esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The self-deployment of nodes with non-uniform coverage in underwater acoustic sensor networks (UASNs) is challenging because it is difficult to access the three-dimensional underwater environment. The problem is further complicated if network connectivity needs to be considered. In order to solve the optimization problem of sensor network node deployment, we propose a maximum coverage and connectivity self-deployment algorithm that is based on ant colony optimization (MCC-ACO). We carry out the greedy strategy, improve the path selection probability and pheromone update system, and propose a self-deployment algorithm based on the foundation of standard ant colony optimization algorithms, so as to achieve energy-saving optimization coverage of target events. The main characteristic of the MCC-ACO algorithm is that it fully considers the effects of the changes in the event quantities and the random distribution of the nodes on the deployment effect of the nodes, ensures that every deployed node can be connected to the sink, and achieves the matching of node distribution density and event distribution. Therefore, the MCC-ACO algorithm has great practical value. A large number of comparative simulation experiments show that the algorithm can effectively realize the self-deployment problem of underwater sensor nodes. In addition, the paper also gives the impact of changes in the number of events in the network on the deployment effect.  
self-deployment; ant colony optimization; network connectivity; energy-saving optimization Article  
A Self-Deployment Algorithm for Maintaining Maximum Coverage and Connectivity in Underwater Acoustic Sensor Networks Based on an Ant Colony Optimization  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] 2019 9 1479 99 00071479
## 3 Mathematical Model and Thermodynamics Analysis
### System Performance Parameters and Evaluation  
The tri-generation total gained output ratio (TGOR), tri-generation gained output ratio for independent systems (TGORind), specific total gained energy (STG), and specific total gained energy equivalent price (STGP) of the proposed basic, IS-I, and IS-II multi-generation systems [20] are as follows.  
\[TGOR_{MGS-I}=\frac{m^{\bullet}_{fresh}(MGS-I)h_{fg}+Q^{\bullet}_{cooling}+W^{ \bullet}_{net}}{Q^{\bullet}_{Eap}} \tag{41}\]  
\[TGOR_{MGS-II}=\frac{m^{\bullet}_{fresh}(MGS-II)h_{fg}+Q^{\bullet}_{Domes}+Q^{ \bullet}_{cooling}+W^{\bullet}_{net}}{Q^{\bullet}_{Eap}} \tag{42}\]  
\[STG_{MGS-I}=\frac{\Big{(}m^{\bullet}_{fresh}(MGS-I)h_{fg}+Q^{\bullet}_{cooling }+W^{\bullet}_{net}\Big{)}\Delta\tau}{A_{SC}} \tag{43}\]  
\[STG_{MGS-II}=\frac{\Big{(}m^{\bullet}_{fresh}(MGS-II)h_{fg}+Q^{\bullet}_{ heating}+Q^{\bullet}_{cooling}+W^{\bullet}_{net}\Big{)}\Delta\tau}{A_{SC}} \tag{44}\]  
\[STGP_{MGS-I}=\Big{[}m^{\bullet}_{fresh}(MGS-I)\times WUR+\Big{(}Q^{\bullet}_{ cooling}+W^{\bullet}_{net}\Big{)}\times EUR\Big{]}\Delta\tau \tag{45}\]  
\[STGP_{MGS-II}=\Big{[}m^{\bullet}_{fresh}(MGS-II)\times WUR+\Big{(}Q^{\bullet}_ {Domes}+Q^{\bullet}_{cooling}+W^{\bullet}_{net}\Big{)}\times EUR\Big{]}\Delta\tau \tag{46}\]  
where,  
\(\Delta\tau\): daylight time (12 h)  
WUR: water unit rate USD/kg  
EUR: Energy unit rate USD/kWh  
The prices for energy and freshwater units vary from one city to another. In the present study, typical values of energy and water unit prices of 0.05 USD/kWh and 2.5 USD/m\({}^{3}\) of Gulf cities [8; 28] are taken. The system governing equations (Equations (1)-(46)) are simulated by using C++ and EES software based on energy and mass balances for all system components to calculate the system performance and productivity parameters for different ranges of design and operating conditions as given in Table 1.
## 3 Communication in the 60 Ghz Band
### Modulation and Coding Scheme  
The modulation and coding scheme (MCS) index value summarizes the modulation type (e.g., BPSK, QPSK, 16 QAM) and the coding rate that is used in a given physical resource block (PRB). Typically, a higher MCS index offers a higher spectral efficiency (which translates to a higher potential data rate) but requires a higher SNR to support it. Depending on the link quality metrics (LQMs) a node will dynamically choose an appropriate MCS in order to provide the best possible performance.  
In the 802.11 ad specification, three different PHY modes are defined based on how they can be used. The Control PHY (MCS0) is designed for low SNR operation with low throughput communication (27.5 Mbps) and is mainly used during the beamforming training (BF) phase. The Single Carrier (SC) PHY enables a power efficient and low complexity transceiver implementation. It provides a good trade-off between average throughput and energy efficiency. SC-PHY defines MCS 1-12, of which MCS 1-4 are mandatory modes to  
Figure 2: Example of wireless backhaul network.  
be implemented in all devices for interoperability [21]. MCS 13-24 provides the maximum 802.11 ad data rates up to 6.76 Gbps and adopts orthogonal frequency-division multiplexing (OFDM) technology, which is very efficient in multipath environments [21]. However, its implementation is complex and therefore targets devices with less stringent power and design constraints. Finally, the DMG low-power SC-PHY with MCS 25-31 is an optional SC mode that can provide lower processing power by using Reed-Solomon instead of low-density parity check (LDPC) codes [22; 23].  
In this work we consider the SC-PHY model, which ranges in value from MCS 1 to MCS 12. Table 1 below lists the MCS values defined in the IEEE 802.11 ad standard and gives their corresponding modulation schemes, coding and data rates. In the SC-PHY model, the lowest SC data rate is 385 Mb/s (MCS 1), which is implemented using BPSK modulation and rate 1/2 code with a symbol repetition of two. MCS 1-5 are all based on pi/2-BPSK modulation. MCS 2, 3, 4 and 5 use code rate 1/2, 5/8, 3/4 and 13/16, respectively. MCS 6-9 are based on pi/2-QPSK modulation, whereas MCS 10-12 are based on pi/2-QPSK [23].
## 2 Related Work
### Fog Computing Architecture
In fact, the simplicity of programming and reconfiguration, made possible by virtualization, simplifies management and ensures the infrastructure's ability to adapt to the changing dynamics of the environment. * Energy efficiency: It remains a challenge in the IoT environment, particularly regarding the energy consumption of large IoT devices. FC addresses this issue by empowering these devices to make intelligent decisions, such as toggling between on/off/hibernate states, ultimately leading to a reduction in overall energy consumption [13]. * Cost: In the context of Cloud computing, where resources are generally billed according to usage (pay-per-use model), some applications may find it more advantageous to invest once in the acquisition of private Fog resources (a form of decentralized processing closer to users) rather than pay regularly for instances in the Cloud. This highlights an alternative economic consideration for specific applications. * Management of services: FC operates as an intermediary layer to furnish computational capabilities. This not only facilitates device control but also permits the customization of services tailored to the specific environment [14]. In Table 2, we compare some studied works based on their objectives among the above Fog characteristics. According to this table, we notice that most works consider the reduction in latency, energy, and cost, and only the works [15; 16] consider data privacy and security.
###### Abstract  
The underwater acoustic (UWA) channel causes large propagation delays and reduces the bit error rate (BER) of wireless communication systems. The t-distribution is the optimal distribution to perform UWA noise. In this study, polar-coded differential chaos shift keying (DCSK) and quadrature chaos shift keying (QCSK) communication with UWA noise are considered. First, we have proposed a PDF for the UWA noise channel, and based on this PDF, the theoretical BER is derived. Second, polar coding's performance is determined to demonstrate the improvement in the BER performance compared to the uncoded UWA system by means of Monte Carlo simulations. The experimental results prove that the nearest model that is applicable to the UWA channel is a t-distribution with five and six degrees of freedom. The BER formulas of the proposed systems are derived and compared with the simulation results. The results confirm the performance improvement of the polar-coded chaotic modulation systems over uncoded systems in UWA channels.  
DCSK; QCSK; underwater acoustic; polar-coded systems Article
## 3 Materials and Methods  
### Vehicle Trajectory Extraction  
Trajectory data are important carriers of vehicle motion characteristics, and the lateral motion parameters of the vehicle can be resolved through the traveling trajectory, such as lateral displacement, lateral speed (the moving speed along the direction of the roadway cross-section) and lateral distance. To obtain trajectory data with sufficient sample size, a UAV is used to take aerial video shots of multiple scenes on two freeways in Chongqing, China (Figure 1a), and vehicle trajectories are extracted by computer vision technology. For fitting the purpose of the study, when labeling the vehicle dataset, all the models are simply divided into passenger cars (class \"car\") and large trucks (class \"truck\"). Figure 1b shows the recognition performance of a single video frame, the interface only shows the \"class-ID-speed\" and the rest of the parameters are saved in the form of a table. Table 2 shows the basic information about UAV video recordings. The selected roads are all two-way six-lane freeways, with lane widths of 3.75 m. The UAV flies at an altitude of 300 m and captures video at a resolution of 4096 \(\times\) 2160 pixels and a frame rate of 25 frames per second, covering a length of the roadway ranging from 405 to 419 m. It is worth mentioning that we have open-sourced part of the extracted data on [[http://www.cgskeyex.com](http://www.cgskeyex.com)]([http://www.cgskeyex.com](http://www.cgskeyex.com)) (accessed on 16 May 2023) and verified by comparing the driving trajectory of real vehicles that the extracted trajectory data can reach centimeter-level error [29], which is sufficient to meet the requirements of this work. The total length of the final valid video is 747 min, and a total of 34,404 vehicle trajectories are obtained, of which 27,334 are car trajectories and 7070 are truck trajectories. In addition to trajectories, the vehicle profile dimensions as well as the relative positions between neighboring vehicles are also extracted.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 4 Photonic Bandgap (Photonic Crystal) Fibers
### Single-Mode PBGFs with Shunts  
Photonic bandgap fibers with regular photonic cladding support the propagation of higher-order modes, while telecom networks require single-mode fibers. Single-mode propagation can be ensured with \"shunts\"--hollow cavities larger than normal cells of the photonic structure, but smaller than the core (Figure 5). In the OFS fibers shown in Figure 5b,c [21], the core and shunt have cross-sections equal to 19 cells and 7 cells, respectively.  
Shunts are traps for higher-order modes: radiation is accepted by the shunt, but leaks from it through the cladding, escaping the fiber. Shunts are designed such that higher-order modes in the core are phase matched to the fundamental mode in them. The loss experienced by the fundamental mode guided in the core is not significantly affected. After a certain length, e.g., 0.5 m [32], only the fundamental mode effectively propagates, despite the large diameter of core (25 um in Figure 5b,c). Single-modedness in a six-shunt HC-PBGF can be better than in an SMF, where the cutoff wavelength is tested on longer samples: 2 m (fiber in primary coating) or 22 m (fiber in the cable) [33].  
Figure 6 shows the deformation of the photonic structure during fiber manufacturing, in particular at the borders with the core and jacketing tube. Each small cell was originally a round glass tube. One undesirable effect is the lack of radial symmetry, causing PMD.  
HCFs and solid-core single-mode fibers exhibit opposite relationship between diameter and the area of the fiber's core and the fundamental mode propagating in it:  
* In solid-core fibers, mode field diameter and effective area are larger than the dimensions of the core, as diffraction causes light to \"spill\" into the cladding;
* In hollow-core fibers, the mode field is \"pushed\" towards the center of the core, away from the photonic cladding, and MFD is smaller than the core diameter (Table 2);
* The mode field diameter of HC-PBGFs decreases with the wavelength, while that of solid-core fibers increases with the wavelength at similar rate (Figure 7).  
Another special property of HC-PBGFs is the characteristic of chromatic dispersion (CD), shown in Figure 8 for OFS AccuCore fiber [22]. Both the CD and CD slopes rapidly increase close to the edges of the transmission band, and the sign of CD is reversed in the middle. As a result, high bit rate transmissions in a part of the low-loss band may require dispersion compensation, which adds undesirable latency.
# Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
## 2 Methods
### Model  
Four different classification and regression models for predicting LOS in the ICU are utilized and their performances are compared. The first is the common logistic/linear regression model (for classification/regression problems), which incorporates a linear combination of all features in modeling. Support vector machine (SVM) is the second, which determines the optimal hyperplane that separates different classes of data in an N-dimensional space, where N is the number of features used. The model computes the optimal hyperplane using a crucial kernel function, here the radial basis function, which performs well in classification problems even in the presence of nonlinear dependencies between features [29; 48]. The next is random forest, an extension of decision trees where an ensemble of many possible trees is generated and selected using an optimization procedure. Random forest is commonly used for LOS prediction and typically shows quite a good performance [14]. The last one is the XGBoost, which is a tree-based method with an integrated end-to-end gradient boosting optimization that enables trees to optimize themselves [49]. These models are also widely used in LOS prediction with highly accurate results [50; 51].
## 6 Experiment
### Results in the White-Box Setting
#### 6.2.4 Effectiveness Under Defensive Mechanisms  
Considering the realistic scenario where recommendation systems might deploy defense mechanisms to detect and mitigate fraudulent rating interactions by \"water armies\", experiments were conducted using the NeuMF recommendation algorithm to study the effectiveness of poisoning attacks under a rating-based detector. RDMA (Rating Deviation from Mean Agreement) is a fake user detection mechanism based on user rating behaviors, reflecting the disparity between a user's ratings and the average ratings of the respective items. For user \(u\), RDMA measures the average deviation in that user's ratings from the item's average ratings. This feature is calculated through the following steps: first, for each item \(i\) rated by user \(u\), calculate the absolute difference between the user's rating \(r_{ui}\) and the average rating \(\overline{y_{i}}\) of that item, and then divide by the number of ratings \(c_{i}\) for that item; next, sum all these values and divide by the total number of items \(\mid I_{u}\mid\) that user has rated. The mathematical expression is as follows:  
\[\text{RDMA}_{u}=\frac{\sum\limits_{i\in I_{u}}|r_{ui}-\overline{y_{i}}|/c_{i}} {|\mid I_{u}\mid} \tag{16}\]  
We conducted experiments on two explicit rating datasets, ML-100K and ML-1M, with the default number of fake users set at 3% and \(T=30\). The results are displayed in Table 5. The experimental data show that, compared to other baseline methods, DRLATack demonstrates higher effectiveness in attacks on both datasets, both before and after the deployment of the RDMA defense mechanism. On smaller datasets, attack methods struggle to counteract the RDMA defense, whereas on the larger ML-1M dataset, due to the vast number of users and items, the proportion of fake users and their interactions is relatively small, which complicates the detection of fake users.  
Figure 4: Attack effects for multiple target items.
## 4 Discussion  
An initial new model of the Earth's heat loading has been developed. Its advantage is the simplicity of the plane strain problem and the original combination of the finite element method and the probabilistic Monte Carlo Method. FEA is performed for the non-stationary and non-linear coupled geomechanical problem (i.e., temperature task and structural task).  
A simple measure of the material Inhomogeneity of the Earth's crust (partitioned into 24 materials) was respected in a simple way.  
The interaction between the crust and the upper mantle is replaced by the bilateral Winkler elastic foundation boundary condition.  
This results in a simplified model, which respects many random (pseudo-random) inputs, i.e., \(2\times 10^{6}\) Monte Carlo simulations. Nonlinear geomechanics tasks, in the case of large deformation caused by Sun heating, were solved.  
From the stochastic modelling, the tangential and radial displacements of the Earth's surface and crustal stresses are obtained, and their values can initiate the possible fractures of the Earth's crust connected with its tectonics.  
The model is considered to be continuous and, therefore, does not allow for the formation of cracks in the Earth's crust. However, the obtained stresses and displacements clearly confirm sufficient capacity to induce tectonic changes in the Earth's surface. The periodic and inhomogeneous thermal radiation from the Sun acting on the Earth's surface is sufficient to induce these tectonic changes. This is the important finding for understanding the complexity of the Earth's crust behavior.  
It can be said that tectonic changes in the Earth's crust are also caused by thermal radiation from the Sun (external heating of the Earth), which is still a new idea. Our results are not inconsistent with earlier foreign works [26; 28; 29] or our works [9; 15; 18; 19; 30; 31].  
As is well known, tectonic changes in the Earth's crust are also due to tidal effects from the gravitational pull of the Sun and Moon, centrifugal forces from the Earth's rotation,  
Figure 17: Histograms and distribution functions of (**a**) tangential component of total crustal surface displacement, (**b**) radial component of total crustal surface displacement (sw MSC.Marc Mentat and Anthill).  
and internal magmatic processes within the Earth. The influence of tidal, centrifugal, and internal magmatic effects is not addressed in this paper.  
In the future, the dynamic and cyclic loading of the Earth's crust from tidal effects and magmatic processes can also be considered, or a more complex spatial (3D) model can be developed that could also account for the other geological and geomechanical phenomena mentioned above. In the future, by applying mechanical contacts between the Earth's plates, it will be possible to deal with tectonic phenomena on our own or foreign planets in a similar but more complex and complicated way.  
The important influence of thermal radiation on the Earth's tectonics are new findings, proved by our work and by the different models, but consistent with the literature [9; 15; 18; 19; 26; 28; 29; 30; 31].
## 3 Formulation of the Wind Power Curtailment Scheduling Problem  
The generation scheduling problem with wind power curtailment is formulated in this section. The objective of this scheduling problem is to determine the generation and spinning reserves of  
Figure 4: Change in the probability density of the state of charge (SoC) of an electric vehicle (EV) by charging for one hour.  
conventional units and also the absolute power limit for wind generators that minimizes the operational cost as follows:  
\[Min\sum_{t=1}^{N_{T}}\sum_{i=1}^{N_{G}}\Bigl{\{}C_{G,i}(p_{G,i,t})+C_{S,i}(u_{i,t })+C_{UR}r_{up,\;i,t}+C_{DR}r_{dn,\;i,t}\Bigr{\}} \tag{13}\]  
where \(C_{G,i}\) and \(C_{S,i}\) are the generation cost and start-up cost of unit \(i\) respectively; \(p_{G,i,t}\) and \(u_{i,t}\) are the scheduled generation and on/off states of unit \(i\) at time \(t\); \(r_{up,i,t}\) and \(r_{dn,i,t}\) represent the upward and downward spinning reserve deployed for unit \(i\) at time \(t\), respectively; \(C_{UR}\), and \(C_{DR}\) are the upward and downward reserve costs respectively; and \(N_{T}\) and \(N_{G}\) are the number of time intervals and the number of conventional units, respectively. The objective function (13) is subject to the following constraints.
## 2 Materials and Methods
### Feature Selection  
Considering HDLSS data, reducing the number of features is crucial to perform non-overparameterized classification-based analyses [54]. Feature selection techniques can be classified into filter, wrapper, and embedded methods [53]. Filter methods can be combined with any machine learning model and are much faster and less prone to overfitting than wrapper and embedded methods [55]. However, no feature selection method outperforms all the others for all tasks [56]. Two widely used filter methods [57], minimum redundancy maximum relevance (mRMR) and Relieff, were chosen and combined with different classification methods to assess the selected features' predictive performance. mRMR ranked all the \(\Delta\) features by calculating redundancy between features and relevance between features and the response vector, composed of zeros for the rousvastatin group and ones for the placebo group [58]. Relieff algorithm uses nearest neighbors' concept to derive feature statistics that indirectly account for interactions [56,59]. Considering the low total \(n\) of the dataset, a low value of nearest neighbors (\(k=3\)) was selected [60]. MathWorks MATLAB version 2019b implementation of both algorithms was used.  
Out of the features selected by both techniques, we rejected variables such as low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol, and triglycerides that were expected to change due to rousvastatin's direct action. Considering that the data were for only 21 subjects and the classification model may overfit when using a high number of features, we selected the top 5 ranked features by both algorithms.
# Frequency-Dependent Schroeder Allpass Filters
###### Abstract  
Since the introduction of feedforward-feedback comb allpass filters by Schroeder and Logan, its popularity has not diminished due to its computational efficiency and versatile applicability in artificial reverberation, decorrelation, and dispersive system design. In this work, we present an extension to the Schroeder allpass filter by introducing frequency-dependent feedforward and feedback gains while maintaining the allpass characteristic. By this, we directly improve upon the design of Dahl and Jot which exhibits a frequency-dependent absorption but does not preserve the allpass property. At the same time, we also improve upon [PERSON]'s allpass filter as our design is both less restrictive and computationally more efficient. We provide a complete derivation of the filter structure and its properties. Furthermore, we illustrate the usefulness of the structure by designing an allpass decorrelation filter with frequency-dependent decay characteristics.  
Keywords:filter design; allpass filter; artificial reverberation; decorrelation; dispersion +
Footnote †: journal: Applied Sciences