esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Improved Deep Learning Model for Day-Ahead Solar Irradiance Forecasting
### Long Short Term Memory Based Sequence Forecasting Model (from RNN to LSTM)
#### 2.3.2 Long-Short-Term Memory  
LSTM network proposed by [PERSON] et al. [53] in 1997 is a variant type of RNN, which combines representation learning with model training without requiring additional domain knowledge. The improved construction of LSTM is helpful for the achievement of avoiding gradient vanishing and explosion problems in typical RNN. This means that LSTM is superior at capturing long-term dependencies and modeling nonlinear dynamics when addressing the sequential data with a longer length. The structure of LSTM cell is shown in Figure 6.  
Figure 5: The structure of Recurrent Neural Network.  
Figure 6: The structure of Long Short-Term Memory Cell.  
Forget  
This part reveals how LSTM controls what kinds of information can enter into the memory cell. After \(\mathbf{h}(t-1)\) and \(\mathbf{x}(t)\) has passed through sigmoid function, a value \(\mathbf{f}(t)\) between 0 and 1 is generated. The value of 1 means that \(\mathbf{h}(t-1)\) will be completely absorbed in the cell state \(\mathbf{c}(t-1)\). On the contrary, if the value is 0, \(\mathbf{h}(t-1)\) will be abandoned by cell state \(\mathbf{c}(t-1)\). The formula of this process is shown below.  
\[\mathbf{f}(t)=\sigma(\mathbf{w}_{f}\cdot[\mathbf{h}(t-1),\mathbf{x}(t)]+\mathbf{b}_{f}) \tag{12}\]  
in which \(\mathbf{W}_{f}\) weight matrix, \(\mathbf{b}_{f}\) is biases vectors, and \(\sigma\) is activation function.  
Store  
This part shows how LSTM decides what kinds of information can be stored in the cell state. First, \(\mathbf{h}(t-1)\) passes through sigmoid function, and a value \(\mathbf{i}(t)\) between 0 and 1 is then obtained. Next, \(\mathbf{h}(t-1)\) passes through tanh function and then a new candidate value \(\mathbf{g}(t)\) is obtained. In the end, the above two steps can be integrated to update the previous state.  
\[\mathbf{i}(t)=\sigma(\mathbf{W}_{i}\cdot[\mathbf{h}(t-1),\mathbf{x}(t)]+\mathbf{b}_{i}) \tag{13}\]  
\[\mathbf{g}(t)=tanh(\mathbf{W}_{g}\cdot[\mathbf{h}(t-1),\mathbf{x}(t)]+\mathbf{b}_{g}) \tag{14}\]  
Then the previous cell state \(\mathbf{c}(t-1)\) considers what information should be abandoned and stored and then creates a new cell state \(\mathbf{c}(t)\). This process can be formulated as follows.  
\[\mathbf{c}(t)=\mathbf{f}(t)\cdot\mathbf{c}(t-1)+\mathbf{i}\cdot\mathbf{g}_{t} \tag{15}\]  
Output  
The output of LSTM is based on the updated cell state \(\mathbf{c}(t)\). First of all, we employ the sigmoid function to generate a value \(\mathbf{o}(t)\) to control the output. Then tanh and the output of sigmoid function \(\mathbf{o}(t)\) are further utilized to generate the cell state \(\mathbf{h}(t)\). Thus we can output \(\mathbf{y}(t)\) after the above process as shown in the following two steps.  
\[\mathbf{o}(t)=\sigma(\mathbf{W}_{o}\cdot[\mathbf{h}(t-1),\mathbf{x}(t)]+\mathbf{b}_{o}) \tag{16}\]  
\[\mathbf{y}(t)=\mathbf{h}(t)=\mathbf{o}(t)*tanh(\mathbf{C}(t)) \tag{17}\]  
The training process of LSTM is called BPTT (backpropagation through time) [54].
## 2 Materials and Methods
### Analysis of Handover Decision Factors  
LEO satellite communications play a vital role in grid scenarios in remote areas, ensuring the provision of reliable power business services. When optimizing handover algorithms, careful consideration must be given to the impact of three key factors on power services: the transmission delay, handover frequency, and data rate. Transmission latency directly affects the real-time availability of monitoring and control commands, particularly in remote areas where the power system may encounter higher latency challenges. Therefore, minimizing the transmission latency is crucial to ensuring real-time responses. Optimizing the number of satellite handovers is essential for reducing communication outages and service disruptions. Frequent handovers can result in signal loss and communication instability, thus impacting the continuity of grid operations. The data rate determines the quantity and quality of data that can be transmitted. For grid monitoring and control, a higher data rate is necessary to support complex data transmission requirements. Thus, optimization of the handover algorithm must consider these three factors to ensure that the LEO satellite communication effectively meets the real-time, continuity, and high-efficiency requirements of power grid operations.
## 3 Results and Discussion
### Industrial-Scale (100 m\({}^{3}\))
If a reduction of 50% in the biomass (Scenario I1) is considered, the environmental load relative to the downstream processing is reduced, as less solvents are used for the extraction, leading to a lower overall impact. On the other hand, the reduction in productivity increases the impact on a unit of product, as the extracts are also reduced by half. When it comes to alternatives for dewatering, Scenarios I2 and I3 have no significant reduction in the environmental load of the system, although in a detailed analysis of subsystem S4 (Figure 8), it is possible to see that spray-drying and tray-drying have a reduced impact when compared to freeze-drying, except for TET, where steam has more impact than electricity mix; overall, tray-drying is the one with less impacts. Moreover, it is important to evaluate, in terms of experimental data, how the _Cyanobium_ sp. biomass would be affected in terms of pigment composition when dried with these different approaches, in order to have a more secure decision. Thus, the semi-continuous cultivation (Scenario I4) reduces the overall impact by 30%, even though it reduces the biomass productivity and consequently the downstream process by 10%. Moreover, the assumption that _Cyanobium_ sp. can revert the metabolism from red-phase to white-phase has not been studied, and if compared to the other two-phase cultures, such as the microalgae _Haematococcus pluvialis_, the switch from the stress phase to the growth phase is very optimistic and does not consider that the culture requires a laborious step to revert the stress conditions [53]. After the evaluation and proposal of scale-up processing, and to give an idea of the advantage of strategic thinking of scale-up, the relative impact of each proposed system was compared considering one litre of culture (Tables 4-6), and it is clear how the outdoor culture, with controlled cultivation productions, led to a reduction in all of the impacts from laboratory to demonstration and industrial scales in a range of 80% to 90%. Moreover, the difference between demonstration and industrial scales is less evident, as it increases in some impact categories and decreases in others, which indicates that the scale-up model was successful.
## 3 Materials and Methods
### Methodology Employed  
As shown in the Figure 2, the data set consists of images that are fed into the GAN network. The network, which is adversarial, in nature processes the given data. Random input images are used as the input. The generator network generates realistic images from the available original data. Simultaneously, the discriminator network learns from the real and generated images. The learning will result in a conclusion having a unique result, termed as prediction. Throughout the learning process, different parameters are updated and finetuned. The performance of the SegNet model is evaluated and modified by different parameters for performance improvement. The model compiles and trains with real data images, and the performance is evaluated with a modified evaluation metric. Upon updating the loss function and optimizing the parameters, the performance is evaluated. The batch size and rate of learning are also adjusted to improve the performance. The performance is improved further by modifying the layers within the system [26]. The final segmented results are generated from the SegNet by transferring the weights.  
Figure 1: SegNet system flow for the proposed system.  
Figure 2: Proposed system for segmentation.  
The proposed system, shown in Figure 2, generates images using a generative adversarial network. After that, the generated images are used to learn a SegNet, and the parameters are updated by using the real data images. Through parameter transfer learning, the SegNet is updated for image segmentation. The results are verified by using a test data set.  
The advantages of the proposed system are evident while looking into various factors and the performance of different architectures mentioned in the literature review. The systems using U Net face a lot of memory constraints, as features are changed into the expansion path. However, the SegNet offers an efficient architecture by using an encoder decoder network. When compared to the fully connected networks, the memory needs are less, and it can effortlessly be used without any memory constraints, even with large data sets. Similarly, when working with small datasets, the proposed SegNet method reduces the chances of overfitting in comparison with the other architectures. The overall design of SegNet can be considered a computationally efficient one that can be deployed in systems with resource constraints. The efficiency of the proposed SegNet architecture is significantly high as compared with other architectures having low efficiencies and so many of the other constraints mentioned above. Volumetric segmentation and analysis can be done by using V-Net. The analysis done here is in terms of volumetric parameters and is suitable for the three-dimensional deep net architecture.
## 3 Results and Discussion
### H\({}_{2}\)S Mechanism  
H\({}_{2}\)S is absorbed by the amine solution and reacts only via acid-base reactions with amine and water. Therefore, H\({}_{2}\)S is present in the solution as H\({}_{2}\)S, HS\({}^{-}\) and S\({}^{2-}\) (but mainly as HS\({}^{-}\)). The aim is to extract H\({}_{2}\)S as solid S(s). To do so, an oxidizer is needed that is capable of oxidizing S(-II) to S(0). An oxidant that is thermodynamically capable of oxidizing H\({}_{2}\)S to S(s) must have an equilibrium potential greater than the one of S/H\({}_{2}\)S. However, we wanted to generate the oxidant at the anode of the electrolyzer. As previously explained, the oxidation taking place at lower potential will happen first on the anode. From a thermodynamic point of view, the oxidation of H\({}_{2}\)S will have to take place first on the anode, which would passivate the electrode. Consequently, to avoid the direct oxidation of H\({}_{2}\)S on the electrode, it is necessary that the oxidation of H\({}_{2}\)S is kinetically disadvantaged, i.e., that the oxidation of H\({}_{2}\)S must have an overvoltage allowing the oxidation of our oxidant before that of H\({}_{2}\)S. The couple Fe\({}^{3+}\)/Fe\({}^{2+}\) was chosen to fulfill this role:  
\[2\;Fe^{3+}+H_{2}S=S_{(s)}+2\;Fe^{2+}+2H^{+} \tag{13}\]  
With  
\[E_{eq}\left(Fe^{3+}/Fe^{2+}\right)\geq E_{eq}(S/H_{2}S) \tag{14}\]  
\[E_{eq}\left(Fe^{3+}/Fe^{2+}\right)+\eta_{electrode}^{Fe}\leq E_{eq}(S/H_{2}S)+ \eta_{electrode}^{S} \tag{15}\]  
In practice, and as shown in Figure 3, the amine solution rich in sulfur elements arrives in the anode compartment of the electrochemical cell. There, the iron anode is oxidized to Fe\({}^{3+}\). The cation Fe\({}^{3+}\) oxidizes H\({}_{2}\)S to solid S, which precipitates. Finally, the Fe\({}^{2+}\) ions will be reduced to solid iron in the cathode compartment of the electrochemical cell.
# Applying Force Plate Technology to Inform Human Performance Programming in Tactical Populations
## 3 Profile Testing for Physical Capabilities
### Use of Loaded Jump Assessments  
Increased load carriage requirements and lifting tasks challenge the physical capabilities of tactical personnel to a greater degree and resultantly place them under greater musculoskeletal injury risk over time [37, 65]. There are many biomechanical movement consequences from being equipped with external loads [66], such as impaired squat jump, drop jump, and CMJ performances [36, 50, 67, 68], as well as hindered occupational physical-movement capabilities [69, 70, 71, 72]. Thus, jump assessments with external loads may be more related to occupational task performances than unloaded jumps [36]. By testing loaded conditions, the strength of an individual may be indirectly assessed without the additional equipment requirements of an IMTP or the fatiguability and time requirements inherent to maximum repetition testing. Thus, force plate testing under unloaded and loaded conditions provides complementary and actionable knowledge [50].  
However, to ensure comparisons across loaded and unloaded conditions are appropriate, necessary precautions must be taken. The CMJ, without an arm swing, performed with a PVC pipe across the shoulders requires the loaded CMJ to be performed with a barbell using a standardized weight (\(\leq\)20 kg) to mimic the unloaded condition. Weighted tests are a viable, and often a more ecologically valid, option for loading CMJs in tactical populations, but must be strapped tight to limit movement (or tightly holding the vast at the collar) and requires similar testing parameters during the unloaded CMJ (i.e., hands at the waist or arms crossed at the shoulders). The load selected should reflect the capabilities of the occupational specialty and be standardized, although too heavy of a load may not be suitable for detecting neuromuscular fatigue. To this end, practitioners should ensure the magnitude of the load and distribution (symmetrical or asymmetrical) appropriately mimic the occupational demands and testing purpose, as both are linked to the ability of the neuromuscular system to maintain postural stability [73, 74].
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 4 Preliminaries
### Function Secret Sharing  
Unlike sharing values between parties above, function secret sharing [41; 42] splits function \(f\) into separate keys. Each party evaluates its own key locally with public input value \(x\) and generates a secret share of \(f(x)\). Note that parties can not infer any information about function \(f\) only from their own keys.  
**Definition 1** (Function Secret Sharing, FSS).: _A two-party FSS scheme is a pair of algorithms (Gen, Eval) such that_* _A PPT key generation algorithm_ \(\mathbf{Gen}(1^{\lambda},\hat{f})\) _inputs security parameter_ \(\lambda\) _and_ \(\hat{f}\in\{0,1\}^{*}\) _(description of a function_ \(f\)_), then outputs a pair of FSS keys_ \(k_{0}^{f}\) _and_ \(k_{1}^{f}\)_._
* _A polynomial time evaluation algorithm_ \(\mathbf{Eval}(k_{b},b,x)\) _inputs_ \(P_{b}\)_'s FSS key_ \(k_{b}\)_,_ \(b\in\{0,1\}\)_, and evaluates on_ \(x\in\mathbb{G}^{in}\)_, then outputs a value_ \(f_{b}(x)\in\mathbb{G}^{out}\)_, which satisfies_ \(f(x)=f_{0}(x)+f_{1}(x)\)_._  
FSS schemes should satisfy two properties: correctness and security. We recommend [41; 42; 43] for more details.
Remote Sens._**2012**, \(4\), 1146-1161; doi:10.3390/rs4051146  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
**Architecture and Methods for Innovative Heterogeneous**  
**Wireless Sensor Network Applications**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2,*}\) and [PERSON] \({}^{2,*}\)**  
\({}^{1}\) Tekever, Rua das Musas 3.30, 1990-113 Lisboa, Portugal  
\({}^{2}\) Dipartimento di Energia, Politecnico di Milano, Via La Masa 34, I-20156 Milano, Italy  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (P.A.); [EMAIL_ADDRESS] (F.G.); [EMAIL_ADDRESS] (M.M.); Tel.: +39-02-2399-4405; Fax: +39-02-2399-8566.  
_Received: 12 March 2012; in revised form: 18 April 2012 / Accepted: 19 April 2012 / Published: 27 April 2012_  
**Abstract:** Nowadays wireless sensor networks (WSN) technology, wireless communications and digital electronics have made it realistic to produce a large scale miniaturized devices integrating sensing, processing and communication capabilities. The focus of this paper is to present an innovative mobile platform for heterogeneous sensor networks, combined with adaptive methods to optimize the communication architecture for novel potential applications in multimedia and entertainment. In fact, in the near future, some of the applications foreseen for WSNs will employ multi-platform systems with a high number of different devices, which may be completely different in nature, size, computational and energy capabilities, _etc_. Nowadays, in addition, data collection could be performed by UAV platforms which can be a sink for ground sensors layer, acting essentially as a mobile gateway. In order to maximize the system performances and the network lifespan, the authors propose a recently developed hybrid technique based on evolutionary algorithms. The goal of this procedure is to optimize the communication energy consumption in WSN by selecting the optimal multi-hop routing schemes, with a suitable hybridization of different routing criteria. The proposed approach can be potentially extended and applied to ongoing research projects focused on UAV-based sensing with WSN augmentation and real-time processing for immersive media experiences.  
**Keywords:** Wireless Sensor Network (WSN); heterogeneous architecture; UAV; multimedia application
# Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches
## 2 Materials and Methods
### Denoising (Postprocessing)  
Two conventional denoising methods (BM3D and NLM) and two deep learning-based approaches (N2N and N2V) were established for imaging denoising. BM3D and NLM are conventional denoising approaches that are supposed to be the basic reference in the field of image restoration. NLM filtering uses the weighted average value of similar blocks with square neighborhood of 10, the search window size of 21 and the Gaussian filtering parameter of 5. BM3D realized denoising by nonlocal-based grouping, collaborative filter and aggregation, with the standard deviation of additive white Gaussian noise set to 15.
# Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
## 2 Materials and Methods
### Assessment of Diagnostic Models Using Machine Learning  
Our research comprehensively evaluated various variables from each mathematical transformation applied to EEG signals. This included assessing parameters such as mean values and peak-to-peak differences. Our selection criteria for these variables were based on their effectiveness in detecting changes across different brain regions and transformations, as we specified in Table 2.  
These features were analysed in relation to the probable cause of the seizure (such as brain haemorrhage, CET, CRA, infection, known epilepsy, and brain ischemia), seizure symptoms (convulsive vs. non-convulsive), and spread (focal vs. generalised). For this analysis, we utilised various machine learning techniques programmed in Python v3.11.7.  
From the initial pool of 247 features, we selected 52 based on their performance in representing means and peak-to-peak values across different lobar regions following each mathematical transformation. These were then applied to supervised machine learning models (see Supplementary Table S1).  
The machine learning algorithms tested for seizure classification included logistic regression, Decision Tree, Random Forest, artificial neural network (ANN), Naive Bayes classifier, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and k-nearest neighbourhood (KNN) with k values of 5, 7, 9, 15, and 21. After training and testing each previous model, the optimal value of k-neighbours was selected according to their precision to detect a specific type of seizure for different patient subgroups (see Supplementary Table S2).  
To assess the efficacy of these machine learning models, we employed training and testing techniques with an 80:20 split ratio, repeated ten times. This approach enabled us to calculate the mean success rate and its standard deviation. Furthermore, we identified the ten variables with the highest F1 scores as our target metric for evaluation.  
Finally, after selecting the top ten variables and evaluating the F1 score of each machine learning algorithm across different subgroups using their confusion matrices, we identified the models with the highest diagnostic capability. This selection was based on their performance in accurately classifying seizure types and characteristics.
# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## 4 Discussion
### Best Single-Date NDVI Is Context-Dependent, Varying According to the Dominant Land Use  
Our results showed that best single-date NDVI for modeling bird distribution patterns is time-dependent but is also modulated by the landscape context of the bird plots. In landscapes dominated by winter crops or summer crops, we found that end summer (14 September ) and begin of autumn (30 September) were the best periods to explain farmland and woodland bird species richness over France. For other seasons, crop cover with high NDVI values affect negatively the estimation of the species richness (leading to an under or overestimation depending on the bird group considered). In landscapes dominated by grasslands, late spring (26 June) and late summer (29 August) were the most adequate periods for SR woodland. This is also the case in woody landscape to explain SR farmland. These time periods match with the peak of woodland and grassland vegetation productivity leading to better accurate estimations.  
Because the proportion of the dominant land use in the surveyed square was sometimes low in the analysis (although a majority proportion was ensured in bird plots), a deeper analysis should be further developed to confirm the results, using a most exhaustive dataset.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 7 Conclusions  
In this paper, we proposed threshold-based opportunistic user scheduling with an information hiding power control mechanism, which achieves secrecy sum-rate scaling as \(M\log(\rho\log N)\) when the number of users scales as \(\rho^{\frac{M(K-1)+1}{1-\epsilon_{0}}}\) for a constant \(\epsilon_{0}>0\) in a high SNR regime. The secrecy performance of the proposed schemes has been evaluated through simulations in two eavesdropping scenarios: internal and external eavesdropping environments. Although the average achievable secrecy sum-rates of the proposed schemes are different depending on the eavesdropping scenario, simulation results show that the proposed schemes achieve the secrecy sum-rate scaling as \(\Theta(M\log(\rho\log N))\) regardless of the eavesdropping scenario when the number of users is sufficiently large. Further, we discussed the possible extensibility of our analysis and proposed scheme to various applications such as satellite communications and IoT networks. In this work, we focused on multiuser SIMO channels in a single-cell environment. The analysis for secrecy sum-rate scaling in various system models (multiuser SIMO channel in a multi-cell environment or multiple-input and multiple-output (MIMO) channel in a single-cell environment) remains for future work.  
Conceptualization, I.B.; formal analysis, I.B.; investigation, S.H.C.; supervision, B.C.J.; project administration, B.C.J.; writing--original draft preparation, I.B.; writing--review and editing, S.H.C. and B.C.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Research Institute for defense Technology planning and advancement (KRIT) grant funded by the Korea government (DAPA (Defense Acquisition Program Administration)) (21-106-A00-007, Space-Layer Intelligent Communication Network Laboratory, 2022).  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.