esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
For this study, we first sought a model that accurately reflects the anthropometry and muscle force-generating capacity of a 50 th percentile adult male to ensure consistency between the software platforms, even with the generic models. Further, we wanted to ensure it includes the three degrees of freedom (DOF) at the GH joint to perform flexion/extension, abduction/adduction, and internal/external rotation. Similarly, two DOFs at the elbow joint perform flexion/extension and pronation/supination rotation as required for the CBA and HBB tests. The Stanford VA upper limb model [18], model of the scapulothoracic joint [19], dynamics arm simulator [20], and upper extremity dynamic model (UEDM) [21, 22] are some commonly used shoulder models from OpenSim. This study uses the UEDM from OpenSim (Version 4.4, developed by the OpenSim team at Stanford University, Stanford, CA, USA, [[https://simtk.org/projects/opensim](https://simtk.org/projects/opensim)]([https://simtk.org/projects/opensim](https://simtk.org/projects/opensim)), accessed on 15 May 2024) because it meets the criteria set for the model requirement as mentioned above. Plus, it can perform kinetics analysis, is easy to use without requiring external scripting or modifications and has been validated by the author for a wide range of motion [21, 22]. Unlike OpenSim, the AnyBody Modeling SystemTM typically includes one primary shoulder model, the shoulder arm model, based on a 50 th percentile European male, and provides a detailed representation of the shoulder joint. This work uses the shoulder arm model from AnyBody Modeling SystemTM (version 7.1.0.5957 developed by AnyBody Technology A/S, Aalborg, Denmark, [[https://www.anybodytech.com](https://www.anybodytech.com)]([https://www.anybodytech.com](https://www.anybodytech.com)), accessed on 5 July 2024) [23].  
The selection of OpenSim and AnyBody Modeling SystemTM was driven by their advanced dynamic modeling capabilities and applicability to perform upper extremity biomechanical analyses. To expand on the selected models, both include the upper extremity bones: thorax, clavicle, scapula, humerus, ulna, radius, and hand. Further, these segments form an articulation surface or joint of the upper limb, enabling the upper extremity to achieve the desired motion for daily activities. The UEDM comprises fifty Hill-type muscle-tendon actuators representing 32 muscles that span the shoulder, elbow, forearm, and wrist with a single line of action. At the same time, AnyBody Modeling SystemTM shoulder models consist of 118 full-blown Hill-type muscle-tendon units with at least two lines of action to define their point of origin and insertion.
# Plenty of Room at the Bottom: Nanotechnology as Solution to an Old Issue in Enhanced Oil Recovery
## 4 Nanocatalysts
### Factors Affecting Nanocatalysts in EOR Applications  
#### 4.2.1 Type, Size, and Concentration of Nanoparticles  
The viscosity-concentration combination can significantly alter the rheology of the produced oil, and therefore the efficiency of the EOR process [138; 143; 169]. This behavior can be attributed to chemical or physical processes generated by the particles. The exothermic chemical reactions are an example of the positive processes. As an example of negative processes, the coordination of the metal species with asphaltene causes an increase of viscosity above a certain concentration (Figure 40).  
Decreasing the particle size increases the surface-to-volume ratio. This improves both the chemical and physical properties of nanoparticles. Furthermore, another significant property is the increased dispersion efficiency. However, at high concentrations, the viscosities of micro- and nano-mixtures become very close (Figure 41) [143]. These results may be connected to the high contact area of nanoparticles, which renders a higher likelihood of particles' aggregation.  
Figure 39: Proposed mechanism of hydrocracking. Figure adapted from Ref. [167].  
This dependence is supported by experiments considering the grinding time of the nanoparticles [144]. As the latter increased (i.e., particle size decreased), the ratio of the viscosity reduction increased (Figure 42). The metal particles may increase the oil viscosity after the aquathermolysis process due to coordination reactions of nanoparticles with heavy oil molecules. This is an important reason why nanoparticle concentrations should be carefully selected beforehand.  
Figure 40: The effect of different metal species on the viscosity of a heavy oil at a fixed temperature (25 \({}^{\circ}\)C) as a function of the nanocatalyst concentration for: \(\blacklozenge\) iron; \(\blacksquare\) copper; and \(\blacktriangle\) iron oxide (III). Figure reproduced from Ref. [170].  
Two important purposes of transition metal species are catalyzing the hydrogenation reactions and improving thermal conductivity [138,142]. However, in the case of oil sands, the effect of temperature and permeability on the nanoparticle mobility should also be taken into account (Figure 43) [37]. The reason is that higher temperatures favor particle collision due to the heavy oil viscosity reduction, yielding higher aggregation rates. Further analyses are then necessary to determine the effective size of the nanocatalyst to secure penetration in the porous media, taking into account that changes in temperature are likely to result in settling, separation, and possible agglomeration of the nanocatalyst.
# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
## 2 Adversarial Examples and Deep Learning Models
### Measurement and Evaluation Metrics
#### 2.5.1 Modification Control  
After modifying the input data, it is necessary to measure the size of the modifications in orderto ensure that they are unnoticeable. Usually, the size of the modifications is measured based on the distance between the original data (or _clean data_) \(x\) and adversarial example \(x^{\prime}\). In the case of text data, the distance between \(x\) and \(x^{\prime}\) must be measured. Correct grammar, syntax, and semantic preservation must be considered as well.  
* **Grammar and syntax measurement.** Ensuring correct grammar and syntax is necessary to ensure that adversarial examples are undetectable. Strategies such as perplexity measure, paraphrase control, and grammar and syntax checkers have been proposed to measure grammar and syntax.
* **Semantic-preserving measurement.*
* The semantic similarity/distance measurement is performed on word vectors using measures of distances (such as the Euclidean distance) and similarity (such as the cosine similarity).
* The Euclidean distance is the distance between two vectors in Euclidean space. \[d(p,q)=\sqrt{(p_{1}-q_{1})^{2}+(p_{2}-q_{2})^{2}+...(p_{n}-q_{n})^{2}}\] (2)
* The cosine similarity computes the cosine value of the angle between the two vectors. \[cos(p,q)=\frac{\sum_{i=1}^{n}p_{i}\times q_{i}}{\sqrt{\sum_{i=1}^{n}(p_{i})^{ 2}}\times\sqrt{\sum_{i=1}^{n}(q_{i})^{2}}}\] (3)
* **Edit-based measurement.** Measuring the number of edits (modifications) quantifies the minimum changes from one text to the next. Different definitions of editing distances use different operations:  
Figure 6: General principle for creating adversarial examples (or adversarial texts) by implementing paraphrases. Modifications are made carefully to ensure that the syntax and grammar of the input are not changed.  
- Word Mover's Distance (WMD). The WMD measures the changes in the space of word embeddings. It measures the minimum distance from the word embeddings of an adversarial text to approach the _word embeddings_ of an original text.
- Levenshtein distance. The Levenshtein distance is a string metric for measuring the difference between two sequences, i.e., the minimum number of single-character edits.
- Perturbation ratio. The ratio of perturbed words in the sentence to the total number of words in the sentence.
- Jaccard similarity coefficient. Used to measure the similarity of finite sets using the intersection and union of the sets: \[J(A,B)=\frac{\mid A\cap B\mid}{\mid A\cup B\mid}\] (4) in two texts, A and B are the original and modified inputs, respectively, \(\mid A\cap B\mid\) indicates the number of terms in both documents, and \(\mid A\cup B\mid\) is the number of unique terms in both documents.
## 2 Materials and Methods
### Dataset Generation  
The program to generate the dataset of SNR values using the simulator is written in MATLAB R2022b Update 9 (9.13.0.2553342), 64-bit (maci64). The SNR values are produced by simulating an mmWave wireless communication system operating at a carrier frequency of 28 GHz, encompassing both Line-of-Sight (LOS) and Non-Line-of-Sight (NLoS) propagation paths. The procedure emulates a Channel Impulse Response (CIR) for each trajectory, followed by multiplication with a random input signal to produce a received signal. The SNR is calculated as the ratio of signal power to noise power, assuming noise power is directly proportional to signal power, and a fixed SNR of 30 dB is maintained.
## 3 Numerical Simulations and Experiments
### Numerical Simulations
The RMS values of the residual error of the two methods are multiplied by \(10^{4}\) and taken logarithmically and plotted, as shown in Figure 7. The reconstruction results of the RBF interpolation algorithm are better than those of the improved Southwell algorithm for the free-form surface at different angles, and the RMS values and PV values can be seen in the Appendix of Table 14. Therefore, the simulations above using ray tracing verify that the RBF interpolation algorithm is more practical for an off-axis PMD system.
## 4 Distribution Method for Observation Scheduling in the AEOS Constellation  
Based on the decentralized PI algorithm, this section proposes a PIDSM for solving the problem of time-dependent observation tasks in the AEOS constellation. Such an algorithm can be deployed and executed concurrently in all AEOSs. By exchanging information with the neighbor satellites through ISLs, each AEOS \(s\in S\) iteratively adds or removes some targets in or from its currently ordered sequence such that the global fitness is somehow optimized, while the constraints defined in Section II are met.
# A Review of Multifunctional Antenna Designs for Internet of Things
## 2 Materials and Methods
### Smart Cities
#### 2.3.3 Necessity of Integrated Social-Technical Systems--Smart Cities
Building on this concept, antennas and electronic devices should, at least in their initial implementation, focus on retrofitting \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Antenna Array** & **Max. Gain (dBi)** & **No. Beams** & **Angle Cover.** & **Polar. (No)** & **Freq.** & **FBW \({}^{*}\) (\%)** & **Freq. Reconf.** & **Ref.** \\ \hline Cubical & 5.6 & 6 & 360\({}^{\circ}\) & LP(1) & 2.45 GHz & 2 & No & [9] \\ Planar & 15.28 & 12 & 60\({}^{\circ}\) & LP(3) & 1.8 GHz & 46 & Yes & [105] \\ Linear & 10.8 & Cont. & 120\({}^{\circ}\) & LP(2) & 27 GHz & 26 & Yes & [106] \\ Ortho-hexagonal & 10.8 & 5 & 100\({}^{\circ}\) & CP(2) & 2.58 GHz & 33 & Yes & [103] \\ Fan shaped & 3.9 & 4 & 360 \({}^{\circ}\) & N/A & 2.4 GHz & 12.5 & No & [104] \\ Cylindrical & 4.29 & 14 & 6 & LP (1) & 2.44 GHz & 3 & No & [108] \\ ESPAR & 4.29 & 12 & 360 \({}^{\circ}\) & LP (1) & 2.65 GHz & 1 & No & [107] \\ Slotring & 7.6 & Cont. & 60\({}^{\circ}\) & LP(2), CP(2) & 6.65 GHz & 37 & No & [109] \\ \(\Gamma\)-Dipole & 6.87 & Cont. & 60\({}^{\circ}\) & ALL & 2.5 GHz & 9.6 & No & [56] \\ Annular & 4.62 & Cont. & 60\({}^{\circ}\)/90\({}^{\circ}\) & LP (3), CP(2) & 4.16 GHz & \textless{}1 & No & [110] \\ \hline \hline \end{tabular}  
* For frequency-reconfigurable antennas the tuning range is presented.  
\end{table} 
Table 3: Comparison table of different antenna designs for smart city antennas. existing infrastructure to make it \"smart\". This approach allows for gradual integration of new technologies while leveraging existing resources, making the transition to smart cities more efficient and cost effective.
# Characterization of a Modified Clinical Linear Accelerator for Ultra-High Dose Rate Beam Delivery
## 2 Materials and Methods  
The LINAC Elekta SL 18 MV was installed in 2016 at the Physics Department of the University of Turin (UNITO) and is entirely dedicated to research. This machine can generate both X-rays (up to 18 MV) and electrons (4-18 MeV), delivering the beam in 2 us pulses at a Pulse Repetition Frequency (PRF) between 6 and 400 Hz. The accelerator head is equipped with multi-leaf collimators (MLC) and diaphragms that precisely shape the beam, allowing radiation fields up to 40 \(\times\) 40 cm\({}^{2}\) at the isocenter position. It also contains a monitoring system consisting of two independent ionization chambers (ICs), one acting as the primary reference and the second as a backup.  
The 10 MeV electron beam can be delivered both in the standard LINAC clinical configuration (conventional mode) and with the high dose rate LINAC upgrade described in Section 2.1 (UHDR mode).  
The beam pulse characterization with a silicon sensor and dose measurements, respectively, described in Sections 2.2 and 2.3, were performed for four irradiation settings, each one featuring different SSDs and collimation of the beam (Figure 1):  
1. At the isocenter position (SSD = 100 cm);
2. At the isocenter position with a PMMA cylindrical applicator with a 5 cm inner diameter;
3. At the crosshair foil (SSD = 52.9 cm) at the exit of the LINAC head;
4. In the wedge tray (SSD = 18.6 cm) inside the LINAC head.  
For all the settings, the irradiation field size was fixed at 10 \(\times\) 10 cm\({}^{2}\) at the isocenter position (i), resulting in smaller field sizes for the configurations closer to the source.  
The crosshair foil position (iii) is the closest point to the source where standard dosimetry procedures can still be applied, while the wedge tray position (iv) can only be evaluated with in-air measurements.
## 3 Materials and Methods
### Deterministic Models  
The deterministic models are classified into static--time is not considered a variable--and dynamic models [18]. A further subclassification is lumped or distributed models [134]. DRAINMOD is a deterministic hydrological model employed for agricultural subsurface drainage for nutrient transport and groundwater salinity problems. Variants of the model are applied for nitrogen subsoil fate DRAINMOD-NII, salinity penetration in soil cropping DRAINMOD-S and phosphorus (P) concentration prediction and dynamic DRAINMOD-P [16; 19; 135; 136; 137; 138]. The model is well tested over a river basin in Belgium for nitrate pollution prediction [139]. Nonetheless, certain limitations have been reported when implemented in artificially drained lands.  
MIKE-11 is a 1-D hydrodynamic model in rivers that simulates dynamic water movement in channels and rivers. It has been applied as a water quality model in England and India. It employs time series of depth, pollutants, and flow data along with water quality parameters [140].  
The artificial neural network (ANN) science field combines bidirectional long short-term memory (BiLSTM) and adaptive neurofuzzy inference system (ANFIS) to predict water quality in different groundwaters by employing single exponential smoothing (SES) as a preprocessing method to adjust the weight of the dataset input (categorized as training and testing data) [141]. The internal operations of a neural network are deterministic, not stochastic, after training is finished.
## 5 Results
### Applying Motion Correction
(**a**) WindCube V1 reference (x) vs. WindCube V1 moving before compensation (y), (**b**) ZephIR 300 reference (x) vs. ZephIR 300 moving before compensation (y), (**c**) WindCube V1 reference (x) vs. WindCube V1 moving after compensation (y), and (**d**) ZephIR 300 reference (x) vs. ZephIR 300 moving after compensation (y) (Courtesy of [PERSON] [38]). The red line represents the Deming regression. Figure 10: Distribution of the error between WindCube V1 moving and WindCube V1 reference before compensation (**a**) and after compensation (**c**). Distribution of error between ZephIR 300 moving and reference before compensation (**b**) and after compensation (**d**).
# UAV-Borne Profiling Radar for Forest Research
## 3 Field Test
### FGI-Tomoradar Data Collection  
During the Tomoradar test, the RF and the IF units were installed on an arm of a Bell 206 helicopter with a 0 degree incidence angle (i.e., with the antenna pointing down). To shorten the length of the RF cables as much as possible for better signal quality (i.e., to reduce the internal reflections), the RF and IF units were installed on the bottom side of the frame on which the parabolic antennas were also installed. The IMU was installed on the frame's top side, and the GNSS antenna was installed on a mast that was attached to the front of the frame (Figure 6a). Although the helicopter blocked part of the GNSS signal, the post-processed position results indicated that the effect of the blockage was minor. The digitizer, control computer and power unit in the cabin were connected to the RF and IF units by umbilical cables.  
Figure 5: Single radar profile at the Evo site. The ground was 84 m and the tree-canopy backscatter ranged from 63 to 73 m from the radar. The signals from the canopy and the ground were approximately 30 dB above noise.  
The digitizer can process the received IF waveform and present the FFT results in real time using a self-developed program. However, rather than the FFT results, it records all raw data at a 2.5 M sample rate. The data is processed in a post-processing way.
# A Numerical Study of the Performance of Point Absorber Wave Energy Converters
## 4 Conclusions  
A numerical investigation was carried out to compare submergence and the SWEC shape subjected to regular waves. This work provides valuable insights for designing and optimizing WECs with CFD using the multiphase VOF model and a coupled fluid-motion solver for submerged and free-floating buoys. The following conclusions can be drawn from this study:  
* The spring constant reduces the efficiency of the WEC but develops a stable floating movement of the submerged WEC.
* Submergence increases with increased power absorption but reduces the conversion efficiency.
* A trapezoidal fin provides higher efficiency than either a rectangular box or a circular cylinder.
* With an increase in wave height, the power absorption increases, but the efficiency decreases.  
The developed model can simulate the interaction between waves and wave energy converters and determine the influence of wave sizes and SWEC shapes on the power generation. The model can thus be applied to different scenarios involving wave energy converters.  
Conceptualization, R.R.; methodology, R.R.; investigation, R.R.; writing--original draft preparation, R.R., S.G. and P.G.V.; writing--review and editing, R.R., S.G. and P.G.V.; supervision, S.G. and P.G.V. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support from the UK Commonwealth Split-Site Commission.  
Figure 13: Effect of the spring constant (\(k\)) on the stability of W1 with an effective density of 1000 kg/m\({}^{3}\), depth \(d_{s0}=0\) m, and wave height \(H=0.04\) m. (**a,b**) Vertical displacement (\(Dz\)) and horizontal displacement (\(Dx\)) of the object as a function of time. (**c**) Power absorption versus time for W1. (**d**) Efficiency versus spring constant for W1.
## 2 Proposed Localization System
### Offline Phase
#### 2.1.1 Constructing Fingerprinting Database  
The process begins by partitioning the target environment into \(L\) grids, each labeled with a unique identifier \(l\) (\(1\leq l\leq L\)). For each grid, RSS values from multiple APs are measured using WiFi scanners, and these RSS values are stored in a fingerprint database. We assume that the total number of APs is \(N\). Fingerprints allow the localization problem to be treated as a classification task, where the goal is to identify the correct grid based on the observed RSS values. Techniques such as kernel-based principal component analysis (KPCA) have been employed in previous studies to reduce data dimensionality while preserving essential localization features [20].  
Let \(G\) denote the maximum number of measurements at each grid \(G\). A fingerprint matrix \(FP\) can be constructed as follows:  
\[FP=\left[\text{Grid}_{1};\text{Grid}_{2};\cdots\text{Grid}_{L}\right]^{T} \tag{1}\]  
where \(\text{Grid}_{l}\) is a dataset for the \(l\)th grid such that  
\[\text{Grid}_{l}=\left[\begin{array}{cccc}rss_{l1}^{1}&rss_{l1}^{2}&\cdots&rss _{l1}^{N}\\ \vdots&\vdots&\ddots&\vdots\\ rss_{lG}^{1}&rss_{lG}^{2}&\cdots&rss_{lG}^{N}\end{array}\right] \tag{2}\]  
where \(rss_{lG}^{n}\) represents the signal form the \(n\)-th AP, which is obtained at the \(g\)-th collection in the \(l\)-th grid (\(1\leq n\leq N,1\leq g\leq G,1\leq l\leq L\)). Let \(M=L*G\); then, the size of matrix  
Figure 1: Architecture of the proposed WiFi fingerprint indoor localization system.  
\(FP\) is \(M\times N\). Let \(FP_{m}\) (\(1\leq M\)) denote the \(m\)-th row vector in \(FP\), which is referred to as the \(m\)-th fingerprint vector in this study.
# An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking
## 5 Evaluation  
In this section, we carried out a series of experiments to evaluate the performance of our proposed AHHM approach. Our evaluation offered a comparison with SSM and BIER. We first used BRITE [26] topology generator to randomly generate a topology. Then, we randomly deployed groups, each of which contains one source and some receivers. Each source and receiver were randomly connected to a router. NRS was deployed as  
Figure 7: The example of the data forwarding process in AHHM.  
an application to provide name resolution service for AHHM. For each experiment, we tested different multicast approaches under the same conditions and counted the relevant experimental results. Table 1 shows the experimental settings used in our evaluation.  
We conducted four sets of experiments to evaluate the performance of AHHM in terms of control overhead, forwarding state, and link load. In the first set of experiments, we tested these metrics of each multicast approach under different thresholds. In the second set of experiments, we tested these metrics of each multicast approach under different numbers of receivers. In the third set of experiments, we tested these metrics of each multicast approach under different numbers of groups. In the fourth set of experiments, we tested the cumulative distribution function (CDF) of these metrics in each multicast approach. The experimental parameters used in each set of experiments are shown in Table 2. In order to make the experimental results more reliable, each set of experiments was repeated 100 times, and the average value was taken as the result.