esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Quality Function and Objective Function  
The task of the quality function is to combine all evaluator losses into one scalar. The following additional requirements must be considered:  
* The function should have a simple mathematical form, in order to not complicate the minimum search.
* The function must be monotonically increasing with respect to the evaluators' losses.  
Figure 8: Influence of factor \(F_{\text{k}}\) on filter calculation.  
* The function contains coefficients to control the relative influence of the evaluators losses.  
The most obvious variant fulfilling these criteria is a linear combination of the losses. The problem with this choice consists of the different and variable order of magnitude of the compliance loss with respect to the other losses. For a given choice of the coefficients, the relative influence of the losses changes for different parametrization and input data points. To avoid this drawback, a quality function in the following form is chosen:  
\[f_{\text{Q}}(c,M,F)=(\alpha c+1)\cdot(\beta M+1)\cdot(\gamma F+1)\cdot(\delta P +1). \tag{30}\]  
The addition of the constant value prevents the quality function from being dominated by one loss when its value is close to zero.  
For every single data point, one value of \(f_{\text{Q}}\) exists. Optimization on the basis of single data points would require a large computational effort and lead to instabilities of the training process (large jumps of the objective function output). Therefore, a given number \(b_{n}\) of training data points (batch) is used, and the corresponding quality function values are combined in one scalar value, which works as an objective function for the optimization that rules the training. The value of the objective function  
\[J =\frac{1}{b_{n}}\sum_{i=1}^{b_{n}}f_{\text{Q}_{i}} \tag{31}\] \[=\frac{1}{b_{n}}\sum_{i=1}^{b_{n}}(\alpha c_{i}+1)\cdot(\beta M_{ i}+1)\cdot(\gamma F_{i}+1)\cdot(\delta P_{i}+1)\]  
is calculated as the arithmetic mean of the quality function values obtained for the single training data points of the batch. Investigations showed that averaging the quality function outputs over numerous training data points stabilizes the training procedure. The disadvantage of this averaging is the possibility of forming prejudices, e.g., if one element is frequently present, then its frequency is also learned, even if the element's contribution to stiffness is in some cases small or non-existent.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 2 Fundamentals of Array Processing  
### Signal Model  
Let us consider an array with \(L\) antenna elements that perceives the superposition of a LOSS of interest and a set of \(M\) undesired replicas. The signal as perceived by the different antenna elements can be arranged into an \(L\times 1\) vector \(\mathbf{x}(n)\), thus leading to the following complex baseband signal model at the array output,  
\[\mathbf{x}(n)=\alpha_{0}\mathbf{a}(\theta_{0})s(\tau_{0},\varphi_{0})+\sum_{m= 1}^{M}\alpha_{m}\mathbf{a}(\theta_{m})s(\tau_{m},\varphi_{m})+\mathbf{e}(n) \tag{1}\]  
with \(s(\tau,\varphi)\doteq c(n-\tau)e^{j\varphi}\) being the signal bearing the useful information \(c(n)\), \(\tau\) and \(\varphi\) being the propagation delay and carrier phase, and the subscript \({}_{0}\) referring to the LOSS. In (1), \(\alpha\) is a complex amplitude encompassing information about the signal power, and \(\mathbf{e}(n)\) embeds the noise introduced by each antenna channel. The term \(\mathbf{a}(\theta)\in\mathbb{C}^{L\times 1}\) is the spatial signature of the signal coming from direction \(\theta\) as perceived by the different antennas in the array, and it depends on the antenna inter-separation and array distribution. In the remainder of the paper, we will refer to \(\mathbf{a}(\theta_{0})\) as simply \(\mathbf{a}_{0}\).  
The samples in (1) collected during an observation interval of \(N\) samples can be arranged into the following \(L\times N\) matrix  
\[\mathbf{X}\doteq\begin{bmatrix}\mathbf{x}(0)&\mathbf{x}(1)&\ldots&\mathbf{x} (N-1)\end{bmatrix} \tag{2}\]and we also define the following \(N\times 1\) signal vector,  
\[\mathbf{s}(\tau,\varphi)\doteq\big{[}s(-\tau,\varphi)\quad s(1-\tau,\varphi) \quad\ldots\quad s(N-1-\tau,\varphi)\big{]}^{T}. \tag{3}\]  
The definitions in (2) and (3) will be used later in Section 3 in the description of the beamforming techniques.
## 3 Materials and Methods
### Classifier Selection  
XGBoost (Extreme Gradient Boosting) is a state-of-the-art machine learning algorithm renowned for its exceptional efficiency and performance in classification tasks [36]. It operates within a gradient boosting framework, iteratively constructing an ensemble of weak learners where each new learner focuses on correcting the residuals from previous predictions. This iterative process unfolds as follows: (1) an initial prediction is made for all data instances; (2) a new decision tree is trained to model the errors, or residuals, of the current ensemble; (3) this new tree is added to the ensemble, scaled by a learning rate to control its impact; and (4) these steps are repeated until the model's performance no longer improves. Mathematically, the ensemble model at iteration \(t\) us updated as:  
\[\overset{\wedge}{\dot{y}}^{(t)}(x)=\overset{\wedge}{\dot{y}}^{(t-1)}(x)+\eta \cdot f_{t}(x_{i}) \tag{7}\]  
where \(\overset{\wedge}{\dot{y}}^{(t)}(x)\) denotes the ensemble model at iteration, \(t\), \(\overset{\wedge}{\dot{y}}^{(t-1)}(x)\) represents the model from the previous iteration, \(f_{t}(x_{i})\) is the newly trained weak learner based on prediction residuals, and \(\eta\) denotes the learning rate and controls the contribution of \(f_{t}(x_{i})\).  
The strength of XGBoost lies in its ability to combine multiple weak learners into a robust ensemble while mitigating overfitting through regularization techniques. Additionally, its parallel processing capability via multi-threading significantly accelerates training on large datasets, and its handling of sparse or missing data further enhances adaptability to real-world scenarios. While other modern gradient boosting algorithms such as LightGBM and CatBoost exist, XGBoost was specifically chosen for this study due to its optimal balance of predictive performance, robustness, and, most critically, its powerful and well-established tools for feature interpretability. These features make XGBoost particularly suitable for processing complex 3D point cloud data with high dimensionality and structural variability.  
In this study, XGBoost is employed to utilize the five-dimensional feature space (smoothed intensity and four geometric features) derived from optimal neighborhoodscales (as detailed in Section 3.3). By integrating both intensity and geometric deviations, the classifier captures subtle distinctions between leakage and non-leakage points. By systematically optimizing the neighborhood scale and feature normalization (Section 3.2), XGBoost effectively models the non-linear relationships within the multi-feature dataset, ensuring accurate leakage detection with balanced precision and recall. This combination of data-driven feature engineering and a robust ensemble learning approach positions XGBoost as an ideal choice for addressing the complexity of tunnel point cloud analysis.
## 3 Results
### Evaluation of Model Prediction Performance
#### 3.5.1 Basic Principle  
Ten-fold cross-validation involves randomly dividing the original dataset into ten subsets, each containing an approximately equal number of samples. In each iteration, nine subsets are combined to form the training set, while the remaining subset is used as the test set. The model's generalization ability is evaluated by averaging the results across all ten trials [30]. This method reduces the variability introduced by a single division of the dataset and provides a robust evaluation of the model's performance.
# Parallel Computation of CRC-Code on an FPGA Platform for High Data Throughput
## 6 Conclusions and Future Work  
In this paper, we have demonstrated the implementation of an efficient hybrid lookup table and matrix transformation algorithm for high throughput parallel computational unit on an FPGA platform. This algorithm will have significant applications in space-based remote sensing instrumentation by ensuring the signal integrity of the data. We  
Figure 11: Simplified diagram of the test system.  
Figure 12: Number of cycles vs. logic elements for different data lengths.  
also analyzed the throughput of the system vs. resource utilization for several cases and showed the effectiveness of the system versus data length. In the future, to further optimize the system, an advanced multi-port ROM topology can be used to reduce the number of logic elements and the possibility to use dual port Random-Access-Memory (RAM) as shared memory to transfer data from the resource to CRC module Another possible improvement is re-architecting the system to use pipelining where both data loading and CRC computation can be performed without the need of using the FSM.  
Writing-original draft, D.T.; Writing-review and editing, S.A., N.G. and G.N.M. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the NASA Grant and Cooperative Agreement Federal Award Identification No.: 80 NSSC20M0017.  
The authors declare no conflict of interest.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 2 SON Operation Rationale, Use Cases, Standardization, Architectures and Dimensioning
### SON EU Research Programs, the NGMN Alliance and 3 GPP Standards  
Since SON seem to be the optimum automation technique for mobile networks, academy, industry, alliance and standardization bodies started analyzing potential scenarios and use cases.  
Organizations such as 5 GPP and European research programs such as SOCRATES, SEMAGUR, SESAME, COGNET, Gandalf, and BeFEMTO describe and propose SON architectures [32; 33; 34; 35; 36; 37; 38; 39], while the NGMN alliance, founded by leading international mobile  
Figure 3: Replacing manual operations with SON.  
Figure 2: RAN planning and configuration lifecycle.  
network operators in 2006 as an industry organization to provide innovation platforms for next-generation mobile networks, was the first to propose SON use cases and prerequisites for deployment in modern mobile networks [27; 40; 41].  
During 2017, NGMN proceeded with the analysis of SON use cases for 5G networks since industry adopted SON solutions for 2G/3G/4G networks with great success in terms of network KPI improvement and CAPEX/OPEX savings [21; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56]. Based on the former NGMN recommendations for SON applicability in 2G/3G/4G networks, NGMN proceeded by proposing SON functions for 5G networks [20].  
Just after NGMN, 3 GPP started working towards standardizing self-optimizing and self-organizing network use cases for 3G/4G networks, starting from Release 8 up to Release 12. The specific standards are described in detail in network automation and management features. 3 GPP releases added enhancements in the prementioned configuration and optimization processes, while proposals allowing inter-radio access technology operation, enhanced inter-cell interference coordination, coverage and capacity optimization, energy efficiency, and the minimization of operational expenses through the minimization of drive tests were added [57; 58; 59; 60; 61; 62; 63].  
As expected, 3 GPP has been working on 5G SON recommendations by further expanding the specific use cases proven to be successful in 2G/3G/4G networks [8; 21; 64].
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 1 Introduction  
The visibility of the IP address in all IP packets is essential for communication across the global Internet, but is vulnerable to privacy-compromising attacks. An adversary on the communication path--a compromised or malicious gateway, router, Internet Service Provider, or other eavesdropper--can correlate to different communication sessions based on address values used across multiple separate communication flows. This allows the attacker to build a detailed picture of an end user's online activity, violating expectations of privacy. As this attack does not exploit the application data, commonplace security measures such as end-to-end encryption are insufficient to disrupt it. A network-layer defence that perturbs the use of address values in correlation attacks is therefore required.  
_Security guidelines_ for Internet-based communication--formalised in RFC3552(BCP) [1], and now a ubiquitous feature of protocol design--typically aim to prevent the misuseof a protocol, rather than to minimise the information leaks that make attacks against privacy possible. It was only after the security and privacy exploitations exposed by [PERSON] [2] that the Internet community hastily introduced _privacy guidelines_[3] to address certain privacy-specific threats. This included a definition for the privacy threat of _correlation:_  
\"Correlation is closely related to identification. Internet protocols can facilitate correlation by allowing individuals' activities to be tracked and combined over time. The use of persistent or infrequently replaced identifiers at any layer of the stack can facilitate correlation\".  
An IP address is one such stable identifier that can be exploited to perform this attack.  
In principle, some network-layer fields do not necessarily need to be visible in transmitted packets for normal network operations, and so could be omitted by protocols that value privacy, further reducing the attack surface [4]. In practice, however, intermediate nodes must be able to inspect:  
* the **destination IP address**, which is essential for packet forwarding and delivery; and
* the **source IP address**, which is essential for transport layer protocol state at end-systems, and which might be required for firewall operations.  
As IP addresses cannot be hidden from on-path nodes, they remain an attack vector for compromising privacy [5].
# Adaptive Segmentation of Streaming Sensor Data on Edge Devices
## 4 Proposed Solution
### Segmentation Heuristic Overview  
The cubic splinelet defined in the previous section gives the locally optimal _approximant_ in the given interval \([0,x_{D}]\). However, the following questions remain unanswered:  
* What should be the value of \(x_{D}\) that corresponds to the locally optimal stream segmentation/partitioning?
* What should be the search space for this optimization task?  
The search space (interval) can be straightforwardly derived from the selected spline adaptation strategy. It may be as simple as:  
\[Dom\left(x_{D}^{(i)}\right)=\left[x_{D_{s}^{\prime}}^{(i)}x_{D_{E}}^{(i)} \right]=\left[\frac{1}{2},2\right]x_{D}^{(i-1)} \tag{15}\]The best \(x_{D}\) can be then computed using an iterative improvement method. At each of its steps:  
1. the search interval is divided into predefined number of sub-intervals,
2. they are then evaluated (see Equation (16) below) by sampling and interpolating (_note_: this step can be accelerated by memoization/caching),
3. the best sub-interval becomes the new search interval.  
The fitness (objective) function, \(\phi\), used in the above search algorithm is defined in the following way:  
\[\phi(s;\Delta R^{2}_{adj};\,L_{1 min},L_{1 max})=\phi_{1}(s)+\Delta R^{2}_{adj} \,\phi_{2}(s;\,L_{1 min},\,L_{1 max}) \tag{16}\]  
where:  
\[\phi_{1}(s)=R^{2}_{adj}(s)=1-\frac{\sum\limits_{k=1}^{n}[y_{k}-s(x_{k})]^{2}} {\sum\limits_{k=1}^{n}(y_{k}-g)^{2}}\frac{n-1}{n-4} \tag{17}\]  
\[\phi_{2}(s;\,L_{1 min},\,L_{1 max})=\left\{\begin{array}{ll}\frac{L_{1 max}-L_{ 1}(s)}{L_{1 max}-L_{1 min}},&\,L_{1 max}-L_{1 min}>0\\ 1,&\text{otherwise}\end{array}\right. \tag{18}\]  
and:  
\[\bar{y}=\frac{1}{n}\sum\limits_{k=1}^{n}y_{k} \tag{19}\]  
\[\Delta R^{2}_{adj}=\max_{s\in\Sigma_{s}}R^{2}_{adj}(s)-\min_{s\in\Sigma_{s}}R ^{2}_{adj}(s) \tag{20}\]  
\[L_{1 max}=\max_{s\in\Sigma_{s}}\text{MAE}(s)=\max_{s\in\Sigma_{s}}\frac{1}{n} \sum\limits_{k=1}^{n}|y_{k}-s(x_{k})| \tag{21}\]  
\[L_{1 min}=\min_{s\in\Sigma_{s}}\text{MAE}(s)=\min_{s\in\Sigma_{s}}\frac{1}{n} \sum\limits_{k=1}^{n}|y_{k}-s(x_{k})| \tag{22}\]  
with \(R^{2}_{adj}\) being the _Adjusted Coefficient of Determination_, _MAE_--the _Mean Absolute Error_, and \(\Sigma_{s}\)--a set of candidate splinelets (corresponding to different values of \(x_{D}\)).
## 3 Results
### Integrated System Performance: Gesture Control and Obstacle Avoidance  
#### 3.4.1 Interaction Between Gesture Control and Obstacle Avoidance  
Integrating gesture control with obstacle avoidance introduced an interactive dynamic where user commands had to coexist with the drone's autonomous safety responses. When a user issued a command directing the drone toward an obstacle, the avoidance system overrode the gesture-based movement, causing the drone to back away from the detected object. This ensured collision prevention but occasionally led to unexpected movements from the user's perspective.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Deviation (cm)** \\ \hline Flight Path & 0 \\ Obstacle 1 & 11 \\ Obstacle 2 & 5 \\ Obstacle 3 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Deviation between the generated 3D map and the actual real-world flight path.  
Figure 9: **(a)** Physical maze setup for drone navigation testing; **(b)** 3D map reconstruction of the drone’s flight path and encountered obstacles.  
Despite this, users retained control over the drone's flight, as they could immediately issue a new gesture command after the avoidance maneuver. The 0.35 m safety threshold and 0.2 m retreat distance traded off safety and responsiveness, preventing crashes without making the drone feel unresponsive. Also, since these values were adjustable in the code, the system could be fine-tuned for different user preferences or environments.  
During testing, users found that while the avoidance system sometimes interrupted their intended flight path, it did not completely disrupt their ability to navigate the drone. However, in confined spaces with multiple obstacles, the drone could enter a cycle of avoidance maneuvers, requiring the user to adjust their gestures to navigate effectively. Overall, the system provided a safe yet intuitive method for controlling the drone, even for users unfamiliar with manual drone operation.
## 2 Materials and Methods
### Inventory Data  
An EC tower was established in the study area to measure carbon and water fluxes (see Section 2.4). In the vicinity of this eddy tower an intensive research site with a size of 1 ha was established. The tree species occurring in the 1-ha study site also correspond to the tree species in the entire forest area. In 20 sparse sampling plots (plot diameter 30 m) in the potential eddy flux footprint area repeated biomass inventories are performed according to ICOS standards to confirm the representativity of the forest area outside the intensive research area (unpublished). Within this 1-ha research area, inventory data for tree positions, their diameters (at breast height, dbh), height and species were acquired by tachymetry (positioning of each tree from a fixed position via distance and angle), gripper (technical instrument to measure the diameter of large trees) and measuring tapes (for circumferences of smaller trees) in spring 2018 for all trees with a dbh \(\geq\) 5 cm. This area of the 'Hohes Holz' forest consists of four dominant species: _Fagus sylvatica_ (36.1% of total stem number), _Quercus petraea_ (27.7%), _Betula pendula_ (2.7%) and _Carpinus betulus_ (33.5%). In total the 1-ha area has 260 trees, a basal area of 28.25 m\({}^{2}\) ha\({}^{-1}\) and an aboveground biomass of 329 Mg ODM ha\({}^{-1}\) (=145 Mg C ha\({}^{-1}\), please note: measurements in units of organic dry matter (ODM) were converted to units of C using C = 0.44 * ODM [29]; values correspond to inventory data for trees with a dbh \(\geq\) 5 cm in 2018). Allometric equations to calculate the species-specific aboveground biomass are taken from [PERSON] et al. [30].
# A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings
## 6 Conclusions  
Square rebars were widely utilized for the RC buildings for decades in a period of history, and the majority of the buildings have been and will be still used due to their historical and cultural value; however, there has been no proper durability evaluation method for these historical square rebar RC buildings. In this study, a reliable and applicable durability prediction method for historical square rebar RC structures is proposed, which can solve the problem where the current standard cannot be used for the durability assessment work for historical square rebar reinforced concrete structures. Based on the comparison between the detailed durability assessment and prediction results of the real cases, it can be considered that the durability prediction result from the proposed method is close to the realistic status and can meet the requirements of engineering applications. The durability prediction method mainly consists of four steps: (i) in-situ investigation,(ii) finite element model simulation, (iii) component importance analysis, and (iv) structural durability prediction. It can be succinctly described as adopting the durability life of the critical components to stand for the durability of the entire historical building, and the critical components are obtained by importance ranking. The novelties and applicability of the proposed method are based on the utilization of structural component importance analysis and the dedicated tests and experiments for historical concretes and square rebar. It should be also noted that being an adaptable model for different needs is of great importance in terms of the prevalence of use, especially for the historical buildings.  
Conceptualization and methodology, Q.C. and H.J.; Software, formal analysis, H.J., C.Z. and Y.H. Investigation, writing--original draft preparation, and visualization, H.J.; Resources, validation, data curation, writing--review and editing, supervision, project administration, and funding acquisition, Q.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jiangsu Provincial Key Research and Development Program, BE2017717, and Jiangsu Province Cultural Relics Research Program, 2017 SK02.  
The data, models, and code generated contributing to the conclusions in this study appear in the submitted article; and some or all data, models, or code generated or used during the study are available from the corresponding author by request.  
Thanks [PERSON] and [PERSON] for taking part in the in-situ investigation works of the Songfengge Building. Thanks [PERSON] for providing advices for the experiments.  
The authors declare no conflict of interest.