esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### The Case Study  
With its approximately 24,000 square kilometers, Sardinia is the second-largest island in the Mediterranean Sea; its population density is rather low (slightly above 68 people per square kilometer), as it counts less than 1.7 million inhabitants according to the 2011 National Census. The vast majority of Sardinia's live close to the sea and especially in the two densely urbanized areas around the two major cities (Cagliari and Sassari), while the inner areas of the island, hilly and mostly badly connected to the rest of the island, are sparsely populated. This uneven concentration of population parallels the island's unbalanced economic development, as in coastal areas, the majority of economic activities takes place.  
It is therefore not surprising, given this context, that the Regional Landscape Plan of Sardinia (RLP), the first statutory landscape plan with regional dimensions produced in Italy under the new legislation, focused on the coastal zone, because of the complexity of the development conflicts arising from tourism (on which a large part of the economy of the island relies) and other development and owing to the fact that thirteen out of the fourteen previous landscape plans covering coastal areas, which contained some restrictions on coastal development, had been quashed in a two-step, five-year long process (1998-2003), some by the President of the Italian Republic and the remaining by the regional administrative court.  
Following approval of the plan in 2006, restrictions and prohibitions (on the development of land and on certain changes in land uses) stemming from the plan are currently in force, in order to protect a part of the island considered economically strategic and environmentally sensitive. Restrictions and prohibitions are set out by the plan by means of a system of rules.  
The planning activity of the regional administration of Sardinia has undergone a deep change after the approval of the RLP, which establishes the directions for nearly any future planning activity in Sardinia and requires that actual sectoral and local plans, as well as plans for protected areas, be changed to comply with its directions.  
The process of adjustment of the municipal master plans (MMPs) of the cities of the coastal strip to the RLP is quite complicated. The MMPs have to conform themselves to the descriptive, prescriptive and propositive contents of the RLP, to the general planning rules and directives established by the RLP for the coastal zones and to the strategic policies for the \"conservation and protection, maintenance, improvement or restoration of the landscape values identified in the landscape units\" [3].  
The question of the adaptation process of the actual MMPs to the RLP is of paramount importance for the effectiveness of the new planning policies of the Sardinian regional administration and for the definition of a general model for the strategic assessment of city planning based on the analysis of the perceived needs and expectations of local communities, through sustainable-development-oriented governance processes [4].
## 3 Results and Discussion
### Volatile Compounds
#### 3.2.1 LOX-Derived Volatile Compounds
Values for the C5 LOX compounds were generally lower in the Organic oils than in the Conventional ones, although in any case the Tukey test revealed only slight differences, especially on week 1. Different trends were found for important C6 compounds: (Z)-hex-3-en-1-ol tended to be more abundant in the Organic oils than in the Conventional ones (differences were significant in week 1, 4 and 6), whereas (Z)-hex-3-enal (weeks 1, 5 and 6), hexanal (weeks 3, 4, 5, 6) and (E)- hex-2-enal (weeks 1, 3, 4, 5, 6) showed the opposite trend. To date, the effect of the organic practices on the volatile compounds has been scarcely studied, and results are not completely consistent. In this sense, our results, from unirrigated Verdial de Badajoz orchards, show a similar trend for hexanal to results for oil from the Leccino and Frantoio cultivars also formed in unirrigated orchards, although for the other compounds no clear trends were reported [9]. Conversely, higher abundances in hexanal in Organic than in Conventional oils from Picaual and Hojiblanca olives from irrigated orchards have been also reported [13]. Therefore, our data might confirm that there is an effect of the organic production on some compounds, such as hexanal, but this effect might depend on other factors, such as irrigation or the olive cultivar. According to the ANOVA results (Table 2), the effect of the harvesting time was significant on five out of the six C5 LOX compounds and six out of the seven C6 ones included in Table 3. It affected all the oil groups to a similar extent (Table 3). The effect was stronger on these compounds than on the chemical and physical-chemical parameters (Table 1). Most C5 LOX compounds fluctuated over time, without a consistent trend, although pentan-3-one and pent-1-en-3-ol decreased significantly as the season went on (Table 3). A similar pattern was reported for C5 LOX compounds in Arbequina and Chettoui olive oils [4]. A general decrease was also found for the C6 compounds over time, especially for (Z)-hex-3-enal, (E)-hex-2-enal and (E)-hex-2-en-1-ol (Table 3), which are related to positive flavour traits [10]. These results for Verdial de Badajoz olive oil match previous results on other cultivars [4; 29], although it has been pointed out that the decrease in C6 LOX compounds might not affect all cultivars [3]. The decrease over time was more marked in the Ground oils, which might indicate that harvesting late would add to the detrimental effect of harvesting from the ground.
# Online Optimization of Pickup and Delivery Problem Considering Feasibility
## 2 Pickup and Delivery Problem
### Preliminaries  
We introduce some notations.  
First, the target area in the pickup and delivery problem is given by the following undirected graph:  
\[\mathcal{G}=(\mathcal{V},\mathcal{E}),\]  
where there are the following:  
* \(\mathcal{V}=\mathcal{S}\cup\mathcal{C}\cup\mathcal{R}\): the set of vertices (\(\mathcal{S}\cap\mathcal{C}\cap\mathcal{R}=\emptyset\));
* \(\mathcal{S}=\{s_{1},s_{2},\ldots,s_{N}\}\): the set of stores;
* \(\mathcal{C}=\{c_{1},c_{2},\ldots,c_{M}\}\): the set of customers;
* \(\mathcal{R}=\{r_{1},r_{2},\ldots,r_{I}\}\): the set of intermediate points between vertices;
* \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\): the set of edges.  
Without the loss of generality, the set \(\mathcal{V}\) can be represented by  
\[\mathcal{V}=\{\underbrace{1,2,\ldots,N}_{\mathcal{S}},\underbrace{N+1,N+2, \ldots,N+M}_{\mathcal{C}},\underbrace{N+M+1,\ldots,N+M+J}_{\mathcal{R}}\}. \tag{1}\]  
Let \(\mathcal{A}=[a_{ij}]\in\{0,1\}^{(N+M+J)\times(N+M+J)}\) denote the adjacency matrix of \(\mathcal{G}\), where if there is the edge between \(v_{i}\) and \(v_{j}\), then \(a_{ij}=1\); otherwise, \(a_{ij}=0\). An example of an undirected graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\) is shown in Figure 1. We suppose that according to a given graph, an agent moves from some vertex to other one in unit time.  
Next, notations about orders are introduced. We assume that new orders are received sequentially. Let \(\tau_{i}\), \(i=0,1,2,\ldots\) denote the time that the delivery route may be changed. The initial update time \(\tau_{0}\) is set as \(\tau_{0}=0\). We assume that at the update time \(\tau_{i}\), there is at least one new order. Let \(\mathcal{O}^{i}\) denote the set of orders that are newly added at time \(t=\tau_{i}\). The element \(o_{k}\) of \(\mathcal{O}^{i}\) is given by the following form:  
\[o_{k}=(s_{k},c_{k},T^{s}_{o_{k}},T^{c}_{o_{k}}), \tag{2}\]  
where there are the following:  
* \(s_{k}\in\mathcal{S}\): the store that received the order;
* \(c_{k}\in\mathcal{C}\): the customer who placed the order;* \(T_{\theta_{k}}^{s}\): the time that the goods are available for pickup;
* \(T_{\theta_{k}}^{c}\): the time limit to complete the delivery.
## 3 System Model
### Uniform Linear and Mesh Arrays  
Beamforming is achieved by applying phase shift to each element in the array such that its mainbeam points towards the desired direction. Assume far-field conditions, where the information regarding the locations of collaborative sensors are shared, then the resultant array factor when the beams from collaborative sensors are directed towards a base station with azimuthal angle \(\phi\in[-\pi,\pi]\) away from the cluster head is given as follows:  
\[AF^{\kappa}(\phi/r,\omega)\triangleq\sum_{K=1}^{N}\omega_{\kappa}\exp^{-\frac{ 2\pi}{\Lambda}r_{\kappa}[\cos(\theta-\hat{p}_{C})]} \tag{6}\]  
Figure 3: Sensor (A) and sensor (B) can communicate with each other, sensor (C) can communicate with sensor (A). Sensor (B) might connect to sensor (D).  
Figure 2: Gain Patterns.  
where \(\omega=[\omega_{1},\omega_{2},\ldots,\omega_{N}]\), and \(\omega_{\kappa}\) is \(x\)th sensor's transmission weight, defined as  
\[\omega_{\kappa}=\xi_{\kappa}\exp^{\mu_{\kappa}} \tag{7}\]  
where \(\omega_{\kappa}\) is the amplitude and \(\alpha_{\kappa}\) is the initial phase of a \(x\)th collaborating sensor is \(\alpha_{\kappa}=\frac{2\pi}{\lambda}r_{\kappa}[\cos(\phi-\psi_{\kappa})]\). For ULA, each element in the array is placed along with distance \(d\) between adjacent elements. The array factor can be expressed as:  
\[AF^{\kappa}(\phi/r,\theta)\triangleq\frac{\sin(\frac{N\psi(\phi)}{2})}{\sin( \frac{\psi(\phi)}{2})} \tag{8}\]  
where \(\psi(\phi)\) is the phase difference between adjacent elements in the direction \(\phi\), which is related to \(d\) by \(\psi(\phi)=\frac{2\Pi}{\lambda}\cos(\phi)+\delta\), where \(\delta\) is the progressive phase shift of adjacent elements, due to the physical deployment of elements. Therefore, the angle of mainbeam is \(\delta=-\frac{2\Pi d}{\lambda}\cos(\phi_{0})\).  
Substituting Equation (8) into Equation (5), we can calculate the antenna gain for any azimuthal angle for ULA. Meanwhile, for UMA the antenna gain can be calculated for any azimuthal angle \(\phi\), by substituting Equation (6) into Equation (5).
# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 3 DR-Net and CTAM
### Channel-Time Attention Mechanism (CTAM)  
An attention mechanism is introduced between two residual blocks to capture the key feature information of the crew behavior, in order to reduce the influence of background noise and enhance the performance of the model. We analyzed the image features and found that the crew overboard behavior has different feature representations at different time steps. We expand the Channel Attention Mechanism (CAM) from 2D to 3D and introduce the time dimension, which constitutes the Channel Temporal Attention Mechanism (CTAM). The CTAM can effectively learn the key features at each time step, flexibly adjust the channel weights, and pay more attention to the crew behavioral characteristics at different time steps.  
Figure 4 shows a sketch of the Channel-Time Attention Mechanism (CTAM). CTAM compresses the spatial dimensions of features by maximum pooling and average pooling, aggregating channel and time dimension features. The compressed features are learned by a multilayer perceptron to obtain two channel feature descriptions containing the time dimension. These two feature descriptions are then added and a weight vector is obtained by a sigmoid activation function. The application of the weight vector to each channel at every time step of the input feature map enables a weighted summation of channel features. This adjustment enables the model to fine-tune the importance of different channels at different time steps, which facilitates a more effective focus on features crucial to the current task. The introduction of CTAM reduces the interference of background information and improves the feature extraction capability of the model, which helps the model to focus more on the behavior of the crew.  
Figure 4: Channel-Time Attention Mechanism.  
Figure 3: Residual blocks.
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 3 CNN-NLP Architecture Design for LSB Steganography Detection
### CNN Architecture
For this layer, a dropout process with a rate of 0.5 is applied, which helps reduce overfitting by preventing the co-adaptation of neurons during training. Finally, the result passes through the \"Softmax\" layer, which transforms the vector of raw outputs (logits) into a probability distribution according to the two classes present in this model. This results in a probability vector (see Equation (10)) associated with an input image belonging to class \(c\) where \(\hat{y}_{c}\) represents the probability of belonging to each class, _With-Steganography_ or _Without-Steganography_, and \(z_{c}\) is the value of the _Logit_ function (output without activating for class \(c\)). \[\hat{y}_{c}=\frac{e^{z_{c}}}{\sum\limits_{j=1}^{2}e^{z_{j}}},\text{ for }c\in\{1,2\} \tag{10}\]  
Then, from the resulting probability, \(\hat{y}\) is compared with the true label, and the weighted cross-entropy loss function is used to quantify the error committed. The loss function is expressed in Equation (11):\[L=-\sum_{c=1}^{2}w_{c}y_{c}\log(\hat{g}_{c}) \tag{11}\]  
Finally, it should be noted that the model was trained using the Adam optimizer, with an initial learning rate of \(1\times 10^{-4}\), which was progressively reduced every 5 epochs (factor 0.5). Likewise, to avoid overfitting, \(L_{2}\) regularization with a coefficient \(\lambda=0.001\) was employed, and the early stopping mechanism was also utilized, which terminated the training when no improvements were observed in the validation metric over 10 consecutive iterations.
## 1 Introduction  
Due to the capability to realize simultaneously multiple filter functions with the same topology, continued researches have focused on the realization of universal filters. The design of biquadratic voltage-mode active filters with high-input impedance has received much interest since they can be directly cascaded to implement higher order filters without the use of additional buffer circuits [1]. Many voltage-mode universal biquadratic filters using current conveyors with multi-input/multi-output were proposed in the literature [2; 3; 4; 5; 6; 7; 8; 9; 10].  
Recently, a symbolic framework for systematic synthesis of a linear active circuit without any detailed prior knowledge of the circuit form was presented [11; 12; 13; 14; 15]. This method, called nodal admittance matrix (NAM) expansion, is very useful to generate various novel circuits in a systematic way. Based on this method, various active networks such as filters, oscillators and gyrators employing OTA, CCII, BOCCII, DVCC and CCCCTA have been synthesized [16; 17; 18; 19; 20; 21]. The circuit synthesis procedure proposed in [12] is suitable to synthesize discrete transfer functions with different circuit topologies. It is difficult to synthesize multiple filter functions using an identical topology. The systematic generation of current-mode filters using NAM expansion was reported in [16]. The trans-impedance filter synthesis based on NAM expansion was reported in [17]. In [19], the synthesis of CCII-based voltage-mode high-Q biquadratic notch filter was reported recently.  
Since the differential difference current conveyor (DDCC) is a circuit similar to a DDA at the input side and a CCII at the output side [22], it enjoys the combined advantages of the CCII and DDA with high-input impedance, low-output impedance, greater design flexibility, larger signal bandwidth and wider current dynamic range, several universal filters using DDCCs have been proposed [23; 24; 25; 26]. Unfortunately, most papers just propose their novel circuits; the design or synthesis methods for DDCC-based universal filters are not available.  
In this paper, a systematic generation of cascadable DDCC-based universal voltage-mode biquadratic filters is presented. The obtained 12 filters configurations with two outputs can be used to realize all the five generic filter functions. They comprise three active elements and five passive grounded capacitors and resistors, with the features of high input impedance, low active and passive sensitivities, and orthogonal adjustability of the resonance angular frequency and quality factor. The filter with grounded capacitors is helpful for easing the elimination/accommodation of various parasitic effects for monolithic integration. HSPICE simulations for two illustrated derived filters confirm the workability of the obtained circuits, and hence demonstrate the feasibility of the proposed approach.
# Targeted Universal Adversarial Examples for Remote Sensing
###### Abstract  
Researchers are focusing on the vulnerabilities of deep learning models for remote sensing; various attack methods have been proposed, including universal adversarial examples. Existing universal adversarial examples, however, are only designed to fool deep learning models rather than target specific goals, i.e., targeted attacks. To this end, we propose two variants of universal adversarial examples called targeted universal adversarial examples and source-targeted universal adversarial examples. Extensive experiments on three popular datasets showed strong attackability of the two targeted adversarial variants. We hope such strong attacks can inspire and motivate research on the defenses against adversarial examples in remote sensing.  
adversarial examples; targeted adversarial attacks; scene classification; remote sensing; deep learning 2022145833
# A Sustainable Composite Cementitious Material Manufactured by Phosphogypsum Waste
## 3 Results and Discussion
### Microstructure of Composite Cementitious Material  
Figure 7 shows the microstructure of a composite cementitious material mortar containing 10% to 90% phosphogypsum after 3 days. In Figure 7a, hexagonal layered calcium hydroxide, a lot of fibrous calcium silicate hydrate and micropore, a few needle-like et-tringite, square particles, and some unhydrated particles are seen wrapped around each other, and the microstructure is relatively dense. The hydration products of mortar with 10% phosphogypsum mainly contained outstanding clustered and fibrous calcium silicate hydrate, a very small amount of needle-like ettringite, short column-like gypsum, and a certain number of pores, which are wrapped around each other, and the microstructure became denser than that of mortar with 0% phosphogypsum. As the amount of phosphogypsum that was mixed in rose from 10% to 90%, clustered calcium silicate hydrate decreased or went away, the length of column-like gypsum, interfacial porosity, and unhydrated particles all increased, and the hydration products piled on top of each other, making the bonding force weaker.  
Figure 8 shows scanning electron microscopy pictures of mortar after 28 days of curing. In the microstructure of mortar containing 0% phosphogypsum, clintheiform and layered calcium hydroxide, fibrous calcium silicate hydrate, needle-like ettringite, and a certain number of pores and unhydrated particles wrapped around each other were observed in Figure 8a, and the hydration products were richer in morphology than that of mortar after 3 days. The hydration products of mortar with 10% phosphogypsum primarily contained fibrous calcium silicate hydrate, column-like gypsum, clintheiform and layered calcium hydroxide, and a certain number of pores that are wrapped around one another, resulting in a microstructure that is denser than that of mortar containing 0% phosphogypsum. When phosphogypsum incorporation was increased from 10% to 90%, square hydration products, long and thin gypsum and unhydrated particles grew, clintheiform and layered calcium hydroxide decreased, and the microstructure was generally loose.
###### Abstract  
Wind power generation is one of the important development projects for renewable energy worldwide. As wind turbines operate in harsh environments, failure of the wind turbines often occurs, thus leading to lower power generation efficiency and high maintenance cost. Earlier detection of the fault type can reduce the maintenance cost. This study proposed a hybrid recognition algorithm based on the symmetrized dot pattern (SDP) and convolutional neural network (CNN) for wind turbine gearbox fault diagnoses. In addition to a fault-free type, four fault types were discussed in this paper, including gear rustiness, broken tooth, wear, and aging. A vibration sensor was used for measurement. The original vibration signals of the gearbox were captured by a NI-9234 high-speed data acquisition card, filtered by a fast Fourier transform, and imported into the SDP to create the snowflake image features. Afterward, CNN diagnosed the gearbox fault type. There were 1500 test data in this study. A total of 200 data items for each fault type were used as training samples, and 100 data of each type were used as test samples. The test result shows that the training accuracy was 98.8%. The proposed method can diagnose the fault condition of the gearbox effectively and identify the fault type of the gearbox accurately. This is favorable for the quick maintenance of wind turbines.  
gearbox; convolutional neural networks; symmetrized dot pattern; wind turbine +
Footnote †: journal: Applied Sciences
# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 2 Materials and Methods
### UCE Preprocessing Techniques
#### 2.3.3 Magnitude vs. Complex FFT  
When computing the FFT, the previous efforts for classification of equipment with UCE mentioned in Section 2.1 used the magnitude FFT computation. One question is whether including phase will improve the classification. If phase is an important feature of the data, either complex FFT data or time domain data are required. In the NILM-acquired UCE classification applications, the value of the phase of the data are yet unknown. In research from [PERSON] et al. [14], some experiments were conducted with classification of communications signals, for which phase information is a feature of high value. In research from [PERSON] et al. [15], neural networks with complex activation functions have  
Figure 1: ResNet architecture for UCE device classification.  
been used in magnetic resonance imaging fingerprinting. In some cases, the real and imaginary components are sent into the deep network as two-dimensional floating point values, as in research from [PERSON] et al. [16]. Here, the authors performed a test with this approach of inputting the real and imaginary components separately into the ResNet with no modification to the ResNet to perform complex math. Results were poor and are not included in Section 3. Efforts are planned to incorporate complex math into the network.