esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Energy-efficient building design needs an accurate way to estimate temperature inside the building which facilitates the calculation of heating and cooling energy requirements in order to achieve appropriate thermal comfort for occupants. Sky temperature is an important factor for any building assessment tool which needs to be precisely determined for accurate estimation of the energy requirement. Many building simulation tools have been used to calculate building thermal performance such as Autodesk Computational Fluid Dynamics (CFD) software, which can be used to calculate building internal air temperature but requires sky temperature as a key input factor for the simulation. Real data obtained from real-sized house modules located at University of Newcastle, Australia (southern hemisphere), were used to find the impact of different sky temperatures on the building's thermal performance using CFD simulation. Various sky temperatures were considered to determine the accurate response which aligns with a real trend of buildings' internal air temperature. It was found that the internal air temperature in a building keeps either rising or decreasing if higher or lower sky temperature is chosen. This significantly decreases the accuracy of the simulation. It was found that using the right sky temperature values for each module, Cavity Brick Module (CB) Insulated Cavity Brick Module (InsCB), Insulated Brick Veneer Module (InsBV) and Insulated Reverse Brick Veneer Module (InsRBV), will result in 6.5%, 7.1%, 6.2% and 6.4% error correspondingly compared with the real data. These errors mainly refer to the simulation error. On the other hand using higher sky temperatures by +10 \({}^{\circ}\)C will significantly increase the simulation error to 16.5%, 17.5%, 17.1% and 16.8% and lower sky temperature by +10 \({}^{\circ}\)C will also increase the error to 19.3%, 22.6%, 21.9% and 19.1% for CB, InsCB, InsBV and InsRBV modules, respectively.  
**Keywords:** sky temperature; building thermal simulation; CFD; simulation vs. reality
## 2 Background and Related Works
### Working Conditions Classification  
In the mission cycle of a spacecraft, there are many different working stages. In space, different environments can also cause different disturbances to a spacecraft. The influence of the space environment mainly comes from the electromagnetic environment, atmospheric environment, space debris, and the influence of the position of other celestial bodies [23].  
The electromagnetic environment of space will interfere with the electromagnetic system of a spacecraft. After the interaction between plasma and surface materials, the spacecraft will be charged, and the accumulated strong electric field will break down the electronic components inside the spacecraft. The solar radiation pressure will generate mechanical forces, which will seriously affect the attitude stability of the spacecraft [24]. Space atmosphere has different effects on a spacecraft in different layers. The drag force of the upper atmosphere is the main orbital perturbation force of a low orbit spacecraft [25]. When the shape of the spacecraft is asymmetric in the relative direction of motion, the drag force of the upper atmosphere will generate torque and affect the attitude control of the spacecraft. The atomic oxygen produced by the interaction between solar ultraviolet and oxygen molecules also has a serious denudation and aging effect on the structure of low-orbit spacecraft. Space debris of different scales and velocities can cause erosion or structural  
Figure 1: The overall flowchart of proposed anomaly detection based on domain adaptive LSTM and working condition classification.  
damage to a spacecraft. The position of the relative celestial body mainly affects the celestial body sensor used for attitude determination on the spacecraft.  
The operation of a spacecraft is a long cycle process, divided into different stages, and will face a different mission environment and mission intensity. The working condition of the spacecraft is defined as the set of the environment state (temperature, electromagnetic radiation intensity), mission state (acceleration, deceleration, attitude adjustment), and life cycle (service time) of the spacecraft, covering all the factors that may affect the threshold of spacecraft anomaly detection. The performance of spacecraft anomaly detection can be improved by classifying the working conditions of the spacecraft. Targeted fault detection under different working conditions has shown advantages in multiple tasks [22; 26; 27; 28]. Reference work [22] points out that the residual life estimation model of bearings trained under a single working condition cannot be well generalized to bearings under other working conditions. Therefore, a transfer learning method based on a multi-layer perceptron is proposed, which can adaptively complete the detection of bearing fault occurrence time under different working conditions.
## 2 The Measurement Methodology and a General Description of the Conducted Measurements
The measurement, although in most cases the vertical direction plays a decisive role, should be conducted simultaneously in three orthogonal directions: the \"X\" direction is taken in all measurements perpendicular to the excitation, the \"Y\" direction parallel, and the \"Z\" direction is the vertical direction. According to the standard [15], the use of a measurement disc is recommended, which should have a weight of at least 30 kg and a diameter of 30 cm [15] (Figure 1). The signal registration should enable the recording of frequencies in the range from 1 to 120 Hz, so that after applying a low-pass filter, frequencies up to 80 Hz could be included in the assessment. Determining the duration of vibrations is a separate issue. The standard [15] stipulates that the duration of vibrations is defined by the interval during which the value of vibration acceleration amplitudes does not fall below 0.2 of the maximum amplitude value in the recorded waveform. The ISO standard [6], primarily used for measuring general-character vibrations, requires that the signal recording time be at least 30 min. The standard [7] contains a provision regarding the duration of vibrations, stating that the recorded signal should suffice to ensure reasonable statistical accuracy. This definition is very broad and imprecise, while the duration of vibrations significantly affects the signal analysis results, especially regarding the widely used and recommended RMS method in the work [7]. In the German standard [17], analysis is carried out in cycles lasting 30 s each, after which data from the cycles are averaged. The measurements described in this work were performed in accordance with the standard [15]. Figure 1: The measurement disc. The last issue related to the analysis of measurement data is the sampling frequency of the recorded signal, which, according to [PERSON]'s criterion [18], should adhere to the principle that the highest frequency recorded in the signal (f\({}_{\text{N}}\)) is half the sampling frequency (f\({}_{\text{S}}\)) (Formula (1)):  
\[\text{f}_{\text{N}}=\frac{\text{f}_{\text{S}}}{2} \tag{1}\]  
This means that for a cutoff frequency f\({}_{\text{N}}\) = 120 Hz, the sampling frequency should be at least f\({}_{\text{S}}\) = 240 Hz. In practice, this minimum is set at 2.5 times the highest recorded frequency, which means that in the case of the impact of vibrations on people, the sampling frequency should be at least 300 Hz. Of course, the higher the f\({}_{\text{S}}\) value, the better the quality of the results, which is why in the analyses described in this work, a frequency of 1024 Hz was used.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 4 Results and Analysis
### Fraction of Absorbed Photosynthetically Active Radiation  
The monthly NDVI and SR maximum and minimum values need to be determined before calculating fPAR based on Equation (11), which are listed in Table 1.  
Figure 3 shows the results for fPAR from March to May 2009. The fPAR results for March, which average 0.47, were less than those for April and May, which fell mostly between 0.32 and 0.57. The fPAR for winter wheat in April continued to increase due to the work of photosynthesis and generally exceeds 0.40. The process of photosynthesis of winter wheat gradually weakened as May approaches, so the fPAR values concentrated in the range 0.40-0.60. The greatest average fPAR of 0.59 occurred in April 2009, whereas the average fPAR was 0.53 in May of the same year. These results reflected the mild weather during April, in which hydrothermal and sunlight conditions were suitable for winter wheat growth, resulting in increased photosynthesis of winter wheat and the subsequent increase in growth. In addition, the April and May fPAR results for Tongzhou and Shunyi districts did not differ significantly, which meant that the geographical environment of these two districts was suitable for winter wheat growth.
# Modeling of Muscle Activation from Electromyography Recordings in Patients with Cerebral Palsy
###### Abstract  
The present study estimated muscle activation from electromyographic (EMG) recordings in patients with cerebral palsy (CP) during cycling on an ergometer. This could be used as an input to the modeling of muscle force following the neuromusculoskeletal modeling technique which can help to understand the alterations in neuromotor processes underlying disabilities in CP. EMG signals of lower extremity muscle activity from 14 adult patients with CP and 10 adult healthy participants were used here to derive muscle activation. With a self developed EMG system, signals from the following muscles were recorded: Musculus tibialis anterior, Musculus gastrocnemius, Musculus rectus femoris, and Musculus biceps femoris. Collected EMG signals were mathematically transformed into muscle activation following a parameter dependent and a nonlinear transformation. Muscle activation values from patients with CP were compared to equivalent reference values obtained from healthy controls. Muscle activation calculated at specific foot positions deviated clearly from reference values. The deviation was larger for patients with higher degree of spasticity. Observations underline the need of muscle force modeling during cycling for individualized cycling training for rehabilitation strategy.  
rehabilitation; cycling; ergometer; spasticity; muscle force +
Footnote †: journal: Applied Sciences
## 3 Results
Specifically, S9, S12, S16, S20, S21, S23, S30, S32, S34, S35, S36, and S43 highlight the importance of implementing proactive defense mechanisms against DDoS attacks and botnets, leveraging AI-driven monitoring systems to detect and mitigate threats before they impact critical infrastructures. Additionally, S19 and S22 focus on minimizing false positives in cyberattack detection and optimizing system efficiency by improving anomaly classification. Meanwhile, S16, S18, and S27 explore behavioral analysis models to refine cybersecurity defenses and enhance the reliability of IoT-based security systems. In the domain of public security and surveillance, studies focused on enhancing real-time security through edge processing for video analysis, early detection of violent incidents, and automated alert generation. Specifically, S26 and S28 implement lightweight deep learning models to process video feeds at the edge, enabling rapid detection of violent activities while reducing reliance on centralized cloud systems. S42 introduces AI-powered motion recognition techniques for the early detection of aggressive behavior, improving response times in high-risk areas. Additionally, studies S26, S42, and S45 develop AI-driven automated alert systems that trigger security notifications upon detecting potential threats, while S28 and S23 emphasize the integration of these systems with existing surveillance networks to ensure seamless operation. Furthermore, S42 extends the application of public surveillance by integrating health risk assessment mechanisms, using AI to detect distress symptoms and abnormal behaviors that may indicate medical emergencies. Finally, addressing ethical considerations and regulatory compliance, studies examine the trade-off between privacy and security, algorithmic transparency, and adherence to legal frameworks for data protection. Specifically, S2 and S15 propose privacy-preserving security models that integrate encryption, anonymization, and federated learning to minimize data exposure risks while maintaining security effectiveness. Studies S13, S28, S45, and S23 assess the ethical implications of surveillance technologies, highlighting concerns over civil liberties and proposing responsible AI governance strategies. Compliance with data protection studies in which surveillance systems are proposed should explore informed consent mechanisms, data governance strategies, and privacy-by-design principles to align IoT security infrastructures with legal and ethical standards.
Therefore, there is still a lack of consensus regarding the specific impacts of VPD and SM on vegetation productivity at the ecosystem scale, highlighting the need for further investigation into how soil and atmospheric drought affect vegetation growth peaks. To address this issue, we first derived the maximum values of gross primary productivity (GPPmax) and the normalized difference vegetation index (NDVImax) as proxies of peak vegetation growth based on remote sensing data from 2001 to 2022 over the TP. Then, we examined the responses of GPPmax and NDVImax to SM and VPD. We aimed to answer several questions in our study: (1) What are the relationships between peak vegetation growth and soil and atmospheric drought? (2) What are the spatiotemporal patterns of these relationships? (3) How do such relationships vary among vegetation types and hydrothermal conditions?
# Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
## 2 Materials and Methods
### Groundwater Contamination Probability Mapping through Machine Learning (ML) Techniques
#### 2.5.5 Implementation and Accuracy Assessment  
This study implements the BRT, SVM, and MDA procedures through the SDM package in the R platform. To calibrate and test the models, the datasets are further divided randomly into the proportion of 70% (datasets utilized for calibration of the models) and 30% (datasets utilized for testing of the model), based on the literature [15; 51; 81; 82; 83; 84]. The Receiver Operating Characteristic (ROC) curve technique and Area Under Curve (AUC) technique are utilized for the performance validation of the models [85; 86; 87; 88]. [PERSON] [89] defined the performance threshold value as AUC \(\geq\) 80%, and the decision-making flow is illustrated in Figure 2. The possible outcomes of all three models utilized in the study are as follows:  
1. All of the models are operated, and the accuracy is assessed. The attained AUC is measured at less than 80% for all the models. Thus, all of the models are recalibrated to achieve the threshold value (a minimum AUC value of at least 80%);
2. One model achieved an AUC value over 80%, and, without further calibration, it is utilized to map the groundwater contamination incidence probability;
3. All of the remaining models achieved accuracy after further recalibration trials (AUC above 80%), and are used to map the groundwater contamination incidence probability without further recalibration procedures. An ensemble modeling process was utilized, which pools the outcomes of the single models, and produces synthesized results in order to achieve further accuracy [15]. In ensemble techniques, weighted incorporation of certain models is primarily employed, such as bagging and boosting. In the present study, the following equation is used to conduct the ensemble modeling: \[EM=\frac{\sum_{i=1}^{n}*(AUC_{i}*M_{i})}{\sum_{i=1}^{n}*AUC_{i}}\] (3) where EM represents the Ensemble models by evaluating AUC values of each model.
# Cloud Computing in Free Route Airspace Research
## 3 FRA Data Structures and Algorithms
### Definition of Air Transport Flights  
In the given airspace FRA, we define _air transport_ as the set of all _flights_. The flight \(p\) is given by a _path_  
\[p=(P_{1},P_{2},\ldots,P_{n}) \tag{1}\]  
where \(P_{i}\in NFRA\) for each \(i=1,2,\ldots,n\) (\(P_{i}\) are the significant points), \(P_{1}\) is a _departure point_, and \(P_{n}\) is an _arrival point_ of flight. Note that \(P_{i}=[LatP_{i},LonP_{i}]\) are the GPS coordinates (latitude and longitude) of the significant points. These values are required for calculating the distance \(d(P_{i},P_{i+1})\) between points \(P_{i}\), \(P_{i+1}\) for \(i=1,2,\ldots,n-1\). The value  
\[l(p)=\sum_{i=1}^{n-1}d(P_{i},P_{i+1}) \tag{2}\]  
is the _length of flight_\(p\). The departure point \(P_{1}\) belongs to the airspace \(\underline{V}=V_{1}(P_{1}\in V_{1})\) and the arrival point \(P_{n}\) belongs to the airspace \(\overline{V}=V_{k}\) (\(P_{n}\in V_{k}\)). Therefore, we can assign an _airspace sequence_  
\[A_{p}=(V_{1},V_{2},\ldots,V_{k}) \tag{3}\]  
to the flight \(p\), where \(P_{i}\in V_{j}\) for each \(j=1,2,\ldots,k\) and \(i=1,2,\ldots,n\). Note that, according to system rules, \(V_{i}\
eq V_{j}\) if and only if \(i\
eq j\) (it is possible to enter and exit each area only once during one flight). The term _air transport optimization_ will represent the optimization of each flight within the airspace. Therefore, it is sufficient to deal with only _one flight optimization_.  
In the \(NFRA\) network, we can define the set of all permissible flights. This is similar to the set of all admissible solutions for optimization problems. In this sense, we can also view the FRA system as an optimization model. However, we still need to define an objective function.
## 3 Results and Discussion  
### Compaction Behavior  
The moisture content-dry density relationships of untreated and treated samples with POFA-based geopolymer of both soils are shown in Figure 3a,b. For untreated CL and CH, the maximum dry density (MDD) was 1.76 Mg/m\({}^{3}\) and 1.31 Mg/m\({}^{3}\), whereas the optimum moisture content (OMC) was 15.5% and 34.4%, respectively. The results indicate a strong relationship between moisture content, dry density and POFA-based geopolymer dosage. At all dosages of geopolymer, MDD revealed an increment while OMC showed a decreasing pattern. Obviously, POFA-based geopolymer-coated soil particles performed as a lubricant which reduced the frictional resistance and enhanced sliding among soil particles resulting in an increase in maximum dry density. This lubrication effect will decrease the needed amount of free water to achieve optimal conditions since the geopolymer effect will replace the function of water, thus decreasing the optimum moisture content [31]. Moreover, the lubrication induced by the geopolymer improved compaction by decreasing the volume of air pockets significantly, which resulted in an MDD increase and OMC decrease [32]. CL- and CH-treated specimens with POFA-based geopolymer with 40% POFA of soil's dry weight (CL-G40 PA, CHG40 PA) exhibited the highest MDD (1.87 Mg/m\({}^{3}\) and 1.51 Mg/m\({}^{3}\), respectively) and lowest OMC (12.8% and 18.6%, respectively) compared to all treated samples. This outcome was anticipated due to the highest availability of alkali source ions accompanying this geopolymer mix that could dissolve the majority of Al and Si found in POFA compared to other geopolymer dosages [33]. However, there is no noticeable enhancement in MDD and OMC for treated CL soil between G20 PA and G40 PA geopolymer dosages. The increase in MDD of soil is usually an expectation of initial strength gain and the modifications in the compaction properties can be seen as a benefit and shall be included in any approach of stabilization using geopolymer.
## 3 Compared Deep Learning Models  
In visual recognition tasks, the computer processes images by their pixel values. To process a \(30\times 30\) color image (in RGB), \(2700\) (\(30\times 30\times 3\)) pixel values need to be processed. Neural network is a sequence of layers with neurons that distinguish underlying relationships in a set of data. In fully connected neural networks, if we take an input image with \(30\times 30\times 3\) pixels, the number of weights in the first hidden layer will be 2700. In real life, we have large images with \(250\times 250\) dimensions and more. If we take an input image with \(250\times 250\times 3\) pixels, then the number of weights in the first hidden layer will be 187,500. Therefore, we need to deal with a massive amount of parameters and need more neurons for deep networks that can cause over-fitting.  
In a convolutional neural network (CNN), the neuron in a layer will only connect to a small number of neurons in the previous layer, instead of all neurons in a fully connected way, which is why we need to handle fewer weights and fewer neurons. This is the reason that CNN performs better than fully connected neural networks for image classification tasks [58]. The CNN architecture has two important layers: convolutional layers and pooling layers. The convolutional layer performs the convolution operation on images to extract feature maps and the pooling layer reduces the size of the feature maps by performing down-sampling.  
Convolutional neural networks with several hidden layers and a large number of parameters are fantastic for image classification tasks [59]. CNN masters spatial patterns from images which are also invariant to translation and learn the different features of images. VGG, ResNet, DenseNet, and Inception [60; 61; 62] are popular due to their outstanding performance in ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [63]. Designing a network architecture is a tedious process and requires a lot of effort. There are various architectures designed for different problems. For the detection of malaria-infected cells, the performance of various architectures of CNN, e.g., customized CNN model, VGG, ResNet, DenseNet, Inception, Xception, and SqueezeNet are analyzed in this research. Each architecture is briefly introduced in the following sections.