esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Power Plant Optimization in Smart Grids: A Narrative Review
## 4 VPP Applications in Smart Grids
### Energy Communities' Sustainability
The scheduling of the VPP aims to maximize revenues and reduce penalties while addressing market constraints. The authors of [99] explore how modeling and fuzzy logic can be used for VPPs in an open context which is characterized by features such as external components that can be included in the system as needed at run-time without any impact, new interfaces that could facilitate the connection of the system with those components can also be added if needed and all the interfaces or components can be changed at any time if required. Moreover, the context and time dependencies are computed with formal methods and the erroneous information is extracted from the result. The high renewable energy sources produce excess energy which in turn generates a burden that creates a notable residual load [105]. The authors of [63] examine a possible implementation of a VPP to aggregate small-scale renewables to be sold on the energy market. It also investigates and implements the optimal VPP configuration for a RES-based power plant. With the aid of this paper, the capability of VPPs to provide secured power market products derived from EEX/EPEX standards is proved. In [64], solutions to optimize coalitions and communities are proposed. The VPPs are created from the prosumers' pool using as the main criteria the energy price during an interval. Using load forecasting and blockchain, the approach contributes to the uptake of local communities and will lead to profit for the customers, offering an alternative for participating in energy markets and paving the way for flexible trading. Indeed, blockchain, smart contracts, and peer-to-peer energy trading are considered key technologies for developing VPP for energy communities [107; 108], especially due to blockchain features of immutability and enhanced security. Interest has increased in using VPPs for community management, offering the citizens access to renewable and storage in urban areas [106]. In [100] is presented a VPP solution for managing both the generation and demand sides using game theory to explore the cooperation among prosumers. The profit of the VPP was allocated based on the Shapley value.
# Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
## 2 Materials and Methods
### Performance Metrics  
The performance analysis of classification models is usually carried out using accuracy (ACC), which measures the proportion of correctly classified instances against the misclassified ones made by the model. The formulae to calculate these performance measurements are described by [PERSON] [45] as follows:  
ACC = TP + TNTP + FP + TN (2)  
PPV = TPTP + FP (3)  
SEN = TPTP + FN (4)  
NPV = TNTN + FN (5)  
SPC = TNTN + FP (6)  
F1 = 2 TP2 TP + FP + FN (7)  
where true positive (TP) is the number of positive instances which have been correctly classified and true negative (TN) is the number of negative instances which have been correctly classified. False positive (FP) and false negative (FN) are the number of misclassifications of positive and negative instances, respectively.  
Accuracy (ACC) represents the ability of the model to identify the correct class of a sample against the total number of samples. Specificity (SPC) is the capacity to return a negative result for a true negative sample. On the other hand, sensitivity (SEN) represents the ability to return a positive result for a true positive sample. finally, predictive positive value (PPV) is the probability that a positive prediction is actually correct, while negative predictive value (NPV) is the probability that a negative prediction is actually correct [46,47].  
In imbalanced binary classification data, the accuracy tends to disrupt the model performance. It is then when SEN and PPV gain more importance and it is convenient to summarize them in a metric called the F1-score, which is the mean of both metrics [48].  
Figure 1: Nested-leave-one-cow-out cross-validation approach used in this work.
## 5 Results and Discussion
### Limitations and Future Directions  
Despite its robust design and significant performance, the MHMM framework acknowledges limitations guiding future research:  
* Simulation-Based Validation: The current reliance on synthetic data is essential for controlled methodological validation but limits claims regarding real-world performance. Operational field validation remains a crucial next step.
* Future Empirical Validation: Empirical validation studies are planned within real-world settings, such as advanced manufacturing environments and surgical training centers. These studies will involve operators performing authentic tasks while monitored through wearable physiological devices, detailed behavioral data, and continuous subjective assessments via adaptive interfaces. Expert-validated proficiency evaluations will be employed to benchmark the MHMM's predictions, ensuring robust real-world applicability and facilitating the refinement of parameters.
* Conditional Independence Assumption: While the conditional independence assumption greatly simplifies computational modeling, real-world data may show interdependencies across physiological, behavioral, and subjective metrics. Future studies could explore more sophisticated coupled HMMs or Bayesian network models to explicitly capture these inter-modal correlations, potentially enhancing accuracy.
* Ethical and Practical Constraints: Multimodal monitoring introduces privacy concerns and necessitates transparent data-handling protocols aligned with regulatory compliance (e.g., General Data Protection Regulation (GDPR)or Health Insurance Portability and Accountability Act (HIPAA)). Ensuring equitable access across diverse industries and populations also remains essential. Future research must prioritize ethical considerations, consent processes, and non-intrusive sensor technologies.  
Moreover, extending the MHMM framework to incorporate bidirectional interactions between operators and AI systems, particularly cognitive feedback loops adjusting assistance levels based on real-time inferred workload, would further advance its alignment with the collaborative ethos of Industry 5.0.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 4 Location and Installation of the Measuring Devices
### Considerations of the WMO
#### 4.1.1 Concerning the Horizontal Scales  
The AWSs should operate on the local scale, in environments of one to several kilometers and with a similar urban development with respect to the built-up surface, the size, and spacing of the buildings. In order to determine the limits of the UCZ, the morphometrics of the urban area should be evaluated using aerial photographs, satellite images, or city planning documents. Then, representative locations should be identified for each AWS, avoiding possible microscale effects (far from such obstacles as trees, buildings or walls) and mesoscale effects (i.e., building envelopes, being close to a concentrated heat source, transition zones between different UCZ types, etc.); furthermore, the streets should preferably be north-south oriented (to guarantee a more constant solar radiation).
## 3 Autonomous Navigation of a Mobile Robot
### SLAM (Simultaneous Localization and Mapping)  
Mapping and localization are directly proportional to each other because if one activity gets an error, then the other activity will get an error automatically. Therefore, these two activities should be estimated in conjunction. These two activities are addressed together using SLAM algorithms [72]. SLAM is the algorithm in which mapping and localization occur at the same time. SLAM can be performed with the help of different sensors such as monocular vision sensors, 2D, 3D LiDAR, and stereo vision sensors [73]. The SLAM algorithms are based on Bayes filtering techniques and SLAM approaches based on the graph. When a mobile robot reassesses an area of its environment, then with the help of loop closure algorithms, similarities in the sensor data are estimated to correct errors of drift in localization and mapping.
## 3 Results and Discussion
### FCT Life and Failure Analysis  
The plasma-spraying process affects the microstructure of the EMAP TBCs, specifically affecting the content and distribution of the second-phase micron-agglomerated particles, thereby affecting the life of the true coating. In this work, the effect of the powder feeding process on the FCT life of EMAP TBCs was systematically studied from three aspects: powder feeding rate, distance between powder feeders, and the type of first-phase powder.  
Figure 8a shows the effect of the first-phase powder feeding rate on the life of EMAP TBCs. When the powder feeding rate was 12 g/min, 16 g/min, and 20 g/min, the results show that the life of the coating decreases with the increase in the first-phase powder feeding rate. Figure 8b shows the effect of the distance between the two powder feeders on the life of EMAP TBCs. When the powder feeding distance was 30 mm, 35 mm, and 40 mm, the results show that the coatings had the highest life when the powder feeding distance was 35 mm. Figure 8c shows the life of EMAP TBCs at different powder feeding rates when the first-phase powder was micron-agglomerated powder and melt-broken. The results show that the life of the coatings when the first-phase powder was micron-agglomerated  
Figure 7: The mechanical properties of conventional and EMAP coatings during the sintering process. (**a**) Elastic modulus; (**b**) hardness.  
powder was higher than that when the first-phase powder was FC. Figure 8d shows the life of traditional coatings and EMAP TBCs when using different powders. The results show that the life of EMAP TBCs was about 200% higher than that of traditional coating regardless of the powder employed.
# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 2 Methods  
The proposed LFRC-Net is introduced in this section, which is illustrated in Figure 2, and the encoder-decoder architecture is taken as the backbone. A frequency recalibration module (FRCM) is placed at the bottleneck layer of the encoder, and the residual attention modules are integrated in the decoder.  
The process begins by randomly choosing centers in regions of interest within the original image to form 256 \(\times\) 256 patches, which are then inputted into the encoder. These encoded feature maps are subsequently refined through the residual attention module (RAM) and further refined by the FRCM to adeptly filter out extraneous features.  
The refined feature maps are directed to the decoder, which involves three stages, with a residual attention module for each. Up-sampling in the feature layer is taken by bilinear interpolation, while the skip connections are accomplished through \(1\times 1\) convolutions, batch normalization (BN) layers, and ReLU activation, where \(1\times 1\) convolutions effectively reduce the channel size of the feature maps from the encoder. The number of convolutions is denoted by K, which can be adjusted to the appropriate parameter size of the model. The segmentation of the four types of DR lesions is obtained through a \(1\times 1\) convolution layer followed by softmax activation.
# Optical Camera Communication as an Enabling Technology for Microalgae Cultivation +
## 2 Proposed Architecture
### Photobioreactor Node  
The transmitter is comprised two parts: the LED flat panel and the LED driver that generates the corresponding OCC driving signal to transmit sensor data provided by probes deployed inside the container (such as temperature, acidity, carbon and nutrient levels). This section is focused on light generation and transmission.  
As was previously mentioned, the competitiveness of any artificial light-driven microalgal production requires improvements in photon harvesting and the conversion efficiency of light sources [2]. Hence, it is crucial to optimise light quality and delivery. In terms of light energy, the photosynthetic rate is directly related to the irradiance power, and excessive or insufficient incident light constrains optimal performance and may induce the photo-inhibition and photo-oxidation of the cells, eventually attaining photo-damage and even leading to culture death [15]. In terms of light spectra, the radiant energy absorbed by microalgae highly depends on the chemical nature of their native pigments, which have specific absorption bands in the visible and near-infrared spectra. Thus, better energy usage can be achieved by adjusting the light source's emission to match the absorption spectrum [16].  
On the other hand, recent experimental studies have shown that combining short and intense light flashes with extended dark periods instead of continuous illumination might increase the culture's growth efficiency, as discussed in [17; 18]. The light frequency and duty cycle are particular to each microalgae species and the expected biomass product result (lipids, carotenoids, etc.). It may vary between a few Hz up to tens of kHz.  
Therefore, the transmitting source's design must consider all these restrictions: light quality (intensity profile, spectra) and light delivery (frequency and duty cycles) concerning the selected strain and the expected results.
## 3 Angular Indexing Error Caused by Coaxiality Error of Mandrel and Radial Error of Spindle  
In the GMM, the upper center is fixed and the lower center rotates with the spindle. As given in Figure 1, the tip of the lower center will experience radial motion due to the spindle error in tilt and radial motion. Figure 4 shows the off-axis radial position of the lower center tip when the angle is at 0\({}^{\circ}\) and 180\({}^{\circ}\), respectively. It is noted that this tip motion has combined the effects of tilt motion and radial motion of the spindle. The spindle tilt is very small and is not shown in the figure.  
Let the radial error motion of the tip of lower center be denoted by \(\delta\). From the geometrical relationship of Figure 4, the angle \(\alpha\) is varied during rotation. Let \(r_{\theta}\) be minimum when \(\theta=0^{\circ}\) and maximum when \(\theta=180^{\circ}\). The following equations can be derived.  
\[\tan\alpha_{\theta=0^{\circ}}=\frac{(e-\delta)}{\sqrt{L^{2}-(e-\delta)^{2}}} \tag{10}\]  
\[\tan\alpha_{\theta=180^{\circ}}=\frac{(e+\delta)}{\sqrt{L^{2}-(e+\delta)^{2}}} \tag{11}\]  
\[r_{\theta=0^{\circ}}=\frac{r_{A}}{\cos(\alpha_{\theta=0^{\circ}})}-h\tan( \alpha_{\theta=0^{\circ}}) \tag{12}\]  
\[r_{\theta=180^{\circ}}=\frac{r_{A}}{\cos(\alpha_{\theta=180^{\circ}})}+h\tan( \alpha_{\theta=180^{\circ}}). \tag{13}\]  
Since \(r_{\theta}\) varies in a sinusoidal form during rotation with maximum at \(\theta=180^{\circ}\) and minimum at \(\theta=0^{\circ}\), it can be expressed by  
\[r_{\theta}=r_{A}-\frac{(r_{\theta=180^{\circ}}-r_{\theta=0^{\circ}})}{2}\cos\theta. \tag{14}\]  
Figure 4: GMM with spindle error and coaxiality error: (**a**) \(\theta=0^{\circ}\); (**b**) \(\theta=180^{\circ}\).  
Similar to Equation (5), the following equations can be obtained.  
\[\theta_{A}=\int_{0}^{t}\omega_{A}dt=\int_{0}^{t}\frac{\omega r_{\theta}}{r_{A}}dt= \int_{0}^{\theta}\frac{r_{\theta}}{r_{A}}d\theta=\theta+\frac{(r_{\theta=180^{ \circ}}-r_{\theta=0^{\circ}})}{2r_{A}}\sin\theta. \tag{15}\]  
Substituting (12) and (13) into (15), the angular indexing error of the mandrel due to its coaxiality error to the spindle and the spindle error induced radial error motion of the lower center tip can be theoretically derived as  
\[\Delta\theta=\theta_{A}-\theta=\frac{\sin\theta}{2r_{A}}(h\tan(\alpha_{\theta =180^{\circ}})+h\tan(\alpha_{\theta=0^{\circ}})). \tag{16}\]  
Equation (16) implies that the angular indexing error (\(\Delta\theta\)) of the mandrel relative to the table top of the rotary stage can be theoretically analyzed as a function of \(e\), \(\delta\), \(h\), \(L\), and \(r_{A}\), among which \(h\), \(L\) and \(r_{A}\) can be measured directly on the GMM. Therefore, if \(e\) and \(\delta\) are obtained by experiments, \(\Delta\theta\) can then be predicted.
## 3 System Design
### Dynamic Core Management and Selection
#### 3.2.2 Core Discovery and Selection  
The multicast source sends the multicast flow identified by the MSEID. After receiving the multicast packet identified by the MSEID for the first time, the edge router queries the NRS for the NA of the core corresponding to the MSEID. If the NRS contains the NA of the core corresponding to the MSEID, it directly returns the core's NA. If the NRS does not contain the information related to the MSEID, it selects a core from the candidate cores corresponding to the FMSEID. The NRS preferentially selects the candidate core in the \"Enable\" state. If there is no core in the \"Enable\" state among all candidate cores, the NRS selects a candidate core in the \"Busy\" state according to some strategies to balance the load, such as using a hash function, random selection, etc. Then, the NRS returns the NA of the selected core and adds the mapping between the MSEID and the NA of the selected core.  
The edge router receives the NA of the core from the NRS. If the returned core is the edge router itself, the edge router directly sends the multicast packets to a group of receivers based on the MSEID. Otherwise, the edge router forwards the multicast packets sent by the source to the core based on the returned NA. Then, the core forwards the multicast packets to a group of receivers based on the MSEID.  
Figure 2a, S1, and S2 show the multicast sources of the multicast service MSEID1. When the edge router R1 receives the multicast packet identified by MSEID1 from S1 for the first time (arrow 1), R1 queries the NRS for the NA of the core corresponding to MSEID1 (arrow 2). Since NRS does not have the information related to MSEID1, the NRS returns NA3, whose state is \"Enable\" from the candidate cores maintained by it (arrow 3) and adds the mapping between MSEID1 and NA3. After R1 obtains that the NA of the core corresponding to MSEID1 is NA3, it forwards the multicast packets sent from S1 to the core (R3) based on NA3 (arrow 4). Then, when the edge router R2 receives the multicast packet identified by MSEID1 from S2 for the first time (arrow 5), R2 queries NRS to obtain that the NA of the core corresponding to MSEID1 is NA3 (arrows 6, 7). R2 also forwards the multicast packets sent from the S2 to the core (R3) based on NA3 (arrow 8).  
As shown in Figure 2b, loads of R1, R2, R3, and R4 all reached the threshold, and they informed NRS to update their states to \"Busy\" (arrows 1-4). S3 was the multicast source of the multicast service MSEID2. When the edge router R4 received the multicast packet identified by MSEID2 from S3 for the first time (arrow 5), R4 queried the NRS for the NA of the core corresponding to MSEID2 (arrow 6). Since NRS does not have information related to MSEID2 and there is no candidate core with the state of \"Enable\", the NRS selects and returns one of the candidate cores in the \"busy\" state, which is R4 (arrow 7). Then, R4 directly forwards the multicast packets sent by S3 based on MSEID2.
## 1 Introduction
It also overcomes the disadvantages of inflexible code length and code rate selection. This paper improves the iterative encoding algorithm and transforms it into a coding algorithm for non-binary LDPC codes. It is called a non-binary iterative coding algorithm. According to the structural characteristics of non-zero element distribution in the LDPC code check matrix, the construction method for LDPC code is divided into a random construction algorithm and structured construction algorithm. In 2005, [PERSON] et al. proposed the progressive edge growth (PEG) random construction algorithm [18]. The check matrix constructed by this algorithm has a large girth, a small Hamming distance and large randomness. The decoding performance is good but the encoding complexity is high. This paper proposes an improved PEG (irPEG) algorithm to reduce the high encoding complexity of the PEG algorithm. The structured code construction algorithm, QC-LDPC [19], creates a corresponding check matrix based on a certain matrix structure. The QC-LDPC construction algorithm is relatively complex and decoding performance is worse than the random construction algorithm but the encoding complexity is significantly reduced. To reduce the complexity of constructing the check matrix and accelerate the convergence speed, this paper introduces an improved QC-LDPC (ir-qc) algorithm, with a lower triangle structure in the check matrix. Using the ir-qc algorithm, a check matrix without short loops was constructed. Then, with a focus on encoding complexity and error correction performance and based on the improved non-binary superposition coding algorithm with linear encoding complexity, a hybrid construction (HC) algorithm is proposed. Combining random construction and structured construction algorithms, the irPEG algorithm constructs a base matrix and then uses the ir-qc algorithm to generate a cyclic shift matrix and a finite field coefficient matrix, eliminating the influence of the fourth and sixth rings. The number of check matrices constructed is set, from which the optimal check matrix is chosen. This algorithm not only has the randomness of a random construction algorithm but also maintains the low encoding complexity of the structured construction algorithm and reduces the loss in the error caused by the structured construction. HC algorithm is a comparative compromise algorithm. It can be seen that the non-binary LDPC code has a good application prospect in a WSN requiring high reliability, low delay and high rate. Moreover, the optimization of the non-binary LDPC-CPM scheme can effectively reduce the energy consumption and computational complexity of the WSN. Therefore, this paper mainly optimizes the non-binary LDPC-CPM scheme in WSNs from four aspects: improving system reliability, reducing computational complexity, improving error correction performance and reducing energy loss.