esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Link and Successful Transmission Probability Analysis  
In this section, the link analysis and successful transmission probability under lognormal channel model are described. The received power at any given distance can be expressed as [35]:  
\[P\big{(}d_{ij}\big{)}=\left(\frac{d_{ij}}{d_{o}}\right)^{-\rho} \tag{1}\]  
Figure 1: (**a**) Network architecture of WBAN; (**b**) Cooperative communication in WBAN.  
where, \(d_{o}\) is a reference distance and \(\rho\) is the path-loss exponent. \(d_{ij}\) is the distance between node \(i\) and \(j\). Afterwards, according to the lognormal channel model, the received power can be expressed as [35]:  
\[10\log_{10}\left(P\big{(}d_{ij}\big{)}\right)=10\log_{10}\left(P_{a}\big{(}d_{ij }\big{)}\right)+\sigma_{ij}^{2} \tag{2}\]  
For the sake of the simplicity, we normalized variables as follows: first, let defined \(G\) as the maximum distance where the received power \(P_{a}\big{(}d_{ij}\big{)}\) is equal to \(P=\left(\frac{G}{d_{o}}\right)^{-\rho}\). Then, by dividing powers by \(P\) and with help of (2), the received power under lognormal channel model can be expressed as:  
\[10\log_{10}\left(\frac{P\big{(}d_{ij}\big{)}}{P}\right)=10\log_{10}\left( \left(\frac{d_{ij}}{G}\right)^{-\rho}\right)+\sigma_{ij}^{2},\]  
then, this yields:  
\[10\log_{10}\left(P^{n}\big{(}d_{ij}\big{)}\right)=10\log_{10}\left(\left(d_{ ij}^{n}\right)^{-\rho}\right)+\sigma_{ij}^{2}. \tag{3}\]  
where, \(d_{ij}^{n}\triangleq\frac{d_{ij}}{G}\) is the normalized distance and \(P^{n}\big{(}d_{ij}\big{)}\triangleq\frac{P\big{(}d_{ij}\big{)}}{P}\) is the normalized power. It is shown that the l0 garithm of a normalized power has a normal distribution with the mean \(10\log_{10}\left(d_{ij}^{n}\right)\) and the variance \(\sigma_{ij}^{2}\). The condition for correct reception of signals at normalized distance \(d_{ij}^{n}\) is that the normalized power at this distance is more than '1' or zero dB. The probability of successful reception at node \(j\) due to transmission of node \(i\) can be expressed as:  
\[P_{ij}^{s}=P\big{[}10\log_{10}\left(P^{n}\big{(}d_{ij}\big{)} \right)>0\big{]}\] \[\qquad=\frac{1}{\sqrt{2\pi}\sigma_{ij}}\int\limits_{0}^{\infty} \exp\left(-\frac{\left(r-10\log_{10}\left(\left(d_{ij}^{n}\right)^{-\rho} \right)\right)^{2}}{2\sigma_{ij}^{2}}\right)dr \tag{4}\]  
this yields:  
\[P_{ij}^{s}=0.5\;erfc\left(\frac{\omega}{U_{ij}}\right) \tag{5}\]  
where \(erfc(x)\;\;(=1-erf\;(x))\) is complementary error function, \(U_{ij}=\sigma_{ij}/\log d_{ij}^{n}\) and \(\omega=10\rho/\sqrt{2}\log 10\).
## 3 Nanostructured Materials for Chromium Removal
### Application of NiO Nanoparticles for Cr(VI) Removal  
Nickel oxide nanoparticles (NiO), as an adsorbent material, have been used to control the mobility of metal ions in the environment, because of their high surface area, low production cost and natural porosity for the removal of heavy metals from aqueous solutions [52]. In a recent study, [PERSON] and [PERSON] (2014) [53] synthesized NiO nanoparticles by a simple precipitation method, using sodium hydroxide, for the removal of Cr(VI) from aqueous solutions. The main parameters (i.e., starting concentration of Cr(VI), dose of material, initial pH value and temperature) were investigated, regarding their effect on adsorption capacity.  
The results show that NiO nanoparticles with a mean size of 11 nm were efficient adsorbents for Cr(VI) with a reported adsorption capacity of 5 mg g\({}^{-1}\) for the initial Cr(VI) concentration 20 mg L\({}^{-1}\), by using 0.6 g L\({}^{-1}\) of absorbent at T = 30 \({}^{\circ}\)C. The pH variation under the aforementioned conditions, indicated that at the pH value of 4.7 the adsorption of Cr(VI) was the highest, approximately 98% [53].
## 2 Problem Formulation  
Microgrids are increasingly used in power distribution grids to integrate renewable energies [5]. They include single-phase components such as loads and solar-photovoltaic units. Therefore, microgrids are usually unbalanced, especially in low-voltage residential cases [7]. To reduce power loss, microgrid designers need to define the right placement of each single-phase unit (load or generation) using a process known as phase balancing [16]. Each three-phase node has six possible configurations as depicted in Figure 1. The problem involves defining each load or generator's configuration to reduce the microgrid's total losses. Therefore, since there are \(6^{n}\) possible configurations, where \(n\) is the number of three-phase nodes, the problem is combinatorial. The problem is not trivial, even in small networks. For example, a microgrid with 10 nodes will have more than 60 million possible configurations.  
A general representation of the problem is as follows:  
\[\min \mathbb{E}(p_{L}) \tag{1}\] \[f(v,M)=0\] (2) \[M_{i}\in\{M_{1},M_{2},M_{3},M_{4},M_{5},M_{6}\} \tag{3}\]  
where \(\mathbb{E}\) represents the expected value; \(p_{L}\) is the active power loss; \(f\) is a vector function that represents the power flow equations; and \(M\) is the phase-swapping included in the set of possible configurations already depicted in Figure 1 (e.g., the configuration \(M_{i}\) with \(i=3\), i.e., \(M_{3}\), implies that the original configuration ABC changes to BCA). Each of this model's equations shows a different source of complexity: (i) the objective function (1) is stochastic; (ii) the equations of the power flow (2) are non-convex, and (iii) the configuration (3) leads to a combinatorial problem. This paper presents a mixed-integer quadratic formulation to this problem, which allows to find an optimal solution. In this way, by using the paradigm of disciplined convex programming, the non-linear integer stochastic problem is transformed into a tractable model [21; 23].
## 3 Results and Discussion
### Experimental Conditions
#### 3.3.2 Drug Release
In GO@Ag-DOX-NGR, the NIR irradiation causes the excitation of electrons from Ag, which due to conjugation results in the hydrolysis of the ester linkage between DOX and the nanocarrier [78; 86; 87]. [PERSON] et al. investigated the drug release under stimulation from NIR and ultrasound. DOX release was controlled under normal conditions; when exposed to stimulation of either NIR or ultrasound, the release rate jumped due to the burst release of DOX that was caused by the sudden increase in energy [73]. Light-mediated drug release by UV or visible light has drawbacks due to the inadequate tissue penetration and potential phototoxicity. Stimulation by NIR is thought to be superior in both aspects due to the longer wavelength(s) of light used (700-1000 nm). Recent evidence has suggested that cancer microenvironments can not only have an acidic environment but can also be alkaline, which is associated with limitations to drug release by an acidic pH [88]. The combination of pH and NIR stimulated response may be the most efficacious in the future as a therapastic technique.
# Detection and Prevention of DDoS Attacks on the IoT
## 2 Scenarios of DDoS Attack
### Network Bandwidth Consumption DDoS Attacks
#### 2.1.1 User Datagram Protocol (UDP) Flood Attack  
UDP is a connectionless transport protocol. When using the UDP protocol to transmit packets, authentication is not required, and a large number of packets can be sent to the victim's computer, which can saturate the bandwidth and make normal services inaccessible. This attack method is shown in Figure 2.
# Consistency Models of NoSQL Databases
## 4 NoSQL Databases Background
### MongoDB  
MongoDB is a document-based NoSQL database. Its architecture was inspired by the limitations on relational databases like MySQL and Oracle. MongoDB tries to join the best of the RDBMS and NoSQL worlds. From RDBMS, MongoDB took the expressive query language, secondary indexes, strong consistency, and enterprise management while adding NoSQL concepts like dynamic schemas and easier horizontal scalability [27].  
MongoDB data model is based on documents. These documents are represented in BSON (Binary JSON). This format extends the well-known JSON (JavaScript Object Notation) to include additional types like int, long, date, and floating point [27].  
Documents that represent a similar class of objects are organized as collections. For example, a collection could be the Car collection while a document could be the data item of a single car. Making the analogy with RDBMS, collections are similar to tables. Documents are similar to rows. Fields are similar to columns. Although left-outer JOIN is a valid operation, MongoDB tends to avoid joins by nesting relationships into a single document, like including manufacturer information into a car document [27].
# Preparations for Galileo PRS in Poland
## 4 Coexistence with RNSS Systems and Other Services
These activities are conducted by the interested countries (administrations), as well as within international regulatory bodies (e.g., CEPT), and they include technical/legal analysis, simulations, attempts to define possible measures to mitigate the interference issue, etc. The global coordination of this work is handled by the ITU-R's working parties (WPs), mostly the WP 5A (ITU-R's Working Party 5A (WP 5A)-Land mobile service, excluding IMT; amateur and amateur-satellite service) with the support of WP 4C (ITU-R's Working Party 4C-Efficient orbit/spectrum utilization for the mobile-satellite service (MSS) and the radiodetermination-satellite service (RDSS)). Among the expected outputs from those working parties are:  
* The new ITU-R report on the amateur and amateur-satellite services characteristics and usage in the 1240-1300 MHz frequency band,
* The new ITU-R report containing studies and guidelines regarding the protection of the primary radionavigation-satellite service (space-to-Earth) from the secondary amateur and amateur-satellite service in the frequency band 1240-1300 MHz,
* Draft of new ITU-R recommendation containing the guidance on technical and operational measures for the use of the frequency band 1240-1300 MHz by the amateur and amateur-satellite service in order to protect the RNSS (space-to-Earth),
* A consolidated report that will serve as a technical basis for the considerations on the 9.1(b) agenda item during the WRC-23 conference. Please note that regardless of the progress on the above documents made in the WRC-23 preparatory period, all binding, regulatory decisions will only be made during the conference itself. Therefore, we have to wait till December 2023 to find out what technical measures (if any) will be established with respect to the amateur services in the E6 band in order to efficiently protect the RNSS from harmful interferences.
# Modelling Prefabricated Construction Safety
## 2 Research Method
### Predictive Modelling of Predabricated Construciton Safety  
The accident frequency was forecast using an autoregressive integrated moving average (ARIMA) for future values using past time values [41; 42], calculated through a function of SPSS ([[https://www.ibm.com/products/spss-statistics](https://www.ibm.com/products/spss-statistics)]([https://www.ibm.com/products/spss-statistics](https://www.ibm.com/products/spss-statistics)), accessed on 21 January 2024), which helps to capture a huge range of time series patterns. This is a suitable approach for short-term forecasting. An ARIMA (p, d, q) model for non-seasonality was followed [43; 44]. The general forecasting model can be expressed as in Equation (4), and as used by [45].  
\[y^{\prime}_{t}=\varphi_{0}+\varphi_{1}y^{\prime}_{t-1}+\varphi_{2}y^{\prime}_{t -2}+\ldots+\varphi_{p}y^{\prime}_{t-p}+\theta_{1}\varepsilon_{t-1}+\theta_{2} \varepsilon_{t-2}\ldots+\theta_{q}\varepsilon_{t-q}+\varepsilon_{t} \tag{4}\]  
where  
\(y\) = regressions on itself lagged by the n-th period  
\(y^{\prime}_{t}\) = differenced series  
\(p\) = the number of autoregressions (AR)  
\(d\) = the number of seasonal differences  
\(q\) = the number of the moving average (MA)  
\(\varphi_{1}\) (i = 1,\(\ldots\), \(p\)) = Weights for AR  
\(\theta_{1}\) (j = 1,\(\ldots\), \(q\)) = Weights for MA  
\(\varepsilon\) = Error term that reflects the previous state at present  
\(\varepsilon_{t}\) = residual term with mean zero  
In this study, these combinations were followed to determine the ARIMA models based on white noise existence [46], such as (0, 0, 0), (0, 1, 0), (1, 1, 0), (0, 1, 1), and (1, 1, 1) [45; 47].  
The performance indicators for selecting the most suitable model are minimum R-squared (coefficient of determination) near to 1, mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and the Bayesian information criterion (BIC). Original data are represented by \(y_{i}\), the prediction data by \(\hat{y}_{i}\), and the average value of original data by \(\overline{y}\). '\(T\)' is the number of samples, '\(n\)' is the number of unknown parameters, and '\(M\)' is the maximum likelihood of the model.  
\[R^{2}=1-\frac{\sum_{i}(g_{i}-y_{i})^{2}}{\sum_{i}(y_{i}-\overline{y})^{2}} \tag{5}\]  
\[MAE=\frac{1}{n}\sum_{i}^{n}\ \big{|}(y_{i}-\hat{y}_{i})\big{|} \tag{6}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\!\big{|}(y_{i}-\hat{y}_{i})/y_{i}\!\big{|}\! \times\!100\% \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=0}^{n}(y_{i}-\hat{y}_{i})} \tag{8}\]  
\[BIC=\ln(T)(n)-2\ln(M) \tag{9}\]  
Initially, the Statistical Package for the Social Sciences (SPSS) expert modeler function was used, and then ARIMA (p, d, q) was applied. An ARIMA model with minimum MAPE was selected based on the interpretability easiness and comparison of forecast error [48]; it was also checked for lower values of MAE, RMSE and BIC, but the maximum (closer to 1) value for R\({}^{2}\). This provides the goodness-of-fit for optimized models [42; 49; 50; 51]. Optimized ARIMA models are provided in Supplementary Material.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Application Layer
#### 3.1.4 Extensible Messaging and Presence Protocol  
XMPP is an XML-based protocol designed for real-time, extensible communications. Originally designed for communication on the internet with ample resources, XMPP has now found relevance in the field of IoT applications owing to its distinctive attributes [56]. The protocol's decentralised architecture promotes heterogeneous and federated communication by facilitating seamless communication between servers and clients. XMPP also support the publish-subscribe communication model, facilitating the simultaneous broadcasting of messages to multiple nodes [57]. The extensibility and decentralised characteristics present distinct advantages in V2I communication, enabling the management of diverse communication services, including real-time traffic monitoring, as noted by [PERSON] and [PERSON] [58]. XMPP could enable immediate communication of safety alerts or evacuation instructions from central control units to all vehicles in a specific geographic location. The flexibility of XMPP also supports the potential for future expansions or adaptations as the requirements of the V2I communication system evolve. Refer to Figure 6 for XMPP architecture.  
Figure 5: DDS architecture.
# Long-Term Effects in Structures: Background and Recent Developments
## 2 Motivation
### The Koror-Babeldaob Bridge, Palau  
The Koror-Babeldaob Bridge was a bridge in Palau that was connecting islands Koror and Babeldaob. The bridge was a balanced cantilever prestressed concrete box girder bridge with a total length of 385.6 m and a main span of 240.8 m. It was primarily constructed for carrying traffic, but it also carried water-supply piping and electrical conduits [10].  
Sagging in the bridge's centre began almost immediately after its completion. After a decade of use, concrete creep led to a sag of approximately 1.2 m in the main span's middle section, causing driver anxiety and government concern (Figure 4). Therefore, the Palau government commissioned two independent studies to determine if the bridge was safe to use. Both studies concluded that the safety of the bridge was sufficient, despite an expected additional sag of 1 m. Encouraged by these studies, the government decided to take only a couple of cosmetic interventions with resurfacing and reinforcement of the bridge.  
The bridge collapsed abruptly and catastrophically on 26 September 1996 during the nice weather and practically unloaded (Figure 5). This disaster occurred only 18 years after  
Figure 4: The Koror-Babeldaob Bridge, sagging of the middle point [10].  
construction, 6 years after engineers declared the bridge safe, and less than 3 months after all the reinforcements were made to address the escalating sagging [10].  
The main cause of the collapse of Koror-Babeldaob Bridge remains unexplained, despite various studies having been conducted. The most likely theory is that the collapse occurred due to excessive long-time deflections of the prestressed box girders [12]. This could be due to factors such as inadequate design assumptions, material properties, construction practices, or environmental conditions, leading to unexpected deformation and ultimately structural failure. However, as the lawsuit regarding the demolition was settled out of court, the final cause has never been disclosed [10; 11].
## 5 Discussion  
Inter-subject classification using either sEMG signals or by comparison acoustic signals/acceleration signals still are a significant challenge in voice research. For example, in [25] the authors performed leave-one-subject-out cross validation for classifying mood in individuals with bipolar disorder. The accuracy they achieved was between 67%-77% based on acoustic signals. Similarly, in a study on acoustic markers of affect [26], leave-one-speaker-out classification was also employed, and the results achieved ranged from 38.8% to 97.8%. More recently, [PERSON]. [27] used neck-surface acceleration to classify vocal hyperfunction, which yielded an accuracy of 83% based on L1 logistic regression. However, when models were evaluated only by cross-validation (some test subjects' samples could be included in training data), they tend to obtain higher accuracies. [PERSON]. [28] proposed and evaluated two different models. The first was a long-short-term-memory (LSTM)-based emotion detector using speech signals that achieved 88% accuracy with five-fold cross-validation. The second model was for mood disorder detection using leave-one-group-out cross validation. The best performance of the model was 73.33%. To the best of our knowledge, there was no comprehensive study conducted by other parties on using neck sEMG to classify vocal fatigue that we could compare our results with.  
However, outside the scope of classification and machine learning, several other studies demonstrated that frequency-based sEMG features could be strong indicators of muscle fatigue. In a review conducted by [29], it was mentioned that the median frequency was superior to amplitude-based measures in detecting muscle fatigue [29]. Another study [30] also suggested a list of sEMG features including ZC, SSC and WA as fatigue indices. These findings are consistent with what we previously demonstrated in Section 4.2.1 and Table 6, which showed that frequency-based features showed superior accuracy performance compared with the rest of sEMG features we selected.
# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 2 Related Work  
### Modeling Methods for Sparse Tabular Data  
In the modeling of structured tabular data, traditional machine learning methods such as logistic regression, decision tree, random forest, and gradient boosting models (e.g., XG-Boost) have long served as strong baselines [11; 29]. As a linear classification method, logistic regression estimates class probabilities by applying a linear transformation to the input features, followed by a nonlinear mapping function. Its simplicity, interpretability, and low computational cost make it a preferred choice in many practical applications; however, its performance is often limited in scenarios involving high-dimensional sparse data, where feature redundancy and missing values can lead to instability and reduced predictive accuracy [30]. Tree-based models such as decision trees and ensemble methods construct recursive partitions over the feature space, and are capable of handling nonlinear relationships. These methods are relatively robust to missing data and offer high interpretability [31]. Nevertheless, they often suffer from overfitting and reduced generalization capability in particularly sparse or high-dimensional contexts [32]. In recent years, deep neural networks (DNNs) have been increasingly applied to structured data tasks. Architectures such as TabNet and TabTransformer integrate attention mechanisms and embedding modules to automatically learn relevant feature representations [33; 34]. These methods outperform traditional baselines in terms of many benchmarks, particularly in capturing complex feature interactions and adapting to sparse distributions. For instance, TabNet dynamically attends to informative features at each decision step, while TabTransformer models dependencies across heterogeneous features through stacked attention layers.  
Despite their promising results, deep models are typically data-hungry and less robust to missing or noisy inputs. Their reliance on large labeled datasets and limited interpretability restricts their effectiveness in real-world scenarios with limited supervision or incomplete data [35; 36]. These challenges underscore the need for more flexible and label-efficient solutions that are capable of modeling sparse structured data with better generalization and interpretability.
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 2 Flight Conflict Network  
In order to analyze the complexity of the air traffic situation and reasonably divide the sectors between idle and busy time, the flight conflict network is constructed using the complex network theory. The flight conflict network \(G=(V,E,W)\) is a complex network that takes the conflict relationship between aircraft in the airspace as the research object, where \(V=\{v_{1},v_{2},v_{3},\ldots,v_{n}\}\) denotes the set of nodes composed of aircraft in the network; \(E=\{e_{1},e_{2},e_{3},\ldots,e_{n}\}\) is the set of connecting edges reflecting the conflict relationship between aircraft; and \(W=\{w_{1},w_{2},w_{3},\ldots,w_{n}\}\) is the set of connecting edge weights reflecting the urgency of potential conflicts.