esa-sceva/satcom-chunk-collection huggingface dataset:
# The Varieties of Agency in Human-Smart Device Relationships: The Four Agency Profiles
## 2 Materials and Methods  
### Toward Developing a Scale for Agency in Human and Smart Device Relationships  
In this paper, we propose a preliminary conceptualization of agency in human-smart device interactions comprising both user agency and device agency. User agency is here conceptualized as a dimension, where at the one end is low agency--a feeling of not mastering one's device optimally--and at the other end is high agency--a sense of mastery of one's device. In a similar manner, device agency is a dimension that evolves from low agency--the device does not have the capacities to do things that are meaningful for the user's goals--to high agency--the device has its own relevant action capacities that can help the user to reach their goals. We will later explicate the literature behind our conceptualizations, and for a quick reference, the reader is advised to look at Table 2.  
We propose that the dimensions of user agency and device agency are independent. This means that a person can be simultaneously high in both or, likewise, low in both these dimensions. In this way, we obtain a matrix of four different ways the user can relate to their device (Figure 1). These user-device dynamics have been named controller, _collaborator_, _detached_, and _victim_. In our theoretical model, a _controller_ is a user who attributes agency to themselves but less to the device. A controller sees oneself as a capable user and the device either as not having any independent capabilities outside the user's control or as completely unable to do things.
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 2 Related Work and Research Motivation
### Related Work
In [9], the random mobility method can be enhanced by sharing reconnaissance information with others. This model uses the pheromones of ants in order to induce a UAV to the area with less pheromone distributions [9]. The pheromone distribution of a UAV is defined by the stored information about paths routed by other UAVs and itself in a bounded region at the current position. UAVs broadcast their own pheromone distributions to other UAVs within a range of communication. When a UAV receives the other UAV's pheromone distributions, it updates its pheromone distributions with the received distribution. Then, each UAV decides its new direction based on the current pheromone distribution in such a way that it avoids the regions which are routed more. If there is no pheromone distribution, a UAV moves based on Markov Process model. Other models using pheromones include H3 MP (Hybrid Markov Mobility Model with Pheromones) [3], Multiple Pheromone UAV Mobility Model [4], etc. H3 MP is proposed on a patrol-surveillance scenario for maximizing target detection in a realistic area which is not square. They use the \(k\)-mean algorithm to divide the area into the similar size. For movement of UAVs, they define the Destination state and the Move state. The Destination state is to choose a destination with transitions probabilities in finite-state machines and the Move state is to search the area based on pheromone. Multiple Pheromone UAV Mobility Model is a reconnaissance model based on the pheromone. It has two kinds of pheromones: repulsive and attractive. Repulsive pheromone is used for searching the area in an ant colony fashion to avoid places which are visited recently. On the other hand, attractive pheromone is used for following a target or to gather surrounding UAVs for tracking a target.
## 3 Metabolomics Applied to Marine Organisms in Cancer Studies
### Structure Elucidation of Bioactive Molecules from Algae
Moreover, this extract exhibited the highest growth inhibition by testing the 12.5 mg/mL concentration against L20 B (32.5%) and against MCF7 (71.5%). These percentages increased after 48 h application (35.5% against L20 B and 78% against MCF7). Other studies, rather than testing the entire extract from microalgae, worked on different fractions obtained with distinct methods. [PERSON] et al. [127] extracted carotenoid and polar fractions from both _Haematococcus pluvialis_ and _Dunaliella salina_ and tested them on HePG2, MCF7, HCT116, and A549 cancer cell lines. Moreover, carotenoids of _H. pluvialis_ identified using LC-MS showed high cytotoxic activity against the HCT116 (100% inhibition at 0.1 mg/mL) and mild activity against both MCF7 and HePG2 lines, while the carotenoid-rich fraction of _D. salina_ showed moderate cytotoxic activity on the MCF7 and HePG2 cancer cell lines. [PERSON] et al. [128] obtained hydrophilic and lipophilic fractions from the diatoms _Phaodactylum tricornutum_ and _Staurosirela pinnata_ that were analysed with \({}^{1}\)H-NMR. They performed a bioactivity assay on human immortalized keratinocytes HaCaT and human melanoma CHL-1 cell lines in a 24 h dose-response test with different concentrations (0.2, 0.4, 0.8, 1.6, 3.2 and 10.0 mg/mL). The most important result showed that _S. pinnata_ extract had an important dose-dependent effect on CHL-1. Extracts and compounds from the macro- and micro-algae analysed in this section are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline
**Algae** & **Compound/Extract** & **Cell Lines** & **Metabolomics** & **Reference** \\ \hline _U. lactuca_ & Ulvan & HepG2, MCF7 and Hela & \({}^{1}\)H-NMR-MS & [114] \\ \hline _U. fascita, U. lactuca, A. anceps, C. mediterranean_ & Organic extract & MCF7 and HTC116 & GC-MS & [115] \\ and _S. filipendula_ & & & & \\ \hline _F. vesiculosus_ & Hydrophilic extract & Panc1, PancTU1, Panc89 & & \\  & & and Colo 357 & & \\ \hline _F. vesiculosus_ & Crude extracts & Panc89 and PancTU1 & \({}^{1}\)H-NMR & [117] \\ \hline _C. okamuranus_ &
\begin{tabular}{c} Fucoxanthin and \\ fucoxanthinol \\ \end{tabular} & Saos-2, MNNG/HOS, 143 & & \\  & & B and LM8 & & \\ \hline \end{tabular}
\end{table} 
Table 2: Algal species, compound or extract, cell lines tested, metabolomics techniques and corresponding reference are reported.
# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 8 Conclusions and Future Work  
This paper proposes a wind power forecasting scheme and a demand management strategy. To take part in the daily market that regulates the supply and demand in the Maine micro grid, a new demand management scheme is proposed that makes use of big data-driven wind power forecasting. The effective demand management is subject to the forecasting accuracy. A deep-learning technique EDCNN is developed to accurately predict the day-ahead hourly wind power on the Maine wind farm data. The numeric results validate the efficiency of the proposed model for wind power forecasting. The proposed DSM algorithm normally distributes the load. The results prove that the proposed DSM method successfully distribute the load, making load profile almost normally distributed. Moreover, the proposed DSM algorithm effectively reduces the consumption cost.  
In the future work, shorter forecasting times will be considered, e.g., 12 h ahead. The day will be divided into daytime and nighttime to determine the impact of current conditions on wind power forecasting.  
Figure 6: Effect of proposed DSM scheme on load profile.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Consumption** & **Cost / Day (\$)** & **Reduction / Day** \\ \hline
**Season** & **Before DSM** & **After DSM** & **Amount (\$)** & **Percentage** \\ \hline
**Spring** & 483,330 & 475,170 & 8153\$ & 1.7\% \\
**Summer** & 793,930.5 & 784,403 & 7527\$ & 1.2\% \\
**Autumnumnumnumnumn{1}{c}{} & 417,980.5 & 413,770.5 & 4210\$ & 1\% \\
**Winter** & 3,347,106 & 3,305,006 & 42,109\$ & 1.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Energy consumption cost reduction by the proposed DSM algorithm.
## 4 Experimental Results and Analysis
### Analysis of Experimental Results  
3.1 Analysis of Correlation between ASE Content and Oil Leakage Stability by CA Average Particle Size of BEM  
The analysis of correlation between ASE content and oil leakage stability by CA average particle size of BEM found that in relation to oil leakage stability at 40 \({}^{\circ}\)C, oil  
Figure 10: Graph of oil leakage stability test results (40 \({}^{\circ}\)C).  
Figure 9: Graph of oil leakage stability test results (20 \({}^{\circ}\)C).  
leakage occurred in some of the samples confirmed to have achieved oil leakage stability at 20 \({}^{\circ}\)C, and oil leakage stability was improved as the CA average particle size decreased, or the ASE content increased as in the temperature of 20 \({}^{\circ}\)C. It was also found that in a temperature environment at 20 \({}^{\circ}\)C, the CA average particle sizes of 1.0 \(\upmu\)m and 1.4 \(\upmu\)m have oil leakage stability regardless of an increase in the ASE content, but in a temperature environment at 40 \({}^{\circ}\)C, the stability of oil leakage was achieved only when the ASE content was added at least twice the standard. In addition, it was confirmed that in the CA average particle sizes of 2.0 \(\upmu\)m and 5.0 \(\upmu\)m, more than 4 times the ASE content should be added to ensure oil leakage stability. In the CA average particle size of 16.0 \(\upmu\)m, oil leakage stability was achieved when more than 5 times the ASE content was incorporated at a temperature of 20 \({}^{\circ}\)C. However, at a temperature of 40 \({}^{\circ}\)C, oil leakage decreased in all specimens. To help understand these results more intuitively, Figure 11 shows the correlation between ASE content and oil leakage stability by CA average particle size of BEM. In Figure 11, red indicates that oil leakage occurs at 20 \({}^{\circ}\)C, while yellow indicates that oil leakage does not occur at 20 \({}^{\circ}\)C, but oil leakage occurs at 40 \({}^{\circ}\)C. Blue indicates that oil leakage stability is achieved because no oil leakage occurs at both 20 \({}^{\circ}\)C and 40 \({}^{\circ}\)C. The analysis of color distribution found that the oil leakage stability is significantly improved as the CA average particle size decreases, and the ASE content increases.
# On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel
## 3 Simulation Results
### End-to-End Performance in Grant-Based Random Access
For the error of the second phase, we use the finite block normal approximation of Polyanskiy-Poor-Verdu ([72], Theorem 54):  
\[\frac{B-J}{n^{\prime}}\,\approx\,C(P)-\sqrt{\frac{V(P)}{n^{\prime}}}Q^{-1}( \varepsilon_{2})\,, \tag{7}\]  
where \(n^{\prime}P\) is the total energy per user, \(C(P)=\frac{1}{2}\lg_{2}(1+P)\) is the AWGN capacity and \(V(P)=\frac{P(P+2)}{2(P+1)^{2}}(\lg_{2}(e))^{2}\) is the AWGN channel dispersion. Given a target error probability \(\varepsilon_{2}\), we can solve (7) with an equality to obtain an achievability estimate \(P^{*}\) on the power \(P\) necessary to attain user-basestation point-to-point error probability at most \(\varepsilon_{2}\). The total energy per transmitted bit (per user) is just  
\[E_{b}/N_{0}=\frac{\frac{1}{2}n^{\prime}P^{*}+J\cdot(E_{b}/N_{0})_{phase1}}{B}\,,\]  
where, as in the previous section, \((E_{b}/N_{0})_{phase1}=\frac{E_{m}}{2\sigma^{2}\cdot 2}\), \(E_{m}\) being the energy of a column of \(A\). For every \(k\), we wanted to find the smallest \(E_{b}/N_{0}\) that achieves total per-user error \(\varepsilon_{1}+\varepsilon_{2}=0.05\). This optimization have has performed numerically. The performance attained by this end-to-end scheme is plotted in Figure 3. We plot the performance corresponding to Phase 1 implemented by the sensing matrix and recovery algorithm introduced in this paper, as well as an i.i.d. Gaussian sensing matrix and AMP recovery. Both implementations for Phase 1 correspond to similar performance, with slight preference for the latter, and substantially improve the state-of-the-art for unsourced random access with (a small amount of) feedback [12].
# Application of Machine Learning Methods for Pallet Loading Problem
## 4 Application and Dataset Description
### Implementation Details  
All the ML models were trained and tested on a uniform dataset; 10-fold cross-validation was used for the 1110 data points for which the algorithm can be applied. Since we were dealing with an imbalanced data set, we first split the data into 10 folds, with 9 folds used for training and the remaining fold used for testing. Since we were dealing with a limited amount of data, we used an oversampling technique called Synthetic Minority Over-Sampling Technique (SMOTE) on the testing dataset in order to address the imbalanced class problem by oversampling the under-represented classes [64]. We used the SMOTE technique implemented in the Python imblearn package [65]. We computed the required metrics using the Python packages scikit-learn [66] and PyCM [60]. For each metric, the average result over 10 rounds of cross-validation is reported. The metrics were computed according to Equations (4)-(14).  
All classification models were implemented in Python 3.8 and were executed for training and testing on the given dataset on a computer with Intel i7 2.4 GHz CPU and 8 GB RAM.  
The classification models used in this study required a number of parameters for the tuning before the actual training phase was initiated. These parameters for each classifier are described in detail as follows. The values of these standard parameters were optimized in order to acquire better results during the training and testing phases.  
For the ANN classifier, since there are three target classes (P1, P2 and P3 pallets) and four inputs (box width, length, height value and demand), a network structure with four inputs and three outputs was created, and two hidden layers each containing 10 neurons were utilized. For the activation function, sigmoid was used. Moreover, the \"adam\" solver was chosen, with max_iter = 10,000. The random state was set to 0. For the DTC, the parameters max depth = none, min samples split = 2, random state = none, and min_samples_leaf = 1 were selected. For the KNN classifier, the parameters n_neighbors = 5, weights = \"uniform\", and algorithm = \"auto\" were chosen. For the Random Forest classifier, the parameters n_estimators = 100, max_depth = none, min_samples_leaf = 1, and random_state = none were used. For the SVM classifier, the parameters kernel = \"rbf\", degree = 3, gamma = \"scale\", coef0 = 0.0, cache_size = 200, class_weight = none, verbose = false, max_iter = -1, and random_state = none were chosen.
## 1 Introduction
used a skip connection within a building block of residual network (ResNet) [7]. The MF for the skip connection is _fixed_ as 1 for identity connection. One building block is composed of two convolution layers and two ReLU operations. These building blocks are concatenated serially for the whole ResNet. Hence, the data processed by a building block are transferred to the next building block, but the original input data are not transferred to most building blocks. ResNet was developed for image recognition [7]. In [8], skip connections are applied for residual function blocks. There is a _fixed_ scaling (multiplication) factor for each residual function block. The output of residual function is multiplied by this scaling factor, whereas the input is not multiplied by this scaling factor. The data processed by the residual function are added to the input data, and the added data are transferred to the next residual function block. Hence, the original input data are not connected directly to the output of most residual function blocks. The structure in [8] was developed for selective pruning of the unimportant parts of CNNs rather than image reconstruction. Figure 1: VDSR network consisting of one layer group with one skip connection (\(l=20,\lambda=1.0\)). In this paper, we propose a deep learning method with CNNs for reconstructing super-resolution images. The proposed method divides entire layers into several layer groups with skip connections. The proposed method is designed to resolve the problem of gradient vanishing by reducing the extinction of data in hidden layer units through skip connections from the input data to layer groups. Experimental results show that the proposed method yields better results than previous methods for super-resolution image reconstruction.
## 3 Mathematical Model and Solution Algorithm
### Model Construction  
#### 3.2.1 Objective Function  
This study aims to minimize both passenger travel costs and enterprise operating costs. Using the linear weighting method, the weight coefficient of passenger travel time cost is set to \(\mu\), transforming it into a single-objective problem, as shown in Equation (1).  
\[minZ=\mu Z_{1}+(1-\mu)(Z_{2}+Z_{3}) \tag{1}\]  
(1) Passenger travel costs  
In this study, we assume passengers opt solely for direct trains, eliminating any transfer behavior (i.e., transfer time is 0). Furthermore, the train follows an all-stop plan with fixed stop and section running times, ensuring passengers' travel times remains constant. Consequently, the passenger's travel cost only encompasses the waiting time cost, as illustrated in Equation (2).  
\[\begin{array}{ll}Z_{1}=&c_{1}\cdot\sum\limits_{i=1}^{I(u)}\sum\limits_{s=1}^{ S-1}\sum\limits_{s^{\prime}=S+1}^{N}\sum\limits_{t=1}^{T}L_{i}^{ss^{\prime}}(t) \cdot\lambda_{i,s}\cdot\lambda_{i,s^{\prime}}\cdot T_{i}\cdot P^{s,s^{\prime}}( t)\cdot(d_{i,s}-t)+\\ &c_{1}\cdot\sum\limits_{i=1}^{I(d)}\sum\limits_{s=N+1}^{S-1}\sum\limits_{s^{ \prime}=S+1}^{2N}\sum\limits_{t=1}^{T}L_{i}^{s,s^{\prime}}(t)\cdot\lambda_{i,s }\cdot\lambda_{i,s^{\prime}}\cdot T_{i}\cdot P^{s,s^{\prime}}(t)\cdot(d_{i,s}- t)\end{array} \tag{2}\]  
Enterprise operating costs mainly consider train purchase costs and travel kilometer costs. To ensure comparability with other cost components, the purchase cost of urban rail transit trains, with an average service life of 30 years, is evenly spread over the research period. This cost is determined by factors such as the actual number of train units in operation, the average purchase cost per unit time, and the spare train ratio, as shown in Equation (3). Additionally, the cost per kilometer of operation is contingent upon the total distance covered by all trains throughout the research period, as shown in Equation (4).  
\[Z_{2}=\theta\cdot c_{2}\cdot T\big{(}\sum T_{i}-\sum Y_{ji}+\sum T_{j}-\sum Y_{ i,j}\big{)},\forall i\in I^{(u)},j\in I^{(d)} \tag{3}\]  
\[Z_{3}=c_{3}\cdot\Big{(}\big{(}\sum\theta_{i}+\sum\theta_{j}\big{)}\cdot L_{1}+ \big{(}\sum T_{i}-\sum\theta_{i}+\sum T_{j}-\sum\theta_{i}\big{)},\forall i\in I ^{(u)},j\in I^{(d)} \tag{4}\]
## 4 Sorting Out Feasible Solutions
### Displacement Analysis
#### 4.2.1 Watt II Six-Bar Slider-Crank Mechanism  
In order to analyze the slider displacement of the Watt II slider-crank mechanism, the position of the joint \(E\) in Figure 1 needs to be determined. To find the coordinates of \(E\), the position of joint \(D\) can be expressed as  
\[\mathbf{D}=\left[\begin{array}{c}D_{x}\\ D_{y}\end{array}\right]=\left[\begin{array}{c}r_{0}\cos\eta\\ r_{0}\sin\eta\end{array}\right]+\left[\begin{array}{c}r_{4}\cos(\alpha+ \beta)\\ r_{4}\sin(\alpha+\beta)\end{array}\right], \tag{48}\]  
where \(\alpha\) is calculated from Equation (45), and \(\beta\) is the angle between link \(r_{3}\) and link \(r_{4}\) that can be determined using the solution of the synthesis equations as  
\[\beta=\tan^{-1}\biggl{(}\frac{r_{4y}}{r_{4x}}\biggr{)}-\tan^{-1}\biggl{(} \frac{r_{3y}}{r_{3x}}\biggr{)}. \tag{49}\]  
Now, the position of the moving pivot \(E\) is  
\[\mathbf{E}=\left[\begin{array}{c}E_{x}\\ E_{y}\end{array}\right]=\left[\begin{array}{c}h\\ D_{y}\pm\sqrt{r_{5}^{2}-(h-D_{x})^{2}}\end{array}\right], \tag{50}\]  
where  
\[h=r_{0}\cos\eta+r_{4x}-r_{5x}. \tag{51}\]  
Equation (50) indicates that there are two solutions for the position of the joint \(E\). That is, the slider and link \(r_{5}\) can be assembled above (with the positive sign: \(E_{y}^{+}\)) or below (with the negative sign: \(E_{y}^{-}\)) the moving pivot \(D\). As mentioned in Section 4.1, however, since the result of this research gives the orientation of each link in its initial position, the sign to be used in Equation (50) and the assembly mode of a synthesized mechanism can be identified.
# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 1 Introduction  
Cyber Physical Systems (CPS) are a new generation of systems that play a key role in interconnecting the physical and virtual worlds. Recently, vast investments have been made globally in the development of CPS, as they have been identified as key technologies that can boost the deployment of the Fourth Industrial Revolution (4 IR). It is expected that CPS will pave the way for solutions to key economic and societal challenges, such as: Coping with an ageing population, improving issues of health and public safety, planning for mega-cities, achieving sustainability and globalisation, as well as profering solutions to mobility challenges. Similarly, Internet-of-Things (IoT) principles are finding their way into next generation CPS, enabling extended interactive functionality between the physical and virtual environments through \"sensing\" and \"actuation\".  
By combining CPS and IoT, we gain the ability to infuse computing and communication capabilities into the dynamics of physical and engineered systems. This combination is expected to provide different ways of interacting and manipulating physical systems, through seamless network connectivity and refined user control over the actuation side. CPS are key 4 IR components whose dependability is paramount to the operation of today's critical infrastructures. As described in [1], the attributes of a dependable system include: Availability--readiness for correct service, reliability--continuity of correct service, safety--absence of catastrophic consequences on the user(s) and the environment,integrity--absence of improper system alteration, and maintainability--ease of maintenance (repair). CPS dependability is therefore among one of the most desirable characteristics of future CPS.