esa-sceva/satcom-chunk-collection huggingface dataset:
# Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain
## 2 Methodology
### Retscreen  
RETScreen is managed by the research center of Natural Resources Canada (NRCan). This software can be used to evaluate the energy production and savings, costs, emission reductions, financial viability, and risk for various types of renewable-energy and energy-efficient technologies (RETs) [32; 33; 34; 35; 36; 37]. In addition, the RETScreen software integrates a series of databases that help to overcome the costs and difficulties associated with gathering meteorological data, product performance data, etc. Hence, global meteorological data are incorporated in the RETScreen software. This meteorological database includes both the ground-based meteorological data and NASA's satellite-derived meteorological data sets.  
RETScreen has a basic user-expandable PV panel. The database covers no other element of the PV facility. The following information must be inputted: type of panel (poly-Si, mono-Si, etc.), total PV installed power of facility, efficiency, estimated electrical losses, losses caused by shadows. The inverter data, defined by the user, are input power, performance and estimated losses.  
The economic analysis of the system requires the definition of estimated operational life, initial investment, loans, and grants, and the program can be used to analyze various economic scenarios.  
As output, it generates different tables and graphs: daily solar horizontal irradiation by month, the estimation of annual electricity production (MWh), environmental benefits (CO\({}_{2}\) emissions savings, equivalent in barrels of petroleum), return on investment period, evolution of the benefits over the operating life of the installation, internal rate of return (IRR), net present value (NPV), and unit cost of energy. In the most recent updated version, RETScreen Expert, has become a paid program.
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 3 Impact Assessment Model for Traffic Operation Status of Large Bridges
### Calculation of Traffic Impact on the Bridge  
The weights of the influence of different components of the bridge are \(\omega_{i},\ i=1,2\ldots\). The results of the inspection of different components of the bridge are \(a_{i},\ i=1,2\ldots\). Then, the impact of the bridge on traffic is:  
\[y(f)=1-\sum_{i=1}^{n}\omega_{i}a_{i} \tag{1}\]  
The maximum traffic capacity of the road section is:  
\[C_{s}=C\times(1-y(f)) \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Lighting Facilities** & **Specific Meaning** \\ \hline Class 1 & The evaluation results of the technical condition of the lighting facilities are all on Class 1 and in good condition. \\  & The evaluation result of the technical condition of the lighting facilities has reached Class 2, and there are a few deficiencies in the lighting facilities, but within a reasonable state. \\  & The evaluation result of the technical condition of lighting facilities has reached Class 3, and there are many deficiencies in the lighting facilities, reaching or approaching the standard value. \\  & The evaluation result of the technical condition of lighting facilities has reached Class 4, with a large number of lighting facilities missing and exceeding the standard value. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Traffic-influencing factors of lighting facilities inspection results.  
Figure 2: Flowchart of bridge traffic impact assessment system.  
Among them, \(C_{s}\) is the current maximum traffic capacity of the evaluated section, \(C\) is the traffic capacity designed for the evaluated section, and \(y(f)\) is the traffic impact value from the evaluation section.  
The degree of traffic impact on different influencing factors of bridges is \(f_{bi},\ i=1,2\). The weights of the influence of different influencing factors are \(\beta_{i},\ i=1,2\). The traffic impact result is recorded as \(f_{2}\). The value of the impact \(f_{2}\) of the bridge on traffic can be calculated using the following formula:  
\[f_{2}=\begin{cases}1-\sum\limits_{i=1}^{2}\beta_{i}(1-f_{bi})&f_{b1}<1.0,\ f_{b2}<1.0\\ 1&f_{b1}=1.0\end{cases} \tag{3}\]  
In the formula, \(f_{b1}=1.0\) refers to the maximum impact of the main structure inspection results on traffic, and \(f_{b2}\) refers to the impact of the bridge deck system inspection results on traffic.
## 3 Structural and Gas Distribution Characteristics of the Kaiping Syncline Turning End
### Gas Content Distribution Characteristics at the Turning End  
Due to significant differences in coal thickness, striatal dip angles, and hydrogeological conditions between the northern and southern limbs of the Kaiping syncline turning end, the Kaiping syncline structural zone can be further divided into Kaiping Syncline Structural Zone I (Zhaogezhuang mine), located on the northern limb, and Kaiping Syncline Structural Zone II (Linxi mine), located on the southern limb.  
Based on the actual geological conditions and measured gas content data from the Zhaogezhuang mine, combined with gas analysis from the Linxi mine, the gas content shows a significant decreasing trend from Zone I to Zone II. Additionally, influenced by the turning end of the Kaiping syncline, the gas content shows a marked increasing trend from Kaiping Syncline Structural Zone I to the wellhead wide syncline zone and the northwest limb fault zone. Similarly, from Kaiping Syncline Structural Zone II to the monoclinic structural zone, the Dujunzhuang Anticline zone, and the Heiyazi Syncline zone, there is also an upward trend in gas content, although the increase is much smaller (Figure 4).  
Figure 3: Ideal stratigraphic distribution model diagram (**a**) and strata dip angle calculation model diagram (**b**).  
According to the gas content contour maps of the No. 7, No. 9, and No. 12 coal seams, the overall trend indicates that gas content increases with coal seam depth. Gas content is relatively lower near the axis of the Kaiping syncline, but increases with distance from the axis on both limbs. However, the rate of increase differs significantly between the two limbs, which is particularly evident in the main coal seams of the Linxi and Zhaogezhuang mines.  
According to gas content statistics from the Zhaogezhuang and Linxi mines, the gas content in the No. 7 coal seam of Zhaogezhuang ranges from 4.0 to 8.0 m\({}^{3}\)/t, while in Linxi it ranges from 2.8 to 5.6 m\({}^{3}\)/t. In the No. 9 coal seam of Zhaogezhuang, the gas content ranges from 4.0 to 9.2 m\({}^{3}\)/t, whereas in Linxi, it ranges from 2.8 to 5.6 m\({}^{3}\)/t. In the No. 12 coal seam of Zhaogezhuang, the gas content ranges from 4.8 to 12.0 m\({}^{3}\)/t, while in Linxi it ranges from 3.2 to 6.6 m\({}^{3}\)/t. A comparison of gas content on both limbs of the turning end shows that the gas content in the coal seams of the Zhaogezhuang mine, located on the northwest limb of the Kaiping syncline, is higher than that in the Linxi mine on the southeast limb. Notably, the maximum gas content in the No. 12 coal seam of Zhaogezhuang is 1.8 times higher than that in Linxi.
## 4 Experimental Results and Discussion
### Summary of Results and Discussion on WOA's Dominance  
In our experiments, the optimization-based imputation method, particularly the WOA approach, consistently demonstrated superior performance across a variety of datasets and missing-data rates. In the first experiment, with synthetic data, WOA outperformed other optimization algorithms, securing the top position, followed by SCA, GWO, and PSO. While differences in performance were noted among the algorithms, no significant outperformance was observed, reinforcing WOA's status as the most effective optimizer. The second experiment with real data further highlighted the effectiveness of the FS-WOA imputation approach, which ranked first against various state-of-the-art methods, showcasing its reliability across diverse datasets. Despite the challenges faced by other imputation techniques, such as KNN, which excelled only on specific datasets, FS-WOA achieved consistently promising results. In the third experiment, with video-surveillance data, WOA excelled in handling missing-data issues in traffic monitoring, proving its capability to deliver high-quality imputations even under high missing-data rates. Overall, the stability, adaptability, and superior performance of the FS-WOA approach position it as the best choice for missing-data imputation in traffic monitoring and other applications.
# Detection of Underwater Targets Using Polarization Laser Assisted Echo Detection Technique
## 1 Introduction
Compared with traditional target detection, the material properties are not only determined by the target temperature but also by the roughness of the target surface, material, observation angle and other factors. Polarization imaging can obtain the surface roughness and internal structure of the measured object according to the polarization characteristics of reflected and transmitted light and can obtain clear underwater images under the thousand perturbations of particle scattered light. This paper aims to verify the function of laser polarization underwater detectors and explore the different polarization of different objects in different environments for identification. Laser underwater target polarization detection provides a new way of thinking for target identification and detection in an underwater environment, overcomes various interference noise underwater, realizes fast measurement and accurate identification, and has important research significance in the field of ocean and space exploration.
# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 3 Dataset Construction
### Data Preprocess  
After data collection, a dataset of TCS environment patterns was constructed for training deep learning models. This dataset consists of two parts: one is the environmental pattern classification labels for training a classification model, and the other is the semantic segmentation labels for training a segmentation model.  
For the semantic segmentation labels, research has identified natural environmental elements such as mountains, rivers, forests, vegetation, and farmland, in addition to their spatial relationships with settlements, as the main components of TCS environment patterns [51; 52]. Mountains are especially important, having a significant influence on the characteristics of a settlement, such as production methods and factor safety [53; 54; 55]. Rivers and lakes provide essential resources such as drinking water and transportation [56; 57], while forests offer materials for settlement development and maintain comfortable microclimates [58]. The distribution of agricultural land dictates the productive and social properties of a settlement [59; 60], and vegetation enhances soil quality, water conservation, and disaster prevention [61]. All these factors combine to form a relationship pattern between the settlement and its environment [62].  
Based on this research, seven categories that represent environmental features have been identified. These categories and their contents for semantic segmentation are outlined in Table 2. Remote sensing images of TCSs are then semantically annotated according to the definitions given in Table 2. In cases where one element conflicts with another, such as mountains and water, the element with higher influence is given precedence, e.g., water. Therefore, this is not a land cover classification, but a description of the human-land relationship in TCS using the settlement environment map. Five samples of TCSs with different environmental patterns and their corresponding settlement environment maps are shown in Figure 4.  
Finally, the dataset is split into training, validation, and testing sets. The training and validation sets come from Qiandongnan and are randomly split by a ratio of 4:1. The test sets come from the Shaanxi and Anhui provinces. The reason for this dataset partition approach is that TCSs are widely distributed and have regional differences. It is not practical to train a model for each region, and collecting all the TCSs' data to build a massive dataset would require a significant amount of manpower. Therefore, we used two different regions to construct the test set to verify the model's generalization ability and performance. The details of the dataset division are shown in Table 3.  
Figure 3: Remote sensing images of hillside pattern settlement. (**a**) Zeli settlement in Qiandongnan of the Guizhou Province; (**b**) Zhangzhuang settlement in the Shaanxi Province.
## 2 Materials and Methods
### Features  
For each recording, 6373 features were extracted using the OpenSMILE tool (by [PERSON](r), Gilching, Germany, [25]) embedding the INTERSPEECH 2016 Computational Paralinguistics Challenge (ComParE 2016) [25] audio feature set, which contains the vast majority of relevant domains used in voice analysis, including time/energy, spectrum, cepstrum [22], RASTA (RelAtive SpecTrAl) [23], prosody, and perceptual features. First, a forward greedy step-wise filter with a correlation-based feature subset (CFS) evaluator was applied [26]. It is a supervised method based on the maximum-relevance, minimum-redundancy principle, computing a merit factor with correlation as a metric according to the following formula:  
\[M_{S}=\frac{k*\overline{r_{fc}}}{\sqrt{k+k(k-1)*\overline{r_{ff}}}} \tag{1}\]  
where \(k\) is the number of features in a subset \(S\), \(\overline{r_{fc}}\) is the average correlation between features and the class label, and \(\overline{r_{ff}}\) is the average correlation between pairs of features in the subset.  
After the CFS, a feature ranker was applied to further reduce the number of features, employing and comparing the following methods:  
* Correlation: the features were ranked according to the value of their cross-correlation with the class.
* Information Gain (IG): measures the change in entropy, according to [PERSON]'s definition, that a given dataset endures when it is the result of a split performed according to some criterion/threshold on each given feature. It is simply computed as a difference between post (conditioned) and prior entropy values [27; 28].
* Gain Ratio (GR): normalized version of the IG, in order to tackle its potential sensitivity to the dataset cardinality, according to the following formula: \[GainRatio(X)=\frac{IG(X)}{-\sum_{l=1}^{n}\frac{N(t_{l})}{N_{tot}}*log_{2} \frac{N(t_{l})}{N_{tot}}}\] (2) where \(IG\) is the IG for the feature \(X\) that presents \(n\) different values, and \(N(t)\) is the number of occurrences of the value \(t\). The denominator is defined as \"split information\". Note that, for continuous features, IG and GR are computed by sorting all the measured values and creating a corresponding number of splits, with each value acting as a below/above threshold [29].
* Genetic Algorithm (GA): a GA classifier was used as a wrapper, used on one feature at a time, to identify the best performing ones. A 10-fold cross-validation was employed to reduce data selection bias [30].  
We started with a large-scale feature extraction (6373 features) rather than using preselected acoustic features to reduce selection bias and enable a thorough acoustical exploration of the voices by means of a statistically sound algorithmic reduction. This was in accordance with the principles behind the \"Curse of Dimensionality\", as defined by [PERSON] [31], which details how the number of features should ideally be comparable to (if not less than) the cardinality of the dataset to avoid overfitting phenomena and loss of interpretability.
## 4 Experimental Setup
### Samples
An index-matched epoxy cured with ultra-violet (UV) light was used to bond the silica fiber holder to the PIC in the position that gave the best insertion loss. Given the \(\mathrm{\SIUnitSymbolMicro m}\)-scale positioning tolerance of the grating-based coupling, the fiber-array ribbons were also glued on the PCB to limit the mechanical stresses on the fiber holder during manual handling (see Figure 6). Figure 5: Spectral measurements made on the RM under test. (**a**) Complete spectrum. The grating coupler (GC) wavelength-dependent response is clearly visible. (**b**) Detail of the ring resonance used throughout the experimental characterization. IL, insertion loss; \(\lambda_{0}\), optical wavelength.
## 6 COTS Hardware Components
### Enclosure  
We mounted all of the electronics to a simple folded aluminum enclosure with cutouts to reduce weight. For the ground user, we enclosed the completed device in a Pelican case and routed cables through the case to connect to the external antennas. For the UAV user, we mounted the completed device directly to the undercarriage of our DJI S1000+. This is depicted in Figure 11. The case itself is approximately 20 cm \(\times\) 20 cm \(\times\) 5 cm and weighs a little less than 1 kg. This UAV already has storage trays for multiple lithium polymer batteries so we placed an additional 14.8 V, 3700 mAh battery to power the electronics next to the one powering the UAV itself, and connected it with a standard XT90 extension cable.
## 4 Advanced SensorNC: In-Network Processing
### Background on Data Scaling in Network Calculus  
Next, we introduce the necessary background on scaling elements as presented in [17].  
**Definition 6**: _(Scaling function) A scaling function \(S\in\mathcal{F}\) assigns an amount of scaled data \(S(a)\) to an amount of data \(a\)._  
Scaling functions are a very general concept and serve as a model for any kind of data transformation in a network calculus model. Note that they do capture any queueing-related effects;consequently, scaling is assumed to be done infinitely quickly. Queueing effects are captured by the service curve element.  
**Definition 7**.: _(Scaling curves) Given a scaling function \(S\), two functions \(\underline{S}\), \(\overline{S}\in\mathcal{F}\) are minimum and maximum scaling curves of \(S\) iff \(\forall b\geq 0\); it applies that:_  
\[\underline{S}(b) \leq \inf_{a\geq 0}\left\{S(b+a)-S(a)\right\}\] \[\overline{S}(b) \geq \sup_{a\geq 0}\left\{S(b+a)-S(a)\right\}\]  
In [17], it is shown that maximum scaling curves should be sub-additive and minimum scaling curves super-additive. Otherwise, they can be enhanced by computing their super-additive resp. sub-additive closure.  
**Theorem 5**.: _(Alternative systems) Consider the two systems in Figure 4, and let \(R(t)\) be the input function. System (a) consists of a server with minimum service curve \(\beta\) whose output is scaled with scaling function \(S\), and System (b) consists of a scaling function whose output is input to a server with minimum service curve \(\beta_{S}\). Given System (a), the lower bound of the output function of System (b), \(R_{S}^{*}\), that is \(S(R)\otimes\beta_{S}\), is also a valid lower bound for the output function of System (a) if:_  
\[\beta_{S}=\underline{S}(\beta).\]  
The implication of this theorem is that performance bounds for System (b) are also valid bounds for System (a), that is a scaling element can be moved in front of a service curve element if we apply the minimum scaling curve to the service curve of the respective component.  
The effect of scaling on the arrival constraints of a flow is stated in the following corollary.  
**Corollary 2**.: _(Arrival constraints under scaling) Let \(R\) be an input function with arrival curve \(\alpha\) that is fed into a scaling function with maximum scaling curve \(\overline{S}\). An arrival curve for the scaled output from the scaling element is given by:_  
\[\alpha_{S}=\overline{S}(\alpha).\]  
Not that, if the arrival curve \(\alpha\) and maximum scaling curve \(\overline{S}\) are tight, then the scaled arrival curve \(\overline{S}(\alpha)\) also is.  
Figure 4: Alternative systems.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Real-Time AI Video Analytics  
This module performs real-time analytics using advanced deep learning models. It includes three functional groups:  
* **Video Analytics Core:** Executes real-time tasks such as object detection, tracking, and activity recognition using models like YOLOv11, DeepSORT, and EventNet.
* **Video Understanding and XAI:** Generates contextual summaries and explainable insights through captioning and attention visualization using models such as VideoBERT, BLIP, and GradCAM.
* **FL and Privacy:** Coordinates distributed model training and aggregation from edge devices, while preserving privacy using frameworks like FedAvg, TFF, and NVIDIA FLARE. To address non-IID data and client drift challenges in federated learning, an asynchronous FedAvg scheme enhanced with FedProx regularization is adopted as the coordination strategy. Model updates are managed through TFF, while edge orchestration is supported via KubeEdge. Privacy guarantees are reinforced using secure aggregation protocols and optional differential privacy, depending on the application context. While FedAvg and FedProx remain effective for scalable and moderately heterogeneous environments, we also evaluate more recent algorithms: MOON [153], which applies contrastive learning to enhance client representation consistency; SCAFFOLD [154], which uses control variates to mitigate client drift; and FedNova [155], which normalizes local updates to address training time imbalance. These methods are currently under consideration for integration into ViMindXAI's FL stack, particularly in deployments with high skew or dynamic data heterogeneity.