esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
## 1 Introduction
To achieve these results, the sensor architecture is composed of tactical-grade micro-electro-mechanical system (MEMS) gyroscopes, quadrant diode fine Sun sensors, photodiodes for coarse Sun estimations, and magnetometers. The system is also equipped with a GNSS receiver for precise on-board orbit estimation. The attitude determination is based on the classical Quaternion ESTimation (QUEST) algorithm [21; 22; 23], which uses on-board calibrated sensor data. The orbit determination exploits GNSS measurements and an orbital propagator fused together in an EKF, which can be easily integrated with accelerometer measurements in the case of orbital control maneuvers. List of Contributions  
The paper describes the sensor architecture to guarantee the attitude determination performance. It illustrates the sensor selection process, focusing on the hardware typologies and specifications. The available data were obtained from hardware-in-the-loop (HIL) functional testing, and the sensor performance is representative of real commercial off-the-shelf (COTS) components. First, the discussion presents the sensor architecture, with a schematic description of the whole attitude and orbit determination block. This part also covers the sensor configuration on the spacecraft, highlighting the hot redundancies in the architecture, while the effectiveness and the efficiency of the proposed subsystem are defined and discussed. Furthermore, the methods and the software elements to fully determine the attitude and orbital states are illustrated and critically discussed with respect to the available sensor measurements. Then, the sensor selection process is extensively described, and for each sensor in the proposed architecture, the main component's specifications are reported and analyzed together with the primary test results. This part is structured according to the different sensor typologies, examining the performance of the gyroscopes, Sun sensors, magnetometers, GNSS receivers, and accelerometers. Finally, an example attitude determination performance is reported and discussed with respect to the sensor performance and specifications. In particular, the direct influence of the sensor architecture on the final output of the determination functions is highlighted. In this part, the presented results were derived from model-in-the-loop (MIL) with hardware calibrated simulations. The aim of this study was to fully characterize the proposed sensor architecture, with respect to the available attitude and orbit determination performance. This was achieved thanks to an extensive analysis of the sensor test results, with particular attention on the critic performance for each sensor family and thanks to an end-to-end verification of the final attitude and orbit estimation accuracy. All the presented results were derived from HIL and MIL verification and testing activities, and they can serve as useful resources and a benchmark for future nano-satellite missions.
## 2 Materials and Methods
### Statistical Analysis  
All data are presented as mean \(\pm\) SD. The normality of the variables was assessed using the Shapiro-Wilk normality test, since the sample size was less than 30. The parametric Student's t-test for repeated or paired measures was used, assuming a normal distribution of the data. The percentage change (\(\Delta\%\)) from T1 to T2 in the outcome variables was calculated using the formula [(T2 \(-\) T1) / T1] \(\times\) 100. To determine the effect size between participants, partial eta squared (\(\eta^{2}_{\text{p}}\)) was calculated. However, since this measure is prone to overestimation of the effect size, the following interpretation was used [91]: no effect if \(0\leq\eta^{2}_{\text{p}}<0.05\); minimal effect if \(0.05\leq\eta^{2}_{\text{p}}<0.26\); moderate effect if \(0.26\leq\eta^{2}_{\text{p}}<0.64\) and strong effect if \(\eta^{2}_{\text{p}}\geq 0.64\). [PERSON]'s correlation analysis and [PERSON] correlation magnitude were also conducted [92]. The magnitude of correlation coefficients was determined as trivial (r < 0.1), small (0.1 < r < 0.3), moderate (0.3 < r < 0.5), high (0.5 < r < 0.7), very high (0.7 < r < 0.9), near perfect (r > 0.9) and perfect (r = 1) [92]. In turn, the effect size variation is indicated providing a convenient (though not exhaustive) representation of the required sample size change [93] and a more intuitive and practical approach to probabilistic inference [94] based only on the ambiguity surrounding the statistic's actual value. At the level of results, it has been considered at a general level that the significance of the difference obtained is in some way significant when it deviates from what is expected in a way that cannot be assumed to be produced by chance alone, since in this way, as we have said, it is unlikely that the result is produced by chance or random fluctuation. Therefore, those results that indicate a correlation coefficient greater than 0.5 will be considered. Following established guidelines [95], BMI was used to determine the prevalence of being overweight in the study sample. An Excel spreadsheet was used to record the values of the measurements collected for later statistical analysis in Windows SPSS(r) 25.0 software (Inc., Chicago, IL, USA). \(P\) 0.05 was the threshold for significance.
## 4 Experiment Analysis
### Analysis of Simulation Results  
#### 4.3.1 Methodology  
In a real environment, each node is selfish. To make the simulation experiment closer to the real scene, we randomly set the selfishness of the node relative to other nodes in the experiment. When a node forwards a message, it forwards the message according to a certain cooperation probability that removes the selfishness. It should be emphasized that we aim to select nodes that have a high probability of encountering the target node and are willing to cooperate to forward the message. In the experiment, the cooperation matrix of the algorithm was not set manually, but was based on the cooperative probability calculated by the number of times the node helps a node to forward the message in the previous \(T\) period.  
The evaluation of the model mainly comes from two aspects: (1) Effectiveness analysis. The experiment compares the difference between our presented model and other existing models in optimizing the network structure and improving the information delivery ratio. (2) Adaptability analysis: The probability matrix is periodically updated by various dynamic change information, and the nodes that can cooperate and have a high probability of encountering with the destination node are filtered to transmit data, thereby increasing the delivery ratio and reducing the average hop count. As a reference, we set a list of performance metrics, which is used for comparison, as follows:  
1. Delivery ratio is the ratio of the number of delivered messages to the total number of generated messages.
2. Overhead is the average number of intermediate nodes used for one delivered message.  
Figure 4: The relationship between delivery ratio and node coverage ratio with different time interval \(\Delta t\): (**a**) node coverage ratio different \(\Delta t\); (**b**) delivery ratio with different \(\Delta t\).  
3. Hop count is the average hops of successfully delivered messages.  
We use these performance metrics to analyze the effects of the model and compare them with other researchers' proposed solutions. To compare the performance of the PEBN protocol with other schemes, the delivery ratio, overhead ratio, and hop count were measured.
## 2 Buildings Overview  
Energym includes 11 simulation models to date (three Modelica models and eight EnergyPlus models). The EnergyPlus models are all updated to the current Energyplus version 9.4. An overview of the installed technical equipment and their controllability is given in Table 1. A description of each model's inputs and outputs is provided in Appendix B. The models differ in size, number of rooms, usage profile, technical equipment, controllability, and climate zone. The seven buildings that are the base for the 11 models are listed below. Four of them are available in two versions, either differing in the control (e.g., controlling thermostat setpoints vs. controlling the equipment directly) or the installed equipment. The buildings have the following characteristics.  
* **Apartments**: A residential building with four stories, each being one apartment, and eight thermal zones (two per story). It is located in Spain and has a central geothermal Heat Pump (HP) providing heat to all apartments. The building envelope is fictive, based on typical Spanish construction materials, but the HP was calibrated with a real HP located in the IREC laboratory (see Section 4.2).
* **Apartments2**: This building shares its envelope with the Apartments building, but differs in the details of the technical equipment: each apartment possesses its own air-to-water HP and its own heating storage tank. In this building, the electrical systems (solar panels, battery) were calibrated with real systems.
* **Offices**: This building consists of 25 thermal zones, 14 of which are controlled with thermostats, and is located in the Attica region of Greece. Both the envelope and the technical systems were calibrated with the corresponding test site data.
* **MixedUse**: A building with 13 thermal zones (eight controlled with thermostats) used for residential and office spaces. It is also located in the Attica region of Greece. Both the envelope and the technical systems were calibrated with the corresponding test site data.
* **Seminxcenter**: This building encapsulates 27 thermal zones, belonging to 22 rooms of which 18 are controlled with thermostats. It is located in the Region of Southern Denmark. Both the envelope and the technical systems were calibrated with the corresponding test site measurements.
* **SimpleHouse**: This building is a standard single room house with HP and sun heating effects through glazing. Two versions exist, one with a standard radiator (SimpleHouseRad) and the other one with floor heating (SimpleHouseSlab). The first order envelope model is designed based on thermal peak power and minimal outdoor temperature.
* **SwissHouse**: This building is a large-scale version of the SimpleHouse building with radiators. It has been designed with parameters (thermal peak power, outdoor temperature) from a real house. The heat pump control differs from the SimpleHouse model, see Table 6.  
The calibration methodology used for the models is explained in Section 4.
## 3 Results
### Experiments and Evaluation Scenarios  
#### 3.5.1 First Evaluation Scenario: Single Client Connection to the Holo-Cloud  
To start the evaluation process, an Android device was connected to the FI3D framework hosted on an EC2 AWS public cloud instance. The Holo-Cloud instance utilized a single CPU core and 4 gigabytes of random-access memory on a general-purpose solid-state shared storage. The connection between the client software and the cloud-hosted server software was established using a 5G cellular network.  
The entire process for the client software to join the server, load the study data, view the overlayed model on the 3D camera environment, view the collaborative 4D CINE MRI, and disconnect from the server was 90 s. In total, 11.534 kilobytes were received by the server, and 8343 kilobytes of data were sent to the client. The network utilization is demonstrated in Table 1.  
CPU utilization of the server software process in this test scenario has been demonstrated in Figure 4. The process of receiving a join request from the client software and replying to the request did not have any noticeable impact on CPU utilization. However, loading the demo study from the computer hard drive to the main memory and sending it to the connected client had a considerable impact on the CPU. Likewise, broadcasting collaborative 4D CINE MRI to the client software required up to 85% of the CPU power.  
Figure 5 shows the memory utilization of the server software during the evaluation test events. Loading the study data from the hard drive to the main memory caused a huge spike in the memory utilization of the server software. However, the maximum amount of memory that the software demanded in this scenario was about 160 megabytes, which is not more than 4% of the total available memory of the system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Average Receive Speed (Kbps)** & **Average Send Speed (Kbps)** \\ \hline FI3D server internet & 272,830.00 & 947,620.00 \\ Client software internet & 347,000.00 & 58,600.00 \\ FI3D–client end-to-end speed & 28,800.00 & 28,800.00 \\ Server network utilization & 1.63 & 743.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network utilization of the Holo-Cloud platform components in the 1 st experiment.  
Figure 4: CPU utilization of the server software in the first experiment. The blue solid line is % CPU utilization and the red horizontal dashed line is the maximum CPU utilization.  
Figure 6 shows the TCP packet transmission rate of the FI3D software during this experiment. The spike during data transmission of the animation data is larger than the packet transmission when the client software loads the static study scene. The data regarding multiple MRI frames for the multislice animation needs to be sent to the client. After the data were cached on the client's device, the transmission of metadata continued until the end of the experiment.
# Application of Pulsed Laser-TIG Hybrid Heat Source in Root Welding of Thick Plate Titanium Alloys
## 4 Discussion  
### Arc Deflection  
The arc deflection is determined by electromagnetic force between the TIG arc and the laser induced plasma. As noted in the paper written by [PERSON] (2015), when the laser acts on a workpiece, there are two currents with the same direction between the electrode and the workpiece. A simplified model is established to calculate the deflected length. Figure 10 shows a schematic illustration of the arc attraction between the laser and arc. In this case, the TIG arc is attracted and repulsed outwards because of the electromagnetic forces.  
Generally speaking, the deflected length \(d\) can be expressed by the following Formula (1) (Kal  
\[d=\sqrt{\frac{h^{2}I_{L}}{2I_{a}}\cos\theta} \tag{2}\]  
Therefore, the required minimum keyhole current, that can provide enough energy to attract the arc root to the laser action point, is calculated to be 142 A.  
Figure 11 shows a comparison of the required keyhole current calculated for different deflected lengths at an arc current of 100, 200 and 300 A. It can be seen that in the case of a certain arc current, with the increase of the deflected length, the required keyhole current increases. For the same deflected length, the required keyhole current increases with the increase of the arc current. Besides, as the deflected length increases, the larger the arc current is, the greater the keyhole current must be.  
In addition, the current \({I_{L}}^{\prime}\) in the keyhole can be given by [PERSON]'s law [22].  
\[{I_{L}}^{\prime}=2\pi E\int\limits_{0}^{R}\sigma rdr=\pi R^{2}\sigma E \tag{3}\]  
and the specific electrical conductivity of the laser induced plasma in the keyhole \(\sigma\) is given by  
\[\sigma=\frac{3}{4}\frac{(kT_{e})}{\sqrt{2\pi m_{e}}\lambda_{e}e^{2}} \tag{4}\]  
where \(E\) is the axial component of the electric field intensity in the keyhole, \(\sigma\) is the specific electrical conductivity of the laser induced plasma in the keyhole, \(R\) is the radius of the current conducting  
Figure 11: Relationship between required keyhole current and deflected length.  
Figure 10: Schematic illustration of arc attraction between laser and arc.  
region. \(R\) is considered as the approximate radius of keyhole, when the keyhole is assumed to be a cylinder. \(k\) is the Boltzmann constant, \(T_{e}\) is the electronic temperature in keyhole, \(m_{e}\) is the electronic mass, \(\lambda_{e}\) is the free path and \(e\) is the electron charge.  
In this experiment, the values of \(\lambda_{e}\), \(T_{e}\) and \(E\) are approximately 0.75 mm, 15,000 K (usually keyhole electron temperature is higher than that in arc) and 3 V/mm, respectively. Therefore, \(\sigma\) can be approximately estimated to be 1.2 \(\times\) 10\({}^{4}\) S/m, and the instantaneous current of keyhole \(\overset{\rightarrow}{I_{L}}\) is about 10\({}^{3}\) A. It is obvious that \({l_{L}}^{\prime}\geq{l_{L}}\) meaning that in this part of experiment the arc is easily attracted by the laser induced plasma produced by a pulse laser.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 3 Genomics in Cancer Risk Assessment
PRSs are calculated by aggregating the effects of numerous genetic variants across the genome, each weighted by their associated risk derived from large-scale genome-wide association studies (GWAS). This weighted sum reflects an individual's genetic predisposition to a particular cancer [41]. PRSs are particularly useful for evaluating cancers like breast, prostate, and lung cancer, where multiple genetic factors, along with lifestyle and environmental exposures, contribute to overall cancer risk. By integrating genetic information with non-genetic factors, a PRS offers a more comprehensive and personalized cancer risk assessment. This approach is further enhanced by the growing role of AI in genomic analysis [4]. AI-driven tools can combine genetic data with environmental and lifestyle factors to create a more holistic understanding of cancer risk, enabling clinicians to make more precise, individualized recommendations for prevention and monitoring. AI can be applied to implement screening programs, such as general population screening, targeted screening, and stratified screening [42]. General population screening invites specific demographic groups to participate in screenings, targeted screening focuses on populations at risk due to genetic variants or pre-existing health conditions, and stratified screening customizes the frequency and type of testing based on the individualized risk level [42]. The integration of genomic testing, including both inherited variants and PRSs, into clinical practice is greatly improving cancer risk assessments. In addition to genetic testing, genetic counseling plays a crucial role in helping individuals understand their genetic susceptibility and make informed healthcare decisions [43]. Genetic counselors assist patients in interpreting test results and discussing evidence-based options for cancer prevention, such as enhanced surveillance, chemoprevention, or preventive surgeries. Recent advances in artificial intelligence have led to the exploration of chatbots as emerging tools to support genetic cancer risk assessment and counseling. Using Natural Language Processing (NLP), chatbots can simulate human conversation to help collect personal and family health history and preliminarily identify individuals at increased genetic risk for hereditary cancer syndromes. A recent systematic review and meta-analysis evaluated their implementation, reporting a completion rate of 36.7% (95% CI, 14.8-65.9%) for genetic risk assessments and 36.0% (95% CI, 27.3-45.7%) eligibility for genetic testing [44]. Among eligible individuals, 47.4% proceeded with testing, and pathogenic variants were identified in 9.0% of cases. While these results indicate potential benefits in improving access and streamlining genetic services, further rigorous studies are necessary to compare chatbot-based models with traditional genetic counseling and assess their long-term clinical utility, cost-effectiveness, and impact on health equity.
## Appendix A Convolutional Layer  
A convolutional layer is one of the fundamental building blocks of convolutional neural networks (CNN). It performs the operation of a 2-D convolution, where a kernel (or filter) slides over the input tensor to produce an output tensor.  
Let the input tensor be \(A\in\mathbb{R}^{X\times Y}\) and the convolutional kernel (filter) be a square tensor \(B\in\mathbb{R}^{K\times K}\). The operation of a 2-D convolution is defined as follows.  
\[C_{x,y}=\sum_{i=1}^{K}\sum_{j=1}^{K}A(i+x-1,j+y-1)B(i,j),\]  
where \(C_{x,y}\) represents the element at position \((x,y)\) in the output tensor \(C\). The resulting output tensor \(C\) has dimensions \(C\in\mathbb{R}^{(X-K+1)\times(Y-K+1)}\).  
A 2-D convolutional layer processes a batch of input tensors. Let the input tensor for the entire batch be denoted as \(Q\in\mathbb{R}^{N\times C\times V\times V}\), where \(N\) is the batch size (number of input tensors in the batch), \(C\) is the number of input channels (number of feature maps in each input tensor) and \(V\times V\) is the spatial dimension of each input channel.  
The weight tensor of the 2-D convolutional layer has a shape \(W\in\mathbb{R}^{T\times C\times K\times K}\), where \(T\) and \(C\) are the number of output and input channels and \(K\times K\) is the kernel size (spatial dimension of the filters).  
Each output channel is computed using its corresponding kernel, applied across all input channels. For every output channel, a tensor of size \((C,K,K)\) represents the set of kernels. The kernels are applied to their corresponding input feature maps and the resulting matrices are summed to produce the output feature map for that channel.  
The convolution operation for the layer is expressed as follows:\[P_{n,t,i,j}=\sum_{s=1}^{C}\sum_{i=1}^{K}\sum_{j=1}^{K}W(t,s,i,j)Q(n,s,i+x-1,j+y-1),\]  
where \(n\) indexes the input tensor in the batch, \(t\) and \(s\) index the output and input channel correspondingly, and \(i\) and \(j\) are the spatial indices of the kernel.  
The resulting tensor \(P\) has dimensions \((N,T,V-K,V-K)\), where \(N\) is the batch size, \(T\) is the number of output channels, and \(V-K\) is the reduced spatial size after applying the convolution.  
For simplicity, we denote the convolution operation as follows:  
\[P=\text{conv}(W,Q,n_{in},n_{out},K,g),\]  
where \(g\) is the group size of the layer, \(K\) is the kernel size, \(n_{in}\) and \(n_{out}\) denote the number of input and output channels, \(W\) is the weight tensor of the layer, and \(Q\) is the input tensor.
## 3 Analysis of Success Probability  
We considered a typical system, where each transmitting node generates and sends packets to its intending receiver following an independent and identically distributed Bernoulli process \(v_{Tr,t}\in[0,1]\) in each time slot \(t\), where \(Tr=p\) for PT and \(Tr=s\) for ST. Since we considered a discrete-time system, where packet generations at various nodes are independent, as in the conventional CRN, packet generation at each node can be modeled as a Bernoulli process. This implies that any packet is transmitted the same time slot it was generated. A failed transmission is repeated in a future time slot until it is successful. A transaction is successful if the received SINR at the intending receiver is greater than the predefined SINR \(\theta_{T}\) threshold. A successfully received packet is removed from the channel. We assumed that each receiver is able to generate an ACK (for successful transmission) or NACK (for failed transmission) through a dedicated error-free channel in a negligible time instant to inform its paired transmitter on the status of its transmission.  
The realization of the network was considered to be static, i.e., the locations of all transmitting and receiving nodes remain constant in all time slots. Hence, the transmission success probability obtained in each time slot is independent of other slots. We henceforth drop the notation \(t\).  
This commonly adopted assumption is necessary to simplify the time-domain evolution and to avoid complicated analysis. The same assumption is made in [16; 27; 28], etc.