esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### System Architecture, Experimental Setup and Protocol  
Figure 1 depicts the overall system architecture, data measurement and flows, data processing, and CNN training for the proposed upper arm gym-workout classification.  
Our exercise monitoring system consisted of a laptop with a custom-developed Unity3D-based software application, an armband type 8-channel sEMG sensor (Myo armband, Thalmic Labs, Brooklyn, NY, USA), a RGB-d camera (Kinect v2, Microsoft, Redmond, WA, USA). Throughout the paper, the sEMG sensor and the RGB-d camera will be referred to as more common name: Myo armband and Kinect v2.  
A professional trainer was recruited as a pilot subject ([PERSON]). The Myo armband was mounted on [PERSON]'s right upper arm under a protocol director ([PERSON])'s supervision. Figure 2 shows two exercises demonstrated by the [PERSON] and the 8-channel deployment on his right upper arm. After being equipped with Myo armband, the [PERSON] was guided by the [PERSON] in front of Kinect v2 and given instructions about experimental procedure and safety. The [PERSON] was instructed to perform a dumbbell curl (target muscle: biceps brachii) at the first visit and a dumbbell kickback (target muscle: triceps brachii) at the second visit. The experimental protocol was as follows:  
1. According to [PERSON]'s hand signal at every 1.6 s, the [PERSON] performed a dumbbell curl one time (one trial).
2. After performing 10 trials, the [PERSON] was supposed to take a 15 min break (the end of one session) to minimize the effect of muscle fatigue.
3. Repeat six sessions.  
While the [PERSON] was performing the exercise, sEMG data and joint skeleton data were sent via Bluetooth and USB+Kinect adapter connections, respectively. The sampling rate of Myo armband was set to 20 ms; For each channel, hence, 80 time series data samples  
Figure 1: The overall system architecture: the measured data from Kinect v2 and Myo armband are merged and visualized. The visualized image is used as an input for CNN training. Both a pilot subject and a protocol director are wearing a mask by following the government quarantine instruction against COVID-19.  
Figure 2: The pilot subject demonstrates dumbbell curl and dumbbell kickback. For Myo armband Ch.1\(\sim\)Ch.3 and Ch.5\(\sim\)Ch.7 correspond to biceps and triceps brachii, respectively. Ch.4 and Ch.8 are placed on boundaries between two muscles.  
were recorded for one trial (corresponds to 1.6 s). An elbow joint angle was calculated by VITRUVIUS using skeleton data from Kinect v2 [10]. The same sampling rate was applied to Kinect v2; therefore, 80 data samples were stored for the elbow joint angle. After two days from the first visit, dumbbell kickback was measured under the same experimental protocol as well.
## 2 Mathematical Modeling
### Geometrical Evaluation
For this case (T-shaped channel), the optimization process was divided into three steps, as shown in Figure 3.  
Figure 3 illustrates that in the first optimization step, the ratio \(L_{1}/H_{1}\) is varied, while the remaining parameters (\(L_{0}/H_{0}\) and \(H_{0}/H_{1}\)) are kept fixed. To clarify how the terms are assigned, note that the minimum value found for the filling time is named the once minimized filling time (\(t_{\text{m}}\)), and the corresponding ratio \(L_{1}/H_{1}\) is called the once-optimized ratio of \(L_{1}/H_{1}\), (\(L_{1}/H_{1}\))\({}_{0}\). In a second step, the same process is repeated by varying \(L_{1}/H_{1}\) for the other ratios of \(L_{0}/H_{0}\) and maintaining \(H_{0}/H_{1}\) constant. At this stage of the process, two degrees of freedom are optimized (\(L_{1}/H_{1}\) and \(L_{0}/H_{0}\)). In this  
Figure 3: Flowchart of the performed simulations in geometric evaluation of the T-shaped empty channel in resin infusion simulation. case, the minimum filling time obtained is twice minimized (\(t_{\rm mm}\)), the \(L_{1}\)/\(H_{1}\) ratio is the twice-optimized ratio (\(L_{1}\)/\(H_{1}\))\({}_{\rm oo}\), and the \(L_{0}\)/\(H_{0}\) ratio is once optimized (\(L_{0}\)/\(H_{0}\))\({}_{\rm oo}\). Finally, the second step is repeated for the different ratios of \(H_{0}\)/\(H_{1}\) evaluated. Thus, the minimum filling time is the thrice-minimized (\(t_{\rm mmm}\)), and the optimal shapes are the thrice-optimized ratio of \(L_{1}\)/\(H_{1}\), (\(L_{1}\)/\(H_{1}\))\({}_{\rm oo}\), the twice optimized ratio of \(L_{0}\)/\(H_{0}\), (\(L_{0}\)/\(H_{0}\))\({}_{\rm oo}\), and the once-optimized ratio of \(H_{0}\)/\(H_{1}\), (\(H_{0}\)/\(H_{1}\))\({}_{\rm oo}\), completing the geometric optimization process.
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 1 Introduction
By using the orthogonal characteristics of the digital matrix, the digital and analog matrix are iteratively updated on the basis of the initial digital matrix. Compared with a traditional OMP algorithm [20] and the MD-HP algorithm in [24], the advantage of this algorithm is that it considers the influence of the beamforming matrix of the transmitter and the combining matrix of the receiver, and alternates on the premise of ensuring minimum error of transmitting and receiving signals. The beamforming matrix and the merging matrix are updated to optimize them to improve the system performance. The rest of the paper is organized as follows. In Section 2, the system model is described. In Section 3, the proposed algorithm is discussed. Section 4 provides the simulation results, while Section 5 concludes the paper.
# Reversed Lempel-Ziv Factorization with Suffix Trees +
## 5 Open Problems
### Applications in Compressors  
Although it seems appealing to use the reversed LZ factorization for compression, we have to note that the bounds for the number of factors \(z\) are not promising:  
**Lemma 3**: _The size of the reversed LZ factorization can be as small as \(\lg n+1\) and as large as \(n\)._  
Proof.: The lower bound is obtained for \(T=\mathtt{a}\cdots\mathtt{a}\) with \(|T|=2^{z-1}\) since \(|F_{1}|=|F_{2}|=1,|F_{x}|=2|F_{x-1}|\) for \(x\in[2\mathinner{.}z]\) with \(F_{1}\cdots F_{x}=(F_{1}\cdots F_{x})^{\mathsf{R}}\) being a (not necessarily proper) prefix of \(T[|F_{1}\cdots F_{x}|\mathinner{.}]\). For the upper bound, we consider the ternary string \(T=\mathtt{abc}\cdot\mathtt{abc}\cdots\mathtt{abc}\) whose factorization consists only of factors of length one since \(T^{\mathsf{R}}=\mathtt{cba}\cdot\mathtt{cba}\cdots\mathtt{cba}\) has no substring of \(T\) of length 2 (namely, \(\mathtt{ab}\), \(\mathtt{bc}\), or \(\mathtt{ca}\)) as a substring (cf. [46] ([Theorem 5])).  
Even for binary alphabets, there are strings for which \(z=\Theta(n)\):  
**Lemma 4** ([46] (Theorem 9)): _There exists an infinite text \(T\) whose characters are drawn from the binary alphabet such that, for every substring \(S\) of \(T\) with \(|S|\geq 5\), \(S^{\mathsf{R}}\) is not a substring of \(T\)._  
**Funding:** This work is funded by the JSPS KAKENHI Grant Numbers JP18F18120 and JP21K17701.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We thank a CPM'2021 reviewer for pointing out that it suffices to store \(W\) in \(z_{W}\lg n\) bits of space in Section 3.3, and that the currently best construction algorithm of the compressed suffix tree indeed needs \(\mathcal{O}(\epsilon^{-1}n)\) time instead of just \(\mathcal{O}(n)\) time.  
**Conflicts of Interest:** The author declares no conflict of interest.
# The Role of an Industrial Alkaline Wastewater in the Alkali Activation of Biomass Fly Ash
## 3 Results and Discussion  
### Characterisation of the Solid Precursors and the Industrial Wastewater  
In Figure 1, the chemical composition of metakaolin and biomass fly ash measured as oxides (by XRF) is presented. Metakaolin is mainly composed of SiO\({}_{2}\) (52.1 wt.%) and Al\({}_{2}\)O\({}_{3}\) (40.1 wt.%). Other relevant oxides include TiO\({}_{2}\) (2.0 wt.%), Fe\({}_{2}\)O\({}_{3}\) (1.9 wt.%), and K\({}_{2}\)O (1.1 wt.%).  
The biomass fly ash is a silica-rich precursor, SiO\({}_{2}\) accounting for 45.4 wt.%. The waste also contains significant amounts of Al\({}_{2}\)O\({}_{3}\) (14.0 wt.%), CaO (12.2 wt.%), K\({}_{2}\)O (6.1 wt.%), and Fe\({}_{2}\)O\({}_{3}\) (4.6 wt.%). Other oxides include MgO (2.6 wt.%) and Na\({}_{2}\)O (1.6 wt.%). The  
Figure 1: Chemical composition of MK and FA.  
differences in the chemical composition of the precursors will affect their alkali activation, metakaolin being more prone to alkali activation compared with the biomass fly ash (additional details in [30]). For this reason, in the present study a mixture of biomass fly ash (70 wt.%) and metakaolin (30 wt.%) was used.  
The sodium content in the alkaline effluent was measured by atomic emission. The results show that it contained 1116.7 \(\pm\) 138.5 ppm of sodium. The molar concentration of Na\({}^{+}\) in the effluent was 0.05 mol/dm\({}^{3}\). The sodium content coupled with the effluent pH suggests the possibility to act as an activator. Nevertheless, the sodium content in the effluent is much lower than the amount present in the sodium hydroxide solution, and for this reason the silica modulus (SiO\({}_{2}\)/Na\({}_{2}\)O) in the activator will increase as the effluent content in the activating solution increases. The silica modulus of the activating solution is known to affect the properties of the alkali-activated materials [31]. It has been reported that optimum modulus ranges are comprehended between one and two, depending on the nature of the precursors [32].  
The chemical composition of the industrial wastewater was evaluated by ICP, and results are presented in Table 2. The results show that sodium is the major element present in the effluent, while lower amounts of chloride and trace amounts of potassium were also detected. The sodium content measured by this technique is rather similar to the value determined by atomic emission, and this provides additional evidence of the wastewater alkalinity.
# Software Fault Localization Based on SALSA Algorithm
## 2 Related Work  
### Spectrum-Based Software Fault Localization  
Spectrum-Based Fault Localization (SBFL) is a typical method in dynamic analysis. The input for SBFL consists of code coverage information from all test cases, and the output is a list of code elements sorted in descending order of their suspicious values calculated by specific formulas. The \"spectrum\" in SBFL refers to the collection of code coverage information during the execution of the tested program, which is composed of information about the parts of the program that are executed during the test case execution. This information can include statements, basic blocks, branches, or functions.  
To more intuitively reflect the information about the location of defective program entities within the spectrum, [PERSON] et al. proposed a statistical formula known as Tarantula [14]. They speculated that statements covered by failed test cases have a higher degree of suspicion than those covered by successful test cases. Since the introduction of the Tarantula formula, researchers have proposed a variety of suspect value calculation methods. These include the Jaccard formula based on cluster analysis [15], the Ochiai formula inspired by biology [16], the Ochiai 2 formula that considers the impact of unexecuted or passed test cases [17], the Op2 formula, which performs optimally in single fault localization [17], and the Dstar formula [18], which has been proven to have the best comprehensive ranking performance. Nevertheless, no single statistical formula can perfectly adapt to the needs of all test programs [19].  
Many current software fault localization methods have been researched and developed based on traditional Spectrum-Based Fault Localization (SBFL) techniques. For instance, [PERSON] et al. [8] considered the variability among test cases. On the basis of the original spectrum information, they utilized the PageRank algorithm to account for the contribution of different test cases and recalculated the spectrum. Subsequently, they applied traditional SBFL techniques on the recalculated spectrum to achieve more effective fault localization. [PERSON] et al. [20] improved the effectiveness of SBFL technology in fault localization by introducing context information and filtering strategies. [PERSON] et al. considered the interactions between software entities and the propagation of influences to construct a Fault Influence Network (FIN) based on the complex network theory, calculating node weights to improve the accuracy of fault localization [21]. Thanks to the efforts of many researchers, Spectrum-Based Software Fault Localization (SBFL) has achieved numerous results. However, as software programs become increasingly complex and larger in scale, relying solely on spectra and statistical formulas is insufficient to pinpoint the location of faults accurately. Building on this, this paper takes into account the interaction between program statements and the propagation paths of faults during the execution process.
# Detecting Colluding Inter-App Communication in Mobile Environment
## 1 Introduction
This is important, as colluding attacks are usually perpetrated by more than two applications [16; 19] in order to have more chances to activate the malicious behavior;
* we developed and evaluated 20 (10 aimed to send data and the remaining 10 for data receiving) Android applications performing a colluding attack by exploiting the _RPC_ communication channel;
* we experimented with an extended set of colluding and not-colluding Android applications to evaluate the proposed method, also taking into account the colluding applications aimed to share information by exploiting _ExternalStorage_, _BroadcastReceiver_, and _RPC_, and we obtain better performance-related results as compared to [18], where an accuracy equal to 0.99 is obtained; our proposed method reaches an accuracy equal to 1. It should be noted that the other works in this research field are focused on the analysis of one application at a time, whereas our method, by analyzing multiple applications together, allows us to identify the presence of an inter-application communication. Furthermore, the other research base their work on the use of machine learning techniques, unlike ours, which is based on model checking techniques. The paper proceeds as follows: in the next section, we provide background information regarding the model checking technique, so that readers can fully grasp the information presented in this paper; in Section 3, the proposed method for the detection of colluding inter-app communication is presented; Section 4 presents the experimental analysis results to demonstrate the effectiveness of the proposed approach; the current literature regarding collusion attack detection is discussed in Section 5; in the last section, conclusions and future research directions are presented.
## 3 Optimization Model
### Initialization Module
#### 3.1.3. Total Cost (\(Ct\))  
Calculations  
Total cost (\(Ct\)) was calculated using Equation (5), which is referred to as direct cost (\(DC\)), indirect cost (\(IC\)), and late penalty fee (\(LPF\)). \(DC\) was calculated using Equation (6), which includes material cost and labor cost, and related costs including freight and transportation. Material cost was a function of material quantity from the BIM model, as shown in Equation (9), and labor cost was the function of daily payrate of workers, as shown in Equation (10). Indirect costs were majorly calculated as overhead cost based on working time with a project delay causing surplus indirect cost. \(IC\) was calculated using Equation (7) and LPF was calculated using Equation (8).  
\[Ct=DC+IC+LPF \tag{5}\]  
\[DC=MC+LC \tag{6}\]  
\[IC=Indirect\ factors\ \times\ Ta \tag{7}\]  
\[LPF=Daily\ penalty\ fee\ \times\ \textit{(Ta}-\textit{Tc}) \tag{8}\]  
where \(Tc\) is the contract construction time.  
\[MC=\sum_{i=1}^{N}[Material\ cost\ unit\ rate_{i}\ x\ Q_{i}\ ] \tag{9}\]  
\[LC=\sum_{t=1}^{T_{n}}[Number\ of\ workers\ per\ day_{t}\ x\ Pay\ rate_{t}\ ] \tag{10}\]
## 3 Off-Line Compression by Longest-First Substitution  
Given a text string \(w\in\Sigma^{*}\), we here consider a greedy approach to construct a context-free grammar which generates only \(w\). The key is how to select a factor of \(w\) to be replaced by a non-terminal symbol from \(\Pi\). Here, we consider the _longest-first-substitution_ approach where we recursively replace as many LRFs as possible with non-terminal symbols.  
_Example._ Let \(w=\mathtt{abaaabbababb}\lx@sectionsign\). At the beginning, the grammar is of the following simple form \(S\rightarrow\mathtt{abaaabbababb}\lx@sectionsign\), where the right-hand of the production rule consists only of terminal symbols from \(\Sigma\). Now we focus on the right-hand of \(S\) which has two LRFs \(\mathtt{aba}\) and \(\mathtt{abb}\). Let us here choose \(\mathtt{abb}\) to be replaced by non-terminal \(A\in\Pi\). We obtain the following grammar: \(S\rightarrow\mathtt{abaa}A\mathtt{ab}A\lx@sectionsign\); \(A\rightarrow\mathtt{abb}\). The other LRF \(\mathtt{aba}\) of length \(3\) is no longer present in the right-hand of \(S\). Thus we focus on an LRF \(\mathtt{ab}\) of length \(2\). Replacing \(\mathtt{ab}\) by non-terminal \(B\in\Pi\) results in the following grammar: \(S\to B\mathtt{aa}ABA\lx@sectionsign\); \(A\rightarrow\mathtt{abb}\); \(B\rightarrow\mathtt{ab}\). Since the right-hand of \(S\) has no repeating factor longer than \(1\), we are done.  
Let \(w_{0}=w\), and let \(w_{k}\) denote the string obtained by replacing an LRF of \(w_{k-1}\) with a non-terminal symbol \(A_{k}\). \(\mathit{LRF}(w_{k-1})\) denotes the LRF of \(w_{k-1}\) that is replaced by \(A_{k}\), namely, we create a new production rule \(A_{k}\rightarrow\mathit{LRF}(w_{k-1})\). In the above example, \(w_{0}=w=\mathtt{abaaabbababb}\lx@sectionsign\), \(\mathit{LRF}(w_{0})=\mathtt{abb}\), \(A_{1}=A\), \(w_{1}=\mathtt{abaa}A\mathtt{ab}A\lx@sectionsign\), \(\mathit{LRF}(w_{1})=\mathtt{ab}\), \(A_{2}=B\), and \(w_{2}=B\mathtt{aa}ABA\lx@sectionsign\).  
Due to the property of the longest first approach, we have the following observation.  
**Observation 1**: _Let \(A_{1},\ldots,A_{k}\in\Pi\) be the non-terminal symbols which replace \(LRF(w_{0}),\ldots,\)\(LRF(w_{k-1})\), respectively. For any \(1\leq i\leq k\), the right-hand of the production rule of \(A_{i}\) contains none of \(A_{1},\ldots,A_{i-1}\)._  
In what follows, we will show our algorithm which outputs a context-free grammar which generates a given string. Our algorithm heavily uses the SLSTree structure.
# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 2 Recommendation System Categories
### Demographic-Based Recommendation Systems  
As various quantitative research papers have displayed, collaborative filtering techniques can be enhanced by demographic correlation [20]. Demographic RSs can generate recommendations by categorizing users based on demographic attributes. Demographic RSs are especially useful when the amount of product information is limited. Demographic RSs aim to tackle and solve the scalability and cold-start problems. This system employs user attributes as demographic data to obtain recommendations (i.e., recommend products based on age, gender, language, etc.) [21]. The key advantage of demographic filtering RSs is that they are fast and straightforward in obtaining results using a few observations. These approaches also do not acquire the user ratings that are essential in content-based and collaborative-based filtering techniques. Demographic-based filtering techniques have several disadvantages. For example, the entire information collection for users is impractical, considering the security and privacy issues involved. Secondly, demographic filtering is mainly based on user interests, which forces the system to recommend the same item to users of related demographic profiles. Another challenge is the difficulty of modifying a customer profile when preferences change; this is known as the stability vs. plasticity problem.
## 3 RRSS Protocol
### RRSS Scheme
#### 3.3.2 Void-Avoiding Algorithm  
In the initialization phase, each sensor node broadcasts a hello packet periodically, and a \"void\" field is included in the hello packet to denote whether it is a void node or not. The procedure of void-avoiding algorithm is showed in Figure 7. Firstly, the sensor node which is unable to communicate directly with sink nodes fills the void field with \"1\" in its hello packet before receiving any hello packet from others, then broadcasts the hello packet periodically. The nodes which can communicate directly with the sink node fill the void field with \"0\". After receiving a hello packet with a stronger RSSB and \"0\" void field from a neighbor, the node fills the void field with \"0\" in its hello packet. After receiving a new beacon signal from the sink nodes, the node updates the beacon seqno in the candidate routing table and initiates the void node field with \"1\" in its hello packet. When a new hello packet is received, the node updates the void field according to the RSSB and void field of its hello packet according to the RSSB and void field in the hello packet from neighbors. A hello packet with a void node field filled with \"0\" indicates the neighbor is not a void node.  
When a node receives a hello packet of the void field filled with \"0\", the source node of the hello packet is qualified to be the candidate next-hop node. However, when the void field fills with \"1\", the source node of hello packet is not qualified to be the candidate next-hop.
## 1 Introduction
In actual signals, the spectrum of the noise and signals will overlap and then result in errors. The noise can modify the numerical value of spectral lines and interfere with the location of spectral lines. [PERSON] and [PERSON] studied the effect of the noise on the correction accuracy of the interpolation method [24]. [PERSON] analyzed the influence of the noise on the interpolation method qualitatively [25]. [PERSON], [PERSON] and other scholars also proposed the ratio correction method and developed the interpolation method of the general spectrum correction method, which solved the problem of the accurate solution to the amplitude, phase, and frequency of the discrete spectrum with large frequency interval [15; 26; 27; 28; 29]. The length of the signal affects the accuracy of the frequency resolution and frequency estimation. The longer the signal is, the higher the frequency resolution will achieve. However, it is not appropriate for real-time processing. The short-time signals make the frequency resolution be limited, which also affects its frequency accuracy. Based on the above analysis, this paper proposes the fault diagnosis method, which combines LMD and the ratio correction method, to improve the accuracy of fault frequencies of rolling bearing, especially for the short-time signals. The feasibility and effectiveness of the proposed method are verified by the analysis of measured signals. This method is also suitable for online real-time monitoring.