esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### LoRa Emulator
#### 3.4.3 Energy Consumption  
The required electric power needed to transmit a message is dependent on the required RF transmit power \(P_{\text{t}}\), the electric-to-RF conversion factor \(\eta\) and the overhead power \(P_{\text{o}}\)[48]. For a given data rate and a message time on air \(T_{\text{m}}\), the energy consumption of an IoT packet can be expressed as follows,  
\[\varepsilon=[\eta P_{\text{t}}+P_{\text{o}}]T_{\text{m}}, \tag{13}\]  
where the time on air is obtained as \(T_{\text{m}}=N_{\text{m}}/R_{\text{b}}\). Energy wasting occurs when a packet is not successfully delivered to its destination. As such, the average wasted energy per transmission is given as follows,  
\[E=\varepsilon\times P_{\text{e}}, \tag{14}\]  
where \(P_{\text{e}}\) is the PER. For practical values of these parameters, we rely on empirical measurements obtained from our previous work in [48]. In case III, the energy consumption is obtained from this following equation,  
\[E=\varepsilon\times L\times P_{\text{e}}. \tag{15}\]  
In order to assess the energy wasted due to synchronization, the overhead power due to synchronization \(P_{\text{NS}}\) is taken into the calculation. Moreover, the energy wasted per transmission can be obtained by the following equation,  
\[E=(\varepsilon+P_{\text{NS}})\times P_{\text{e}}. \tag{16}\]  
Note that we did not add any extra power overhead since we can simply program the IoT devices to transmit at certain periods of time.
# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
## 3 Materials and Methods
### Imperviousness Dataset Generation
\begin{table}
\begin{tabular}{l l} \hline \hline
**Month** & **Product Name Identifier** \\ \hline March & S2A\_MSIL2A\_20170329T095021\_N0204\_R079\_T33 TVF\_20170329T095024 \\ June & S2A\_MSIL2A\_20170613T101031\_N0205\_R022\_T32 TQR\_20170613T101608 \\ July & S2A\_MSIL2A\_20170720T100031\_N0205\_R122\_T33 TUH\_20170720T100027 \\ July & S2A\_MSIL2A\_20170706T102021\_N0205\_R065\_T32 TMQ\_20170706T102301 \\ August & S2A\_MSIL2A\_20170806T095031\_N0205\_R079\_T33 TVF\_20170806T095744 \\ August & S2A\_MSIL2A\_20170802T101031\_N0205\_R022\_T32 TQP\_20170802T101051 \\ October & S2A\_MSIL2A\_20171014T102021\_N0205\_R065\_T32 TNR\_20171014T102235 \\ October & S2A\_MSIL2A\_20171015T095031\_N0205\_R079\_T33 SVB\_20171015T095944 \\ October & S2A\_MSIL2A\_20171028T100121\_N0206\_R122\_T33 TUG\_20171028T134729 \\ December & S2A\_MSIL2A\_20171220T101431\_N0206\_R022\_T32 TPR\_201712207122057 \\ December & S2A\_MSIL2A\_20171224T095421\_N0206\_R079\_T33 TVG\_20171224T122256 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of Sentinel-2 products used for the generation of the dataset. Figure 4: Schematic representation of the impervious surface dataset generation. Sentinel-2 bands with lower spatial resolution were up-sampled to 10 m per pixel using cubic convolution. When necessary, ground-truth raster and acquired Sentinel-2 images have also been reprojected to a common coordinate system to have two perfectly stackable files. It is worth noting that a typical Sentinel-2 tile has a size of 10,980 \(\times\) 10,980 pixels for the 10 m spatial resolution. Since these dimensions are computationally infeasible, we extracted non-overlapping patches of 244 \(\times\) 244 pixels from each ground-truth image. Finally, a data-cleaning process excludes chips without corresponding ground truth, i.e., chips containing no data values or chips with clouds. To this aim, a cloud mask is computed using the Scene Classification Layer within Sentinel-2 Level-2A products.
## 2 System Architecture and Requirements
### Receive Chain
#### 2.1.2 Requirements for FD  
In full duplex transceivers, the simultaneous transmission and reception of signals at the same time and frequency makes self-interference cancellation an even greater challenge. For this reason, FD transceivers typically target communication links over shorter distances, where lower transmitted power levels and receiver sensitivities can be tolerated. In this work, we envision a dual antenna FD system with a transmitted power of 10 dBm and a sensitivity of \(-82\) dBm. Assuming 25 dB antenna isolation, the TX leakage is \(-15\) dBm and the total cancellation needed to have the SI below the RX sensitivity is (\(82-25+10=67\) dB. The use of an RF canceler would not be able to achieve this cancellation due to nonidealities and low resolution constraints in the canceler design. Assuming 27 dB of cancellation in the RF domain, the further 40 dB would be done in digital domain (e.g., using an auxiliary path). Notice that both the TIA and the ADC must have a dynamic range (DR) greater than 40 dB. The effective receiver in-band IIP3 requirement can be computed assuming a 3 dB sensitivity degradation when the transmitted power is maximum, i.e., \(-15+(-15+82)/2=18.5\) dBm.
## 5 Discussion and Future Research Directions
### Machine Learning, Advanced Analytics, and Explainability  
Machine learning and advanced analytics play a pivotal role in predictive maintenance, enabling intelligent decision making through data-driven insights. Core techniques include classical models such as regression, decision trees, random forests, and ensemble methods, as well as deep learning architectures like CNNs, LSTMs, GRUs, and hybrid networks that capture spatial and temporal dependencies. Specialized models address prognostics and anomaly detection, incorporating physics-based constraints or methods like one-class SVM and isolation forests. Optimization strategies such as hyperparameter tuning and transfer learning further enhance model adaptability and scalability. Explainability and hybrid intelligence, with approaches like SHAP, LIME, and rule-based systems, offer greater transparency and help build trust in automated diagnostics (see Figure 12).  
Notwithstanding these advancements, two major concerns curtail their wider deployment. First, concept drift arises when equipment undergoes continuous changes in load, wear, or operating regimes. Models that are not updated to reflect these variations are prone to degrade in accuracy over time. Incremental learning and domain adaptation have shown potential, but practical demonstrations are still scarce. Second, computational overhead remains high when dealing with large, high-frequency datasets. Pruning, quantization, and more efficient architectures represent viable paths to address latency constraints in real-world scenarios. Further exploration of hybrid (physics-data) approaches could increase reliability and interpretability, especially in safety-critical settings where purely data-driven black-box models face scepticism.
# Deep Pixel-Level Matching via Attention for Video Co-Segmentation
## 2 Related Work  
**Video co-segmentation.** Some proposal-based methods, such as the models in [6; 9; 15], extract the common information among the proposals from video frames, but proposal-based methods easily lead to a pixel misclassification problem. Therefore, we need to extract the common information between frames in pixel-level. On the other hand, the algorithm in [4] extracts the color, texture, and relative motion features from video frames and then applies the co-feature Gaussian mixture models to capture the common feature between video frames. A generative multi-video model [5] and a trainable apparent model [7] were presented for video co-segmentation. In addition, [PERSON] et al. [8] proposed a framework containing clustering and Markov random field model to separate the common objects across videos; the weakly supervised VODC approach in [10] was developed for video object discovery and co-segmentation; a co-saliency strategy was applied to obtain the common objects from videos in [11]. However, all these methods cannot generate reliable features, because these methods do not have high ability in feature learning, they may not accurately capture the features belonging to the foreground object when the object's properties (i.e., size, pose and viewpoint) change in the video. Although the model in [13] applies FCN [16] to extract features from video frames, the common information is not refined by the deep model after being captured by a spatial-temporalgraphical model and the clustering algorithm; thus, the common information cannot be obtained accurately and utilized efficiently. Therefore, different from these methods, our network is built for extracting the common information in pixel-level within the deep learning architecture so that the objects' features can be refined with their common information by the convolutional layers in the deep model to enhance the accuracy of segmentation.  
**Attention modules.** Because attention modules do well in information matching, they are widely used in image co-segmentation [17], video segmentation [3], and scene segmentation [2]. Specifically, the model in [17] adopts the attention mechanism to refine the feature maps; the DANet in [2] leverages the attention modules in two branches architecture, including a position attention module and channel attention module to segment each object in the street scene; the COSNet in [3] also presents a co-attention model within a deep model for video segmentation, but this architecture is too complex to implement since it needs a lot of memory space. Therefore, we try to develop a new deep learning architecture that includes an attention module for common information extraction.
## 4 Fire and Firefighter Monitoring
### Firefighter Monitoring  
The objective is to keep at the Base Station the updated measurements of the firefighter locations and the conditions the firefighters are facing. Each firefighter carries a mobile node equipped with the aforementioned sensors. The Base Station registers not only the instantaneous conditions but also the accumulative gas concentrations. Thus, the participation of each firefighter in a fire episode can be managed considering the accumulative gas concentrations they have been exposed to. Also, having the updated firefighter location is critical to improve their safety. Localization in indoor environments is not a solved problem. GPS fails and methods based on Received Signal Strength Indicator (RSSI) face problems related to reflections and other interactions of radio signal with the environment. To reduce these effects, we adopt a training-based method in which each static node computes its own RSSI-range model. Then, these trained models are employed to estimate the localization of the mobile nodes.  
Each mobile node periodically computes its location and gathers measurements from its sensors and inserts them in _FiremanSense_ packets to be transmitted to the Base Station through the static WSN. The information contained in these packets includes a packet identifier, an identifier of the sender node, the \(X\) and \(Y\) coordinates of the mobile node and the concentrations in parts per million of \(CO\), \(CO_{2}\) and hydrocarbon gases. Each mobile node dynamically selects the static node with which it currently has the best link quality, e.g., that with the best RSSI. The mobile node will forward data packets to that static node, which will route them to the Base Station using the multi-hop channels computed by CTP. Firefighter sensor measurement and packet transmission rates are configurable. For efficiency, the transmission of _FiremanSense_ packets is also unconfirmed. Firefighter monitoring measurements are considered more critical than fire monitoring data. To reduce the impact of loss of _FiremanSense_ packets in firefighter monitoring, _FiremanSense_ transmission rate was set higher than _FireSense_ packets.
# A Two-Phase Approach for Single Container Loading with Weakly Heterogeneous Boxes
## 2 Block-Loading Hybrid Metaheuristic
### Block Loading
#### 2.2.1 ILP Model  
Loading FSBs on the container floor is a two-dimensional C&P problem. We implement a well-known ILP model for orthogonal two-dimensional C&P problems [28]. Let \(u_{ijk}\) be a binary decision variable standing for the decision of whether or not to load an FSB of type \(i\) at coordinate \((j,k)\) of the container floor.  
\[u_{ijk}=\begin{cases}1,&\text{if an FSB of type $i$ is placed at}\\ &\text{coordinate }(j,k),\\ 0,&\text{otherwise.}\end{cases}\]  
The elements \(j\) and \(k\) belong, respectively, to the following discretization sets:  
\[X=\{x|x=\sum_{i=1}^{|\overline{B}|}a_{i}l_{i},x\leq L-\min_{1\leq i\leq| \overline{B}|}\{l_{i}\},\alpha_{i}\in\mathbb{Z}^{+}\}\]  
\[Y=\{y|y=\sum_{i=1}^{|\overline{B}|}\beta_{i}w_{i},y\leq W-\min_{1\leq i\leq| \overline{B}|}\{w_{i}\},\beta_{i}\in\mathbb{Z}^{+}\}\]  
To avoid overlapping of FSBs, the incidence matrix \(g_{ijkpq}\) is defined as:  
Figure 4: A representation of a guillotine block, its MBC, and the residual space on top of it.  
\[g_{ijkpq}=\begin{cases}1,&\text{if }j\leq p\leq j+l_{i}-1\\ &\text{and }k\leq q\leq k+w_{i}-1,\\ 0,&\text{otherwise}.\end{cases}\]  
which is computed a priori for each FSB of type \(i\), for each coordinate \((j,k)\), and for each coordinate \((p,q)\). Finally, the ILP model is defined as follows:  
\[\max\sum_{i=1}^{|\overline{B}|}\sum_{j\in X}\sum_{k\in Y}v_{i}u_{ ijk} \tag{1}\] \[\text{s.t.}\sum_{i=1}^{|\overline{B}|}\sum_{j\in X}\sum_{k\in Y}g_ {ijkpq}u_{ijk}\leq 1,\forall p\in X,\forall q\in Y\] (2) \[\sum_{i=1}^{|\overline{B}|}\sum_{j\in X}\sum_{k\in Y}q_{i}^{t}u_{ ijk}\leq q_{t},t=1,\ldots,m\] (3) \[u_{ijk}\in\{0,1\},i=1,\ldots,|\overline{B}|,\forall j\in X, \forall k\in Y \tag{4}\]  
The objective function (1) maximizes the volume of loaded FSBs inside the container. Constraints (2) avoid overlapping of FSBs in any feasible solution. Let \(q_{i}^{t}\) represent the quantity of boxes of type \(t\) in an FSB of type \(i\). Constraints (3) impose that, for each box type \(t\), the quantity of loaded boxes of this type must not exceed the number of boxes available. Finally, Constraints (4) specify the domain of variables. The ILP model (1)-(4) contains \(O(|\overline{B}|\times|X|\times|Y|)\) variables and \(O(|X|\times|Y|)\) constraints. Since the product \(|X|\times|Y|\) may be too large for practical instances of this problem, the number of constraints and variables can easily reach the order of millions, which discourages the straightforward use of classical ILP techniques.
## 3 Results
### Quantifying Energy Input and Electrode Loss
#### 3.5.2. Settling Velocity  
For both the means and S/N ratio, the analysis shows that factor A is the most significant factor (_p_-value (means) = 0.008 and _p_-value (S/N) = 0.034) and the most important factor (pC (means) = 63.20% and pC (S/N) = 50.17%). For mean values, factor B is significant (_p_-value = 0.033 and pC = 26.25%), while for S/N, it is marginally significant, with a _p_-value of 0.072, and its pC is 29.66%. Factor C is not statistically significant and has a negligible impact on settling velocity. The optimal combination of factors for means is A1, B2, C2, while for S/N is A1, B2, C1. It is interesting to note that there is a small difference in the optimal level of factor C between the S/N ratio analysis and the mean values analysis. However, given the negligible impact of factor C, this difference is not of practical significance. The error value is 10.36% (means) and 20.17% (S/N), which is considerably higher than in the COD analysis. The predicted S/N ratio for A1, B2, C1 is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Response** & & & **COD Reduction** & & \\
**Factor** & **A** & **B** & **C** & **Error** \\ \hline Response & & & Settling velocity & & \\ Factor & A & B & C & Error \\ DF & 1 & 1 & 1 & 4 \\ SS & 142.958 & 84.496 & 0.009 & 57.458 \\ MS & 142.958 & 84.496 & 0.009 & 14.365 \\ pC, \% & 50.17 & 29.66 & 0.00 & 20.17 \\ _p_-value & 0.034 & 0.072 & 0.981 & - \\ Significance & significant & marginally & not significant & - \\ \hline Response & & & Electrode loss & \\ Factor & A & B & C & Error \\ DF & 1 & 1 & 1 & 4 \\ SS & 0.211 & 0.338 & 72.25 & 2.716 \\ MS & 0.211 & 0.338 & 72.25 & 0.679 \\ pC, \% & 0.28 & 0.45 & 95.68 & 3.60 \\ _p_-value & 0.607 & 0.519 & 0.000 & - \\ Significance & not significant & not significant & highly significant & - \\ \hline Response & & & Turbidity & \\ Factor & A & B & C & Error \\ DF & 1 & 1 & 1 & 4 \\ SS & 54.76 & 106.16 & 1582.22 & 225.19 \\ MS & 54.76 & 106.16 & 1582.22 & 56.26 \\ pC, \% & 2.78 & 5.39 & 80.39 & 11.43 \\ _p_-value & 0.380 & 0.242 & 0.006 & - \\ Significance & not significant & not significant & highly significant & - \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._11.6971, while the predicted means for A1, B2, C2 is 3.1405 cm/min. The obtained values are slightly higher than in experiments T3 and T4, which have the same factor values as the optimal combinations.
## 3 Results
### Meteorological Forecasting and Assimilation Performances
In Romania, four different time windows corresponding to various stages of the 2024 summer crop season were selected: 29 June-3 July, 22-26 July, 4-8 August, and 6-11 September, all related to irrigation management needs. For the French site, the selected time window of 18-19 June 2024 featured a convective event characterized by notable spatial variability in rainfall intensity, elevated atmospheric moisture, and the availability of Meteorone profiles, making it a representative case for testing improvements in short-term convective forecasting.
# Smart Clothing Framework for Health Monitoring Applications
## 2 Wearable Technology and Smart Clothing
### Wireless Communication  
Several technologies are integrated with smart clothing as the medium of wireless communication. These include 3G/4G/5G, ultrasounds, infrared, ZigBee, Long-Range Wide Area Network (LoRa WAN), Ultra-Wide Band (UWB), Wireless HART, SigFox, ANT+, Weightless-P, Wi-SUN and IEEE 802.11 ah [7]. Data transmission is performed with the use of both a source and storage centre, including a computer or cloud system communicating with Central Processing Units (CPUs), microcontrollers, Field-Programmable Gate Arrays (FPGAs), or System-on-Chips (SOCs). Information is exchanged by a communications gateway with smart clothing through cloud servers or a block chain [45]. Wireless data communication is performed through a Body Area Network (BAN), which distributes information from electronic sensors embedded into smart clothes. Then, data from smart clothing are collected by a Local Area Network (LAN), sent via a cloud-based server. A mesh wireless network is responsible for communicating with each object and machine that are connected around the setup. The Wide Area Network (WAN) is responsible for many aspects of the technology, such as internet connection [46]. A summary of current wireless technologies mostly used for developing smart clothing is provided in Figure 7.
# Risk Assessment of a Coastal Ecosystem from SW Spain
## 3 Results and Discussion
### Multivariate Analysis
Furthermore, the bioaccumulation of As and Zn was shown in factor two which was correlated with the sediment contamination. These results are expected since mussels exposed to trace elements in seawater can accumulate high amounts in their tissue [6]. In addition, a previous study reported that the bioaccumulation in _Tellina deltoidalis_ showed that the bioavailability of metals was likely to be a result of the continued cycling between the pore water and the surface sediments due to the bioturbation process [42]. According to [45], the bioaccumulation of metals may be influenced by some factors, such as metal specificity, environmental influences, exposure routes, and species-specific characterization. Results of metal bioaccumulation in mussels may be associated with different parameters such as CO\({}_{2}\) enrichment in seawater, sediment contamination, metals behaviour, etc. To confirm the factors' descriptions and to establish the relationship between components associated with contamination, biological effects and acidification at each studied site, it was proposed as a representation of estimated factor scores per case (Figure 5). The factor scores have demonstrated that the contamination levels of sediment influence the obtained results. Overall, the RSP sediment that is considered a noncontaminated sediment site did not correlate with the contamination levels and biological responses (factor one) for the higher pH values. Moreover, the results of factor scores confirm those previously reported (Table 2) with lower levels of metal contamination in sediments (factor two). Furthermore, as expected, the results of factor two were correlated even at the control pH values using the TRO which also confirms the results obtained using the tabular matrix and pie charts. Furthermore, it was noted that factor two decreases with the concentration of protons increase (pH reduction) while the opposite results were demonstrated for factor one, which increases its value as the pH values decrease. In this sense, the pH value of 6.5 was correlated to provoke adverse effects on marine organisms that could also be associated with the increase in the mobility of metals. Moreover, it is important to perform a complete risk assessment in areas affected by CO2 enrichment considering different LOEs associated with biological responses and sediment characterization for a proper interpretation of the impacts caused by the acidification in a marine environment.
## 7 The Experimental Results and Discussion
### The Maintainability Index Computation for State-of-the-Art Solar Tracking Systems
Thus, the time hour reduction using the accessibility metric is 1.27 h. Concerning predictive maintenance, assuming that for \(PME=100\%\) we obtain a 1 h reduction in repair time; it can be written as follows:  
\[f_{PME}(PME)=k_{3}\times\frac{PME}{100}=0.2105\times\frac{21.05}{100}=0.2105 \times 2=0.42\text{ h}, \tag{45}\]  
where \(k_{3}=0.21\) represents the hour reduction per predictive maintenance percentage, and \(PME=21.05\%\) is the computed predictive maintenance value from Equation (40). Thus, the time hour reduction using the predictive maintenance metric is 0.42 h. Finally, the total hour reduction can be calculated as in Equation (46):  
\[ \begin{array}{l}T_{HR}=f_{MF}(MF)+f_{AS}(AS)+f_{PME}(PME)=k_{1}\times\frac{ MF}{100}+k_{2}\times\frac{AS}{100}+k_{3}\times\frac{PME}{100}\\ =1.41+1.27+0.42=3.1\text{ h}\end{array}  \tag{46}\]The improved \(MTTR\) was established in Equation (22), and by substituting the computed values we obtain the following:  
\[MTTR_{I}=MTTR_{B}-\sum_{i=1}^{n}TR=MTTR_{B}-T_{HR}=6.25-3.1=3.15 \tag{47}\]  
Consequently, the proposed maintenance-oriented system achieves a 3 h reduction in the baseline \(MTTR\) by taking advantage of the modularity, accessibility, and predictive monitoring tools of modern solar tracking devices.