esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation Metrics
#### 2.4.5 Micro-Average Metric  
Different from the macro-average, when calculating micro-average, we have to sum up individual true positive, false positive, and false negative and then take the average.  
\[\text{Micro average of precision}=\frac{\text{TP1}+\text{TP2}}{\text{TP1}+ \text{TP2}+\text{FP1}+\text{FP2}}\]  
\[\text{Micro average of recall}=\frac{\text{TP1}+\text{TP2}}{\text{TP1}+\text{TP2 }+\text{FN1}+\text{FN2}}\]  
\[\text{Micro average of F1}-\text{score}=2\times\frac{\text{Micro AvgPrecision}\times\text{Micro AvgRecall}}{\text{Micro AvgPrecision}+\text{Micro AvgRecall}}\]  
For example, we have two classes and TP1, FP1, FN1, TP2, FP2, and FN2 are true positive, false positive, and false negative of class 1 and class 2. Then each micro-average of precision can be calculated and recalled as described in the formula.
# Hybrid Beamforming for Reduction of Inter-Beam Interference in Millimeter-Wave Cellular Systems
## 2 Hybrid Beamforming Techniques for a Multicell Multiuser Environment
### Type-1 Precoder
However, we cannot find the pseudo inverse matrix for \(\widetilde{\mathbf{H}}^{b_{n}}\in\mathbb{C}^{BU\times U}\) because the effective network channel is given by a rectangular matrix with a large number of rows (\(\widetilde{\mathbf{H}}^{b_{n}}\left(\widetilde{\mathbf{H}}^{b_{n}}\right)^{ \dagger}\
eq\mathbf{I}\)). Thus, the zero-forcing (ZF) precoder, which is widely used in multiuser communication, cannot be obtained in a multicell multiuser environment. Note that the ZF precoder in a single-cell multiuser environment is given by an inverse (square) matrix of \(\widetilde{\mathbf{H}}^{b_{n}}\in\mathbb{C}^{U\times U}\). In the Type-1 precoder, the ZF precoding technique for a single-cell multiuser is applied to each cell. It is expected that the Type-1 precoder cannot achieve a high capacity because it cannot reduce the IBI from neighboring cells. When the Type-1 precoder is used, the achievable rate of the \(MS\)\(u_{n,m}\) served by the \(BS\)\(b_{n}\) is given by  
\[R_{u_{n,m}}^{b_{n}}=\log_{2}\left(1+\frac{\frac{\beta}{\beta}\left|\left(\mathbf{ w}_{RF}^{u_{nm}}\right)^{*}\mathbf{H}_{u_{nm}}^{b_{n}}\mathbf{F}_{RF}^{b_{n}} \mathbf{\tilde{\theta}}_{BB,u_{m}}\right|^{2}}{\frac{\beta}{\beta}\sum_{S\
eq n }\left|\left(\mathbf{w}_{RF}^{u_{nm}}\right)^{*}\mathbf{H}_{u_{nm}}^{b_{n}} \mathbf{F}_{RF}^{b_{n}}\mathbf{\tilde{\theta}}_{BB,u_{m}}\right|^{2}+\sum_{l \
eq n}\left\{\frac{\beta}{\beta}\sum_{S\
eq l}^{U}\left|\left(\mathbf{w}_{RF}^ {u_{nm}}\right)^{*}\mathbf{H}_{u_{nm}}^{b_{n}}\mathbf{F}_{RF}^{b_{n}}\mathbf{ \tilde{\theta}}_{BB,u_{l}}\right|^{2}\right\}+\varepsilon^{2}}\right) \tag{8}\]  
where \(\beta\) denotes the total transmission power of the \(BS\). Note that \(b_{l}\) and \(u_{l,g}\) denote the \(l-\)th \(BS\) and the \(g-\)th \(MS\), respectively, which are served by the \(b_{l}-\)th \(BS\). In addition, \(\mathbf{\tilde{\theta}}_{BB,u_{n},m}^{b_{n}}\) and \(\sigma^{2}\) denote the \((U\)\({}^{*}(n-1)+m)\)-th column vector of \(\mathbf{F}_{BB}^{b_{n}}\) and the variance of Gaussian noise, respectively. The first term in the denominator represents the interference caused by adjacent beams in the serving cell, and the second term represents the interference caused by beams from neighboring \(BS\)s. The sum-rate and average-rate of the system are given by  
\[R_{sum}=\sum_{n=1}^{B}\sum_{m=1}^{U}R_{u_{n},m}^{b_{n}},\ \ R_{avg}=\frac{R_{sum}}{BU} \tag{9}\]
# Utilizing Duplicate Announcements for BGP Anomaly Detection
## 2 Related Work
### Duplicate Announcements  
BGP update message consists of the network prefix and network layer reachability information. Let us say a prefix \(p\) is dispatched by any random router \(r\), and is said to be its corresponding duplicate; if the update message containing the attributes is similar to the latest prior update message for the same prefix, which is dispatched by the same router, then this type of repetition of the message from the same router is called a Duplicate announcement. In the stated announcement, both the messages updated and the earlier received prior update message must pertain to the identical routing session of the Border Gateway Protocol. The utilization of the protocol takes place in both inter-domain routing and intra-domain routing. In the case of inter-domain routing, the communication takes place between two different autonomous systems. It is referred to as the exterior Border Gateway Protocol (eBGP). In contrast, in the case of intra-domain routing, the communication takes place within the same autonomous system and is referred to as interior Border Gateway Protocol (iBGP).  
When the utilization of the protocol is performed in order to carry the network layer reachability information (NLRI) among the routers, which are located in two separate autonomous systems and establish the session, then the session established among the routers is called an eBGP session. In different BGP attributes, routing information is carried out separately in every update message. The commonly utilized attributes are the Next-hop attribute, the second is Multi Exit Discriminator attribute (MED), the Local preference attribute (Local-pref), and the more relevant one is the community attribute. When the protocol is utilized to dispense the network layer reachability information between the routers, which are located in the same autonomous system and create the session, then the session established among the routers is called an iBGP session. The loop should not be formed during the routing of the information and must be cast aside and prevented from the formation of the loop. It is essential for all the iBGP speaking routers to be fully connected, and the reachability obtained through different routers must not be transmitted to any other different iBGP speaking router connected in the network. In a practical scenario, this outlook is not applicable as it is non-scalable and, at the same time, it is highly expensive to handle. This gives a direction for the usage of route reflection [32]. This eliminates the need for a fully connected mesh between the neighboring routers. When an update message is traveled within the ASes among the iBGP peers, then there are chances of the creation of loops, so in order to avoid it, novel routes are designated by the AS confederation.
## 1 Introduction  
In the literature many authors have proposed advantageous long-term results for implant-supported single-unit crowns, as well as, implant-supported short-span fixed dental prostheses (FDP) [1, 2]. Implant success depends on tissue biological response and on several other factors such as smoking habit, periodontal status and surgical technique [3, 4, 5, 6]. Primary and secondary stability are determining factors for successful implant osseointegration [7] and the absence of micro-movements is a necessary condition [8, 9, 10]. A combination of multiple variables could influence primary implant stability such as:  
* The quality and quantity of bone at the recipient site;
* The surgical technique used in order to place the implant;
* The macro-/microscopic morphology of the implant [11, 12, 13, 14, 15, 16].  
Secondary stability is the progressive increase in stability as a consequence of the dynamic interrelationship between new bone formation and remodelling occurring at the bone-implant interfacial zone [17]. In the literature it is demonstrated that the implant success depends on the quality and quantity of the bone as most important factors [18, 19, 20]. Bone resorption and healing delay are the result of implant failure due to weak bone quantity and quality. [PERSON] et al. showed a 35% failure rate in type 4 bone. In their study, the major risk factors for implant failure were weak bone quantity and quality [21]. Moreover, [PERSON] and co-workers demonstrated that the primary stability of the implant depends only on jaw and the bone type and not on other factors such as diameter, length and deepening of the implant [22].  
Therefore, the aim of the present study was to evaluate the influence of implant dimensions (length and diameter) and position (anterior and posterior maxilla) on the primary and secondary stability of implants placed in the upper arch.
## 4 Application to the L'Aquila 2009 Mw6.1 Seismic Sequence
### Application to L'Aquila Dataset  
We used the HYDE algorithm to define the parameters (strike, dip, length and fault zone thickness) of the Mw6.1 L'Aquila seismogenic fault, which generated the Mww6.1 mainshock and the aftershock sequence.  
Following the algorithm steps, we first used the earthquake distribution to identify a set of 1000 pivot hypocenters, assumed to be the rotation centers of the fault plane. We conducted different tests, both changing the possible length of the fault plane from 4 km to 32 km (with a 4 km sampling rate) and allowing the fault zone thickness to vary from 100 m to 800 m (with a 100 m sampling rate), according to previous studies [32]. The initial chosen thickness of 100 m takes into account the order of the localization error, which implies a flat fault. Increasing fault thickness values will comprise non-planar faults, as is often observed in real cases.  
Figure 8 shows the results for a fault zone thickness fixed at 500 m. The results (Figure 8a) show a clear density peak, which represents the presence of a well-defined fault. Figure 8b confirms the existence of a fault with a well-defined nucleus, which approximatively individuates the estimated dip and strike of the main L'Aquila fault.  
In particular, i Figure 9a,b presents, respectively, the estimated dip and strike of the L'Aquila fault as a function of the fault dimension and thickness. Both fault dip and strike are individuated where the represented functions have stable behavior. In particular, the estimated dip value is approximately 52 degrees and the strike value about 36 degrees, which matches the estimated values by [32; 35; 36] well.  
For a fault with thickness of 500 m and a length of 24,000 m, we obtain the solution shown in Figure 10, where it is clear that the main shock is contained in the parallelepiped, which individuates the main fault plane. This is a very important result considering that the algorithm does not use constraints, for example, ones based on the main shock location  
Figure 8: (**a**) A density diagram (normal to the fault) of L’Aquila distribution events: fault detection. (**b**) Occurrence events as a function of rotations around the x and y axes (the barcode gives the values).  
Figure 9: Estimated dip (**a**) and estimated strike (**b**) as a function of the fault dimension and thickness.  
to drive the solution. The main shock does not affect the exploration of spatial parameters at all. The algorithm does not impose any constraints forcing the fault plane to pass through the main shock. Moreover, the structure responsible for the main shock is generally the same as the one that triggers the aftershocks. Other substructures--whether parallel or even orthogonal to the main fault--are usually characterized by a lower number of earthquakes and are initially disregarded by the algorithm. This indicates that our algorithm is a very useful and reliable tool.
# Translating Speech to Indian Sign Language Using Natural Language Processing
## 4 Proposed Work  
The proposed system presented in this paper is a real-time audio to Indian Sign Language conversion system which will assist hearing-impaired people to communicate easily with other hearing people. The system comprises mainly six components:  
1. Audio-to-text conversion if the input is audio.
2. The tokenization of English text into words.
3. Parsing the English text into phrase structure trees.
4. The reordering of sentences based on Indian Sign Language grammar rules.
5. Using lemmatization along with part-of-speech tagging so that synonyms of words or the root form of a word can be used if the exact word is not present in the database.
6. Indian Sign Language video output.  
The overall efficiency of the system is improved, as it splits the word into letters. If the video for the corresponding word is not present in the database then it shows video output letter by letter so that it does not skip any word. Another unique feature of the system is that it can recognize phrases in the sentence and show sign language videos corresponding to the phrase if present in a database instead of word by word. The database contains around 1000+ videos which are a combination of self-recorded videos by the author and open-source ISL faculty videos. Thus, it increases the horizon and literacy of the system.
## 2 IEEE 1588 (PTP)  
### Overview of IEEE 1588  
IEEE 1588, also referred to as the PTP, is an international standard for the precise time clock synchronization of measurement and control systems used in various environments, such as communications, local computing, and distributed systems. IEEE 1588v1 (first published in 2002) operated only on local area networks (LANs), while IEEE 1588v2 (published in 2008) supports unicast and multicast modes. Through these standards, packets containing time-synchronization information are transmitted through routers and switches, providing precise time synchronization for various Ethernet-based protocols. IEEE 1588 specifies several functions for time synchronization; these include the best master clock (BMC) algorithm, which selects a clock master in a master-slave structure, and a method that calculates the delay and offset to synchronize the time clock of the slave nodes with that of the master. The standard also specifies several measures in the case of failure of the device selected as the master.
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.3 State-of-the-Art Thresholding Techniques  
In what follows, we review some of the state-of-the-art thresholding techniques that have been developed for general image processing tasks.  
The [PERSON]'s thresholding technique calculates an adaptive threshold for each heatmap position \((u,v)\) using the maximum and minimum values of a sliding window. If \(I_{max}(u,v)\) and \(I_{min}(u,v)\) are the minimum and maximum values, respectively, found in an \(n\times m\) sliding window, then the [PERSON]'s threshold, \(T_{u,v}\) is given as [51]:  
\[T_{u,v}=\frac{I_{min}(u,v)+I_{max}(u,v)}{2} \tag{6}\]  
Equation (6) depends on the value of a local contrast, \(c(u,v)\), as long as \(c(u,v)=I_{max}(u,v)-I_{min}(u,v)\) is above a certain limit \(c_{min}\). However, if the limit exceeds \(c(u,v)\), then the pixels are assumed to belong to a single class [51].  
Unlike the previous method, [PERSON]'s thresholding technique calculates an adaptive threshold for each heatmap position \((u,v)\) using the mean \(m(u,v)\) and the standard deviation \(\sigma(u,v)\) of the pixels within an \(n\times m\) sliding window. If \(k\) is an arbitrary constant that can take any value between 0 and 1, then [PERSON]'s threshold \(T_{u,v}\) is given as [52]:  
\[T_{u,v}=m(u,v)+k\times\sigma(u,v) \tag{7}\]  
Figure 6: Architecture of the shallow CNN model for simultaneous maize leaf disease classification and quantification. From the left, the maize leaf image is processed by three convolutional layers that learn the disease features. A dropout layer is added to reduce overfitting before converting the feature maps into a 1D array through flattening. Finally, the fully connected layers consist of two dense layers (with a ReLu and softmax transfer function, respectively) to yield the class probabilities. This architecture produces about 6,451,812 trainable parameters. The neural network was drawn using the PlotNeuralNet LaTex package [50].  
From Equation (7), it is clear that the value of the threshold is affected by \(k\) and the size of the sliding window.  
[PERSON]'s ThresholdingAs shown in Equation (8), [PERSON]'s technique also uses the mean and the standard deviation to determine the adaptive threshold, just like [PERSON]'s method. However, [PERSON]'s method adapts the threshold based on the contrast of the pixels in the sliding window [53].  
\[T_{u,v}=m(u,v)\bigg{[}1+k\bigg{(}\frac{\sigma(u,v)}{R}-1\bigg{)}\bigg{]} \tag{8}\]  
where \(m(u,v)\) is the mean, \(0.2\leq k\leq 0.5\) and \(R=max(\sigma_{(u,v)})\)[53].
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 5 Future Opportunities  
The studies discussed above demonstrate access to dynamics below ms and up to several hours by means of XPCS at storage rings, on length scales from about 1 \(\upmu\)m down to molecular and atomic scales. On the other hand, fast time scales between ns and fs can be reached at FEL sources. The gap between both regimes will be closed soon with the start of XPCS experiments at diffraction-limited storage rings [7; 8; 184; 185; 186], and the availability of new fast detectors (see Section 3.2). The accessible time and length scales of XPCS (and XSVS) experiments are summarized in Figure 13 and compared to further techniques. In general, XPCS and XSVS will thus cover more than 18 orders of magnitude in time, ranging from femtoseconds up to more than 1000 s at different facilities.  
New type of experiments will be enabled, in particular at diffraction-limited storage rings. Here, we will outline few directions.
## 3 Materials and Methods
### Twitter Communication Represented by Using Multilayer Network  
Given the framework \(\mathcal{MF}\) defined according to the (3), we model a Twitter network as the multilayer framework consists of four layers, thus \(m=4\). Each layer represents one aspect of communication on Twitter as follows.  
The first layer is the tweet layer, \(G^{1}=(V^{1},E^{1})\), in which nodes represent Twitter messages and two nodes \(i\) and \(j\) are connected with the directed link if message \(i\) and \(j\) have at least three words and/or hashtags in common. The direction of the link is defined according to the timeline; from the first tweet to the second tweet. The link weight is defined as the number of common words/hashtags. The second layer is the follower layer, \(G^{2}=(V^{2},E^{2})\), in which nodes represent Twitter users and two nodes \(i\) and \(j\) are connected with the directed link if user \(j\) follows user \(i\). This is an unweighted network; however, the whole network is weighted, and thus all weights in this layer are set to 1. The third layer is the reply layer, \(G^{3}=(V^{3},E^{3})\), in which nodes represent Twitter users and two nodes \(i\) and \(j\) are connected with the directed link if user \(j\) replies to user \(i\). The weight is defined as the number of replies. The fourth layer is is a mention layer, \(G^{4}=(V^{4},E^{4})\), in which nodes represent Twitter users and two nodes \(i\) and \(j\) are connected with the directed link if user \(j\) mentions user \(i\). The weight is defined as the number of mentions. Next, we define a set of interconnections between nodes of different layers. The first layer of tweets (tweet layer) is connected with the second layer (follower layer) in the way that there is a directed link from the user (follower) to every tweet that he/she posts. The reply layer is connected with the tweet layer in the way that there is a directed link from the user to the tweet if the user replies to this tweet. Analogously, the mention layer is connected with the tweet layer in the way that there is a directed link from the user to the tweet if the user mentions this tweet. The rest of the layers, \(G^{2}\), \(G^{3}\) and \(G^{4}\) represent a multiplex network in which interconnections are established between the same nodes. A multiplex network is a special case of the multilayer network in which interlayer links can only connect nodes that represent the same node in different layers.  
The model of Twitter represented as the multilayer network is illustrated in Figure 1. The figure is taken from [4] and adapted to the experiment of this study.  
Further explanations and details of the multiplex, which is illustrated in Figure 1, the connections between the nodes of the same or different layers, and the weights can be found in [4].
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
## 3 Our Approach
### Bilinear Pooling  
At the first and second stages, a deep network extracts local features \(\mathbf{X}\in\mathbb{R}^{D\times N}\), where \(N\) is the number of feature vectors, and \(D\) is their dimension. Bilinear pooling works as a global pooling approach that aggregates \(\mathbf{X}\) into the bilinear representation \(\mathbf{A}\in\mathbb{R}^{D\times D}\):  
\[\mathbf{A}=\frac{1}{N}\mathbf{X}\mathbf{X}^{T}+\epsilon\mathbf{I}, \tag{1}\]  
where \(\epsilon\) is a small value 1 \(\times\) 10\({}^{-7}\) and adds to diagonal values in the matrix \(\mathbf{A}\) for the sake of stability during training. Like the representation after average pooling and max pooling, the diagonal values in \(\mathbf{A}\) also preserve representative features in each separate feature dimension. Furthermore, non-diagonal values show richer information, i.e., the correlation between each pair of dimensions. Hence, bilinear pooling is more powerful and validates its effectiveness in many computer tasks [4; 5; 8; 9; 10; 11; 12; 19].