esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 8 Conclusions and Future Research
Let us define its consumption by \(J_{\text{tx},s}^{(j)}\) with a probability of \(P_{\text{tx},s}^{(j)}\):  
\[P_{\text{tx},s}^{(j)}=\left(1-FER^{(j)}\right)\cdot P_{s}^{(j)} \tag{13}\] \[J_{\text{tx},s}^{(j)}=\rho_{\text{tx}}^{(j)}\cdot T_{\text{tx}, \text{data}}^{(j)}+\rho_{\text{rx}}^{(j)}\cdot T_{\text{ack}}+\rho_{\text{ idle}}^{(j)}\cdot\left(SIFS+2\delta+DIFS\right) \tag{14}\]  
where \(P_{s}^{(j)}\) represents the probability of success of station \(j\):  
\[P_{s}^{(j)}=\tau^{(j)}\prod_{i=1,\ i\
eq j}^{|\mathcal{S}|}\left(1-\tau^{(i)}\right) \tag{15}\]  
We consider a corrupted transmission event when a transmission does not collide but fails due to noise influence:  
\[P_{\text{tx},\sigma}^{(j)}=FER^{(j)}\cdot P_{s}^{(j)} \tag{16}\] \[J_{\text{tx},c}^{(j)}=\rho_{\text{tx}}^{(j)}\cdot\overline{T}_{ \text{data}|c}+\rho_{\text{idle}}^{(j)}\cdot\left(EIFS+\delta\right) \tag{17}\]  
Analogously, the probability that station \(j\) suffers a collision transmission event is given by \(P_{\text{tx},c}^{(j)}\), whose consumption is defined by \(J_{\text{tx},c}^{(j)}\):  
\[P_{\text{tx},c}^{(j)} =\tau^{(j)}\cdot\left(1-\prod_{i=1,\ i\
eq j}^{S}\left(1-\tau^{(i )}\right)\right) \tag{18}\] \[J_{\text{tx},c}^{(j)} =\rho_{\text{tx}}^{(j)}\cdot\overline{T}_{\text{data}|c}+\rho_{ \text{idle}}^{(j)}\cdot\left(EIFS+\delta\right) \tag{19}\]  
The reception probabilities can be derived from the aforementioned transmission probabilities. Notice that stations and access points may behave different. For example, the successful packet reception event is characterised by consumption \(J_{\text{rx},s}^{(j)}\) with probability \(P_{\text{rx},s}^{(j)}\):  
\[\left\{ \begin{aligned} & P_{\text{rx},s}^{(j)}=\sum_{i=1,\ i\
eq j}^{S} \left(P_{\text{tx},s}^{(i)}\right),&\text{if }j=AP\\ & P_{\text{rx},s}^{(j)}=P_{\text{tx},s}^{(AP)}\cdot\lambda_{\text{ rx}}^{(j)}/\lambda^{(AP)},&\text{if }j\
eq AP\\ & J_{\text{rx},s}^{(j)}=\rho_{\text{rx}}^{(j)}\cdot T_{\text{rx},\text{data}}^{(j)}+\rho_{\text{tx}}^{(j)}\cdot T_{\text{ack}}\\ &+\rho_{\text{idle}}^{(j)}\cdot\left(SIFS+2\delta+DIFS\right) \end{aligned} \right. \tag{20}\]In addition, stations may hear frames whose destination are not themselves, we term this event as overhear:  
\[ \begin{cases}P_{o,s}^{(j)}=0,&\text{if }j=AP\\ P_{o,s}^{(j)}=\left(\sum_{i=1,\ i\
eq j}^{S}P_{\text{tx},s}^{(i)}\right)-P_{\text {rx},s}^{(j)},&\text{if }j\
eq AP\\ \end{cases}  \tag{12}\] \[ \begin{split} J_{o,s}^{(j)}=\rho_{\text{rx}}^{(j)}\cdot \overline{T}_{\text{data}|s}+\rho_{\text{rx}}^{(j)}\cdot T_{\text{ack}}\\ +\rho_{\text{idle}}^{(j)}\cdot\left(SIFS+2\delta+DIFS\right)\end{split}
# Deep Clustering of Student Code Strategies Using Multi-View Code Representation (CMVAE)
## 3 Methodology  
### Problem Formulation  
In programming education, students often encounter the same algorithmic problem and submit various solutions. Despite implementing the same functionality, these solutions can vary significantly in terms of the coding strategy used. For example, the same functionality that implements array sorting can have many different strategies, such as selection sorting by constantly selecting the smallest element to swap positions, or insertion sorting by inserting elements into a sorted section. However, due to the lack of explicit labels, it is challenging to directly determine which solutions employ similar strategies. Identifying and grouping student codes based on the strategies underlying these problem-solving solutions is critical to providing more personalized feedback and enabling students to optimize their problem-solving approach.  
The main challenge lies in automatically extracting and clustering solution strategy-related code representations from student code submissions. Given a set of code submissions, our goal is to group them based on the similarity of their solution strategies. The point of difficulty lies in capturing the structural and semantic aspects of the code, which are crucial for identifying code strategies beyond the functionality of the code.  
Formally, we define the problem as follows:  
Given a set of code snippets \(C=\{c_{1},c_{2},\ldots,c_{n}\}\), where each code snippet \(c_{i}\) represents a solution to a common algorithmic problem, we aim to group these snippets into clusters \(Z=\{z_{1},z_{2},\ldots,z_{k}\}\), where each cluster \(z_{j}\) contains code snippets that implement similar solution strategies. The challenge is to design a model that can automatically learn a meaningful representation of the code, accounting for both structural and semantic features, and use these representations for clustering.  
The objective is to learn a function \(f:C\to R\), where \(R\) is the space of code representations, and then cluster the learned representations into \(k\) groups that reflect the underlying solution strategies. The model should capture both the syntactic structure and the semantic meaning of the code snippets.
## 3 Results and Discussion
### Adsorption of 4-Ethylphenol and 4-Ethylguaiacol
#### 3.2.1 Kinetic Studies of 4-Ethylphenol and 4-Ethylguaiacol  
The kinetic curves exhibited an initial stage in which the adsorption of 4-EP and 4-EG was fast, followed by a second step, in which the uptake steadily increases up to equilibrium concentrations. The single-component kinetic provides information regarding the mechanism involved and the rate-controlling step on the 4-EP and 4-EG adsorption [2].  
The removal of 4-EP and 4-EG reached 75 \(\pm\) 2% and 72 \(\pm\) 2% of the maximum amount removed within 3 h. The relatively slow adsorption of 4-EG might be a result of its higher solubility in water as well as its possessing the largest molecular size, compared to the 4-EP. As reported in a previous paper, an attractive adsorbent for organic compound removals must exhibit a large volume of micropores with widths of approximately 1.5 times the kinetic diameter of the target adsorbate [26]. The four commercial ACs present a mean pore varying between 0.97 and 1.11 nm, and 4-EG and 4-EP present an area of 29.5 and 20.2 A\({}^{2}\), respectively. The 4-EG could find resistance, to some extent, to enter the narrow micropores.  
With the Merck AC, only 7 h were needed for both phenolic compounds to achieve their thermodynamic equilibrium. In order to facilitate and quicken laboratory work, and as in any industry, \"time is money\", an equilibrium time of seven hours was chosen to carry out the remaining tests. However, results not shown here revealed that an increase in the equilibrium time, up to 48 h, allowed an increase in the 4-EP and 4-EG maximum adsorption quantities, mostly on granular ACs, such as those from Norit.
## 2 Experimental Programme
### Shear Capacity Calculation by Various Standards
#### 2.1.1.1 Aci 318-14  
ACI 318-14, established by the American Concrete Institute in 2014 [39], is a concrete design standard that incorporates a specific shear design section for assessing the shear strength of hollow slabs. The shear resistance of reinforced concrete members arises from the combined actions of concrete (\(V_{c}\)) and shear reinforcement (\(V_{s}\)). Equation (1) provides a basic estimate of \(V_{c}\), while a more refined expression incorporating the influence of concrete compressive strength is given by \(f_{ck}\); \(b_{w}\) represents the width of the cross-section (\(mm\)), \(d\) is the effective depth of the cross-section (\(mm\)), and the detailed formula of the combined actions of concrete is given by \(V_{c,detailed}\). Furthermore, shear span-to-depth ratio (\(a\)/\(d\)), main tensile reinforcement ratio (\(\rho_{w}\)), lightweight concrete factor (\(\lambda\)), shear span-to-depth ratio (\(\frac{V_{c}d}{M_{u}}\)), and coefficient shear force (\(V_{u}\)) are presented in Equation (2). In addition, Equation (3) quantifies the shear reinforcement contribution (\(V_{s}\)), considering the cross-sectional area (\(A_{v}\)), spacing of the shear reinforcement (\(s\)), and inclination angle (\(\alpha\)) of the shear reinforcement. These equations form the basis for a comprehensive understanding of shear behaviours in reinforced concrete members.  
\[V_{C}=0.17\lambda\sqrt{f_{ck}}b_{w}d \tag{1}\]  
\[V_{c,detailed}=\left(0.16\lambda\sqrt{f_{ck}}+17\rho_{w}\frac{V_{u}d}{M_{u}} \right)b_{w}d \tag{2}\]  
\[V_{s}=\frac{A_{v}(\sin\alpha+\cos\alpha)d}{s} \tag{3}\]
# Analysis of Blasting Efficiency Using Crack-Inducing Holes and Pre-Splitting Method in Blasting
## 4 Discussion and Consideration  
In tunnel blasting, the location where vibrations are most significant is the cut area, where the confinement is greatest and the charge amount per blast is the highest. Various patented methods have been developed to reduce vibrations in the cut area [39]. However, the results observed in this experiment by the author showed a different outcome.  
Analyzing the vibration propagation characteristics through time-lag analysis, as shown in Table 6, the maximum vibration point for general blasting was found in the expansion section. Therefore, to reduce vibrations, it would be more effective to apply controlled blasting in the peripheral areas that can block vibrations in the direction of sensitive structures. In addition, for combined blasting, the largest vibrations were observed at the pre-splitting blast holes. It is believed that the vibrations were higher due to constraints such as the drilling conditions of the crack induction holes and the size of the hole diameter during pre-splitting blasting.  
[PERSON] et al. (2011) stated that, in the case of Smooth Blasting with pre-split cracks induced by empty holes, smooth fracture surfaces are formed up to a spacing of 40 cm [28]. [PERSON] et al. (2018) explained that the shape of the guide hole (circular, notched, or diamond-shaped) affects the crack control effect through experiments on crack control in blasting [17]. Therefore, to increase crack formation and suppress significant vibration during the preliminary blasting of peripheral holes, we plan to research the shape, size, and spacing of crack-inducing holes, as well as the explosive charge and firing sequence of pre-split  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**General Blasting** & **Combined Blasting** & **Remarks** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum blasting vibration occurrence area.  
blasting holes. To reduce errors from these various variables, dynamic numerical analysis and scaled model experiments should be conducted, and validation through full-scale field experiments is required.  
When the principles of combined blasting are established and applied to underground tunnel blasting, it is expected to reduce overbreak, lower reinforcement costs, and minimize vibrations, thereby preventing damage to adjacent structures. This would ultimately result in more economical blasting operations, both directly and indirectly.
## 5 Cryptosystems Choice
### Cryptosystem for Product Encryption
#### 5.3.2 ElGamal  
ElGamal [24] is a public key cryptosystem whose security holds on the assumed intractability of the _Discrete Logarithm Problem_ (DLP).  
This cryptosystem is set by choosing a large prime \(q\) satisfying that \(p=2q+1\) is also prime. The cryptosystem is built on the order-\(q\) multiplicative subgroup of \(\mathbb{Z}_{p}^{*}\). An order-\(q\) element \(g\) is chosen during the setup.  
A private key is generated by choosing a random \(x\in\{0,\ldots,q-1\}\) and the corresponding public key is computed as \(y=g^{x}\pmod{p}\).  
A randomly selected element from \(\mathbb{Z}_{p}^{*}\) turns out to be a public key if its order is \(q\). This happens exactly with a \(1/2\) probability since \(\mathbb{Z}_{p}^{*}\) contains exactly \(p-1=2q\) elements with \(q\) of them having the desired order.
# Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations
## 4 Experiments
### Experimental Results
#### 4.3.1 English-Korean Adaptation  
HuBERT consists of an encoder and a decoder. We utilized self-supervised pre-training on the HuBERT encoder using an English speech corpus, Librispeech [33]. We then attached a phoneme predictor to the pre-trained HuBERT encoder, and fine-tuned it on the Korean speech corpus, NIKLSEOUL, using the CTC loss for adaptation.  
**Was the English speech representation prior helpful?** Based on Table 2, even when there was no Korean data in the pre-training dataset, fixing the HuBERT encoder, which was pre-trained on a dataset of 960 h of English speech data from Librispeech, and fine-tuning only the attached phoneme predictor on the NIKLSEOUL dataset of 180 h, resulted in a performance of approximately 21.28%. Additional fine-tuning of the encoder without fixing it yielded a performance of 7.44%, after adapting to Korean. Compared to the baseline that trained directly from scratch, this reflects an improvement in performance of 17.55%. In this case, the self-supervised methods exhibited an ability to generalize beyond the language system of their input data, as their targets were not specific language labels, even when the input data came from a single specific linguistic system. The margin between the training from scratch and the fine-tuning of the pre-trained priors was 31.19% of phoneme-level error rate.
## 5 Conclusions  
In this study, we developed a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model. The proposed model is expected to help managers monitor the status of the assembly line process and to identify faults in the process, thus unexpected losses caused by faults can be prevented. Through this study, we showed that integrating IoT-based sensors with a big data processing system is effective for processing and analyzing large amounts of sensor data in real-time. The big data processing system developed in this study utilizes Apache Kafka, Apache Storm, and NoSQL MongoDB. The experimental results showed that the system is scalable and can process a large amount of continuous sensor data more efficiently than traditional models. Furthermore, the performance of the IoT-based sensor was analyzed with various metrics such as the network delay, CPU, and memory usage. For all experimental scenarios, the IoT-based sensor provided an efficient solution as it successfully collected and transmitted the data within an acceptable time with low computational cost.  
Fault detection is an important issue in the manufacturing process as it can identify whether the process is functioning normally or abnormally. We propose a hybrid prediction model that consists of DBSCAN-based outlier detection and Random Forest classification. DBSCAN was used to separate outliers from normal sensor data, while Random Forest was utilized to predict faults--given the sensor data as input. The results showed that the proposed hybrid prediction model is effective with high accuracy compared to the other models tested. The results of the study are expected to support management and improve decision-making during manufacturing, helping prevent unexpected losses caused by faults.  
Security is a big issue when more IoT devices are adopted, implemented, and connected. Therefore, the security of IoT devices and platforms should be considered in a future study. Furthermore, a variety of abnormal conditions during the manufacturing process should be further identified and collected so the proposed hybrid prediction model can be utilized to learn from a complex dataset in the near future.  
Conceptualization, M.S. and J.R.; Methodology, M.S. and G.A.; Software, M.S.; Formal Analysis, N.L.F.; Data Curation, N.L.F.; Writing-Original Draft Preparation, M.S.; Writing-Review & Editing, M.S. and G.A.; Visualization, N.L.F.; Supervision, J.R.  
**Funding:** This research was financially supported by the IT R&D program of MOTIE/KEIT [10052972, Development of the Reconfigurable Manufacturing Core Technology based on the Flexible Assembly and ICT Converged Smart Systems].  
**Acknowledgments:** This paper is a tribute made out of deep respect of a wonderful person, friend, advisor, and supervisor, [PERSON] (1965-2017).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 3 Related Work  
### Definition  
This study is dedicated to predicting the remaining service life of optical cables. During operation, optical cables are subject to various weather-related factors, including strong winds, high temperatures, low temperatures, and cumulative impacts, all of which can cause the cables to expand or contract. These environmental changes consequently affect the residual length of the internal fibers and thereby the lifespan of the cables. It forecasts future cable length trends based on historical changes and associated characteristics, considering the key factors influencing cable life. When the cable length reaches a critical threshold, it is determined that the cable has reached the end of its service life. The prediction of optical cable lifespan can be seen as a form of time series forecasting. This involves using a historical data series containing \(C\) features, \(X_{h}=\left\{X_{1}^{t},\cdots,X_{C-1}^{t}\right\}_{t=T+1}^{T}\), along with a known future data series following \(T\) timesteps, \(X_{f}=\left\{X_{1}^{t},\cdots,X_{C-1}^{t}\right\}_{t=T+1}^{T+1}\), to predict the cable length for the upcoming \(L\) timesteps, represented as \(Y=\left\{y_{T},\cdots,y_{T+L}\right\}\). The calculation formula is shown in Equation (1), where \(F()\) represents the fitted model, and \(\theta\) denotes the model's learnable parameters, which are optimized during the training process to minimize the prediction error.  
\[Y=F\left(X_{h},X_{f},\theta\right), \tag{1}\]
## 4 Results and Discussions  
### Results  
System Performance  
Multiple tests were conducted to evaluate the system's response time, recording the time from when the user entered the 600 m radius of a shopping center to the reception of a promotional ad. The results are summarized in Table 4, which shows the test conducted with the HEB, GranD Toreo, and Soriana Carrera shopping centers in Ciudad Victoria, Tamaulipas.  
During the tests, the geographic coordinates of these shopping centers were configured in the API's database.  
The data show that the average response time for ad delivery is approximately 131 ms.  
The offers recorded in the API database are for testing purposes. Figure 9 displays the available offers in three different shopping centers (HEB, GranD Toreo, and Soriana Carrera) as seen on a mobile application interface. Each screen represents a specific shopping center, showing the store's name, the calculated distance between the user and the shopping center, and a list of items on sale. For each item, details include the item name, regular price, discount percentage, and discounted sale price.  
* Soriana Carrera (Distance: 349 m): The available items include a professional notebook and school scissors, with their regular and discounted prices. The user's location (latitude and longitude) and an execution time of 131 ms are also presented.
* HEB (Distance: 344 m): The displayed offers include a soccer ball, a tennis ball, a baseball, and a bath towel, each with respective prices, discounts, and final sale prices. The latitude and longitude coordinates are shown at the bottom, with an execution time of 136 ms.
* GranD Toreo (Distance: 185 m): Offers here include a professional notebook, school scissors, and a soccer ball, with details on regular and sale prices. The geographic coordinates and execution time (126 ms) are also displayed.  
Each screen allows the user to retrieve offers with the button labeled \"OBTAIN OFFERS\" at the bottom, suggesting that the application can fetch real-time data based on the user's proximity to these shopping centers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & **Distance (m)** & **Response Time (ms)** \\ \hline
1 & 349 & 131 \\ \hline
2 & 344 & 136 \\ \hline
3 & 185 & 126 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Response time results and detection accuracy in different tests.  
One of the key aspects evaluated was the accuracy of the Haversine formula in detecting proximity. The data demonstrate that the implementation enabled precise identification of when the user was within the configured proximity radius for each shopping center, ensuring timely delivery of promotional advertisements.
# Heat-Only-Driven Vuilleumier Refrigeration
## 5 Discussion and Summary  
In this short note we present our findings on the cooling power optimal design of Vuilleumier refrigerators for heat-only operation. In particular, we investigated three competing designs: The D1 design, in which the hot displacer piston features two different volume displacements in the hot and medium working space thus providing the mechanical power needed for the entire refrigerator; the D2 design, in which the cold displacer piston provides the mechanical power needed for the entire refrigerator; and the DA design, where both displacer pistons each provide their own mechanical power. The surprising result we found is that the D2 design leads by far to the highest cooling power, followed by the DA design and the D1 design, which provide less than half the cooling power with an otherwise unchanged refrigerator. Even the DA design, which seemed to promise high performance with its increased optimizing potential due to the possible adjustment of both piston displacement ratios only provides approximately 3/4 of the cooling power compared to the D2 design. We caution the reader that these results of course depend on the parameter values chosen. The sensitivity investigations we performed for the medium regeneration extent, however, support that our results are very stable against variations of the two relevant thermodynamic transport coefficients. Moreover, it is obvious that future work must address the question of to what extent the results change if, in particular, different transport parameters are allowed for each individual flux present in the refrigerator. Nonetheless, this exploratory investigation shows that surprising features for the heat-only operation of Vuilleumier refrigerators can occur, which might have impact on the design of future devices operating on waste heat. Finally, we would like to mention that work carried out on the optimization of Stirling engines [52; 53; 54; 55; 56; 57; 58] of course provides interesting ideas to further optimize Vuilleumier refrigerators, and, in particular, those using heat-only-driven operation.  
[PERSON], [PERSON], [PERSON] and [PERSON] contributed equally to this article. All authors have read and agreed to the published version of the manuscript. This research was funded by the German Federal Ministry of Education and Research under support code 01 LY1706B. The publication of this article was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project number 491193532 and the Chemnitz University of Technology. The authors declare no conflict of interest.  
Figure 14: The rescaled displacement ratios \(d_{1}\) and \(d_{2}\) are shown as functions of the rescaled heat transport coefficient for the three engine designs D1, D2, and DA, and \(r=0.7\) regeneration extent. The rescaling is with respect to the properties of the maximum cooling power operating point for the base case.
## 4 Energy Efficiency Optimization  
Generally speaking, macrocell BSs are used to provide basic coverage, so their transmit power and density settings must ensure basic coverage. At the same time, due to the mobility and randomness of MUEs, their density cannot be controlled. Hence, we intend to improve the energy efficiency by jointly optimizing the transmit power and density of picocell BSs. Based on the above considerations, in this section, the density and the transmit powers of macrocell BSs and the density of MUE (i.e., \(\lambda_{1}\), \(P_{T1}\), and \(\lambda_{u}\)) are assumed to be given. It is difficult to directly optimize the density and transmit power of picocell BSs jointly, so we first discuss the optimization based on density and power separately.