esa-sceva/satcom-chunk-collection huggingface dataset:
# Student Behavior Prediction of Mental Health Based on Two-Stream Informer Network
## 5 Conclusions  
In view of the fact that most of the existing studies on predicting students' mental health status use qualitative methods and do not adequately collect students' relevant characteristics, this paper obtains students' daily behavior data from three aspects (canteen consumption, Internet access and daily routine), and proposes an informer network based on a two-stream structure (TSIN) to analyze students' daily behavior data to determine whether they need mental-health-focused attention for classification prediction. This model uses Time Encoder and Behavior Encoder to capture students' time cycle trend and behavior dependence, respectively, prevents information loss through an Intermediate Fusion Module, and uses a gating mechanism for classification prediction. Experimenting with 10 multivariate time series from public datasets, the TSIN obtained the highest accuracy in seven experiments, and was able to obtain higher accuracy in predicting students' mental health status compared to other time-series classification models, verifying the advantages of the model.  
However, the experiments in this paper have the following disadvantages: (1) When collecting data from students, we only considered some of their behavioral data in daily life, while in real life students with abnormal mental health conditions often have their learning affected as well. (2) In making predictions about mental health status, we treated each student as an individual, and students with abnormal mental health status usually have some influence on those around them. In the future, we will try to collect more behavioral characteristics of students, especially in terms of student learning, including data related to the library and classrooms, to more comprehensively describe students' daily behaviors. We will also use the graph structure to construct a network of relationships among students, and give higher weights to students who are closely related to students with abnormal mental health status in the model to improve the prediction accuracy. Since each student's mental health status has abnormalities, we will try to let the model learn  
Figure 6: Test accuracy of the TSIN on different student behavior sets.  
from psychologists' ranking of the number of students' psychological abnormalities so that schools and teachers can allocate resources more rationally and cover all students who need psychological counseling as much as possible.  
Conceptualization, X.D.; methodology, J.X.; validation, J.X.; formal analysis, J.X.; writing--original draft preparation, J.X.; writing--review and editing, X.D., H.K., C.X. and H.Z.; supervision, X.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 DeepFogSim: The Considered System Model
### Models of the Per-Flow Networking Power and Energy
#### 4.6.1 Power Wasted for Receiving Network Operations  
Let \(\mathcal{P}_{NET}^{(IDLE;Rx)}(j,m)\) (Watt) be the idle power consumed by each receive port of \(\text{Fog}(j,m)\) in Figure 6. Hence, the corresponding receive dynamic power can be modeled as [41]:  
\[\mathcal{P}_{NET}^{(DYN;Rx)}(j,m)=\Omega_{NET}^{(Rx)}(j,m)(\psi_{m}R_{m})^{ \zeta^{(Rx)}(j,m)},\quad 2\leq m\leq M. \tag{13}\]  
Now, the idle time is still \(T_{EXE}^{(1,M)}\). However, the receive time is:  
\[T_{NET}^{(Rx)}(j,m)=\frac{v_{I}(j,m)}{R_{m-1}\times FanIn(j,m)},\quad 2\leq m\leq M \tag{14}\]  
because the overall workload \(v_{I}(j,m)\) received by \(\text{Fog}(j,m)\) is evenly split over its \(FanIn(j,m)\) input ports, each one working at \(R_{m-1}\). Hence, the total network energy \(\mathcal{E}_{NET}^{(Rx)}(j,m)\) consumed by \(\text{Fog}(j,m)\) for receiving operations over a sensing interval equates to:\[\mathcal{E}_{NET}^{(Rx)}(j,m) \triangleq\mathcal{E}_{NET}^{(IDLE;Rx)}(j,m)+\mathcal{E}_{NET}^{( DYN;Rx)}(j,m)\] \[\equiv FanIn(j,m)\times\left\{\mathcal{P}_{NET}^{(IDLE;Rx)}(j,m) \times T_{EXE}^{(1,M)}\right.\] \[\quad+\left.\Omega_{NET}^{(Rx)}(j,m)(\psi_{m-1}R_{m-1})^{\zeta^{(Rx )}(j,m)}T_{NET}^{(Rx)}(j,m)\right\} \tag{15}\] \[= FanIn(j,m)\times\mathcal{P}_{NET}^{(IDLE;Rx)}(j,m)\times T_{EXE}^ {(1,M)}+v_{I}(j,m)\times\Omega_{NET}^{(Rx)}(j,m)\] \[\quad\times(\psi_{m-1})^{\zeta^{(Rx)}(j,m)}\times(R_{m-1})^{\zeta ^{(Rx)}(j,m)-1}.\]  
The above expression holds for \(m\geq 2\) because, in our framework, sensors are co-located with FNs at _tier_ #1 (see Figure 3). \(\Omega_{NET}^{(Rx)}(j,m)\) and \(\zeta^{(Rx)}(j,m)\) in (15) depend on the communication technology employed at the Physical layer and can be modeled as follows [41]:  
* \(\zeta^{(Rx)}(j,m)\geq 2\) is a positive dimension-less exponent, in which the actual value depends on the transmission technology actually implemented at the Access sub-layer of the functional stack of Figure 4[42; 43];
* \(\Omega_{NET}^{(Rx)}(j,m)\) is a positive coefficient (measured in \(\text{Watt}/((\text{bit}/\text{s})^{\zeta}\times(\text{s})^{\eta})\)), which accounts for the effect of the round-trip-time \(RTT_{m-1}\) of the received flows. Specifically, in the case of single-hop connections, it can be formally modeled as in the following [41; 44]: \[\Omega_{NET}^{(Rx)}(j,m)=\frac{\left(RTT_{m-1}\right)^{\eta}\chi(j,m)^{(Rx)}}{ 1+\left(l_{m-1}\right)^{\alpha}},\] (16) where: (i) \(\eta\approx 0.6\) is a dimension-less positive exponent; (ii) \(l_{m-1}\) is the length (i.e., coverage, measured in meter (m)) of the wireless connections in Figure 3 going from _tier_ #\((m-1)\) to _tier_ #\(m\); (iii) \(\alpha\geq 2\) is a fading-induced path-loss exponent; and (iv) the positive coefficient \(\chi(j,m)^{(Rx)}\) accounts for the power profile of the receive ports.
## 2 Literature Review
### Recent Trends in Multi-Objective Optimization  
Recent advancements in multi-objective optimization provide methodologies that can inspire solutions for ship routing, fleet deployment, and demand acceptance in liner shipping. For instance, skip-stop strategies in urban rail transit optimize total travel time and equity in service distribution. [PERSON] et al. [44] formulate a multi-objective mixed integer nonlinear programming model to minimize both inequity and total travel time through train stop planning and scheduling, with a case study on Seoul's urban railways. [PERSON] et al. [45] propose a robust multi-objective model for skip-stop scheduling, minimizing travel and waiting times while ensuring equitable service through robust scheduling under demand uncertainty. [PERSON] et al. [46] address passenger crowding risks and revenue losses in oversaturated networks, offering insights for port call sequence optimization in liner shipping under congestion or regulatory constraints. Similarly, accessible routing strategies provide inspiration for customized routing; [PERSON] et al. [47] develop an accessible taxi routing model based on the travel behavior of people with disabilities, using a Gaussian mixture model to optimize routes, which can guide prioritization of ports with specialized cargo needs. Additionally, [PERSON] and [PERSON] [48] develop a multi-objective model for submerged floating tunnel routing, balancing structural safety and travel time using Non-dominated Sorting Genetic Algorithm II (NSGA-II), achieving 9.9% to 10.5% time savings on the Mokpo-Jeju route. These techniques can inform ship routing under complex constraints like safety regulations or emission control areas.  
Recent progress in liner shipping has focused on integrating multi-objective optimization with heterogeneous fleet assignment under regulatory constraints. [PERSON] et al. [27] propose a multi-objective MILP model for tactical-level planning in liner shipping, optimizing ship deployment and routing for heterogeneous fleets while balancing costs and CO\({}_{2}\) emissions under carbon tax regulations, achieving up to 15% cost savings and 20% emission reductions on a transatlantic route. [PERSON] et al. [9] investigate co-management of heterogeneous ship fleets in liner alliances, optimizing profit-sharing, fleet utilization, and service reliability under emission caps, improving profit by 10% and reducing delays by 12% on an Asia-Europe route. [PERSON] et al. [49] develop a multi-objective MILP model for ship scheduling, addressing port congestion and environmental regulations using queuing theory, optimizing costs, emissions, and service unreliability with improvements of 4%, 6%, and 44% over NSGA-II, applied to Maersk's Trans-Pacific route with nine ports and five ships of 10,000 TEU. These studies highlight the potential of multi-objective MILP frameworks to balance economic, environmental, and operational objectives in liner shipping under stringent regulatory environments.
## 4 SAR System Requirements Effect on Satellite Design  
Designing a SAR (synthetic aperture radar) satellite, especially a micro-sized one, involves a delicate balance between the power subsystem (solar panels and batteries) and the SAR antenna, including its mass and folding/deployment mechanism if needed. SAR system power is needed for the SAR system during its operation. This includes the transmitter, receiver, and processing systems. Satellite bus power includes power for communication, onboard computers, attitude control, and other satellite functions. Based on the peak power requirement and the satellite's orbit (which affects sunlight exposure), one can calculate the size of the solar panels and size the battery to support operations during an eclipse (when the satellite is in the Earth's shadow). Considering the active system which can operate during the day or eclipse time, the battery should provide the necessary power for the operation of the SAR system. The mass depends on the size, material, and technology of the antenna. Microsatellites often use lighter and smaller antennas. For microsatellites, deployable mesh reflector antennas or inflatable antennas can be used to reduce launch mass and volume. The mechanism should be reliable and tested for the harsh conditions of space as well.
## 5 Approaches of Optimal State Estimation Utilizing Filtering
### Real Data Collection from GNSS Receivers and Sensors Fusion  
GNSS constellation presently composed of GPS, GLONASS, Galileo, BEIDOU and SBAS augmentation satellites such as WAAS (USA), EGNOS (Europe), SDMC (Russia), GAGAN (India) are widely integrated and used in major navigation applications. Table 6 presents the current Space Vehicles (SV) employed in GNSS. Furthermore, many recent works were dedicated to counter the vulnerability of these constellations and their positioning accuracy, which can be degraded in harsh or denied GNSS environments. To achieve better performances, and based on several recent surveys, different data fusion, sensors fusion and information fusion with modified filtering algorithms were developed and proposed in the literature. Even with hundreds of satellite channels and multi frequency tracking capabilities with dual code/phase positioning methods, GNSS receivers may suffer from spoofing, jamming and interference, NLOS and multipath problems, and therefore need to be coupled with alternative higher-data-rate navigation sensors. In this survey, however, the benchmark and analysis were given to geolocate and target tracking algorithms and systems independently of GNSS receivers. The best alternative innovative solution is the use of LEO satellite signals of opportunity for localization and tracking. Presently, for the reasons mentioned above, they represent the best alternative Positioning, Navigation, and Timing (PNT) solutions.
# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 3 Experimental Setup  
### Dataset: Fundus Images  
Digital color fundus images (this study followed all guidelines for experimental investigation on human subjects, was approved by the Samsung Medical Center Institutional Review Board, and adhered to the tenets of the Declaration of Helsinki) were used from a database created by the glaucoma clinic of Samsung Medical Center in Seoul, Korea. All patients gave their informed consent to have their data used in our experiments. When the fundus images were taken, the camera had a magnification setting of \(30^{\circ}\) or \(35^{\circ}\). Moreover, the macula was center-positioned near the intersection of the crosshairs in the ocular. A fixation target was used to direct the gaze of the subject in the primary (straight ahead) position, so that the optic disc did not appear directly behind the lens. The color images were evaluated for quality by an experienced investigator. If the quality was not deemed adequate for assessment of the key features of the studied eye (such as optic disc morphology) and if no irremediable cause of inadequate quality was present (such as lens opacities or a pupil that did not dilate adequately), then the images were retaken.  
The fundus dataset used for training contained 540 normal eye images and 1363 glaucomatous eye images, thus resulting in the use of a total of 1903 eye images. This dataset was randomly split into two sets, namely a training and a validation set, using a split ratio of approximately 4:1 (that is, the training dataset contained 1503 images and the validation dataset 400 images). The training set was used to fine-tune our models, whereas the validation set was employed to assess the generalization power of the models created during training. Apart from these two datasets, for testing purposes, 220 unseen fundus images were used: 55 normal eye images and 165 glaucomatous eye images. The images used came with different spatial resolutions, ranging from 1172-2500 pixels horizontally and from 1500-3200 pixels vertically.  
Starting from the set of original fundus images, we created two derived datasets, as shown in Figure 2. For the first derived dataset, we center-cropped all images using a 1:1 ratio by cutting off unnecessary image areas (e.g., a black background sometimes containing image numbers and text).  
For the second derived dataset, we cropped the optical disc part from each image. The former set of images was named Dataset-C, and the latter set of images was named Dataset-OD. We created the two different datasets in order to be able to investigate the importance of the optic disc, given that most related work only made use of this part of the fundus images.
## 3 Results  
### Water Quality Variables  
Through the two projects on which this work is based, a total of 52 field campaigns were carried out and 177 samples were taken. However, approximately 20% of the samples could not be used due to various factors such as: increment weather, non-coincidence with image acquisition, or due to problems with the instrumentation. For this reason, the total number of samples drops to 146 corresponding to 41 campaigns, although this maximum amount decreases depending on four factors: (1) the quality variable, due to problems with the instrumentation; (2) in situ radiometry, limited to coincidence with image acquisition, (3) AC, due to the respective quality flags, and (4) the match-up exercise for C2-Nets.  
The values of the five studied variables cover a wide range of ecological states of water bodies. All are within the ranges used to retrieve the water quality algorithms to be validated in this work. Descriptive statistics for each variable are given in Table 3.  
The database of each variable was displayed in a box plot to understand the distribution of sample values over the variables ranges (Figure 2). All variables showed a skewed distribution, with the average in the lower values, except for SDD. The CDOM, TSS, and Chl_a have a minor spread of the data; the 90 th percentile is concentered in lower values.  
The cross correlations between the variables were established to understand their relationships (Table 4). The SDD is inversely related to the other four variables since they produce a reduction in transparency as their concentrations increase but strongly with TSS (R\({}^{2}\) = 0.96, \(p\) < 0.001), indicating that inorganic matter has also a great presence. CDOM shows the same correlation with all variables (R\({}^{2}\) = 0.6, \(p\) < 0.001). TSS and Chl_a are well correlated (R\({}^{2}\) = 0.77, \(p\) < 0.001), indicating that, in most cases, TSS is mainly composed of phytoplankton and not suspended minerals. Chl_a and PC are strongly related (R\({}^{2}\) = 0.75,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(N\) & **Min.** & **Max.** & **Mean** & **Median** & **SD** \\ \hline SDD (m) & 133 & 0.17 & 11.40 & 3.51 & 2.95 & 2.86 \\ CDOM (\(\upmu\)g/L QSE) & 104 & 0.03 & 10.18 & 0.83 & 0.35 & 1.67 \\ TSS (mg/L) & 102 & 0.67 & 162.33 & 12.2 & 3.30 & 27.1 \\ Chl\_a (mg/m\({}^{3}\)) & 146 & 0.28 & 309.62 & 18.1 & 2.25 & 51.2 \\ PC (mg/m\({}^{3}\)) & 107 & 0.00 & 751.10 & 95.9 & 5.63 & 175.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics of the measured water parameters: Secchi disc depth (SDD), colored dissolved organic matter (CDOM), total suspended sediment (TSS), chlorophyll a (Chl_a), and phycocyanin (PC). \(N\): data number; Min.: minimum; Max.: maximum; SD: standard deviation.  
\(p<0.001\)), indicating the importance of the Cyanobacterial group in the phytoplankton composition of our dataset.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 4 Results and Analysis
### Spatial Distribution of UBD and LST
In general, (a) the overall trend of the UBD and LST is a decline from the core area of the city to the periphery; (b) the density of buildings and the spatial distribution of the LST are affected to some extent by the terrain; and (c) the two data distribution characteristics are the same, but there are differences in local areas.
## 2 Materials and Methods
### Tri-Axial Acceleration Data Processing and Physical Activity Classification  
Immediately after each match, the activity monitors were removed, and the data were downloaded to a personal computer using the manufacturer's software. The raw tri-axial acceleration data were processed to create a single, omnidirectional measure of body acceleration. This was achieved by calculating the vector magnitude from the three axes and subtracting the gravitational constant, following the formula  
\[ENMO=\sqrt{x^{2}+y^{2}+z^{2}}-1 \tag{1}\]  
where \(x,y,\) and \(z\) represent the acceleration values along the three axes, expressed in units of gravitational acceleration (g), with g = 9.81 m/s\({}^{2}\). Any negative values resulting from this calculation were set to zero, as defined by the Euclidean Norm Minus One (_ENMO_) approach [63]. This method isolates the acceleration due to body movement by removing the constant effect of gravity. The data were then averaged over 1 s epochs to reduce variability and facilitate analysis. All data processing was performed using custom scripts written in Python 3.9.  
To categorize the measured accelerations into PA intensity zones, we utilized specific cutoff values based on established metabolic equivalent (MET) thresholds [64]. The classification was as follows:  
* **No Physical Activity (NPA):** Accelerations below **0.03 g**, indicating minimal physical movement;
* **Light Physical Activity (LPA):** Accelerations between **0.03 g** and **<0.1 g**, corresponding to activities with an intensity of \(\geq\)2 METs;
* **Moderate Physical Activity (MPA):** Accelerations between **0.1 g** and **<0.4 g**, aligning with \(\geq\)3 METs;
* **Vigorous Physical Activity (VPA):** Accelerations of \(\geq\)0.4 g**, associated with \(\geq\)6 METs.
# Optimal Control of an Ultraviolet Water Disinfection System
## 4 Algorithm Using Hybrid Topology Particle Swarm Optimization
### Hybrid Topology Particle Swarm Optimization
#### 4.1.2 Hybrid Topology PSO  
The algorithm described in the sequel is based on hybrid topology particle swarm optimization (HTPSO) [18,19], which is an improved version of the PSO metaheuristic with better communication abilities among particles. It is also endowed with adaptive control of the particle speed through a continuous change of some of the algorithm's parameters.  
The proposed HTPSO algorithm is devoted to solving UV_OCP1 and is called HTPSO_UV1.  
HTPSO also uses a local topology of the swarm, regarded as a communication network. The local topology involves the existence, for any particle #i, of a \"social neighborhood,\" i.e., a set of 3-5 particles that inform particle #i about their best personal experience. These neighborhoods are decided in a deterministic or random manner at each step of the algorithm (see [23]). The particle will determine the local best position \(P_{lbest_{i}}\) as the best experience of the particles belonging to the \"social neighborhood\" and the particle itself. It holds that:  
\[P_{lbest_{i}}\ =\ \Big{(}p_{lbest_{i}}^{1},\ldots,p_{lbest_{i}}^{d}\ldots,p_{ lbest_{i}}^{n}\Big{)};\ d\ =\ 1,\cdots,n; \tag{19}\]  
A new term appears in the speed equation containing \(C_{3}\) and \(rand_{3}\) (see [18]). Hence, the update of the speed and the position is performed using the following equations:  
\[\left\{\begin{array}{c}v_{i}^{d}(t\ +\ 1)=w\ \times\ v_{i}^{d}(t)\ +\ C_{1}\ \times\ rand_{1}\ \times\ \Big{(}p_{best_{i}}^{d}(t)\ -\ x_{i}^{d}(t)\Big{)}\ +\\ +\ C_{2}rand_{2}\Big{(}p_{lbest_{i}}^{d}(t)\ -\ x_{i}^{d}(t)\Big{)}\ +\ C_{3}rand_{3} \Big{(}p_{gbest}^{d}(t)\ -\ x_{i}^{d}(t)\Big{)};\ d\ =\ 1,\ldots,n\\ x_{i}^{d}(t\ +\ 1)\ =\ x_{i}^{d}(t)\ +\ v_{i}^{d}(t\ +\ 1)\end{array}\right. \tag{20}\]
# Rotational-Shearing-Interferometer Response for a Star-Planet System without Star Cancellation
## 2 Theory
### Special Cases
#### 2.1.2 Second Case, Star on Axis and OPD Equal to \(\lambda/2\)  
When the star is aligned with the optical axis and it is orbited by a planet, the RSI receives the star and the planet wavefronts simultaneously. If additionally, we adjust the OPD to \(\lambda/2\), the star wavefront is canceled by destructive interference. This case is analyzed in the most of interferometric methods to detect extra-solar planets. The incidence equation is considerably reduced because the terms \(M_{SS}\) and \(M_{SP}\) are canceled; additionally, \(\theta_{P}\) is small enough to use the paraxial approximation. The resulting equation may be rewritten as:  
\[M=M_{P}\bigg{(}2+2\cos\bigg{[}\frac{2\pi}{\lambda}2\rho\theta_{P}\sin\Delta \varphi\sin(\varphi-\varphi_{P}-\Delta\varphi)+\pi\bigg{]}\bigg{)}. \tag{8}\]  
Equation (8) represents a fringe pattern with spatial frequency equal to \(2\theta_{P}\sin\Delta\varphi\) and orientation equal to \(\varphi_{P}-\Delta\varphi\). Both expressions are dependent on the rotation angle, \(\Delta\varphi\). This demonstrates that the frequency and orientation on the fringes may be controlled by the operator as it is shown in laboratory implementations [22]. The maximum fringe density is reached when the angle between the Dove prisms is \(90^{\circ}\). The minimum fringe separation is \(\frac{\lambda}{2\theta_{P}}\) (about 2 m for a Jupiter-like planet at 10 parsecs from the Earth, observed at 10 \(\mu\)m).  
Figure 4: Star–planet system viewed for the interferometer when the star is aligned with the interferometer optical axis.  
In this case, the fringe visibility is only limited by the coherence function of the incident beam. Unfortunately, the incidence is too low because the planet incidence \(M_{P}\) is just a few photons/s per m\({}^{2}\)[15].