esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Background to Deblurring Algorithms
#### 2.1.2 The Overlapping Group Sparse TV Regularization Term  
The overlapping group sparse TV regularization term is expressed as:  
\[R_{OGSTV}(\mathbf{F})=\varphi(\mathbf{K}_{h}*\mathbf{F})+\varphi(\mathbf{K}_{v}*\mathbf{F}) \tag{1}\]  
where \(*\) represents the convolution operator, \(\mathbf{K}_{h}=[1,-1]\) is the first-order horizontal difference convolution kernel, \(\mathbf{F}\) is the original image, and \(\mathbf{K}_{\varphi}=[1,-1]^{T}\) is the first-order vertical difference convolution kernel. \(\varphi(\mathbf{V})\) is the function for calculating the combined gradient and is expressed as:  
\[\varphi(\mathbf{V})=\sum_{i=1}^{N}\sum_{j=1}^{N}\left\|\widetilde{\mathbf{V}}_{i,j,K, K}\right\|_{2}\quad\left(\mathbf{V}\in\mathbb{R}^{N\times N}\right) \tag{2}\]  
where \(\widetilde{\mathbf{V}}_{i,j,K,K}\) represents the overlapping group sparsity matrix, which is defined as:  
\[\widetilde{\mathbf{V}}_{i,j,K,K}=\begin{pmatrix}\mathbf{V}_{i-K_{i},j-K_{i}}&\mathbf{V}_{ i-K_{i},j-K_{i}+1}&\dots&\mathbf{V}_{i-K_{i},j+K_{r}}\\ \mathbf{V}_{i-K_{i}+1,j-K_{i}}&\mathbf{V}_{i-K_{i}+1,j-K_{i}+1}&\dots&\mathbf{V}_{i-K_{i}+ 1,j+K_{r}}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{V}_{i+K_{r},j-K_{l}}&\mathbf{V}_{i+K_{r},j-K_{l}+1}&\dots&\mathbf{V}_{i+K_{r},j+K_{ r}}\end{pmatrix}\in\mathbb{R}^{K\times K} \tag{3}\]  
where \(K\) represents the length or width of the matrix of the combined gradient, \(K_{l}=\left\lfloor\frac{K-1}{2}\right\rfloor,\;K_{r}=\left\lfloor\frac{K}{2}\right\rfloor,\) and \(\left\lfloor.\right\rfloor\) means round down to the nearest integer.  
As indicated in Equation (3), the overlapping group sparse TV regularization term uses the gradient of the pixel point \((i,j)\) as the center, constructs a \(K\times K\) matrix, combines it using the L2 norm, and replaces the independent gradient of the pixel point. Compared with the traditional anisotropic TV regularization term, the overlapping group sparse TV regularization term fully mines the gradient information of each pixel and considers the structural information of the image, thereby increasing the difference between the smooth and the edge areas of the image.
## 4 Threading Statistical Disclosure Attack Algorithm with EM (TSDA-EM)
### Fixed Values Table  
In order to manage all feasible tables generated to calculate the communication probability, an auxiliary table called the Fixed Values Table is used. It is worth mentioning that the table of fixed values is represented as a matrix with the complete set of senders, \(S\), and receivers, \(R\). This table contains only three different values, \(\mathbf{-1}\), \(\mathbf{0}\), and \(\mathbf{1}\): (i) \(\mathbf{-1}\) is the initial value for all the cells in the table and represents an unexplored cell; (ii) \(\mathbf{1}\) is assigned when a pair of sender and receiver appears in all the rounds; (iii) finally, \(\mathbf{0}\) is the representation of uncertainty, i.e., it is not certain if the communication happened.  
Using the information from Table 2, the values at cells \([s_{1},r_{2}]\), \([s_{1},r_{4}]\), \([s_{1},r_{6}]\) and \([s_{1},r_{7}]\) of the Fixed Values Table (Table 3) is set to \(\mathbf{1}\). This is because at this point, all these communication pairs appear to be active in this round. The same operation needs to be performed for \([s_{3},\ldots]\), and \([s_{5},\ldots]\).  
After the first round, there are a lot of \(1\)s, but those will be removed by subsequent rounds. For example, if there is a round where the sender \(s_{5}\) appears again but the recipient \(r_{7}\) does not, the cell \([s_{5},r_{7}]\) will be set to \(\mathbf{0}\) because this pair does not always appear in the same round. We only change the cell values for the cells with values \(\mathbf{-1}\) or \(\mathbf{1}\). Once we are finished with all rounds, we will change all the \(\mathbf{-1}\) to \(\mathbf{0}\) in order to work only with two different values.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline \(\boldsymbol{S^{\prime}}\backslash\boldsymbol{R^{\prime}}\) & \(\boldsymbol{r_{2}}\) & \(\boldsymbol{r_{4}}\) & \(\boldsymbol{r_{6}}\) & \(\boldsymbol{r_{7}}\) & \(\boldsymbol{MS}\) \\ \hline \(s_{1}\) & & & & & 2 \\ \hline \(s_{3}\) & & & & & 3 \\ \hline \(s_{5}\) & & & & & 2 \\ \hline \(MR\) & \(1\) & \(3\) & \(2\) & \(1\) & \\ \hline \end{tabular}
\end{table}
Table 2: Example of a round. \(MS\): number of messages sent by \(s_{i}\), \(MR\): number of messages received by \(r_{i}\).  
Figure 4: Example of the set of feasible tables for each thread.
## 5 Behavioral Demonstration of RANs  
This section exhibits two distinct aspects of RANs modeling via separate experiments. Both investigations present a different view of RANs methodology, highlighting the capabilities of the RANs approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline RBM & 90.87 \(\pm\) 01.26 & 85.25 \(\pm\) 2.61 & 82.34 \(\pm\) 3.85 & 85.25 \(\pm\) 2.61 \\ K-NN & 99.96 \(\pm\) 00.08 & 99.95 \(\pm\) 0.11 & 99.94 \(\pm\) 0.12 & 99.95 \(\pm\) 0.11 \\ LR & 99.65 \(\pm\) 00.07 & 99.64 \(\pm\) 0.07 & 99.64 \(\pm\) 0.07 & 99.64 \(\pm\) 0.07 \\ MLP & 95.62 \(\pm\) 11.18 & 96.82 \(\pm\) 7.56 & 96.02 \(\pm\) 9.95 & 96.82 \(\pm\) 7.56 \\ RANs & 99.12 \(\pm\) 00.09 & 99.12 \(\pm\) 0.09 & 99.12 \(\pm\) 0.09 & 99.12 \(\pm\) 0.09 \\ SGD & 96.00 \(\pm\) 02.81 & 95.25 \(\pm\) 2.86 & 94.57 \(\pm\)3.76 & 95.25 \(\pm\) 2.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RAN’s Comparative Study for Toy-data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Time Complexity** & **Description** & **Source** \\ \hline K-means & \(O(n^{k+2/p})\) & \(n\): n\_samples; \(k\): n\_clusters; \(p\): n\_features & [59] \\ \hline Affinity Propagation & \(O(n^{2})\) & \(n\): n\_samples & [59] \\ \hline MLP & \(O(n\cdot m\cdot h^{k}\cdot o\cdot i)\) & \
\begin{tabular}{c} \(n\): n\_samples; \(m\): features; \(k\): no. of hidden layers; \\ \(h\): number of hidden neurons \\ \(\sigma\): output neuron; \(i\): no. of iterations \\ \end{tabular} & [59] \\ \hline RBM & \(O(d^{2})\) & \(d\): max(n\_components, n\_features) & [59] \\ \hline KNN & \(O(m\cdot n\cdot i)\) & \(m\): n\_components; \(n\): n\_samples; \(i\): min(m, n) & [59] \\ \hline LR & \(O(n\cdot m^{2})\) & \(n\): n\_samples; \(m\): n\_features & [59] \\ \hline SGD & \(O(k\cdot n\cdot p)\) &
\begin{tabular}{c} \(n\): n\_samples; \(k\): n\_iterations; \\ \(p\): the average number of non-zero \\ attributes per sample \\ \end{tabular} & [59] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time Complexities of Models used in the Article.
# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 2 UAV Reconnaissance Image Tank Database  
### Image Capturing and Annotation  
In order to collect the military object image of UAV data being close to the real battlefield scenes, we built a low-altitude reconnaissance simulation platform. First, we constructed the models of three main battle tanks according to the ratio of 10:1, marked as Tank A, Tank B, and Tank C, respectively. Seven kinds of battle scenes were simulated, which are denoted as Grass Ground I (GG-I), Grass Ground II (GG-II), Waste Land I (WL-I), Waste Land II (WL-II), Waste Land III (WL-III), Bush Land I (BL-I), and Bush Land II (BL-II), respectively. Then, we simulate the low-altitude reconnaissance scene of the UAV. One or more tanks were allocated in the simulated battlefield, and a small car equipped with an RGB camera moved on a fixed elliptical and captured the images of the tanks. It aims to simulate the UAV reconnaissance of military objects on the ground from different angles at a certain height. Then we simulated UAV reconnaissance at different heights by adjusting the track height. The simulated height \(H_{\text{S}}\) can be obtained by  
\[H_{\text{S}}=(S_{\text{T}}/S_{M})\times H_{\text{T}}, \tag{1}\]  
where \(S_{T}\) and \(S_{M}\) denote the size of the tank and its model, and \(H_{\text{T}}\) denotes the track height. Figure 1 shows some examples of the collected images, and 1263 images were finally selected.  
Next, the collected images were annotated by hand. We annotated the image samples using the labeling tool Labellmg, where each annotated sample includes the following information. Class type denotes which class (Tank A, Tank B, or Tank C) the object belongs to. Object center points \((x,y)\) were obtained by \(x_{t}/W_{I},y_{t}/H_{I}\), where \(x_{t}\) and \(y_{t}\) denote the object coordinate, and \(W_{I}\) and \(H_{I}\) represent image width and height. Object width \(w\) and height \(h\) were obtained by \(w_{t}/W_{I}\), \(h_{t}/H_{I}\), where \(w_{t}\) and \(h_{t}\) denote object width and height.  
Figure 1: Visualization of the UAVT-3 dataset.
# Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
## 1 Introduction  
Diminishing information privacy in communication ecosystems often requires consumers to directly manage their own preferences. Yet, this easily becomes a tedious task considering the amount of information exchanged on a daily basis. Research indicates that, most often, consumers lack the information to take appropriate privacy-aware decisions [1], and even where sufficient information is available, long-term privacy is traded-off for short-term benefits [2] (indicatively, a Facebook-focused empirical user study pointed out that 20% of participants and 50% of interviewees reported direct regret after posting sensitive information publicly) [3]. Automatically identifying sensitive information is critical for privacy-preserving technologies. Sensitive data most often appear as unstructured text, making it vulnerable to privacy threats, due to its parseable and searchable format. This work solely concentrates on text data. The sensitivity of a piece of information is directly shaped by the context it is provided in. For the purposes of this analysis, we offer a taxonomy of four distinct context classes, for use as sensitivity categories. We derive these respectively by the meaning, interaction, precedence, and preference associated with any piece of information:  
**Semantic Context:** formed on the basis of the semantic meaning of a term. As, for instance, in the case of homonyms or homographs, the semantic meaning of a sequence affects its sensitivity. **Agent Context**: depending upon the agents participating in the transmission of information. Here, the relationship between participating actors determines sensitivity. For instance, patient-doctor sharing of medical history is non-sensitive for this set of actors, but sensitive otherwise. **Temporal Context:** defined by information precedence that affects the significance of a term. Here, previously introduced definitions of a term qualify its sensitivity. If, for example, a string sequence is introduced as a password, it carries higher sensitivity than if it was introduced as a username. **Self Context:** defined by the user's personal privacy preferences. Notably, what is considered private varies across users due to cultural influences, personal experiences, professional statute, etc. For example, ethnic origin may be considered sensitive information for one individual but not for another. The above list of context classes is not exhaustive, nor are these mutually exclusive. One or more contexts may simultaneously influence sensitivity differently. Information may, however, be sensitive, regardless of context (e.g., credit-card numbers, email, passwords, insurance numbers). In this work, we use sensitive information as any coherent sequence of textual data from which a third party can elicit information that falls under the categories proposed in the P3P standard (Table 1). This paper proposes a context-aware predictive deep learning model that can annotate sensitive tokens in a sequence of text data, more formally defined as a \"token sensitivity labelling\"
# Consumer Acceptance of Cultured Meat: An Updated Review (2018-2020)
## 3 Studies Reviewed  
Table 2 contains the key details of the 26 studies included in this review. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Inclusion Criteria** & **Exclusion Criteria** \\ \hline
1. & Focus on consumer perceptions of & 1. & Sources which do not discuss consumer perceptions of cultured meat \\  & cultured meat & 2. & Review articles which do not include original empirical data \\
2. & Presents original empirical data & 3. & Book chapters, conference papers, and \\
3. & Published in a peer-reviewed journal & 3. & unpublished theses and white papers \\
4. & Published since 2018 (inclusive) & 4. & Articles already reviewed in our previous \\
5. & English language & 4. & Articles already reviewed in our previous \\  & & review [12] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Inclusion and exclusion criteria. Figure 1: Process for identifying the relevant literature. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Method** & **Sample** & **Key Findings** \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] (2019) [13] & Experimental & 1185 US adults, census balanced & The arguments that cultured meat is natural, and that naturalness should not matter tend not to persuade consumers, and resulted in a lower acceptance than discussing the general benefits without addressing naturalness. Arguing for the unnaturalness of conventional meat was relatively effective, though this argument may not be politically feasible for a co-operative market strategy. The name used to describe cultured meat has a significant effect on consumers’ attitudes and behavioral intentions, with “clean meat” resulting in significantly more positive attitudes than “1 ab-grown meat” (“cultured meat” and “animal-free meat” were not significantly different from either of the first two names). This effect was mediated by the positivity of word associations, suggesting that the mechanism is associative. A frame focused on the high-technology/scientific nature of cultured meat resulted in significantly less positive attitudes and intentions compared to frames focused on the societal benefits of cultured meat, or its similarity to conventional meat. & Consumers are more likely to say they would eat cultured meat when asked about it in a foreign language (vs. their native language). The effect was mediated by disgust. \\ \hline [PERSON] and [PERSON] (2020) [18] & Experimental & 145 Dutch participants & Cultured meat was perceived to violate norms, which caused moral disgust and subsequent rejection by consumers. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key details of the 26 studies included in the review.
# Seismic Response Analysis of Prestressed Concrete
## 2 Rigid Body Model of the PCRF  
The PCRF has the connecting interfaces set at the column feet and at the vertical inner faces of the beam-column joints, allowing the columns to be uplifted with the accompanying separation of the beam-column interface and rotation of the beam and column around the interface. The theoretical model based on rigid body of a one-story single-span PCRF is shown in Figure 1, which consists of two columns, a beam and prestressing tendons. Relative slips between the rigid members on each interface are not allowed. The tendons are arranged along the centerline of the beam and columns. The connections between the beam and columns and the anchoring of columns are accomplished by prestressing the tendons. The column has a mass \(m_{\rm c}\), height \(2h\) and width \(2b\). Its semi-diagonal and slenderness ratios are \(R_{\rm c}=\sqrt{h^{2}+b^{2}}\) and \(b/h=\tan\alpha\), respectively. The beam has a mass \(m_{\rm b}\), height \(2h_{\rm b}\) and length \(2L_{\rm b}\). Its half-diagonal length is \(R_{\rm b}=\sqrt{h_{\rm b}^{2}+L_{\rm b}^{2}}\) and its height span ratio is \(h_{\rm b}/L_{\rm b}=\tan\gamma\). The inclined angle between line AB and AD is \(\beta\). The coordinate system and other geometric parameters are shown in Figure 1 and the clockwise rotation of the beam and column are positive. The mass centers of the columns and beam are \(G_{\rm AB}\), \(G_{\rm CD}\) and \(G_{\rm BC}\).  
The motion law of the PCRF is the same as that for the planar four-bar linkage mechanism shown in Figures 2-4 when the PCRF is rocking. The rigid body model of the PCRF is a single degree of freedom system and can be described by a generalized coordinate. The rocking mechanism and planar four-bar linkage mechanism of the PCRF during counterclockwise rotation are shown in Figure 2. The dotted lines in Figures 1 and 2 are used to illustrate the four-bar mechanism presented in Figures 3 and 4. Hence, the planar four-bar linkage model can be used to obtain the relationship between the angles of the beam and columns. That is, the motion of the columns and beam when the PCRF is rocking can be represented by the motion of the bars in the four-bar linkage model. The governing equation of motion of the PCRF can be derived by the Lagrangian equation method. The angles of rotation of bars AB, BC and CD are denoted as \(\theta_{1}\), \(\theta_{2}\) and \(\theta_{3}\), respectively. Figure 4 illustrates the planar four-bar model for the case \(\theta_{1}<0\) and the case \(\theta_{1}>0\). The angle \(\theta_{2}\) is positive and \(\theta_{3}\) is negative in the case \(\theta_{1}<0\), while the opposite is positive for the case \(\theta_{1}>0\).  
Figure 1: Rigid body model of prestressed concrete rocking frame (PCRF).  
Figure 2: Rocking mechanism and planar four-bar linkage mechanism of PCRF during counterclockwise rotation.
## 2 Ad hoc On-Demand Distance Vector Routing and Hotline Based Routing
### AODV is Loop Free  
AODV keeps the routing paths loop free by using sequence numbers. Every destination has a monotonically increasing sequence number which is known as destination sequence number. Intermediate nodes with higher sequence number have more recent route information to the destination node and are given preference in case of multiple RREP messages. The AODV protocol prevents routing loops by maintaining an invariant that destination sequence numbers along any valid route [1].
## 2 Methodology
### Measurements with a Common Directional Speaker  
For measurements with the common directional speakers, the same source and microphone positions were used. According to the measurements with the dodecahedron speaker, an ESS excitation signal was used with the same sampling frequency of 44.1 kHz. The same appropriate sequence length and time constant for the ESS signal was chosen according to the expected RT. Three iterations were performed for each of the measurement points for each of the loudspeaker positions. Averaging was used for better signal to noise ratio and to reduce the temperature fluctuation effect. The same microphone was used in the same position for each of the measurements. Different placements of the loudspeakers were performed (twelve positions similar to the twelve positions of the faces of a dodecahedron speaker, different rotations of the loudspeakers for a total sum of twenty six and fourteen positions).  
For all the measurements with a directional speaker, the speaker was handheld in the appropriate positions described in the following sections. For all the measurements the same person operated the directional speakers without any contact with the body. We followed this approach in order there will be no differences caused by different absorption by different operators. However, variations of the results are expected when different operators perform the method since they will have different absorption. Also, the combined absorption of the operator and the directional speaker is different from the absorption of the dodecahedron speaker. This is expected to affect the results of the acoustic parameters to some extent e.g., measurement of reverberation time. The effect of these differences remains to be studied in a future work.  
We followed this approach so that no further equipment would be needed to conduct measurements in order to maintain the low-cost method. The evolution of the method, which is now underway, is going to address this problem. However, additional equipment will be necessary that will increase the cost.  
In order to compare the impulse responses, impulse responses created with the proposed method were normalized. Impulse responses created by the proposed method have generally greater amplitude compared with the impulse responses measured with a dodecahedron speaker. Audio normalization is the application of a constant amount of gain to a recording to bring the amplitude to a target level. Because the same amount of gain is applied across the entire recording, the signal-to-noise ratio and relative dynamics are unchanged. Peak audio normalization was especially applied in the impulse response created with the proposed method, which adjusted the recording based on the highest signal level present in the impulse response measured with the dodecahedron speaker.
# An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks
## 3 Network Model and Assumptions  
Let us consider a WSN with dense enough sensor nodes evenly deployed in a two-dimensional sea area of interest--called the sensor area--at the initial moment of event occurrence. We use the topology control scheme proposed in a previous work [19]. When an event occurs, the relevant environmental parameters in the related area are likely to change, that is, the event reports a time-varying sensing signal to its neighbour region. Normally, the intensity of the signal will decrease with increasing distance from the event centre. An event area is the domain around the actual boundary of the event where the intensity of signal exceeds the given threshold value _Th_. Then all sensors in this sensor sea region receive radio wave signals from the event area. The proposed algorithm recommends a method for identifying boundary nodes.  
We assume that sensor nodes are similar in terms of their computation, battery life and communication capability. Each node works well and has a loose synchronization. It assumes that the sensor's location information is correct since we require the right sensor's location information for the accurate detection of boundary sensor nodes. A series of prior definitions are given below:  
* The term sensor area, denoted as \(\sum\), not only refers to the geographical area covered by the WSN but also to the set of nodes in this area. We denote an event region as \(\omega\), which is the sub-region of \(\sum\) covered by an event and \(\varpi\) is the remaining region. Thus, \(\sum=\omega+\varpi\). Hence, a sensor node \(S_{i}\in\omega\) means it is an affected node while \(S_{i}\in\varpi\) means it is a normal node.
* A sensor node \(S_{i}\) with its location information, that is \(S_{i}(xi,yi)\), is considered to be a boundary node when it is on the actual boundary. Let us consider a boundary width \(R\) defined as the communication radius of the sensor \(S_{i}\) according to [13]. Let \(N(S_{i})\) denote the disk centred at node \(S_{i}\) with the radius \(R\). Therefore \(S_{i}\) is a boundary node if \(\|S_{i},B\|\leq r\) where \(\|S_{i},B\|\) is the geographic distance between \(S_{i}\) and B the actual boundary. Then the event boundary \(B(S_{i})\) is the collection of such boundary nodes.  
Suppose that the sensor data of nodes in the event area \(\omega\) forms a gaussian distribution \(N(\mu_{1},\sigma^{2})\) and the readings of other sensors in \(\varpi\) form another distribution \(N(\mu_{2},\sigma^{2})\), where \(\sigma\) is small compared with \(\left|\mu_{1}-\mu_{2}\right|\). A sensor node is considered to be an affected node when its sensing information exceeds the threshold \(\mu_{th}=\frac{\mu_{1}+\mu_{2}}{2}\), while the others are called normal nodes. Let us suppose that the event boundary is a circular boundary centred on the event source. The sensor area is shown in Figure 1, the black nodes are the boundary nodes. Figure 1 on the right shows the local area of a single boundary node.  
Figure 1: An illustration of a sensor area.