esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Energy (T and R)-Caused Vegetation Anomalies  
The presented results indicate that nearly all VPs are highly sensitive to solar radiation over cold and polar biomes. These results are similar to the findings of [PERSON] et al. [74], who investigated the GC links between photosynthetically active radiation (PAR) on SIF. The strong dependency of vegetation over cold and polar biomes, especially in Northern Russia and Northern Scandinavia (see Figure 3), can be explained by the linkage of snowmelt and solar irradiance. The snow cover dominating over higher latitudes typically causes high reflectance within visible to NIR wavelength domains. In contrast, being a near-black body, (cold) snow absorbs and re-emits most thermal infrared radiation [75; 76]. This infrared component can melt snow under bright conditions when the temperature is below zero, and, consequently, the increased SM aids photosynthetic activity [77]. Thus, the causality by radiation is higher as opposed to that by temperature over these areas: (e.g., cold biome T\(\rightarrow\)LAI mean F stat = 1.09 versus cold biome R\(\rightarrow\)LAI mean F stat = 2.43). The greater influence of irradiance than the air temperature on snowmelt was also confirmed by [PERSON] and [PERSON] [78]. Favorable growing conditions over colder biomes, where the cyclic variations in CO\({}_{2}\) fluxes are controlled by light-limited gross primary productivity (GPP), also reveal the underlying driving force from solar radiation [79]. The general tendency for the primary control of R on higher latitudes is also visible in Figure 6, with additional influences from SM. Analogous findings were presented by [PERSON] et al. [57]. The low causality from radiation in tropical areas can be linked to the long convective season caused by the moist atmospheric boundary layer [80]. This results in nearly omnipresent inter-tropical convergence zone cumulus cloud layer clouds blocking incoming solar radiation [81] and hindering the consistency of gap-free data streams observed by satellites [82]. Moreover, the weak response to R over lower latitudes can be further reasoned by the biotic factors controlling canopy photosynthesis [83].  
Temperature leads to a relatively even distribution of F stats. Consequently, T seems to drive F stats that are more evenly spatially distributed, thus being primary causal factors over areas, where other factors have no significant effect on VPs, as per Figure 6. Hence, T is the dominant driver over North America, Europe, and Eastern Asia for SIF and over Western Australia for the FAPAR. This spatial distribution agrees with the findings of [PERSON] et al. [79]. A stronger T\(\rightarrow\)VP causality over temperate regions can possibly be linked to the higher dependency of vegetation on temperature during growing seasons. Similar results were also presented by [PERSON] and [PERSON] [84], [PERSON] and [PERSON] [85]at European continental scales, and by [PERSON] et al. [86], who also confirmed the reliance of the NDVI on T over various climatic zones in China.
## 3 Proposed Method
### Refinement of Camera Calibration Using Lane Detection
Using Equation (5), the camera calibration can be updated to account for changes in the vertical movement of the vehicle. Equation (6) provides the formulation for the camera calibration, where \(x_{i}\) represents the image coordinate system, \(K\) is the intrinsic matrix, \(R\) is the rotation matrix, \(T\) is the translation matrix, and \(X_{w}\) denotes the world coordinate system [27]. \[x_{i}=K[R|T]X_{w} \tag{6}\]  
The rotation matrix, \(R\), from Equation (6) is structured as shown in Equation (7). In Equation (7), \(R_{x}\) denotes the rotation matrix about the \(x\)-axis, \(R_{y}\) denotes the rotation matrix about the \(y\)-axis, and \(R_{z}\) denotes the rotation matrix about the \(z\)-axis, respectively [27]. \[R=R_{x}R_{y}R_{z} \tag{7}\]  
The movement in the \(y\)-axis position of the vanishing point can be computed as a function of the angle from the \(x\)-axis, as shown in Equation (8). In Equation (8), \(S_{d}\) represents the \(y\)-axis movement of the vanishing point calculated from Equation (5), \(f_{y}\) denotes the focal length along the \(y\)-axis, and \(\theta_{o}\) represents the existing \(x\)-axis rotation angle. \[\theta_{n}=\arctan(S_{d}/fy)+\theta_{o} \tag{8}\]  
Applying \(\theta_{n}\) from Equation (8) to the rotation matrix \(R_{x}\) yields Equation (9). The camera calibration parameters in Equation (6) should be revised using the modified rotation matrix parameters. \[R_{x}= \begin{bmatrix}1&0&0\\ 0&\cos\theta_{n}&-\sin\theta_{n}\\ 0&\cos\theta_{n}&\cos\theta_{n}\end{bmatrix}  \tag{9}\]
## 3 Experimental Study  
### Measure the Round-Trip TM  
Before imaging the object, the round-trip TM \(T_{O\to E}(x,y;\xi,\eta)\) of the scattering medium should be measured in advance.  
The experimental setup is shown in Figure 2. A He-Ne laser with a wavelength of 632.8 nm is split into two by a beam splitter (BS1). The transmitted beam is reflected off by a mirror, after being reflected at a second beam splitter (BS2), the beam is distorted via the scattering medium to illuminate the DMD, then, the beam reflected by the DMD is distorted again via the scattering medium to generate the output wave. Meanwhile, the beam reflected by BS1, with the wave front being modulated by the LCVR, is used as the reference wave. At last, the output wave and the reference wave are combined to form an interference image at the CCD. In this way, the complex field distribution (both the amplitude and the phase) of the output wave can be acquired by using the phase-shifting digital holography technology [24].  
Figure 1: Schematic of imaging an object hidden behind a scattering medium.  
To acquire the round-trip TM, we divide the DMD into \(64\times 48\) segments (the size of each segment \(219\)\(\upmu\)m \(\times\)\(219\)\(\upmu\)m) and turn on only one segment in sequence. The sketch map of the measured \(T_{\mathrm{O\to}E}(x,y;\xi,\eta)\) are shown in Figure 3a,b for the amplitude part and the phase part, respectively (Two-dimensional input DMD segments \((\xi,\eta)\) and output CCD segments \((x,y)\) are both stretched to one-dimensional to facilitate the computation, and only part of the DMD segments and CCD segments are shown). What we want to emphasize is that the measured \(T_{\mathrm{O\to}E}(x,y;\xi,\eta)\) has the ability to overcome the round-trip distortion simultaneously without information loss, which is owing to the fact that it not only has played the role of the one-way TM, but has also recorded the complex field distribution of the distorted illumination wave.
# Hybridizing Lithography-Based Ceramic Additive Manufacturing with Two-Photon-Polymerization
###### Abstract  
Stereolithography processes such as lithography-based ceramic manufacturing (LCM) are technologies that can produce centimeter-sized structures in a reasonable time frame. However, for some parts specifications, they lack resolution. Two-photon-polymerization (2 PP) ensures the highest geometric accuracy in additive manufacturing so far. Nevertheless, building up parts in sizes as large as a few millimeters or even centimeters is a time-consuming process, which makes the production of 2 PP printed parts very costly. Regarding feedstock specification, the requirements for 2 PP are different to those for LCM, and generally, feedstocks are designed to meet requirements for only one of these manufacturing technologies. In an attempt to fabricate highly precise ceramic components of a rather large size, it is necessary to develop a feedstock that suits both light-based technologies, taking advantage of LCM's higher productivity and 2 PP's accuracy. Hybridization should bring the desired precision to the region of interest on reasonably large parts without escalating printing time and costs. In this study, specimens gained from a transparent feedstock with yttria stabilized zirconia (YSZ) particles of 5 nm at 70 wt% were presented. The resin was originally designed to suit 2 PP, while being also printable with LCM. This work demonstrates how hybrid parts can be sintered into full YSZ ceramics.  
ceramic; additive manufacturing; transparent feedstock; hybridization; powder processing; yttria stabilized zirconia +
Footnote †: journal: Applied Sciences  
1
## 5 Recommendations Related to Access and Connectivity
### Technological Solutions  
Although many technological options are proposed in the literature, satellite communication has been identified as the only possible solution for most rural areas, due to its global coverage. [PERSON] [60] points to the combination of cable with wireless technologies as the most cost-effective option. [PERSON] et al. [17] suggest implementing satellite or wireless connections, as expanding coverage through satellite systems would improve geographic coverage and connection speed. They stress the difficulty of installing xDSL (Digital Subscriber Lines) and propose BFWA (Broadband Fixed Wireless Access), DVB-S (Digital Video Broadcasting by Satellite) and WiMAX (Worldwide Interoperability for Microwave Access) as alternatives. They also recommend redefining the models of collaboration between telecommunications service providers and the public sector.  
[PERSON] et al. [64] propose the combination of backhaul satellite communication with 4G fronthaul networks. Likewise, they prove that caching the most popular content to reduce traffic carried over the satellite link leads to a significant decrease in operating cost (more than 60%), which also implies a significant decrease in average cost per user.
# Analytical Evaluation of Laser Cleaning Effectiveness in the Context of Contemporary Muralism
## 2 Materials and Methods
### Instrumentation
#### 2.2.2 Micro-Attenuated Total Reflection-Fourier Transform Infrared (Micro-ATR-FTIR)  
Micro-ATR-FTIR was used to analyze the paint material and cleaning efficiency in a non-invasive manner. For each cleaning test section, the spectra were collected before and after cleaning using a Perkin Elmer Spectrum Spotlight 300 FT-IR spectrometer (Waltham, MA, USA) equipped with a Germanium ATR crystal. All spectra were collected in the wavenumber range of 750-4000 cm\({}^{-1}\), with a resolution of 4 cm\({}^{-1}\) and 16 scans per spectrum.
# Vectorized Numerical Algorithms to Solve Internal Problems of Computational Fluid Dynamics
## 4 Addressing Mesh Cells
### Addressing to Internal Cells  
Implementation of computational algorithms requires addressing to current cell or node and values in neighboring cells or nodes included in the finite-difference template. The traditional way to address flow quantities is to specify two indices defining line and column of two-dimensional array.  
Using one-dimensional array of indices, it is possible to specify a subset of cells of mesh (Figure 1). For example, denoting by \(\mathbf{pC}\) the vector (set) of indices of all internal cells (Figure 1a), subsets of cells that are above (Figure 1b) or below a current layer of cells are addressed. To address neighboring cells, it is necessary to add or subtract one from all components of the vector \(\mathbf{pC}\)  
\[\mathbf{pU}=\mathbf{pC}+1,\quad\mathbf{pD}=\mathbf{pC}-1.\]  
Using similar procedure, the index vectors for left and right cells of computational domain are defined  
\[\mathbf{pL}=\mathbf{pC}-m,\quad\mathbf{pR}=\mathbf{pC}+m.\]  
The integer value \(m\) determines the number of cells in the column of computational domain.  
Figure 1: Addressing subsets of central cells (**a**), upper cells (**b**) and left cells (**c**) in a rectangular computational domain.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Cloud-Based Batch Processing Vbda (2013-2016)  
As video data grew in volume and complexity, VBDA systems shifted from on-premises architectures to cloud-based infrastructures. Distributed frameworks like Apache Hadoop ([[https://hadoop.apache.org](https://hadoop.apache.org)]([https://hadoop.apache.org](https://hadoop.apache.org)), accessed on 12 May 2025) and Apache Spark ([[https://spark.apache.org](https://spark.apache.org)]([https://spark.apache.org](https://spark.apache.org)), accessed on 12 May 2025) facilitated large-scale batch analytics, allowing efficient processing of video archives within elastic cloud infrastructures [24; 25]. Simultaneously, cloud object storage platforms like Amazon S3 and Google Cloud Storage offered scalable, cost-effective alternatives to traditional systems, supporting flexible data ingestion and on-demand compute provisioning [26].  
This period also marked the integration of deep learning models such as VGGNet and ResNet into VBDA pipelines, improving object detection and scene interpretation through cloud-native AI services [27]. Elasticsearch, widely adopted during this phase, enabled scalable indexing and fast metadata retrieval across distributed video repositories [25]. However, batch-based processing introduced significant latency and lacked responsiveness. These limitations rendered it less suitable for real-time scenarios such as surveillance and traffic analytics [24]. These limitations spurred the shift toward more agile, edge-driven architectures in the next phase.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 3 Overloading Mitigation Strategies
### External Frequency Support  
The last approach to limit overloading in GFM inverters is to decouple synchronization duties and frequency support, similar to GFL inverters [38]. Based on the active power controller proposed in [39], the EFS introduces a flexible control structure that decouples primary frequency regulation, inertia, and synchronization.  
The proposed controller is shown in Figure 10. This approach is based on a cascaded control structure for the active power controller, composed of an inner and an outer loop. The inner controller is a power regulator, based on a PI, which provides power-based synchronization with the grid and ensures GFM behavior. The outer controller provides inertia and primary frequency regulation by modifying the power setpoint of the inner controller. In the event of a frequency excursion, if the active power reaches its limits, the outer loop power setpoint, \(P_{EFS}\), is saturated, thereby limiting frequency support. During overloading scenarios, the inertial contribution is only determined by the dynamics of the inner controller, without disrupting the synchronization.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 1 Introduction
Another challenge faced by the D2D communication-based IoT 5G network is that mobile devices are equipped with limited battery power; therefore, energy utilization must be optimized to maintain the network's activity. Various strategies have been adopted in order to save energy, including limiting the broadcast to a cluster to share the information of the required energy or remaining within the metric of the routing protocol [21; 22; 23]. Most of the suggestions to save energy were solely based on single-path routing protocols. However, selecting the same path every time to forward the traffic from the source to the destination node impacts the routing protocol efficiency and affects the battery life of the intermediate devices. Thus, low efficiency was obtained when the same path to a destination was selected to forward the traffic, reducing the battery life of the devices. On the other hand, a large proportion of overhead traffic generation to repair the routes when established routes become obsolete due to the random movement of the devices leads to higher consumption of energy, and affects the performance of the routing protocol.
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 4 Data Acquisition
### Metering Devices
#### 4.1.1 Sampling Frequency  
The data sampling can be classified into high-speed sampling and low-speed sampling. Depending on the target application, the sampling rate for electricity consumption may vary. Some authors [21; 43; 45] define a fairly high sample rate as from 1 kHz to almost 100 kHz. For higher sampling rates, the authors in [45] state that the identification results are more precise, typically allowing to capture state transitions and eventually separating brands in the same category. To monitor the electro-magnetic interference generated by the switch-mode power supplies, the sampling frequency is required to be at least hundreds of kilohertz [43].  
At present, most commercial devices cannot achieve high-speed sampling. Furthermore, the complexity of data storage, transmission and processing for high-speed sampling is significantly increased compared to low-speed sampling. Therefore, high-speed sampling is not currently considered a practical approach for large-scale solutions. The low-speed sampling rate is usually set to 1 Hz or even less. As a result, the resolution of the data drops significantly. With low-speed sampling, the transient state of the electrical information can no longer be captured and features may overlap [43]. The type of information that can be inferred from metering devices data has proven to be slightly related to the sampling frequency [21]. Figure 8 illustrates the common sampling frequencies used in Data Acquisition, including the features which can be extracted from the data at each sampling frequency. This figure was built based on the information given in [21].
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 3 Experimental Evaluation
### Loss Function  
The loss function is used to measure the inconsistency between the predicted value \(f(x)\) and the real value y of the model. It is usually expressed by \(L(y,f(x))\) and can also be called the Cost Function. The smaller the value of loss function, the better the fitting of the model.  
The loss function used in this experiment is Mean Absolute Error (MAE), also known as L1-loss. MAE is the sum of absolute values of the difference between the target value and the predicted value, which can be used to measure the difference between the predicted value and the real value. The calculation formula is as follows:  
\[L(y_{i}-\hat{y_{i}})=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y_{i}}\right| \tag{14}\]  
where \(y_{i}\) is the target value and \(\hat{y_{i}}\) is the predicted value.
## 4 Statistical Analysis
### Simple Regression Analysis  
Regression analysis expresses the relationship between the independent and dependent variables in the data as a functional formula to use the independent variable to predict the dependent variables. Equation (5) shows a form of a linear regression model using actual data. The intercept means the initial value, and the slope is the influence (weight). Depending on the sign, the slope can be interpreted as a positive or negative correlation between the two variables. The larger the absolute value, the greater the influence (weight) of the independent variable on the dependent variable [26].  
\[Y_{i}=b_{0}+b_{1}x_{1} \tag{5}\]  
\(Y_{i}\): dependent variable; \(x_{t}\): independent variable; \(b_{0}\) : intercept; \(b_{t}\) : slope.  
Table 6 shows the simple linear regression analysis results between the natural water content ratio, liquid limit, plastic limit, and compression index. According to the single-factor linear regression results, the highest Coefficient of Determination was found in the regression equation between the natural water content and compression index. The plastic limit's Coefficient of Determination was the lowest, just as in the Pearson correlation analysis results. In addition, all independent variables were positively correlated with the dependent variable (Cc). The reliability of the simple regressions in Table 6 can be assessed using R\({}^{2}\), and the results are similar to those of previous studies in similar geographic regions [8; 9; 27; 28]. This was likely due to the analysis using relatively little data from a wide target area. Therefore, it is necessary to apply statistical analysis and machine learning techniques to propose a regression model for predicting the compression index using multiple factors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Regression Equation** & \(\mathbf{R^{2}}\) \\ \hline Natural Water Content & \(C_{c}=0.0167W_{n}-0.2913\) & 0.687 \\ \hline Liquid Limit & \(C_{c}=0.0125\)LL \(-0.0146\) & 0.425 \\ \hline Plastic Limit & \(C_{c}=0.0284\)PL \(-0.0019\) & 0.223 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Linear regression analysis results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **VIF** \\ \hline Natural Water Content & 15.025 \\ \hline Liquid Limit & 93.882 \\ \hline Plasticity Index & 60.218 \\ \hline Plastic Limit & 9.899 \\ \hline Initial Void Ratio & 14.976 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The VIFs of the initial data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **VIF** \\ \hline Natural Water Content & 2.149 \\ \hline Liquid Limit & 3.262 \\ \hline Plastic Limit & 2.101 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The VIFs of the final data.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 3 A Festival Mobility Model
### The Sonar Festival Dataset  
#### 3.1.1 Description  
The Sonar Festival dataset [13] offers information about the mobility of participants at the Sonar festival in Barcelona, Spain, which took place on 18-20 June 2015. This festival is a multistage event with more than 100,000 attendees in two main venues, Sonar by Day and Sonar by Night. The data were collected during the daytime in six locations of the venue and have been anonymized in order to reduce privacy issues.  
The data consist of time points recorded by Raspberry Pi 2 nodes distributed in strategic points across the festival grounds, in order to maximize the covered area. Around six million Wi-Fi events were recorded during this experiment.