esa-sceva/satcom-chunk-collection huggingface dataset:
# Recommendation System for a Delivery Food Application Based on Number of Orders
## 4 Proposed Recommendation System
### Clients and Restaurants Vectors  
For analyzing patterns and calculating distances among clients or restaurants, we represent them as vectors containing their number of orders. The vector \(c^{i}\) represents the \(i\)-th client. Its entries correspond to the number of orders to every restaurant. On the other hand, the vector \(b^{j}\) represents the \(j\)-th restaurant, whose values correspond to the number of orders from each client. It is essential to note that some clients could have the same preferences for restaurants, but their number of orders differs. Imagine the hypothetical case of having selected only eight restaurants, and the number of orders of Clients 1 and 2 are represented by the vectors \(c^{1}=\{12,0,0,0,0,20,0,4\}\) and \(c^{2}=\{6,0,0,0,0,10,0,2\}\), respectively. Those clients have the same preferences, meaning that their preferred restaurant is the sixth, followed by the first. The preference is the same, but the number of orders is different. Considering those cases, and for identifying clients' similarities, we use normalized vectors (see Equation (3)). In the example, \(\widehat{c^{1}}=\{0.333,0,0,0,0,0.555,0,0.111\}\) and \(\widehat{c^{2}}=\{0.333,0,0,0,0,0.555,0,0.111\}\).  
\[\widehat{c^{i}}=\frac{c^{i}}{\sum_{m}c^{i}_{m}} \tag{3}\]  
Figure 5: Similarity among clients and restaurants in 2021. The length of vertical lines represents the distance between items. The shorter the line, the closer the items. Different colors represent possible groups of items.
# Docker Performance Evaluation across Operating Systems
## 2 Docker Architectures
### Docker on Linux
#### 2.1.3 Network Namespaces  
Another namespace used by virtualization tools is the network namespace. This feature allows each container to have its own routing table, ARP table, and network device with IP address.  
Docker networks utilize Linux network namespaces in such a manner that constructing multicontainer applications--especially using docker compose--is easy and convenient. Docker network provides such features as isolation, where users can define inter-container connections. Containers can communicate in the same network using their names thanks to internal DNS resolution. Ports of docker containers can be exposed for external access, and mapped to avoid port collisions.
_applied sciences_  
_Article_  
**Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio**  
**[PERSON] 1,*-\(\copyright\), [PERSON] 1, [PERSON] 1,+, [PERSON] 1, [PERSON] 2\(\copyright\) and [PERSON] 3,*  
1 School of Engineering, Deakin University, Geelong, VIC 3216, Australia; [EMAIL_ADDRESS] (M.D.N.); [EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (N.M.)
2 Future Regions Research Centre, Federation University Australia, Churchill, Gippsland, VIC 3841, Australia; [EMAIL_ADDRESS]
3 Department of Civil Engineering, La Trobe University, Melbourne, VIC 3086, Australia
*Correspondence: [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (H.A.-N.)
4 Current address: Investment Department, Diwan Endowment, Baghdad, Iraq.  
**Abstract:** Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (\(R^{2}\) values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer.  
**Keywords:** alum sludge; soil stabilization; artificial intelligence; California bearing ratio; genetic programming
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 1 Introduction
### The Desire to Update P.372  
The predicted man-made noise levels in P.372 were derived from a limited set of measurements performed between 1950 and 1970 and has since remained unaltered. However, since that time, the number of electrical and electronic appliances has increased exponentially, and one would expect the man-made noise to increase with the number of appliances. On the other hand, the introduction of EMC (electromagnetic compatibility) requirements could have mitigated this increase. Objective information to this end is scarce.  
From the side of equipment manufacturers, there is a constant pressure to increase the predicted man-made noise levels in P.372. Their reasoning is that higher predicted levels of man-made noise in P.372 will allow higher levels of unwanted emissions in compatibility studies and higher emissions levels in EMC norms, as these emissions will be masked by the already existing man-made noise levels. And allowing higher unwanted emissions will make the design and production of their products cheaper. However, the P.372 curves are no protection curves, but they describe a current, potentially unwanted, situation. Some spectrum users--the victims of increased noise levels--also promote increasing the predicted man-made noise levels in P.372. They hope that raising the noise levels in P.372 will alert the authorities and result in stricter EMC and unwanted emission regimes to reduce the man-made noise. The latter is a misconception: the given noise levels are taken as a reference in compatibility studies without further discussion about trends and the necessity to act on them.
## 6 Implementation and Case Study  
In this section, we present the implementation of our proposed dynamic Distributed Unit (DU) sleep scheduling framework, supported by a case study evaluating its performance under both normal and adversarial conditions. We begin by formulating the sleep scheduling problem as an optimization task using an FL approach, trained on real-world datasets. We then describe the datasets utilized, including DAWN and OpenRAN Gym, and finally analyze the robustness of our solution against adversarial threats such as data poisoning and signaling attacks.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 4 Proposed Protocol
### Vehicle Registration Phase  
Each vehicle transmits a registration request to a fully trusted cloud server in this phase and receives a smart card.  
**Step 1:**: \(V_{i}\) inputs a user identity \((ID_{i})\) and password, \(PSW_{i}\), and chooses an integer, \(2^{4}\leq s_{0}\leq 2^{8}\). \(V_{i}\) computes \(RPSW_{i}=h(ID_{i}\parallel PSW_{i}\parallel s_{0})\) and \(PID_{i}=h(ID_{i}\parallel RPSW_{i}\parallel s_{0})\) and sends \((ID_{i})\) to \(CS\) in a secure manner.
**Step 2:**: After responding to the request of \(V_{i}\), \(CS\) randomly selects \(x_{i}\in Z_{p}^{*}\) and calculates \(N_{i}=h(PID_{i}\parallel s\parallel x_{i})\). Then, \(CS\) stores \(N_{i}\) on a smart card and sends it to \(V_{i}\) through a secure channel. \(CS\) also stores the pair \((PID_{i},x_{i})\) in a database.
**Step 3:**: Then, \(V_{i}\) computes \(M_{i}=h((h(ID_{i})\oplus PSW_{i})mods_{0})\) and \(N_{i}^{*}=h(RPSW_{i}\parallel M_{i})\oplus N_{i}\). \(V_{i}\) stores \((N_{i}^{*},M_{i},s_{0})\) on a smart card and deletes \(N_{i}\).  
Figure 2 presents the vehicle registration phase.
# Software Fault Localization Based on SALSA Algorithm
## 5 Results and Analyses
### RQ1: How Accurate Is SA-SBFL in Fault Localization?
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{**Technique**} & \multicolumn{2}{c}{**Top-1**} & \multicolumn{2}{c}{**Top-3**} & \multicolumn{2}{c}{**Top-5**} & \multicolumn{2}{c}{**EXAM**} \\ \cline{3-10}  & & **SBFL** & **SA-SBFL** & **SBFL** & **SA-SBFL** & **SBFL** & **SA-SBFL** & **SBFL** & **SA-SBFL** & **Improvement** \\ \hline \multirow{4}{*}{Chart} & Dstar & 5 & 13 & 16 & 19 & 19 & 25 & 0.042 & 0.035 & 26.19\% \\  & Jaccard & 6 & 13 & 17 & 19 & 20 & 24 & 0.038 & 0.032 & 15.79\% \\  & Tarantula & 7 & 14 & 20 & 21 & 22 & 25 & 0.037 & 0.033 & 10.81\% \\  & Ochiai & 6 & 11 & 17 & 20 & 19 & 25 & 0.039 & 0.036 & 7.69\% \\  & Ochiai & 6 & 12 & 17 & 19 & 21 & 23 & 0.028 & 0.025 & 10.71\% \\  & Op2 & 5 & 8 & 14 & 19 & 16 & 22 & 0.061 & 0.057 & 6.56\% \\ \hline \multirow{4}{*}{Lang} & Dstar & 23 & 28 & 45 & 54 & 55 & 56 & 0.044 & 0.035 & 20.45\% \\  & Jaccard & 22 & 29 & 45 & 53 & 56 & 56 & 0.043 & 0.035 & 18.60\% \\  & Tarantula & 21 & 30 & 45 & 52 & 57 & 58 & 0.042 & 0.032 & 23.81\% \\  & Ochiai & 22 & 29 & 44 & 55 & 56 & 56 & 0.043 & 0.034 & 20.93\% \\  & Ochiai & 21 & 30 & 45 & 54 & 55 & 56 & 0.042 & 0.035 & 16.67\% \\  & Op2 & 23 & 31 & 45 & 51 & 56 & 57 & 0.046 & 0.037 & 19.57\% \\ \hline \multirow{4}{*}{Math} & Dstar & 22 & 32 & 56 & 59 & 69 & 81 & 0.102 & 0.076 & 25.49\% \\  & Jaccard & 22 & 32 & 57 & 60 & 69 & 79 & 0.109 & 0.087 & 20.18\% \\  & Tarantula & 22 & 31 & 57 & 60 & 69 & 79 & 0.102 & 0.086 & 15.69\% \\ \hline \multirow{4}{*}{Math} & Ochiai & 22 & 32 & 58 & 63 & 70 & 81 & 0.102 & 0.092 & 9.80\% \\  & Ochiai & 22 & 32 & 56 & 61 & 69 & 79 & 0.104 & 0.087 & 16.35\% \\  & Op2 & 21 & 27 & 52 & 58 & 61 & 78 & 0.110 & 0.085 & 22.73\% \\ \hline \multirow{4}{*}{Time} & Dstar & 6 & 10 & 11 & 12 & 12 & 14 & 0.039 & 0.029 & 25.64\% \\  & Jaccard & 5 & 9 & 9 & 12 & 18 & 17 & 0.033 & 0.030 & 9.09\% \\  & Tarantula & 5 & 9 & 11 & 14 & 16 & 17 & 0.032 & 0.030 & 6.25\% \\  & Ochiai & 6 & 9 & 11 & 14 & 18 & 18 & 0.031 & 0.028 & 9.68\% \\  & Ochiai & 25 & 5 & 8 & 11 & 13 & 16 & 17 & 0.037 & 0.032 & 13.51\% \\  & Op2 & 8 & 11 & 12 & 11 & 14 & 14 & 0.041 & 0.037 & 9.76\% \\ \hline \multirow{4}{*}{Closure} & Dstar & 14 & 20 & 38 & 42 & 39 & 87 & 0.044 & 0.030 & 31.82\% \\  & Jaccard & 13 & 17 & 27 & 36 & 37 & 80 & 0.046 & 0.031 & 32.61\% \\ \cline{1-1}  & Tarantula & 12 & 15 & 26 & 33 & 36 & 79 & 0.031 & 0.025 & 19.35\% \\ \cline{1-1}  & Ochiai & 14 & 15 & 29 & 35 & 39 & 81 & 0.037 & 0.026 & 29.73\% \\ \cline{1-1}  & Ochiai & 13 & 14 & 27 & 33 & 37 & 77 & 0.036 & 0.026 & 27.78\% \\ \cline{1-1}  & Op2 & 17 & 15 & 33 & 32 & 42 & 85 & 0.053 & 0.039 & 26.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fault localization results of SA-SBFL and SBFL using different statistical formulas.
## 3 Piperine: Human Clinical Trials
### Clinical Trials on Piperine Effect on Human Health
#### 3.2.2 Vitiligo  
Two clinical trials have evaluated the effect of topical piperine on vitiligo treatment and found positive effects (Table 2). In patients with facial vitiligo, topical treatment with piperine combined with narrowband UVB (NB-UVB) therapy resulted in significantly higher repigmentation rate at time intervals of 1, 2, and 3 months after treatment than in the control group [182]. In another study [183], a _Piper nigrum_ fruit extract and pure piperine were integrated into different ointments and applied for 12 weeks to different skin areas of the vitiligo patients. The results showed a higher efficiency of the black pepper extract than that of the pure piperine. In a study of [PERSON] et al. [187], an Ayurvedic preparation consisting of dried ginger, black pepper, pippali and leadtwort root fermented in cow's urine was given twice daily before a meal to vitiligo patients as a drink. After six months of the treatment, only 40% of the patients showed relief of symptoms.
# Development of Energy-Based Impact Formula--Part I: Penetration Depth
## 3 Development of New Energy-Based Penetration Formula
### Concept of New Formula  
#### 3.1.1 Impact Mechanism and Energy Conservation  
[PERSON] et al. [7] defined the impact mechanism and derived the consumed energies. In this paper, the impact mechanism, regardless of the panel's thickness, is redefined, and each consumed energy is newly derived, by referring to [PERSON] et al. [7]. The energies involved in the impact mechanism can be divided into five types, and two additional types in the case of special conditions, as shown in Figure 2. The first is the kinetic energy of a projectile (\(E_{K}\)), considered as external energy, which is an essential critical energy for concrete failure. The second is the deformed energy of a projectile (\(E_{DP}\)) at the moment of contact. The third is the elastic deformed energy of a concrete panel (\(E_{EP}\)). The fourth is the overall deformed energy of a concrete panel (\(E_{SD}\)). The fifth is the spalling-resistant energy (\(E_{SP}\)), which determines the type of failure mode. This energy is the energy generated by the concrete drag force around the collision point. One of the additional types of energy is the tunneling-resistant energy (\(E_{T}\)), which occurs when a flying projectile has energy over the spalling-resistant energy. The other type includes the scabbing-resistant energy (\(E_{SC}\)), which occurs when the panel thickness is insufficient for absorbing \(E_{SP}\). In this case, all the kinetic energy, except for \(E_{DP}\), is transferred to the concrete panel. However, if the concrete panel's thickness is insufficient to absorb the collision energy, it is reflected from the opposite face and can cause scabbing failure. Conversely, if a panel is thick, the scabbing-resistant energy (reflected energy on the rear face of the concrete panel) is weak or does not occur. Thus, \(E_{SC}\) can occur under certain conditions, and is not included in the energy conservation law between the kinetic energy of a projectile and consumed energies defined as Figure 2a-e.  
Figure 1: Nose shape and definition (redrawn from [PERSON] and [PERSON] [26]).
## 1 Introduction
### Related Work
#### 1.4.1 Spoken Term Detection  
Spoken term detection has been a hot topic in the past few years due to its many applications and has received a great deal of interest from many outstanding companies and research institutes, such as IBM [14; 32; 33; 34; 35; 36], BBN [37; 38; 39], SRI & OGI [40; 41; 42], BUT [17; 43; 44], Microsoft [45], QUT [46; 47], JHU [16; 48; 49; 50], Fraunhofer IAIS/NTNU/TUD [15], NTU [31; 51], IDIAP [52] and Google [21], among others. Within an STD system, the ASR subsystem uses mostly word-based speech recognition [53; 54; 55; 56; 57; 58; 59; 24; 53; 59] due to its better performance in comparison with subword-based approaches.  
However, subword-based ASR [60; 61; 62; 63; 64; 60; 65; 22; 24; 66] is also being used, sometimes in combination with word-based ASR. One of the main challenges of using word-based ASR in this context is that, in principle, only in-vocabulary (INV) terms can be detected. Subword-based ASR, on the other hand, can detect terms even though they are not in the vocabulary of the recognizer (i.e., out-of-vocabulary (OOV) terms). A more robust system could be obtained by combining both approaches [17; 24; 25; 38; 39; 42; 50; 67; 68; 69; 70; 71; 72; 73].  
The availability of ASR toolkits, such as Kaldi [50; 74] and ESPnet [75], among others, facilitates the development of STD systems. For instance, Kaldi includes the tools to build a complete STD system since it integrates an ASR subsystem, a term detector and a decisionmaker [74; 76; 77]. It uses a word-based approach and proposes a method based on proxy-words for OOV detection that replaces each OOV word by the most similar in-vocabulary word (or word sequence) [78].  
In current state-of-the-art neural end-to-end ASR approaches, it is common to use plain characters or word fragments as units [75; 79], which theoretically avoids the OOV issue. However, in practice, these systems are combined with language models to improve recognition accuracy, which again limits the vocabulary of the ASR system.  
Deep learning and, in particular, end-to-end systems were also recently investigated to solve the STD problem directly. In this direction, several end-to-end ASR-free approaches for STD were proposed [13; 34; 35; 36]. In addition to exploring neural end-to-end approaches, deep learning is extensively used to extract representations (embeddings) of audio documents and query terms that facilitate the search [20; 21; 23; 25].
# Test-Retest Reliability of a 6 DoF Marker Set for Gait Analysis in Cerebral Palsy Children
## 2 Materials and Methods
### Participants Selection  
A convenience sampling of eight children (two females and six males) with cerebral palsy was recruited from two Portuguese cerebral palsy centres to participate in the study. Firstly, the participants' clinical history was reviewed, and a clinical exam was performed with the subject laid on the table, seated on a chair, or standing. The eligibility criteria were as follows: male and female children, between 4 and 16 years of age; with a clinical diagnosis of Unilateral Spastic Cerebral Palsy or Bilateral Spastic Cerebral Palsy of crural predominance, grades I and II in the Gross Motor Function Classification System(GMFCS) [28]; able to walk independently with or without walking aids; cooperative and able to comply with simple orders; feet size between 20 and 33; who had a clinical recommendation to use an ankle foot orthosis, but have never used it before, or during the trials; who have not undergone orthopaedic surgery of the lower limb in the last 12 months, and who are not expecting to have a surgical intervention in the next 6 months; and who were not given botulinum toxin in the last 6 months [29]. The protocol was approved by and executed in accordance with the Faculty of Human Kinetics Ethics Committee (CEFMH-2/2019). An informed consent was previously signed by the parent or the legal guardian of the participant.
# Non-Classic Atmospheric Optical Turbulence: Review
## 6 Influence in Applications
### Wireless Optical Communications
#### 6.1.4 Mean Bit-Error Rate  
The probability of error of a beam wave after propagation in the atmospheric turbulence is the conditional probability that must be averaged over the PDF of the random received signal to determine the unconditional mean BER. In terms of a normalized signal with unit mean, if a direct detection scheme is used, this leads to the following expression [95]:  
\[PE=\frac{1}{2}\int\limits_{0}^{\infty}P_{u}(u)\text{Erfc}(\langle SNR\rangle \frac{1}{4}u\sqrt{2})du. \tag{50}\]  
where Erfc is the complimentary error function and the PDF of the instantaneous intensity is assumed to be log-normal distributed with the unit mean. In Figure 20, we show the mean BER for different values of power law \(\alpha\) as a function of the mean SNR (in dB), for several sets of anisotropy factors (shown as \(z_{x}\)eff and \(z_{y}\)eff in the legend) and a given value of the structure function (kept fixed) [100]. The effect of anisotropy on the mean bit-error rate is well visible: as for the probability of fade and the signal-to-noise ratio, the decrease in scintillation, due to anisotropy leads to a decrease in the bit-error rate.  
[PERSON] and [PERSON] also showed that the removal by adaptive optics of several Zernike modes from the turbulence-affected phase can be very effective in reducing scintillation in non-classic turbulence [101].
# Wireless Home Assistive System for Severely Disabled People
## 3 Results and Discussions
### The Experimental Results of MCT
#### 3.1.1 The Efficiency of MCT used as a Mouse  
In order to evaluate the efficiency of MCT with a button used as a mouse, a mouse function test interface for MCT was designed and shown in Figure 8. During the test of MCT set in mouse mode, there are nine icons on the screen (17\", 1920 \(\times\) 1080) in which the arrangement of icons is shown in Figure 8: one start button is on the center of the screen, four icons numbered 1, 3, 5, and 7 are at the four corners of the screen and four icons numbered 2, 4, 6, and 8 are on the four edges of the screen. The subject was asked to move the cursor to each flower icon position numbered sequentially from 1 to 8 and click each icon by inputting the corresponding Morse codes of mouse control commands to MCT. The software can count and show the total time cost when the user finishes all the procedures per cycle, so that the user can know how fast he/she finishes this mouse mode test of MCT. Each subject is asked to play the mouse function test 3 times; the subject can rest for 3 min between two cycles. The 8 severely disabled participants were invited to attend the mouse function test, and the test result is shown in Figure 9; the average time cost of the first, the second, and the third cycle are 86.45, 74.62, and 67.76 s, respectively.
## 5 Experiment and Discussion
### Experiment Result and Discussion
#### 5.3.2 Node Clustering Task  
This paper uses the K-mean algorithm to cluster the generated node embeddings. The number of clusters is set as the number of category types of the target node. The clustering task is also repeated 10 times, and the average value of the Adjusted Rand index (ARI) is shown in Table 3.  
It is easy to find that MRDE is always better than other comparison methods in Table 3. Combined with the results of the node classification task, it is found that most comparison methods have different degrees of over-smoothing; that is, the node embeddings in the local structure become too similar. In other words, similar node representations are beneficial to the classifier to classify nodes to a certain extent. On the contrary, during node clustering, similar node representations will make nodes clustered together and become indistinguishable. By comprehensively considering the distribution of the nearest neighbors under multiple adjacency matrices and selectively extracting useful information from them, MRDE can effectively prevent the occurrence of the over-smoothing problem.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Dataset** & **Training Set** & **Indicator** & **Raw** & **GCN** & **GAT** & **M2V** & **JUST** & **CTDNE** & **C2V** & **MRDE** \\ \hline \multirow{4}{*}{DBLP} & Micro-F & 0.715 & 0.699 & 0.819 & 0.593 & 0.824 & 0.899 & 0.906 & **0.930** \\  & Macro-F & 0.725 & 0.698 & 0.813 & 0.513 & 0.835 & 0.