esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments of Molecular Deposition Film Throughput  
### Experimental Plan  
#### 4.1.1 Injection Concentration Optimization Experimental Plan  
The injection concentration is a critical parameter for determining the economic effectiveness of molecular deposition film flooding. Variations in rock components and fluid properties within the reservoir lead to differences in the adsorption of molecular deposition film agents on the rock surface and their consumption at the oil-water interface. Research results on the mechanism of molecular deposition film flooding have revealed that different concentrations of these agents exhibit varying diffusion coefficients depending on the water quality, with specific concentration ranges capable of significantly increasing the activation energy of water molecules. Therefore, selecting an appropriate injection concentration for molecular deposition film flooding is particularly crucial.  
Experiments were initially conducted using natural rock cores or model samples filled with natural crude oil shale; air permeability was assessed after vacuuming, and the samples were saturated with brine to determine water-phase permeability. Subsequently, crude oil was injected into the sample at a controlled rate to establish bound water, allowing time for even distribution throughout the core; following this, heating was applied to reach reservoir temperature before flooding with water at a specified rate until 98% or higher water content was reached. Finally, various concentrations of molecular deposition film agent solutions were injected into the sample at consistent rates until no further crude oil production was possible. Throughout these experiments, parameters such as injection pressure, produced crude oil volume, and liquid volume were recorded while calculating both water flooding effects and data necessary for enhancing oil recovery by employing molecular deposition film agents. By comparing this data set, it becomes feasible to determine an optimal injection concentration.
## 2 Materials and Methods
### Determination of Emulsifying Properties  
The emulsifying activity index (EAI) and emulsion stability index (ESI) were determined spectrophotometrically as described in [15; 18] by mixing 0.3 g of mushroom powder with 30 mL of distilled water and 10 mL of sunflower oil, followed by homogenization using an Ultra Turrax (T18 digital model, Ika, Germany) at 20,000 rpm for 1 min. Then, 2 aliquots of emulsion were diluted, after 0 and 10 min of homogenization, with 0.1% (\(w/v\)) sodium dodecyl sulfate (SDS) solution. The absorbance of the diluted solution was measured at 500 nm using a spectrophotometer (Specord 200 Plus UV-Vis, Analytik Jena, Germany).  
The EAI and ESI were calculated according to Equations (3) and (4), respectively:  
\[EAI\Big{(}m^{2}g^{-1}\Big{)}=\frac{2\times 2.303\times A_{0}\times DF}{c\times \theta\times L\times 10000} \tag{3}\]  
\[ESI(min)=\frac{A_{0}\times\Delta t}{A_{0}-A_{10}} \tag{4}\]  
where:  
\(A_{0}\) and \(A_{10}\) = absorbance measured at initial time and after \(\Delta\)t = 10 min;  
\(DF\) = dilution factor;  
\(c\) = weight of powder per unit volume (g/cm\({}^{3}\));  
\(L\) = width of the optical path (1 cm); and  
\(\theta\) = oil volumetric fraction of the emulsion.
## 5 Machine Learning, Dependability, and 6G
### Background in Brief  
FL [66] was conceived by Google researchers back in 2016. Since then, it has experienced wide adoption in both industry and academia. The idea behind FL is to move the training towards the end devices while federating local models and learning, to build a privacy-preserving ML framework by keeping all raw data on devices and aggregating local model updates, while also reducing the communications overhead. The FL process is conformed by several communication rounds between a server and the clients, performed in the following fashion [4; 67]:  
* A number of clients is selected by the server based on certain conditions, such as being idle or its bandwidth limitation, to download the model parameters and use them to initialize their local model.
* Using their local data, each device trains and optimizes the downloaded model. This is done by using stochastic gradient descent, a determined number of minibatch steps, and several epochs in order to increase the update quality and reduce the communications cost.
* When the training is done, clients send their updates towards the server. It is important to notice that some clients might drop out due to connectivity issues, lack of processing power, etc. Nevertheless, the round continues with the received updates. If there are too many dropped out clients, the current round is abandoned.
* The server receives the updates, weights them based on their training set sizes, and finally, aggregates them. A new model is built on the server, and the next round begins.  
Figure 2 shows a simplified flowchart of the previously explained FL process. \(\theta\) represents the global model parameters, \(n_{k}\) corresponds to the data size of the client \(k\), \(K\) is the total number of clients, and \(t\) is the communication round.
###### Abstract  
In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance. With the advancement of deep learning, this paper introduces \"ZWNet\", an end-to-end zero-watermarking scheme that obviates the necessity for specialized knowledge in image features and is exclusively composed of artificial neural networks. The architecture of ZWNet synergistically incorporates ConvNeXt and LK-PAN to augment the extraction of local features while accounting for the global context. A key aspect of ZWNet is its watermark block, as the network head part, which fulfills functions such as feature optimization, identifier output, encryption, and copyright fusion. The training strategy addresses the challenge of simultaneously enhancing robustness and discriminability by producing the same identifier for attacked images and distinct identifiers for different images. Experimental validation of ZWNet's performance has been conducted, demonstrating its robustness with the normalized coefficient of the zero-watermark consistently exceeding 0.97 against rotation, noise, crop, and blur attacks. Regarding discriminability, the Hamming distance of the generated watermarks exceeds 88 for images with the same copyright but different content. Furthermore, the efficiency of watermark generation is affirmed, with an average processing time of 96 ms. These experimental results substantiate the superiority of the proposed scheme over existing zero-watermarking methods.  
2024 14 435 10.3390/app14010435
# Miniaturized Antenna Array-Based Novel Metamaterial Technology for Reconfigurable MIMO Systems
## 3 MTM Analysis and Discussion
### Design Methodology
#### 3.1.3 Mender Line Effects  
The authors introduced a meander line to examine the effects of coupling between the ground plane and the antenna parts. Such coupling is due to the effects of the electrical field fringing from the antenna edges [12]. The effects of such fringing could realize a further loss in the antenna performance, specifically in terms of gain [13]. We decided to apply a parametric study to the proposed meander line by changing the turn number from one turn to five turns concerning S\({}_{11}\) and gain spectra variation. As shown in Figure 7a, it was found that the proposed antenna operates over several frequency bands and they increased with increasing the turn number. Such an observation is due to the fact that the current path can be increased further on a meander line [16]. Therefore, the introduction of the proposed meander line was found to be an excellent candidate to realize a significant enhancement to the antenna size reduction.  
Figure 7: The obtained S\({}_{11}\) and gain spectra for the proposed antenna with changing IDC iteration: (**a**) S11 and (**b**) gain spectra..  
Now, the antenna gain was found to increase when increasing the turn number, as seen in Figure 7b. This realizes a significant enhancement due to the reduction in storing losses. Such a reduction is attributed to the negative effects of the induction part by the mender line on the capacitive behavior of the ground plane concerning the other antenna parts. It is good to mention that the authors did not increase the turn number further, because the effects of increasing the turn number on the antenna performance become very slow after the third turn, and due to antenna size limitations.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 5 Experimental Setup and Evaluation  
To the best of our knowledge, there is no existing open-source library or well-documented method explicitly addressing memory-efficient batching via sliding windows for time series models. Existing approaches typically rely on the default PyTorch tensor-stacking workflow, incurring substantial data replication. Thus, we compare our proposed system against PyTorch's standard data pipeline as the baseline.  
To comprehensively evaluate the efficacy and effectiveness of our zero-copy sliding window techniques in optimizing memory usage for time series models, we conducted a set of experiments. Our experiments are designed to evaluate memory consumption and runtime performance. We evaluated our design implemented in Python (v3.10.16, Python Software Foundation, DE, USA) with PyTorch (v 2.5.1, PyTorch Foundation, CA, USA) using a custom PyTorch dataset, collate_fn, and the custom DataLoader against the default PyTorch data-loading pipeline to provide a comprehensive comparison.
## 3 Results
### Main Results from Studies
#### 3.3.2 Histomorphometric Analysis  
[PERSON] et al. [47] reported the highest mean newly formed bone at 56.5 \(\pm\) 22.2%, showing a steady increase from 26.3% at four months to 56.5% at five months, and ultimately reaching 66.5% at six months. Residual graft percentages declined over time starting from 10.4% and reduced to just 0.9% by the six-month mark, with 40% of the sockets presenting no residual graft after six months.  
[PERSON] et al. [48] observed a mean newly formed bone of 37.6 \(\pm\) 8.9% in the AMD group, the lowest newly formed bone mean out of all four studies, and 48.4 \(\pm\) 11.6% in the ADD group. Regarding residual graft percentages, the AMD group showed 17.1 \(\pm\) 5.6%, while the ADD group exhibited 11.5 \(\pm\) 4.1%.  
In contrast, [PERSON] et al. [23] found a mean newly formed bone of 47.3 \(\pm\) 14.8% in the AMD group, which was significantly higher than the 34.9 \(\pm\) 13.2% reported for the xenograft group (\(p\) < 0.001). The residual graft percentage was also significantly lower in the AMD group (12.2 \(\pm\) 7.7%) compared to the xenograft group (22.1 \(\pm\) 10.9%, \(p\) = 0.001).  
[PERSON] et al. [46] reported a mean newly formed bone of 38.4 \(\pm\) 16.5% and the highest residual graft percentage of 29.9 \(\pm\) 14.4%. Their study also included a detailed analysis of the interaction between bone and dentin particles, with a mean direct contact of 69.1 \(\pm\) 22.8% between new bone and dentin particles.  
Thus, while all studies demonstrate bone formation over time, [PERSON] et al. [47] reported the most favorable outcomes in terms of both bone formation and minimal residual graft, followed by [PERSON] et al.'s [23] AMD group, with [PERSON] et al. [46] presenting the higher residual graft levels and [PERSON] et al. [48] showing the lowest newly bone mean.
# FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN
## 4 FiQ--A Fastener System Inspection and Quantization Method Based on Mask FRCN
### A Quantization Method Based on Cluster Method to Estimate Fastener Hat and Shim Status
#### 4.5.1 Fastener Shift  
In this section, as Figure 5 shows, the shift calculation is performed by selecting the Euclidean distance \(L_{\text{left}}\) between the left fastener cap and the left shim, the Euclidean distance \(L_{\text{right}}\) between the right fastener cap and the right shim, and the Euclidean distance \(L_{\text{hat}}\) between the two fastener caps.  
The calculation process involves clustering the existing dataset to obtain the normal and abnormal center values of these three features. The distance between the feature that needs to be judged and the normal and abnormal center values is calculated, and if the distance is closer to the normal center value, there is no problem. If it is closer to the abnormal center value, the next step of judgment is entered: by calculating the normal and abnormal center values of two fastener caps and shims corresponding to each other twice, it is determined which side of the fastener has shifted. Finally, classification is performed based on the degree of shift to determine if there is a problem.  
The method is shown as Algorithm 1 and fastener shift detection definition is shown as Table 2.
## 3 Preliminaries and System Model
### ECDSA  
The elliptic curve digital signature algorithm (ECDSA) is a simulation of the digital signature algorithm (DSA) based on the elliptic curve cryptography (ECC) algorithm. Let \(q\), \(P\) be the public parameters as discussed above. ECDSA can be divided into the following three steps [33]:  
* \(ECKeyGen(\cdot)\): Select a random integer \(k\) as the private key, where \(k\in[1,q-1]\). Compute the public key \(K=kP\). The entity can obtain the key pair \((k,K)\).
* \(ECSign(M)\): When receiving the message \(M\), the entity first selects a random number \(r\in[1,q-1]\), then generates \(rP\). Set \((x,y)=rP\) and \(R=x\ mod\ q\). Compute \(h=SHA1(M)\), then generate \(S=(h+k\cdot R)r^{-1}\ mod\ q\). Finally, the entity obtains the signature \((R,\ S)\). It is noted that if one of \(R\) or \(S\) equals 0, this algorithm should be rerun.
* \(ECVerify(M,(R,S))\): When the entity receives \(M\) and \((R,S)\), it first checks whether \(R\) and \(S\) are in the range \([1,q-1]\). If yes, the entity calculates \(h^{\prime}=SHA1(M)\), \((x^{\prime},y^{\prime})=h^{\prime}\cdot S^{-1}P+R\cdot S^{-1}K\) and \(R^{\prime}=x^{\prime}\ mod\ q\). If \(R^{\prime}=R\), the entity can accept the signature.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 5 Simulation Study and Results
### Debris-Mass and Compliance Relation
#### 5.2.2 Discussion: The Need for Hybrid Compliance  
The analysis of the diagram in Figure 16 leads to a conclusion of major importance regarding the design of the ADR system itself. When the required minimum contact time is very small, there is indeed an appropriate stiffness coefficient for targeting most debris masses in the desired range of 0-100 kg. However, for more realistic minimum contact times required, none of the stiffness coefficients are adequate; one must be able to modify the equivalent spring's stiffness to target the whole desired range of debris.  
Considering the use case where the system only uses the benefits of passive compliance, the equivalent stiffness would remain constant without any possibility of being tuned. In that regard, the range of debris that can be targeted is consequently constrained. Assuming a passive spring of 0.5 N/m, and the required minimum contact time is 10 s, based on the diagram in Figure 16 and the more detailed visualization of it provided in Figure 20, the range of debris masses that can be targeted is 12-100 kg. Nevertheless, to capture debris of smaller mass, e.g., 4 kg, an equivalent stiffness coefficient of 0.2 N/m would be required for the equivalent spring, as shown in Figure 20. To achieve the tuning of the spring's stiffness from 0.5 N/m to an equivalent spring's stiffness of 0.2 N/m, active compliance should be added, realized by an impedance controller, adding versatility to the system.  
To decrease the equivalent spring stiffness \(k_{m}\) to make it equal to 0.2 N/m, the additional active compliance \(k_{a}\) should be in series with the already-manufactured and integrated passive one \(k_{s}\), with \(k_{s}\) equal to 0.5 N/m in this study. Hence, based on Equation (20) for springs in series, the stiffness coefficient of the active compliance \(k_{a}\) should be equal to 0.333 N/m. Employing this active compliance in the presence of the passive one, the debris of 4 kg can be successfully captured, thus allowing the ADR system to target a wider range of debris than the one targeted by employing only the passive compliance.  
\[k_{m}=\frac{k_{s}k_{a}}{k_{s}+k_{a}}. \tag{20}\]  
Figure 19: Relative position of ADR system’s tip with regard to the debris contact point for the indicative case.  
Figure 20: Detailed visualization of Figure 16 for minimum contact time achieved of 10 s. Range of debris masses for different stiffness coefficients.  
One could wonder why not use only active compliance. For reliability reasons, integrating flexibility with passive compliance at the first impact interface would avoid a hard shock and, thus, avoid pushing away the debris. Moreover, if the debris has an unexpected mass variation, tuning the stiffness brings more reliability and safety, reducing the risk of damaging either the servicer or the debris itself.
## 3 Data Denoising
### Singular Value Decomposition (SVD)  
Singular value decomposition (SVD) is a numerical processing method. According to mathematical linear theory, for any real matrix \(A\in R^{m\times n}\) of arbitrary dimensions with rank r = min(m, n), there exist orthogonal matrices \(U\in R^{m\times n}\) and \(V\in R^{m\times n}\) such that the following equation holds:  
\[A=USV^{T} \tag{13}\]  
In the equation, A is the matrix to be decomposed, \(U=(u_{1},u_{2},...,u_{m})\in R^{m\times n}\), and\(u_{i}\) are the column vectors of U, forming the left singular matrix; \(V=(v_{1},v_{2},...,v_{m})\in R^{m\times n}\), and \(v_{i}\) are the column vectors of V, known as the right singular matrix; S is the diagonal matrix composed of singular values. \(S=\begin{bmatrix}S_{1}&0\\ 0&0\end{bmatrix}\), \(S_{1}=diag(\sigma_{1},\sigma_{2},...,\sigma_{r})\), and \(\sigma_{1}\geq\sigma_{2}\geq...\geq\sigma_{r}>0\). Singular values have good stability, scale invariance, and rotation invariance, and they are intrinsic features of the matrix.
## 2 Materials and Methods
### Data Processing  
For the marker trajectories and GRF data, the measurements of at least 20 steps (15 s) were recorded intermittently every 2 min during the fatiguing run [22]. The sagittal plane kinematic data of the dominant lower extremity, defined as the preferred kicking leg, were filtered through a Butterworth fourth-order, low-pass filter at a cut-off frequency of 14 Hz with V3D software (v5, C-Motion Inc.,  
Figure 1: Marker set and experimental setup.  
Germantown, MD, USA) [27]. The kinematic variables of the hip, knee and ankle joints included the following: (1) joint angles (\(\theta_{0}\)) at initial contact (Figure 2), (2) maximum joint extension/dorsiflexion angles (\(\theta_{max\_ext}\)) and peak joint flexion/plantar flexion angles (\(\theta_{max\_fix}\)) during stance, (3) joint ranges of motion (\(\theta_{\text{ROM}}\), \(\theta_{\text{ROM}}=\theta_{max\_ext}-\theta_{max\_fix}\)) during stance, (4) changes in joint angle (\(\Delta\theta\), \(\theta_{max\_flex}-\theta_{0}\)), and (5) maximum extension/plantarflexion angular velocity (\(\omega_{max\_ext}\)) during the stance.  
GRF variables included (1) first and second peak vertical GRFs (F\({}_{\text{zmax1}}\) and F\({}_{\text{zmax2}}\)) and the occurrence time of F\({}_{\text{zmax1}}\) and F\({}_{\text{zmax2}}\) (t\(\_\)F\({}_{\text{zmax1}}\) and t\(\_\)F\({}_{\text{zmax2}}\)), (2) maximum and average loading rates (LR\({}_{\text{max}}\) and LR\({}_{\text{avg}}\); Figure 2), (3) contact time (CT) and (4) maximum propulsive and braking GRF (F\({}_{\text{ymax}}\) and F\({}_{\text{ymin}}\)). Loading rate was calculated on the basis of the method described by [PERSON] et al. [28]. In brief, a point of interest (POI) was defined as the first point above 75% of a participant's body weight with the instantaneous loading rate less than 15 body weight/s. LR\({}_{\text{max}}\) (i.e., the maximum instantaneous slope) and LR\({}_{\text{avg}}\) (the average slope) were calculated from 20% to 100% and from 20% to 80% of the force at the POI, respectively (Figure 2).  
Kinetic variables included the peak moments and joint stiffness [29] [\(k_{j}\), Equation (1)] of hip, knee and ankle the vertical stiffness of the lower extremity [30] [\(k_{\text{wert}}\), Equation (2)], and they were expressed as follows:  
\[k_{j}=\frac{\Delta M}{\Delta\theta} \tag{1}\]  
where \(\Delta M\) is the joint moment difference between initial contact and mid-stance, and \(\Delta\theta\) is the joint angle difference between initial contact and mid-stance.  
\[k_{\text{wert}}=\frac{GRF_{i}}{\Delta y} \tag{2}\]  
where \(GRF_{i}\) is the vertical ground reaction force at the lowest position of the center of gravity (CoG), and \(\Delta y\) is the maximum vertical displacement of CoG.
## 2 Study Objective  
The objective of this study is to develop an ANN-based real-time mode detection methodology that utilizes spatial analysis techniques and improves mode detection accuracies that would support various ITS applications in transportation geoinformatics. This paper assesses the feasibility of using proposed mode availability GIS layers for enhancing performances of transportation mode detection ANNs with data from smartphone sensors in Dubai. The GIS layers contain spatial information of available modes of transportation in a 2D space on a map of the study region. The developed ANNs are tested with three different experimental scenarios with varying choice sets of transportation modes, monitoring duration, device querying frequency, data collection time periods, and routes. The experimental scenarios are:1. Universal mode detection with a mode availability layer;
2. Auto mode detection with mode availability layers;
3. Peak (rush hour) versus non-peak (non-rush hour) period comparisons.  
In all scenarios, performance of the newly-proposed method with mode availability GIS layers are compared with existing methods using conventional GPS data loggers and smartphone sensors.