esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Poultry Egg Production  
Around 400 million laying hens are kept in the EU, of which, according to Eurostat, 53% are housed in enriched cages, 27% in barns, 15% are free-range, and 5% organic [3]. These generally range from more intensive to less intensive, respectively, whereby enriched cages are characterized by high stocking densities and closed cages, while in the other systems, hens have lower stocking densities and generally have access to an outdoor space [53]. There is relatively little information available on chicken egg production in the EU, which makes it difficult to draw conclusions. [PERSON] et al.'s (2011) study looked at four egg production systems in the Netherlands, which, although geographically limited, covered the main production systems in the EU [19]. This study found that to produce 1 kg of eggs, 20.5-23.5 MJ of energy inputs were needed and that in all cases, at least 50% of all energy inputs were associated with feed (Figure 7 and Table A5).  
Comparing the above results, [PERSON] et al. (2011) showed higher energy use. They claimed that this was caused by the inclusion of more processes and more process details for subsystems concentrate and litter production and laying hen husbandry.  
[PERSON] et al. (2006) and [PERSON] et al.'s (2012) study conducted an LCA on the environmental impacts of chicken egg systems in the UK. [PERSON] et al. (2006) found that for non-organic, caged, and free-range egg production systems, around 14.1, 13.6, and 15.4 MJ/kg of energy inputs were required, respectively [57]. [PERSON] et al.'s (2012) study found that feed represented between 54 and 75% of the total energy use. This was followed by on-farm electricity use (mainly for ventilation, automatic feeding, and lighting), consuming between 16-38% of the total energy use and gas and oil (used mainly for heating and incineration of dead layer birds) which accounted for 7-14% of the total energy [58]. Similarly, [PERSON] et al. (2017) found that feed represented around 83% of the total energy consumption. Regarding direct energy consumption, this study found that ventilation accounted for 33% of energy use, lighting for 15%, transportation for 17%, thermal energy for 3%, waste removal for 2%, packaging for 14%, watering for 1%, and feeding for 15%. The specific energy carriers were found to be 55% electricity, 12% LPG, and 33% diesel [51].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Country** & **Thermal** & **Electrical** \\ \hline [PERSON] et al., 2009 & France & 1.37 & 0.18 \\ [PERSON], 2010 & France & 1.51 & 0.25 \\ [PERSON], 2011 & Spain & 5.45 & 0.19 \\ [PERSON], 2011 & Spain & 2.73 & 0.18 \\ [PERSON], 2011 & Spain & 1.64 & 0.17 \\ [PERSON], 2008 & Italy & 1.40 & 0.01 \\ [PERSON] et al., 2013 & Italy & 2.15 & 0.23 \\ [PERSON], 2008 & Sweden & 1.73 & 0.30 \\ [PERSON] et al., 2006 * & Finland & 4.07 & 0.68 \\ [PERSON] et al., 2017 1 & Greece & 1.116 & 0.36 \\ \hline \hline \end{tabular}
* MJ/kg carcass weight. 1 MJ/kg live weight.  
\end{table}
Table 5: Thermal and electrical energy consumption in broiler systems MJ/kgmeat (based on [7; 55; 56]).
## 2 Materials and Methods
### Data Augmentation
#### 2.3.1 Augmentation Setting for Training Data  
The number and range of rotations applied to the accelerometer signals might affect the success of data augmentation. Therefore, we initially tested which rotation pattern resulted in the largest performance improvement for our model during cross-validation. Two augmentation training datasets were considered: the first set consisted of seven rotations between 0 and 90 degrees, while the second set consisted of seven rotations between 0 and 180 degrees. Rotations were applied separately along the frontal and sagittal axis of the human body. The frontal axis splits the body into a dorsal and ventral parts, while the sagittal axis splits the body into an upper and lower halves. To compare the two augmented sets, tests were made for rotations from 0 to 360 degrees with a step of 5 degrees.  
Based on this preliminary analysis, the final augmentation settings for the training set of the augmented model consisted of ten rotations from 0 to 180, with a 20 degree step on the frontal and sagittal axis separately. While all the original acceleration signals were considered in the training data, only a randomly selected portion (11.1%) of these signals was kept when applying a single rotation. The baseline model was trained using the data collected by the five upper body sensors in the WPS dataset, including a total of 73,683 segments (windows of 6 s overlapped by 4.5 s). The resulting training size of the augmented model was three times the size of the baseline training set.  
Figure 2: Convolutional Neural Network model architecture composed of an input layer, four blocks including multiple layers (B1, B2, B3, B4), flattening and ending softmax layer.
## 4 Experiments Section
### Experimental Results
#### 4.2.3 Leave-One-Subject-Out Assessment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Subject**} & **LSTM-Raw** & **LSTM-Features-1** & **LSTM-Features-5** & \multirow{2}{*}{**SVM (AUC)**} \\  & **(AUC)** & **(AUC)** & **(AUC)** & \\ \hline A & 0.62 & 0.82 & 0.86 & 0.74 \\ B & 0.60 & 0.84 & 0.80 & 0.81 \\ C & 0.78 & 0.87 & 0.87 & 0.83 \\ D & 0.77 & 0.78 & 0.79 & 0.70 \\ E & 0.85 & 0.84 & 0.85 & 0.85 \\ F & 0.55 & 0.82 & 0.83 & 0.70 \\ G & 0.85 & 0.91 & 0.94 & 0.84 \\ H & 0.70 & 0.77 & 0.84 & 0.87 \\ I & 0.70 & 0.79 & 0.82 & 0.82 \\ J & 0.65 & 0.76 & 0.76 & 0.72 \\ K & 0.51 & 0.71 & 0.72 & 0.75 \\ L & 0.70 & 0.85 & 0.87 & 0.92 \\ Average & 0.69 & 0.81 & 0.83 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification results for each test subject for all LSTM models and SVM. gradually, while the validation AUC stabilized, indicating that the model achieved convergence. With the early-stopping strategy, the training stopped at epoch 78 when the validation AUC stopped improving after 15 epochs to prevent overfitting. As shown in Figure 11, the gaps between the training and validation curves in the binary cross-entropy loss were small. Figure 12 shows that the gaps between the training and validation curves in terms of the AUC were exceedingly small. These results indicated that the dropout technique was effective in preventing overfitting. After the learning curves stabilized and the model achieved a good fit, training beyond that point could lead to overfitting. To prevent this, the early-stopping strategy was employed. could see that LSTM-Features-1 outperformed the SVM by an average of 2%. This meant that LSTM-Features-1 with post-processing was the most effective model, as it exceeded LSTM-Features-5's classification performance by 5%. This finding suggested that post-training stride aggregations could lead to better classification results, and they could be more effective than incorporating multiple strides during the training stage.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 2 Related Work
### Existing Research
In a study by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [32], a valuable methodology was provided for classifying exchange sites and customers into three geotypes (rural, suburban, and urban) based on precise customer locations rather than relying solely on the number of connected customers. The classification was defined by household density per square kilometer, with rural areas having up to 10 households/km\({}^{2}\), suburban areas ranging from 11 to 500 households/km\({}^{2}\), and urban areas exceeding 501 households/km\({}^{2}\). A distribution analysis in Ireland highlighted a predominance of rural and suburban regions, thereby offering valuable insights for refining the classification of query points or locations in diverse scenarios. Lastly, [PERSON], [PERSON], and [PERSON] [3] studied vehicle routing problems (VRPs) on road networks, called Steiner VRPs. Unlike traditional VRPs, where customers and depots are nodes in a complete graph, Steiner VRPs consider road networks, thereby introducing challenges in terms of distance computation. The authors proposed a three-phase heuristic approach: (1) create an approximation using Euclidean distances multiplied by a constant; (2) solve the approximated instance; and (3) convert the solution to the original instance. Computational experiments were conducted on Steiner versions of the travelling salesman problem (TSP) and capacitated VRP (CVRP) using global road network data from twelve cities. The result of this research showed that the use of Euclidean distances instead of actual road distances was found to yield acceptable results for the Steiner travelling salesman problem (TSP) and Steiner capacitated vehicle routing problem (CVRP), particularly when only a tiny proportion of nodes require service.
## 5 Summary and Conclusions
Recent advances that support and build on these state-of-the-art shortwave ADMs include machine learning and semi-physical approaches. Machine learning of either the anisotropic factors or the scene type has been shown to produce promising results that can be applied in the absence of imager information. This is currently used operationally in CERES data processing when imager retrievals are missing. Semi-physical approaches have demonstrated that further combining theory with the traditional empirical approach can help to represent the additional influence of unaccounted variables in cloudy scenes over ocean such as above cloud water vapor and cloud drop effective radius. This demonstrates that shortwave ADMs developed for ERB can be applied more broadly, and possibly even to other remote sensing applications involving observations of anisotropic scenes. Looking forward, continuity of the high-quality ERB data record will be crucial. As such, consistent processing of ERB observations to produce a backward compatible ERB data record, including the application of shortwave ADMs, should be a priority. The recently selected _Libera_ mission that will continue the CERES ERB data record recognizes this requirement by matching the CERES observation strategy very closely. _Libera_ is planned to fly alongside the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Joint Polar Satellite System-3 (JPSS-3) satellite, which is scheduled to launch by December 2027. In addition to continuity goals, _Libera_ will also explore a different proof-of-concept approach that will utilize a WFOV camera to develop ADMs for the new split-shortwave channel. This approach will be explored further in subsequent studies. This work was funded by the _Libera_ project under NASA Contract 80 LARC20D006. J.J.G. acknowledges support from the NOAA Cooperative Agreement with CIRES, NA17 OAR4320101. Not applicable.
## 2 Methods and Materials
### Statistical Machine Learning Models and SHAP Method
#### 2.4.1 The \(k\)nn  
The first model is the \(k\)-NN algorithm, one of the most straightforward machine learning techniques. When handling regression tasks, it predicts the value by utilising a weighted mean function \(\hat{f}(x)=\sum_{i=1}^{k}\frac{f(x_{i})}{k}\), where \(\hat{f}(x_{i})\) represents the estimated value. Alternatively, more sophisticated functions can be utilised, including inverse distance weighting, as depicted in Equation (4).  
\[\hat{f}(\vec{x})=\begin{cases}\sum_{i=1}^{k}\frac{w_{i}(x_{1},\dots,x_{k})f(x_ {i})}{\sum_{i=1}^{k}w_{i}(x_{1},\dots,x_{k})}&\text{if }\rho(\vec{x},\vec{x}_{i})\
eq 0 \ \forall\ i\leq k\\ f(x_{i})&\text{otherwise}\end{cases} \tag{4}\]  
Within the \(k\)-NN algorithm, the model has the flexibility to change the distance function. A common approach involves exploring various values for the Minkowski distance (shown in Equation (5)), which allows for adapting the distance metric to better suit the characteristics of the dataset. More mathematically intricate distance functions can also be employed, such as exponentially weighting by distance or utilising a Gaussian function (Gaussian kernel).  
\[\rho(\vec{x},\vec{y})=\left(\sum_{i=1}^{n}|x_{i}-y_{i}|\right)^{\frac{1}{p}} \tag{5}\]
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 2 Related Work
### Approach-Based Routing Protocols (ARP)
An essential characteristic of SPEED is its stateless architecture, which contributes to its exceptional scalability and efficiency, especially in IoT networks where sensor node resources are constrained. Several surveys have discussed different protocols that have been used in IoT networks. Table 2 summarizes the most common IoT networks' routing protocol and classifies each one of them to AI or traditional-based protocols. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Routing Protocol** & **Classification** & **Contribution/Methodology** \\ \hline \multirow{2}{*}{BFS} & \multirow{2}{*}{AIRP} & Reducing energy consumption, extending the lifespan of IoT networks \\ \hline \multirow{2}{*}{ECQSR} & \multirow{2}{*}{AIRP} & Utilizing the NN algorithm for finding low-cost paths, and imposing delay restrictions for real-time data \\ \hline \multirow{2}{*}{GA-UCR} & \multirow{2}{*}{AIRP} & Focusing on unequal clustering incorporating Genetic Algorithm technique \\ \hline \multirow{2}{*}{NSI-MOALO} & \multirow{2}{*}{AIRP} & Designed for a multi-objective optimization \\ \hline \multirow{2}{*}{SALMA} & \multirow{2}{*}{ARP} & Focusing on the activeness of sensor nodes in the IoT networks operations, and it is defined three states of sensor nodes, which are black, white, and gray \\ \hline \multirow{2}{*}{SAR} & \multirow{2}{*}{ARP} & Multiple paths routing protocol that assists in routing decisions \\ \hline \multirow{2}{*}{LEACH} & \multirow{2}{*}{ARP} & A clustering based protocol that applies a randomized selection the CH to distribute the power load between the sensor nodes in the IoT networks evenly \\ \hline \multirow{2}{*}{APTEEN} & \multirow{2}{*}{ARP} & Sensor nodes not only react to critical time events but also periodically share comprehensive network details while minimizing energy consumption \\ \hline \multirow{2}{*}{GAF} & \multirow{2}{*}{ARP} & Turning off useless sensor nodes at the same time maintaining the stability of process fidelity \\ \hline \multirow{2}{*}{GEAR} & \multirow{2}{*}{ARP} & Selecting the next hop of the path according to the geographical location closeness or cost \\ \hline \multirow{2}{*}{SPEED} & \multirow{2}{*}{ARP} & Supports three classes of real time transmission services, namely, real-time area multicast, real-time area anycast and real-time unicast \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison between most common routing protocols in IoT networks. Figure 3: GAF state transitions.
## 3 Experimental Results and Analysis
### Security Analysis
#### 3.2.2 NIST SP 800-22 Randomness Test  
The National Institute of Standards and Technology (NIST) statistical test [38] was adopted to test the randomness of sequences generated by the 2D-SLMM to ensure that they are suitable for cryptosystems. NIST SP 800-22 consists of 16 statistical tests, and each test provides a \(p\)-value between 0 and 1 under the significance level \(\alpha\). If \(p\)-value \(\geq\alpha\), the sequence passes the randomness test successfully with the confidence of \(1-\alpha\). Otherwise, the sequence fails the test. In our experiment, we set the default value, \(\alpha=0.01\) for testing.  
Table 5 shows the NIST test results. From the table, we can see that all \(p\)-values are larger than 0.01, indicating that the 2D-SLMM sequence passes the randomness tests with 99% confidence.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Name** & **Resolution** & **Capacity** & \(\textit{ER}_{\textit{rea}}\) (**bpp**) & **MSE (RGB)** & **TM PSNR** & **TM SSIM** &
\begin{tabular}{c} **TM** \\ **Q-Index** \\ \end{tabular} \\ \hline
1 & Apartment & 2048 \(\times\) 1536 & 24,225,583 & 7.7
# Stochastic Generator of Earthquakes for Mainland France
## 2 Methodology
### Main Shock Generation
#### 2.1.3 Rupture Plane's Parameters  
Seismic hazard produced by an earthquake at a given site is computed by ground-motion prediction equations. These equations accept the definition of earthquakes as points. However, the geometry of rupture plane associated to an earthquake could play a significant role in seismic hazard computation. Knowing this, a rupture plane needs to be described for each generated earthquake.  
In this study, a rupture plane is considered as a plane at a particular depth and is parametrized by a length (\(L\)) and three orientation angles: (i) azimuth (clockwise angle from north between \(0^{\circ}\) and \(360^{\circ}\)), (ii) dip (angle between \(0^{\circ}\) and \(90^{\circ}\) from the horizontal) and (iii) rake (angle between \(-180^{\circ}\) and \(180^{\circ}\)) that define the components of fault movement (normal, reverse, strike-slip).  
\(L\) is evaluated from moment magnitude \(M_{\text{zw}}\) of the earthquake using the following relation, e.g., [24; 25]:  
\[L=10^{(M_{\text{zw}}-l_{1})/l_{2}}\,, \tag{3}\]  
with \(L\) being the rupture plane's length in km and \(l_{1}\) and \(l_{2}\) being two constants. For the other parameters, they are defined per region, where intervals of values are set according to data. Classically, these kinds of data are used:  
* Instrumental seismic catalogue for depth;
* Focal mechanism catalogues for azimuth, dip and movement;
* Fault map for azimuth.  
See Appendix A for an example of definition of these intervals for an application in mainland France.  
Values for each parameter are randomly drawn in a uniform distribution when a main shock in generated in a given region.  
This step is referred as 3A in Figure 1.
## 4 Conclusions  
The present work considers the surface renewal and coalescence intensity of dropwise condensation within a hybrid surface. The developed model illustrates the process of drop growth, drop generation, departing, and merging into film regions. Drops' population density and surface renewal for various ratios of maximum drop radius to DWC region width, \(R_{D}\), have been studied. The drop size distribution for DWC within a hybrid surface was developed based on modifying the exponent \(n\) of \(1/3\) in the empirical formula for large drops and the population model for small drops. The present work provides \(n\) in a polynomial form that corresponds to various effective and maximum radii. 1. From simulation work, area fraction \(f\) occupied by drops was estimated for \(R_{D}\) values of 0.125, 0.25, 0.5, 0.75, and 1 and a total temperature drop of 2, 4, and 8 \({}^{\circ}\)C. The area fraction, \(f\), ranged from 0.28 and 0.296, and an average of 0.288 was selected to reflect the variation of \(R_{D}\). 2. The average \(f\) of 0.288 for DWC within the hybrid surface was different from the reported value of 0.55 for full DWC due to the consideration of surface renewal by sweeping and merging. 3. Surface renewal was improved by the hybrid surface for \(R_{D}\) values greater than 0.5 due to the contribution of merging. When \(R_{D}\) reached unity, the surface renewal rate was enhanced by 85% at \(\Delta T=2\)\({}^{\circ}\)C and 64% at \(\Delta T=8\)\({}^{\circ}\)C.
4. Drop size distribution for DWC in a hybrid surface generally shows a steeper slope on the logarithmic scale, where the population of larger drops is less than that of full DWC due to the combined effect of surface renewal and intensive coalescence. 5. Increasing drops' nucleation or lowering the effective radius accelerates the surface renewal and coalescence process for a given maximum radius. 6. An exponent \(n\) of \(1/3\) in the drop size distribution of large and small drops was modified for DWC on a hybrid surface with the consideration of surface renewal by sweeping and merging and intensive coalescence. The modification is in a simple polynomial form of effective and maximum radii. In future work, the modified drop size distribution will be utilized to develop and evaluate the thermal performance of hybrid dropwise condensation. Figure 12: Model results for exponent \(n\) with respect to \(r_{max}\) and \(r_{e}\). **Author Contributions:** Conceptualization, A.A.; Formal analysis, A.A., M.A. and H.A.-Z. ; Methodology, A.A.; Resources, A.A.; Software, A.A. and M.A. ; Validation, A.A. and M.A. ; Writing--original draft, A.A., M.A. and H.A.-Z. ; Writing--review & editing, A.A., M.A. and H.A.-Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within the article. **Conflicts of Interest:** The authors declare no conflicts of interest.
# A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition
## 4 Robustness Strategies
In addition to pose-related challenges, real-world FER systems must contend with variability in ambient lighting conditions and individual appearance factors, such as skin tone, makeup, or the presence of facial accessories. These variables can introduce artifacts that distract attention modules and undermine the statistical integrity of feature representations. FERONet addresses this issue through a dual-pronged approach that combines architectural robustness with training-time augmentation techniques. This ensures that the model not only learns invariant features under challenging conditions but also remains resilient when deployed in diverse and unpredictable environments. Before feeding RGB images into the encoder, an LCN layer standardizes local patches by subtracting the local mean and dividing by local standard deviation [38]:  
\[I^{\prime}_{x,y}=\frac{I_{x,y}-\mu_{x,y}}{\sigma_{x,y}+\epsilon} \tag{29}\]  
where \(\mu_{x,y}\) and \(\sigma_{x,y}\) are the local mean and standard deviation computed over a \(5\times 5\) window centered at pixel \((x,y)\), and \(\epsilon\) is a small constant. This operation reduces the impact of global illumination shifts and improves invariance to shadows and highlights. To enhance the model resilience against appearance-related variability and ensure robust generalization in real-world conditions, FERONet incorporates a diverse suite of data augmentation techniques during training. These augmentations are designed to simulate a wide range of visual distortions and environmental inconsistencies commonly encountered in unconstrained settings. Variations in lighting are emulated through stochastic adjustments to brightness, contrast, and gamma levels, while sensor imperfections are mimicked by injecting additive Gaussian noise into the input images. To further improve the model's robustness to partial occlusions and visual ambiguity, random patches of the image are obscured or blurred, encouraging the network to rely on global and context-aware cues rather than localized patterns alone. Additionally, color jittering is applied to reflect the variability introduced by makeup, skin tones, and artificial lighting, helping the model learn to identify expressions independent of superficial visual traits. These transformations are applied stochastically across training batches, ensuring a dynamic and unpredictable input space that prevents overfitting to any single visual configuration. Complementing these augmentations is a channel dropout strategy, in which individual RGB or depth channels are randomly zeroed out. This further compels the model to learn from incomplete or degraded information, fostering an adaptive learning process that is resilient to corrupted, missing, or modality-specific noise. Collectively, these augmentation and dropout techniques serve to regularize the training process, enhance feature invariance, and ultimately improve the model performance in diverse, real-world environments.