectance of the reference surface in relation to the base surface made it possible to correct the reflectance of the lake surface.
## 3 Evaluation and Results
### Experimental Setup  
All the experiments were carried out in a laboratory environment. The sensitive optical components such as the MRR and the coupling fiber pigtails were suspended on a mechanically isolated optical table. The MRR was coupled through vertical coupling with the SMF pigtails. Optical polarization controllers were included in the setup to optimize the performance of polarization-sensitive devices such as the MZM and MRR. Since polarization-dependent grating couplers were used as the couplers between the PIC and the optical fibers, which can only effectively couple TE polarized light, the polarization needs to be precisely tuned for efficient coupling. To eliminate the need to tune the polarization, we can rely on polarization-maintaining fibers, employ a polarization-diversity approach [42] or use a polarization-insensitive MRR [43].
the OSA and the other to a PIN-FET receiver, and finally a CLC system whose output is connected back to the input of the VCCS. A tunable RF modulation frequency is provided by the VNA.