esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Protocol and Visual Biofeedback  
A total of 42 reflective markers were attached to anatomical landmarks on the trunk, pelvis, and lower limbs (acromioclavicular joints, jugular notch of the clavicle, the xiphoid process of the sternum, spinous process of C7 and T10, anterior superior iliac spine, posterior superior iliac spine, lateral femur epicondyle, lateral malleolus, the first, second, and fifth metatarsal heads, and calcaneus), 4-marker rigid clusters were used for tracking the thigh and shank, and additional medial knee and ankle markers were added during static standing calibration trial (Figure 1).  
Participants wore a harness solely for safety purposes during treadmill walking. To determine their comfortable walking speed (CWS), participants gradually increased the treadmill speed in 0.1 m/s increments every 15 s until they reported their preferred speed. Following this, participants walked at their CWS for 3 min as a warm-up. A 3 min baseline walking trial was then conducted at a fixed speed of 1.2 m/s to minimize the influence of speed variations on gait parameters such as joint kinematics and kinetics [29].  
The biofeedback training phase consisted of three 6 min bouts of walking at a fixed speed of 1.2 m/s. The first bout served as familiarization with the VGRF biofeedback system. The second bout was designated as the acquisition phase, during which the last minute of walking with visual biofeedback active was used to define the acquisition trial. The third bout reinforced the training and was immediately followed by a one-minute walking trial without visual feedback, defined as recall 1. Participants were provided with a 5 min seated rest period between each biofeedback bout (Figure 2). After completing the third bout and the recall 1 trial, participants rested for another 5 min in a seated position. This was followed by a 3 min walking trial without visual feedback, defined as recall 2. During both recall trials, the visual biofeedback was removed, and participants were instructed to attempt to maintain the same increased VGRF as during the biofeedback trials.\"  
Real-time VGRF biofeedback was displayed on a screen using D-flow software (version 3.20.0), with VGRF trajectories for the right and left limbs shown as blue and red graphs, respectively. A horizontal target line (yellow line), set at 10% above baseline during  
Figure 1: Front (**A**) and rear (**B**) views of marker placement, showing tracking-only markers (gray) and calibration markers (black) on key anatomical landmarks, including the shoulders, pelvis, thighs, knees, shins, ankles, and feet.  
Figure 2: The VGRF biofeedback training protocol. The alternating stripes and blanks indicate that biofeedback was on and off, respectively, during that minute.  
familiarization and 15% above baseline during training, guided participants to increase their VGRF (Figure 3). To reduce reliance on visual feedback, intermittent biofeedback (1 min on, 1 min off) was employed during the training bouts (Figure 2).
# Exploiting Outage and Error Probability
## Appendix A Area of Overlapping Region  
Figure 11 shows the overlapping area of same transmission ranges of two sensors. Transmission range is denoted by \(r\). Therefore, the area of overlapping region is found by subtracting area of triangle SEG from area of sector [32].  
Area of sector is given by:  
\[AS=\frac{\theta}{2}r^{2} \tag{1}\]  
where \(\theta\) is the angle of sector in radians. In order to find \(\theta\), we know that triangle SEG is an isosceles triangle with height \(d_{SD}/2\). Using half angle identity:  
\[\cos(\frac{\theta}{2}) = \frac{d_{SD}/2}{r}=\frac{d_{SD}}{2r} \tag{2}\]solving for \(\theta\), we get:  
\[\theta=2\arccos(\frac{d_{SD}}{2r}) \tag{12}\]  
Putting Equation (12) in Equation (11), we get:  
\[AS = \frac{2\arccos(\frac{d_{SD}}{2r})}{2}r^{2}=r^{2}\arccos(\frac{d_{SD }}{2r}) \tag{13}\]  
Now area of triangle SEG is given as:  
\[AT=\frac{1}{2}\;base\times height. \tag{14}\]  
Using Pythagorean theorem to find \(\overline{OE}\) of the right angle triangle SOE.  
\[\overline{OE}=\sqrt{r^{2}-\frac{d_{SD}^{2}}{4}} \tag{15}\]  
Therefore, base of the triangle SEG is \(2\times\overline{OE}\).  
\[base=\sqrt{4r^{2}-{d_{SD}}^{2}} \tag{16}\]  
Area of the triangle SEG is given as:  
\[AT = \frac{1}{2}\sqrt{4r^{2}-d_{SD}^{2}}\times\frac{d_{SD}}{2} \tag{17}\] \[= \frac{d_{SD}}{4}\sqrt{4r^{2}-{d_{SD}}^{2}} \tag{18}\]  
Area of the overlapping region is \(2\times(AS-AT)\).  
\[A = 2\times\left[r^{2}\arccos(\frac{d_{SD}}{2r})-\frac{d_{SD}}{4} \sqrt{4r^{2}-{d_{SD}}^{2}}\right] \tag{19}\] \[A = 2r^{2}\arccos(\frac{d_{SD}}{2r})-\frac{d_{SD}}{2}\sqrt{4r^{2}-d _{SD}^{2}}\]
# Protecting Private Communications in Cyber-Physical Systems through Physical Unclonable Functions
## 3 Proposed Encryption Scheme  
In this section, we described the proposed encryption scheme for CPS. The proposed scheme includes a simple encryption mechanism based on a XOR logic gate, and the PUF-based key generator.
## 2 Data Analysis of Load
### Outlier Treatment  
A detailed data analysis was performed on the considered load data. The inter quartile method (IQR) method was used to detect outliers. A data entry that was caused due to experimental error, variation in measurement, data anomaly, or any other reason was called an outlier [30]. Although it is difficult to separate bad outliers from good outliers with precision, data that are extremely unfamiliar without any considerable reason are considered outliers. The presence of outliers may produce errors in the output by affecting the mean and standard deviation. In this paper, outliers were detected and removed. The following equations were used to detect outliers in the data.  
\[IQR=Quart_{3}-Quart_{1} \tag{1}\]  
\[LB=Quart_{1}-1.5\times IQR \tag{2}\]  
\[UB=Quart_{3}+1.5\times IQR. \tag{3}\]  
The difference between the third quartile (Quart\({}_{3}\)) and the first quartile (Quart\({}_{1}\)) was determined in (1). This difference was called IQR. The lower bound (LB) and upper bound (UB) of the data using IQR were calculated in (2) and (3). Once outliers that were observed beyond the LB ad UB were detected, they were removed from the system.
# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## 4 Evacuation Simulation Model
### Evacuation Strategies  
In order to find out (a) whether adding static information depositories will improve the evacuation performance, and (b) whether deployment of these depositories in staircases or corridors makes a difference, simulations compare three evacuation strategies during evacuation:  
* Decentralized evacuation with no static information depositories (denoted by \(NoDepot\)), in order to establish the baseline. Evacuees update awareness of the dynamic environment only through exploration and peer-to-peer communication with other agents.
* Decentralized evacuation with information depositories at _staircases_ (denoted by \(DepotSt\)). Evacuees update awareness of the dynamic environment not only through exploration and peer-to-peer communication with other agents, but also through peer-to-peer communication with close information depositories. In this strategy, twenty static depositories have been deployed, placed at each floor at locations near the four staircases of this building.
* Decentralized evacuation with information depositories in _corridors_ (denoted by \(DepotCr\)). In this strategy, twenty static depositories have been deployed at random locations along the corridors of this building.  
The locations of the information depositories are labeled with red squares (for DepotSt) and blue circles (for DepotCr) in Figure 4. Level one has been occupied by spacious labs. In this case, information depositories for DepotCr have been placed at graph nodes that are close to the doors of the rooms.
## 4 Proposed Routing Algorithm for Wireless Sensor Networks
### Routing Strategy for Flat Architecture  
When a node has data to transmit to the sink, it sends a broadcast message to the nodes that are within its coverage area to create a temporary table with the energy and number of hops needed to reach the sink. In the Figure 4, the node possessing the most energy and the requiring the smallest number of hops to the sink is chosen to send the data packet. This process is repeated until the data packet reaches the sink. If a data packet fails to reach the sink, this process is repeated, beginning at the source, until it successfully reaches the sink.  
Figure 4: Routing Strategy for flat architecture.
# Short-Run Contexts and Imperfect Testing for Continuous Sampling Plans
## 5 Analytical Comparisons
### Illustrations  
In this section, we revisit the analysis of Plan 1 with perfect testing. Table 3 compares the output of the simulation for \(AOQ\) and \(APS\) with the approximations in Section 5.1 and with analytical formula available in the previously discussed references.  
The calculation using the Section 5.1 formula is close to the calculation of the simulation because \(F/N\) is small. The simulation results agree nicely with the analytical formulas available in the literature that can be used in short-run contexts provided there is perfect testing.  
As a second illustration we revisit the analysis of Plan 1 with imperfect testing. Table 4 compares the output of the simulation for \(AOQ\) and \(APS\) with results obtained from naively using the imperfect testing formulas in [2]. The naivette results from ignoring the effect of the short-run context, since the formulas in [2] are valid only under long-run contexts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Simulation** & **Section 5.1 Approximation** & **Formulas in References [3; 4]** \\ \hline \(AOQ\) & 0.66\% & 0.69\% & 0.65\% \\ \(APS\) & 67.38\% & 65.34\% & 67.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Plan 1 with perfect testing.  
The differences in the results shown above expose the fact that even in relatively large batches, the naive use of the formulas in [2] can lead to non-trivial discrepancies with the correct simulation answers. As a sensitivity study, we evaluated a modification where the batch size was doubled to 6400 and the number of failures was also doubled to 128 (preserving the initial 2% probability of a defect). With the larger batch size, the short-run context becomes closer to a long-run context and the simulation estimates of \(A\)\(O\)\(Q\) and \(APS\) change to 1.09% and 56.94%, respectively, which are closer to the results given by the long-run formulas.
## 6 ABT-RRMAC Comparison with DA-MAC
### Performance Evaluation  
The simulations of the DA-MAC are accomplished in Omnet++ by using the switched beam SAS module that we designed in [46]. The number of antenna elements of the array is set to 10; because DA-MAC does not allow for a plane sectorization, the overall MAC performance of the considered approaches are evaluated in function of the number of mobile nodes in the network; the number of nodes are increased from 10 to 100. The first statistic is referred to the PDR.  
Figure 12 illustrates the comparison plot of the considered approaches related to the PDR statistic. When the number of nodes is low, the DA-MAC performs quite similarly to the ABT-RRMAC. Indeed, as demonstrated in [26], the peak of DA-MAC is obtained for a number of nodes set to 20; however, this value, for DA-MAC, tends to linearly decrease when the number of nodes is higher than 50; the average reduction changes from 4 to 12%; This is probably closely related to the different kinds of SAS technology used by the two approaches; in fact, the higher the number of nodes is, the higher the correlation matrix associated to the network is, and the lower the accuracy in the DOA (direction of arrival) estimation is; more specifically, the adaptive SAS technology used in ABT-RRMAC, due to its complex and efficient logical processing unit, is able to reduce the overall error introduced by the direction estimation better than the switched beam technology; once again, it is easy to observe how the gain introduced by ABT-RRMAC against RR-MAC in terms of PDR is considerable. For a further investigation, the energy consumption of the considered approaches varying the number of nodes has been analyzed.  
The trend of curves plotted in Figure 13 is consistent with the results of Figure 12. In particular, ABT-RRMAC and DA-MAC operate very similarly with a small number of nodes. As the number of nodes enhances, DA-MAC energy consumption increases considerably and reaches a value of 85.13 J against the 75.16 obtained by ABT-RRMAC in the worst case. This behavior is probably motivated by the fact that the DA-MAC algorithm tends to reinforce the basic backoff procedure provided by classical directional MAC (which is the same used by ABT-RRMAC) increasing the number of retransmissions in the case of collision; as a consequence, the highest number of retransmissions of DA-MAC with respect to ABT-RRMAC translates into the highest energy consumption of DA-MAC compared to our proposal. Finally, based on considerations given in Section 4.3, it is possible to further justify the obtained results. Indeed, in the case of DA-MAC, the overall complexity of the algorithm can be assumed to be \(O(N)\), namely it is linear with the number of nodes because the loop operation is performed by all nodes in the network. This translates into a higher number of operations of the DA-MAC with respect to ABT-RRMAC and then into a higher energy consumption.  
Figure 12: PDR-considered approaches.
# Convolutional Adaptive Network for Link Prediction in Knowledge Bases
## 4 Experimental Setup
### Datasets  
The extensive evaluation was carried out on the FB15k, FB15k-237, WN18, WN18 RR, and YAGO3-10 KB datasets. All of them contain a large number of entities and relations and are composed of training, validation, and test sets. Table 1 shows the details of all of the datasets.  
* _FB15k_: FB15k [16] is a large-scale knowledge base containing massive amounts of knowledge and facts; it is a subset of Freebase [7]. It comprises approximately 1345 relations and 14,951 entities. FB15k describes information about awards, athletics, movies, stars, and sports teams, among other things, as part of a broad knowledge base about the human world.
* _FB15k-237_: FB15k-237 [13] is a subset of FB15k. Due to the inverse relations, it can observe that almost all of the test triplets in FB15k can be predicted easily. Thus, the FB15k-237 dataset was created so that the inverse relations were deleted. There are 14,541 entities in all, with 237 various relations between them.
* _WN18_: WN18 [16] is a knowledge base featuring lexical relations between words, and it is a subset of WordNet [8]. This dataset also contains massive numbers of inverse relations. There are 18 relations and 40,943 entities in this dataset. Its relations define lexical relationships between entities, while the entities define word senses.
* _WN18 RR_: Similarly to the FB15K-237 dataset, WN18 RR [16] is a subset of WN18 and was obtained by removing the inverse relations. It has a total of 40,943 entities and 11 different relations.  
* _YAGO3-10_: YAGO3-10 [13] is a subset of YAGO [9]. There are 37 various relations with 123,182 entities. Each entity has a minimum of 10 relations connected to itself. It defines a person's characteristics, such as their occupation, ethnicity, and citizenship.
###### Abstract  
Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.  
hyperspectral image; dimensionality reduction; spatial-spectral feature; hypergraph embedding; sparse representation +
Footnote †: journal:
# Digital Authorship Attribution in Russian-Language Fanfiction and Classical Literature
## 5 Methods
### Open Set Attribution
#### 5.2.3 Statistical Methods  
Method Based on Thresholding Using the Euclidean Distance  
The Euclidean distance method is also based on a threshold value. A vector of 400 informative features is calculated for each text. Feature selection is completed using the method chosen as the best according to the results of closed attribution. Then, for each author (positive class), based on the training sample, the centroid is calculated as the arithmetic mean for each feature from the vector. As a result, for each of the known authors, a centroid vector of the \(i\)-th class is formed (3):  
\[C_{i}=\underset{j}{\cup}\left[\frac{n}{j}(j)\right] \tag{3}\]  
where \(n\) is a number of test samples, and \(j\) is the index of the feature vector element \(\in[1;400]\).  
The Euclidean distance is used to predict the class of the new text. That is, the \(i\)-th test sample calculates its own feature vector, \(\mathrm{T_{i}}\), and then determines the distance of this vector to each centroid according to the Euclidean distance (4):  
\[dist\left(T_{i},C_{j}\right)=\sqrt{\left(x_{T_{i}}-x_{C_{j}}\right)^{2}+\left( y_{T_{i}}-y_{C_{j}}\right)^{2}+\ldots+\left(z_{T_{i}}-z_{C_{j}}\right)^{2}}, \tag{4}\]  
where \(\mathrm{T_{i}}\) is the feature vector of the anonymous text, \(\mathrm{C_{j}}\) is the centroid of any positive class, and \(j\) corresponds to positive classes set size.  
The threshold distance is defined for each positive class as the maximum deviation of one of the training samples from the centroid. If the Euclidean distance of the current test pattern exceeds this distance for all classes, it will be classified as an example belonging to the set of negative classes. An illustration of the method principle is shown in Figure 6.  
Figure 6: Illustration of the thresholding using Euclidean distance method.  
Method Based on Thresholding Using Cosine Similarity  
The last open set attribution method is also based on a thresholding value considered as a cosine similarity. In this method, similar to the previous one, the feature vector is calculated for each text. After that, the centroid is calculated for each positive class. A cosine similarity measure is calculated for each training sample regarding class centroids (5):  
\[similarity=\frac{T*C}{\|T\|\|C\|}=\frac{\sum\limits_{i=1}^{n}T_{i}\times C_{i}}{ \sqrt{\sum\limits_{i=1}^{n}\left(T_{i}\right)^{2}}\times\sqrt{\sum\limits_{i= 1}^{n}\left(C_{i}\right)^{2}}} \tag{5}\]  
The minimum value per class is recognized as the threshold value. Test samples that do not exceed the threshold value for any class are classified as the negative class.
# Limited-Angle Computer Tomography with Truncated Projection Artifacts Removal
## 2 Materials and Methods  
### System and Data Set  
The image and the geometry of the second-generation GE prototype digital tomosynthesis system for DBT imaging are shown in Figure 2. The distance from the X-ray focal spot to the rotation pivot point is 640 mm and the X-ray source rotation plane is parallel to the chest wall and perpendicular to the detector panel. Additionally, the distance between the detector panel and the bottom of the object is 20 mm. The system has a 1920\({}^{\circ}\)2304 matrix flat panel digital detector with a 0.1 mm \(\times\) 0.1 mm pitch size, and the signals received in the experiments were provided by all the detectors. The system used a step-and-shoot design and acquired PV images with 21 angles at 3-degree increments ranging from \(-\)30 to 30 degrees [15; 16].  
Two types of data sets were used in our study: simulation data and human breast data. To create simulation data set, the positions of ligaments and tumors were sketched in 3D space and then the corresponding 3D images were projected to obtain projection data. Tumors and ligaments with different shapes were created in different layers to make our simulation data representative. The human breast data were obtained from the projections of full view DBT images of the Breast Cancer Screening [22]. The projection diagram is shown in Figure 3.  
Figure 2: (a) GE GEN2 prototype tomosynthesis system at the University of Michigan. (b) Description of DBT system structure used in the research. The top of the image is an X-ray source (60\({}^{\circ}\) total scanning angle, 21 projection view, 3\({}^{\circ}\) angular increment), the middle is a shelf for placing the measured object, and the bottom is a 1920 \(\times\) 2304 digital detector.
## 3 From EPOV to Content Neutrality
### Definition of Content Neutrality  
Our thesis is that _every_ single point of view is lopsided and that, consequentially, _there is no neutral point of view_. We assert that a knowledge management platform should adhere to strict content neutrality in the following sense: **Content neutrality** demands _to store and provide content without any evaluation of its merit regarding to any chosen cultural, social or ethical standard or even any other measure of notability, correctness or suitability of any kind_.  
The concept is closely related to **network neutrality**, which requires the internet service provider to transport bits independently of their economic status, origin or merit of content [15]. We are convinced that the strong arguments for network neutrality [16] apply accordingly.  
Note the different semantics of the adjective \"neutral\": Whereas in \"neutral point of view\" it denotes requirements for a specific text document, in \"content neutrality\" it _does not require the content to be neutral_ but rather the _system_ which delivers the contents to be _neutral in its treatment_ of said contents.  
_Consequential content neutrality_ goes so far as to accept _seemingly_ meaningless contributions, not claiming any authority at all over their value. For example: A distinction whether \"_Babalu Argu Manu_\" is malevolent spam or a highest artistic and Dadaistic expression of deep feelings simply is not made based on the content alone.  
**Differentiation:** Content neutrality must not be confused with providing free disc storage for meaningless randomized character-streams. The distinction comes with the pragmatics and the use of content. If \"_Babalu Argu Manu_\" is repeatedly _read_ by _users_ of an information storage system, then obviously it has _some_ relevance for them. In this case the _operator_ is not in the position to interfere, claiming POV, non-notability or other criteria. If the same data, however, gets written only once and is read nearly never, it may be regarded as spam. Content neutrality in the sense of our definition does not mean _publications [...] without bias_, since we believe that bias-free publications are not possible at all; it does mean _representing all views fairly_, which we believe can be achieved only by a dynamic writable collection of POV-variants. It is important to point out this subtle difference, since these two positions are sometimes (we believe: incorrectly) equated [17].  
**Terminology:** We shall use the word _version_ to designate the linear history of different revisions of a text, whereas the word _variant_ is reserved for different points-of-view, modalities or contexts of content.