esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The existing trend towards increased penetration of renewable energies in the traditional grid, and the intermittent nature of the weather conditions on which these energy sources depend, make the development of tools for the forecasting of renewable energy production more necessary than ever. Likewise, the prediction of the energy generated in these renewable production plants is key to the implementation of efficient Energy Management Systems (EMS) in buildings. These will aim both to increase the energy efficiency of the building itself, as well as to encourage self-consumption or, where appropriate, collective self-consumption (CSC). This paper presents a comparison between four different models, the former one being an analytical model and the remaining three machine learning (ML) based models. All of them will forecast the photovoltaic (PV) production curve for the next day. In order to validate these models, a case study of a PV system installed on the roof of a university building located in Bidart (France) is proposed. The model that most accurately forecasts the PV production during the period of July 2021 is the support vector regression (SVR), which has a mean R\({}^{2}\) of 0.934 for July, being 0.97 on sunny days and 0.85 on cloudy ones. This is an improvement of 5.14%, 4.07%, and 4.18% over the nonlinear autoregressive with exogenous inputs (NARX), feedforward neural network (FFNN), and analytical model, respectively.  
PV production forecasting; artificial intelligence; machine learning; feedforward neural network; support vector regression; nonlinear autoregressive exogenous; OpenModelica; analytical model model
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 2 Related Work  
### Point Cloud-Based  
In the domain of human skeleton estimation from mmWave FMCW radar point clouds, the RadHAR system proposed by [PERSON] et al. [12] marks a significant milestone. It innovatively leverages 5D point cloud data--comprising 3D spatial coordinates, Doppler velocity, and reflection intensity--captured from commercial radars. To mitigate the inherent sparsity of mmWave point clouds, RadHAR constructs spatiotemporal voxel grids using a sliding time window. Experimental results demonstrate a classification accuracy of 90.47% across five daily activity recognition tasks, indicating strong practical utility. Building on this, [PERSON] et al. [13] introduced the MARS system to meet the specific demands of rehabilitation healthcare. By applying intelligent 5D point cloud dimensionality reduction and transforming the data into 3D feature maps, MARS estimates the 3D coordinates of 19 skeletal keypoints with a Mean Absolute Error (MAE) of 5.87 cm. Further advancing the field, [PERSON] et al. [14] proposed the mmMesh system, which extends the 5D representation by incorporating distance and reflection intensity into a 6D point cloud. Through the integration of anchor modules and attention mechanisms, mmMesh achieves a mesh vertex reconstruction accuracy of 2.47 cm, establishing a new benchmark for sparse radar point cloud-based human modeling.  
For complex motion scenarios, [PERSON] et al. [15] proposed the VirTeach framework, introducing a virtual point cloud augmentation strategy that simulates point distributions under rapid motion. This approach reduces the MAE by 20% in high-intensity motion sequences. Building on this, their subsequent mmPose-NLP system [16] adopts a Natural Language Processing (NLP) paradigm, utilizing a sequence-to-sequence architecture to process voxelized point cloud data. It achieves 3 cm MAE for 17 anatomical keypoints, demonstrating the promise of interdisciplinary modeling approaches. Recent advances in temporal modeling have also yielded significant progress. [PERSON] et al. [17] addressed the limitations of single-frame data by proposing a multi-frame 5D point cloud processing pipeline that combines bidirectional LSTM networks with a reliability assessment module for denoising, reaching an MAE of 2.84 cm. [PERSON] et al. [18] developed the Mikey system, which integrates adaptive GCN with Transformer architectures, maintaining robust performance (MAE \(<\) 6 cm) across diverse motion patterns. [PERSON] et al. [19] introduced the mmHPE framework, which adopts a three-stage \"detection-augmentation-estimation\" pipeline. By leveraging bounding-box-guided point cloud augmentation and multi-scale networks, mmHPE achieves an MAE of 4.5 cm while maintaining computational efficiency for real-time applications.
# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation
### Evaluation Using Synthetic Mobility Patterns  
We here discuss the experimental results obtained simulating 10 of the protocols discussed in Section 2. We categorize the protocols into three classes of protocols: simple, single technique and advanced protocols. For each class, we discuss how and why the proposed techniques affect the performance metrics. In order to conduct the comparative study, we implemented three DTN routing protocols, namely Fuzzy-Spray [25], FAD [6] and SCAR [26]. Our protocol implementations are available online along with the other extensions made to the ONE simulator.  
We here include the results for the random walk mobility model and the Levy Walk mobility model (see Figures 2-4). The results related to the Community model were not included in full detail; instead, they are presented in comparison to the other two models in Section 3.4.
## 4 Simulation Experiment and Discussion
### Experimental Results
(5) Scenario 4 The results for scenario 4 are tabulated in Table 5. The acquired results indicate the following conclusions: * For UAV1, the CC-MPSO algorithm achieves optimal results in \"BEST\", \"MEAN\", and \"MEDIAN\", while the RI-MAC algorithm achieves optimal results in \"BEST\", \"MEAN  
(6) Scenario 6  
The results for scenario 6 are tabulated in Table 7. The acquired results indicate the following conclusions:  
* For UAV1, CC-MPSO achieves optimal results in \"BEST\", \"WORST\", and \"MEAN\"; RI-MAC is optimal in \"MEDIAN\"; and ECPO-ME is optimal in \"SD\". * For UAV2, CC-MPSO is optimal in \"BEST\", \"MEAN\", \"MEDIAN\", and \"SD\", and RI-MAC is optimal in \"WORST\". * For UAV3, CC-MPSO is optimized in \"MEAN\" and \"SD\", and RI-MAC is optimized in \"BEST\", \"WORST\", and \"MEDIAN\". * For UAV4, CC-MPSO achieves the best results in \"BEST\", \"MEAN\", and \"MEDIAN\", and RI-MAC achieves the best results in \"WORST\" and \"SD\". Figure 12 depicts the most efficient search routes produced by all the algorithms examined in scenario 6. In scenario 6, there are two sets of regions with high likelihood, each consisting of two adjacent regions. One set is located above the map, while the other is located below the map. The four UAVs are positioned centrally between the two clusters of regions. The pathways produced by the PSO, MPSO, and ACO algorithms do not immediately lead to the optimal region. In addition, the paths generated by the ECPO-ME and RI-MAC algorithms can reach the high-probability target area, but their paths produce some invalid nodes. Compared to other algorithms, the CC-MPSO algorithm shows excellent performance in generating search paths that are not only unattracted by interfering target regions but also find the optimal region quickly and directly.
# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 2 Materials and Methods  
### Object Detection Algorithm  
YOLOv8 is an advanced object detection algorithm introduced by Ultralytics in January 2023. Compared to previous versions, it shows significant improvements in detection accuracy and speed on the COCO128 dataset. This model inherits the basic architecture of YOLOv5 while incorporating the latest technologies, such as an anchor-free prediction structure, stacked convolutions for feature extraction, and a new weighted loss calculation method. The entire architecture is divided into four main parts: the input, backbone, neck, and head [21; 22; 23].  
In the structure of YOLOv8, as shown in Figure 1, the initial step involves mosaic data augmentation of the input image to increase data complexity. Then, the image proceeds to the backbone, where it progressively extracts and generates feature maps of different sizes. Next, the neck module is responsible for effectively merging these multi-scale feature maps. Finally, the head module adjusts the scale of the feature maps according to specific task requirements and outputs the final predictions.  
In the backbone of YOLOv8, the original C3 module from YOLOv5 is replaced by the new C2F feature extraction module. The C2F module consists of the following steps: a slicing operation, two \(1\times 1\) convolutions, multiple bottleneck operations, and a final concatenation. First, the \(1\times 1\) convolution is used for cross-channel information interaction. Then, the slicing operation divides the feature map into two parts along the channel dimension, one of which undergoes multiple bottleneck operations, with each result being concatenated with the feature map from the first convolution. Another \(1\times 1\) convolution compresses the channel number of the concatenated feature map back to that of the input feature map, a design that ensures model lightness while enriching gradient information.  
In the head part, YOLOv8 adopts a different design approach from YOLOv5, changing from a coupled head to a decoupled head, which separates the classification and localization prediction heads and removes the object branch. This change shifts the model from an anchor-based prediction method to an anchor-free method, eliminating the need to design prior boxes and reducing the time spent by the model in removing redundant boxes during prediction.
# A Real-Time DAO-YOLO Model for Electric Power Operation Violation Recognition
## 4 Experimental Results and Analysis
### Subjective Evaluation  
To better demonstrate DAO-YOLO's enhanced detection capabilities, Figure 8 presents comparative results on representative power operation images before and after our improvements.  
Figure 8a-d demonstrates that while YOLOv8 fails to detect irregularly shaped locked hooks, high-altitude falling objects, and operators, our model successfully identifies all objects, confirming the DC2f module's efficacy in extracting features from irregular objects. Given the notable size variations among these three types of missed objects, the model's detection results further demonstrate the effectiveness of the AMFE module in extracting multi-scale object features. As illustrated in Figure 8e, the bounding box predicted by the YOLOv8 model for the crane has an aspect ratio similar to the ground truth, but its  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Method** & **[EMAIL_ADDRESS](\%)** & **[EMAIL_ADDRESS]-0.95(\%)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]-0.95** & **Parameters/M** & **GFLOPS** & **FPS** \\ \hline YOLOv5n & 83.2 & 57.3 & 0.133 & 0.137 & 2.5 & 7.1 & 88.7 \\ YOLOv6n & 82.5 & 56.9 & 0.159 & 0.141 & 4.2 & 12.1 & 83.3 \\ YOLOv7t & 84.3 & 58.5 & 0.119 & 0.129 & 6.0 & 13.1 & 79.6 \\ YOLOv8n & 85.0 & 59.5 & 0.104 & 0.120 & 3.0 & 8.1 & 86.2 \\ YOLOv9t & 82.3 & 57.7 & 0.144 & 0.136 & 2.0 & 7.7 & 87.8 \\ YOLOv10n & 81.9 & 56.8 & 0.162 & 0.151 & 2.3 & 6.5 & 89.7 \\ YOLOv11n & 84.3 & 59.0 & 0.117 & 0.125 & 2.6 & 6.3 & 90.3 \\ DAO-YOLO & 88.2 & 63.9 & 0.070 & 0.101 & 2.9 & 7.9 & 86.7 \\ \hline \end{tabular}
\end{table}
Table 4: Quantitative comparison between our DAO-YOLO and other YOLO models.  
overall size is significantly smaller. In contrast, the bounding box predicted by our model aligns much more closely with the ground truth, demonstrating the effectiveness of the OCIoU loss. Figure 8f shows that the YOLOv8 model fails to completely enclose the left worker within its predicted bounding box. This issue occurs due to two factors: firstly, the inherent limitation of the CIoU loss function when optimizing aspect ratios, and secondly, the cluttered background around the worker's feet combined with color similarities between the operator clothing and the environment, requiring stronger feature discrimination. In contrast, the proposed model achieves accurate and complete operator detection, validating both the improved feature representation capability of the DC2f and AMFE modules and the enhanced object localization accuracy provided by the OCIoU loss.  
Figure 8: Subjective evaluation of detection results between our DAO-YOLO and YOLOv8 models. (**a–f**) are from the EPOVR-v1.0 dataset.
# Design of A Parallel Decoding Method for LDPC Code Generated via Primitive Polynomial
## 3 Parallel Decoding Method for m-Sequence Codes
### Parallel Decoding Method with Multiple Sub-Decoders
```
1:The LLR value \(\mathbf{L}_{ch}\) of channel output; Maximum iteration number \(t_{0}\); Basic parity-check matrix \(H_{0}\); Parity-check matrices \(H_{1},H_{2},\ldots,H_{N}\). 2:The decoding result \(\mathbf{\hat{c}}\). 3:Begin procedure
4:Initialize: Let \(S\) be the set of candidate codewords, \(t_{i},i=1,2,\cdots,N\) is the half of the girth of \(H_{i}\). 5:In sub-decoder \(D_{0}\), \(\mathbf{\hat{c}}_{0}=BP_{h}(H_{0},\mathbf{L}_{ch},t_{0})\). 6:if\(H_{0}(\mathbf{\hat{c}}_{0})^{T}=0\)then
7:\(S:=S\cup\{\mathbf{\hat{c}}_{0}\}\)
8:endif
9:for\(i:=1;\ i<N;\ i++\)do
10:In sub-decoder \(D_{i}\), \(\mathbf{L}_{ex}^{i}=BP_{s}(H_{i},\mathbf{L}_{ch},t_{i})\). 11:\(\mathbf{\hat{c}}_{i}=BP_{h}(H_{0},\mathbf{L}_{ch}+\mathbf{L}_{ex}^{i},t_{0})\). 12:if\(H_{0}(\mathbf{\hat{c}}_{i})^{T}=0\)then
13:\(S:=S\cup\{\mathbf{\hat{c}}_{i}\}\). 14:endif
15:endfor
16:if\(S=\emptyset\)then
17:\(S:=\{\hat{c}_{0},\mathbf{\hat{c}}_{1},\mathbf{\hat{c}}_{2},\ldots,\mathbf{ \hat{c}}_{N}\}\). 18:endif
19:\(\mathbf{\hat{c}}=\operatorname*{argmin}_{\mathbf{s}\in S}d(\mathbf{L}_{ch}, \mathbf{s})\)
20:return\(\mathbf{\hat{c}}\). ```  
**Algorithm 2**Parallel decoding method
# Refractive Index Sensing through Surface Plasmon Resonance in Light-Diffusing Fibers
## 2 SPR Sensor Based on an LDF  
The LDF chosen as the host platform (Fibrance(r) by Corning(r), New York, NY, USA) is composed of a silica core with a diameter of 170 \(\upmu\)m, and a low-index polymeric cladding with a diameter of 230 \(\upmu\)m [15] (see Figure 1a). In the chosen LDF, the guided light is scattered radially outward through a plurality of helical voids randomly distributed in the core, and wrapped around the long axis of the optical fiber (see Figure 1b). The pitch of the helical voids controls the amount of the side-emitted light, with smaller pitches scattering more light than larger pitches, while their diameter ranges in size from 50-500 nm; consequently, they scatter the propagating light almost independently of the wavelength of light used [15].  
For our tests, a 2 cm length of the fiber was unclad by means of a Miller stripping tool. In the first experimental configuration, one end of the fiber was coupled to the white light emitted by a tungsten halogen light source (HL2000, Ocean Optics), while the transmitted light was collected through a spectrophotometer with a detection range from 350 to 1023 nm (FLAME-S-VIS-NIR-ES, Ocean Optics). The outline of this sensor system (intensity-based LDF sensor) is shown in Figure 2a.  
The SPR sensor was then obtained by depositing, through sputter coating (Bal-Tec SCD 500), a thin gold film over the same unclad portion of the LDF. Gold was chosen due to its high stability and ease of functionalization for biosensing applications. The sputtering process was repeated twice, upon rotating the fiber by 180\({}^{\circ}\), in order to metalize the whole fiber circumference. The so-obtained gold film had a nominal thickness of 60 nm, and presented a good adhesion to the substrate, tested by its resistance to rinsing in de-ionized water. As shown in Figure 2b, the changes in the transmission spectrum induced by changes of the SRI have been recorded by using the same equipment previously described (halogen lamp and spectrometer).  
Figure 1: **(a)** Schematic cross-section of the light-diffusing fiber (LDF) used for surface plasmon resonance (SPR) sensing; (b) Schematic lateral view of the LDF used for SPR sensing, showing the portion of unclad fiber.
## 3 Methods
### Flow Simulator  
As depicted in Equation (3), a local velocity distribution (\(v_{ar}\)) is required to obtain the heat transfer coefficient, \(h_{c}\). It is difficult to measure the airflow velocity in the cooling space because of its very small value, except around the outlet of the air conditioner. CFD is effective in this regard as the flow is easily changed by the measuring device or the person measuring. Thus, CFD was used herein for obtaining the airflow velocity distribution.  
To obtain air velocity information in the cooling space, the space shown in Figure 2 was modeled, with the velocity measured with an anemometer at the discharge position of the desiccant cooler as a boundary condition. No-slip boundary conditions were imposed on the solid walls. Pressure outlet boundary conditions were imposed on the exits.  
The CFD analysis was performed using commercial software Star CCM+, considering the cooling space shown in Figure 4b as the computational domain for which a grid system was constructed using 3 million hexahedral cells. This was slightly greater than in a recent study by [PERSON] et al. [29], which had used 1.6 million grids. Reynolds-averaged Navier-Stokes analysis was performed to solve the 3D Navier-Stokes equation for incompressible flow. Recently, simulation results, including buoyancy [30] and radiative heat transfer [29], have been published. They were included in the analysis considering heating elements such as occupants and lighting equipment in the cooling space. In the present simulation, these heating elements were not included, so buoyancy and radiant heat transfer were not considered.  
Figure 4: A measurement location on the cooling space, the computational domain for CFD and code validation data: (**a**) thermal environmental measurement points (The blue axis represents the line from which the data for code validation were extracted.); (**b**) computational domain with grid system; (**c**) comparison of velocity profile obtained by CFD with measured data in dining room.  
The RNG (Re-Normalization Group) \(k\)-\(\varepsilon\) model, which has been verified for similar problems in previous studies [19; 20; 22; 24; 30], was adopted as the turbulence model. [PERSON] et al. [30] mentioned that the RNG \(k\)-\(\varepsilon\) model gives the best results because the flow generated by the air conditioner is close to the free jet flow. Pressure-velocity coupling was handled with the SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm. The convection terms were discretized with a second-order upwind scheme. Moreover, the convergence level was set at \(10^{-5}\).  
In Figure 4c, the numerical analysis results were validated based on the velocity profile in the dining room. First, it can be confirmed that the result obtained using 3 million grids agrees well with the result using 2 million grids to obtain a grid converged solution. Next, when comparing with the measurement data, the velocity profiles obtained via simulation at the four velocity measurement points agree well within the measurement error range.
## 6 Discussion and Conclusions  
### Discussion  
From the above research, it can be seen that a key aspect of using the complex variable function method to solve the underground rock stress problem is to accurately determine the mapping function of the plane around the hole, because it directly affects the accuracy of the solution results. The accuracy of the mapping function can be effectively improved by increasing the number of conformal transformation terms. When compared with the three functions in the traditional rectangular roadway complex function method, the four functions adopted in this paper have a higher mapping accuracy for roadways with larger aspect ratios, and the shape of the mapping is almost the same as the actual situation. This accuracy can better show the stress distribution characteristics and stress concentration of roadway surrounding rock with a large aspect ratio.  
The complex variable function method provides a robust and mathematically rigorous method for solving the stress field in underground mining design, but it is not devoid of limitations. Firstly, the application of complex variable function theory necessitates simplifications in both the calculation model and boundary conditions. In this study, given the uniformity and integrity of the rock mass, it has been treated as a homogeneous elastic material. Nevertheless, in certain underground engineering practices, geological conditions can be intricate and varied--such as faults, inclined strata and layered roofs. Under these circumstances, when dealing with heterogeneous rock masses or those containing defects, such simplifications become evidently untenable. In such cases, relying solely on numerical simulations proves challenging. Therefore, supplementary analyses through similar simulation or field observations may be conducted to more effectively address issues encountered in practical engineering applications.