esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Fibre Stresses for Cyclic Loading  
The experimental results of pullout tests under cyclic tensile loading show that bond failure (fibre pullout) or material failure (fibre rupture) can occur (cf. Section 4.2.2 \"Cyclic Loading\"). In [1], extensive experimental investigations were carried out with regard to the fatigue strength of bare high-strength micro steel fibres under centric tensile loading. According to [1], and if e.g., a fatigue resistance of \(N=10^{6}\) load cycles should be maintained, the load amplitude in the bare steel fibre may not exceed \(\Delta\sigma_{\text{st}}^{\text{f}}\)=178.8 N/mm\({}^{2}\), resulting into a utilization rate of  
\[\eta_{\text{fat}}\Big{(}N=10^{6}\Big{)}=\frac{\Delta\sigma_{\text{st}}^{\text {f}}}{f_{\text{st}}^{\text{f}}}=\frac{178.8\text{ N/mm}^{2}}{3575.8\text{ N/mm}^{2}}=0.05 \tag{12}\]  
As mentioned above, in case of an inclined fibre extension a bending tensile stress has to be added to the centric tensile fibre stress. At first, this will not result in a higher amplitude, since the bending tensile stress will be present at top load as well as at bottom load. However, it will increase the load level.  
At the current stage of investigation and based on the obtained test results, it appears not feasible to formulate the utilization rate as a function of the fatigue stress, as the load amplitude could apply at different stress levels, and as the load cycles have a considerable influence. Based on current knowledge, a distinction between low-cycle (\(N\approx 10^{4}\)) and high-cycle fatigue (\(N\geq 10^{5}\)) is feasible in regard to the fibre design.
## Materials and Methods  
### Analysis  
TLC (thin-layer chromatography) and GC (gas chromatography) analyses were used to monitor the progress of biotransformation processes and to determine the purity of isolated compounds. We used silica gel-coated aluminium plates (DC-Alufolien Kieselgel 60 F254, Merck, Darmstadt, Germany) for TLC analysis. The GC analysis was performed on a CP03380 instrument (Varian, Agilent Technologies, Santa Clara, CA, USA). The temperature program, which was used in GC analysis on DB-1 column ((dimethylpolysiloxane, Agilent), 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m), was as follows: injector 250 \({}^{\circ}\)C, detector (FID) 300 \({}^{\circ}\)C, column temperature: 75 \({}^{\circ}\)C (hold 3 min), 75-80 \({}^{\circ}\)C (rate 2 \({}^{\circ}\)C/min), 80-150 \({}^{\circ}\)C (rate 17 \({}^{\circ}\)C/min), 150-300 (rate 40 \({}^{\circ}\)C/min), 300 \({}^{\circ}\)C (hold 1 min) (\(\alpha\)-acetylbutylrolactone **1** R\({}_{\text{t}}\) = 5.9 min; _anti_ stereoisomer of \(\alpha\)'-1'-hydroxyethyl-\(\gamma\)-butyrolactone **2** R\({}_{\text{t}}\) = 6.7 min; _syn_ stereoisomer of **2** R\({}_{\text{t}}\) = 7.2 min). To determine the enantiomeric excess of the products obtained during biotransformation, GC analysis was performed using chiral column Gamma DEX\({}^{\text{TM}}\) 325 (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Supelco) under the following conditions: injector 150 \({}^{\circ}\)C, detector (FID) 250 \({}^{\circ}\)C, column temperature: 80 \({}^{\circ}\)C (hold 20 min), 80-107 \({}^{\circ}\)C (rate 1 \({}^{\circ}\)C/min), 107-200 \({}^{\circ}\)C (rate 30 \({}^{\circ}\)C/min), 200 \({}^{\circ}\)C (hold 1 min) (substrate **1** R\({}_{\text{t}}\) = 27.9 min; (35,1\({}^{\circ}\)S)-**2**a** R\({}_{\text{t}}\) = 34.9 min; (36,1\({}^{\circ}\)R)-**2**b** R\({}_{\text{t}}\) = 35.5 min; (35,1\({}^{\circ}\)R)-**2**c** R\({}_{\text{t}}\) = 43.8 min; (36,1\({}^{\circ}\)S)-**2**d** R\({}_{\text{t}}\) = 44.3 min).  
Products of biotransformation were purified by column chromatography in silica gel (Kieselgel 60, 230-400 mesh) with eluent: hexane: diethyl ether 1:1. NMR spectra were recorded with a Bruker Avance DRX-500 (Bruker, Billerica, MA, USA) spectrometer in CD\({}_{\text{3}}\)OD solution.  
Optical rotation was measured with the P-2000 polarimeter (Jasco, Easton, PA, USA).
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 3 Proposed Method
### DFS-Based K-Shortest Path Search Algorithm  
Algorithm 2 is the pseudo-code representing the DFS-based K-Shortest Path algorithm. The goal of this algorithm is to search through DFS for all routes that can be reached from the starting node to the destination node and then select the top K routes with the lowest cost. First, initialize the array required for the discovery. Set the allPaths array to store paths, the allCosts array to store path costs, and the visited array to record whether or not to visit. After verifying that the current node is the destination node, DFS extends the current path to each unvisited neighbor and adds the corresponding edge cost to the allCosts. When the destination node is reached during the discovery, it stores the path and cost to this day. Then, the search to another path is continued through backtracking, which allows all possible paths to be discovered. When the discovery is complete, the saved paths are sorted in ascending order of cost and the top K paths are stored in the paths array to return. DFS, implemented through its iterative structure and stack, facilitates the implementation of pipelining.
# Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application
## 4 Conclusions  
The DFT-based channel estimation algorithm is proposed for GFDM system. From simulations results, the following points can be obtained:  
* The pilot symbol with \(\Delta k=3\) has a better performance in estimating the channel than the pilot symbol with \(\Delta k=6\). However, on the other hand, the efficiency of the information data decreases.
* DFT channel estimation improves channel estimation accuracy with a 9 dB MSE improvement for the Umi scenario, 4 dB in the Uma scenario and 3 dB in the Rma scenario for an MSE value of \(10^{-2}\) compared to LS estimation.
* The magnitude of the channel frequency response fluctuations is influenced by the number of tap channels and the magnitude of the gain of the power delay profile. The number of fluctuations decreases the accuracy of channel estimation. Although the number of tap channels in the Uma scenario is more than in the Umi scenario, the gain of each tap in the Umi scenario is greater, so that the estimation accuracy in the Umi scenario is decreased.  
Conceptualization, E.Y.H.; Supervision, E.Y.H., Validation, E.Y.H., A.K.P.; Writing--original draft, A.K.P.; Writing--review and editing, E.Y.H., A.K.P. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Politeknik STMI Jakarta through national funds and the Program Penelitian, Pengabdian kepada Masyarakat dan Inovasi, Institut Teknologi Bandung ITB (P3 MI-ITB).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Simulation Results and Security Evaluation
### Brute Force Attack
#### 4.7.2 Secret Key Space Analysis  
The control parameters of the sine, logistic, and Henon maps are crucial in determining the level of security provided by the encryption mechanism. As the number of keys utilized in the encryption process increases, it becomes increasingly difficult for potential attackers to break the security measures. In this study, a total of three different keys were utilized; these secret keys are \(k_{m}\) obtained from the fuzzy number, \(k_{1}\) obtained from the hybrid map of the logistic and sine maps, and \(k_{2}\) obtained from the hybrid of the triangular membership function and the Henon map. Ideally, key space used in encryption should be larger than \(2^{100}\)[34] to handle brute force attack. According to the IEEE [34] standard, the precision number of floating points is \(10^{15}\). As part of this experiment, where three floating-point keys are utilized, the total number of key spaces can be approximated using Equation (26).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Rice** & **House** \\ \hline MSE & 107.136 & 88.5046 & 93.44519 & 100.1538 & 131.523 \\ PSNR & 8.3611 & 8.5085 & 8.4240 & 9.3281 & 9.2477 \\ SNR & 2.7773 & 1.2926 & 1.7061 & 2.7145 & 4.3285 \\ \hline \hline \end{tabular}
\end{table}
Table 7: MSE, PSNR, and SNR.  
This equation serves as a crucial tool in estimating the size of the key space. This increased key space is better for resistance against brute-force attacks.  
\[\text{Keys}=\left(10^{15\times 3}\right)\approx 2^{149} \tag{26}\]
# Smart System for the Optimization of Logistics Performance of the Pruning Biomass Value Chain
## 1 Introduction  
Biomass is an important source of renewable energy sources. Biomass comprises among others, vegetation, energy crops, biosolids, forestry and agricultural residues, organic municipal waste, as well as some industrial waste [1]. Agricultural residues from the pruning operations of fruit plantations can be used as a renewable energy source. However, such utilization of pruning residues has been limited due to logistic-related constraints in harvesting, processing and transportingactivities [2; 3; 4]. Although there is a potential source of renewable energy from agricultural pruning residues, the challenges and opportunities of the logistics operations have not been assessed well considering the entire chains from pruning harvesting to transport to end users.  
It is therefore important to develop new improved logistics for pruning residues, which covers harvesting, transport and storage for agricultural pruning (fruit tree, vineyards and olive groove prunings and branches from up-rooted trees). This study aims to develop an integrated concept where location and quality allow a wise decision tool to support decisions for logistic operators. This paper describes the users' need for a smart logistics system (SLS), defines the components of SLS with technical and functional requirements and specifications, and determines relevant information/data to be documented and managed by the SLS.
## 2 Hardware Design and Implementation
The wireless transmission range is 50 to 300 m and can be extended by adding more routers. The working frequency is 2.4 GHz and the system power voltage is 3.0-5.5 VDC, which can be powered by battery or by solar energy based on the users' needs. The system has a relative low energy consumption, which is mainly due to the onboard sleep mode. In the normal operation mode the system has a working current of 10 mA, while in the sleep mode the current drops to 10 \(\,\)mA. The normal operation mode activates only when the data acquisition interval is arrive. The sleep mode activates by the onboard software when the data transmission completed. Considering the transmission rate of the ZigBee system, the data transmission is done in less than 5 ms. Thus, most of the time the system is working in the sleep mode. Based on the experiment data and the on-site experience a single sensor node powered by a typical 2000 mAh lithium battery has a life of more than two years working at a data acquisition interval of two minutes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Performances** \\ \hline Temperature Range & 0 \(\,\)C\(\rightarrow\)+125 \(\,\)C \\ Resolution & 0.1 \(\,\)C \\ Precision & \(\pm\)0.5 \(\,\)C \\ Data acquisition interval & 1 s\(\rightarrow\)24 h \\ Wireless transmission range & 50\(\rightarrow\)300 m \\ Max sensing nodes & 65,535 \\ Working frequency & 2.4 GHz \\ System power voltage & 3.0–5.5 V/DC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performances of hardware  
Figure 6: Schematic design of PCB.
## 2 Related Works
### He-MPC Hybrid SNNI  
HE-MPC Hybrid SNNI judiciously combines HE and MPC. In particular, HE-MPC Hybrid SNNI frameworks compute linear layers, e.g., fully connected and convolutional layers, using HE and non-linear activation functions, e.g., ReLU using MPC. [PERSON] [15] was the first work to combine an LHE scheme, i.e., BFV, to compute linear layers with GC to compute non-linear layers. [PERSON] [15] opened a new approach, which outperformed other approaches, i.e., LHE-based, TFHE-based, and MPC-based, at the time of publication (2018). Based on the idea of [PERSON], MP2 ML [16] was proposed, which combines the CKKS scheme with ABY [57] (BFV was replaced by CKKS and GC was replaced by ABY). Although Gazelle and MP2 ML can evaluate the ReLU function, offer efficient solutions, and keep the NN architecture secret from the client, the main bottleneck of these frameworks is the GC/ABY block, where the client and the server have to interact to jointly compute non-linear functions. Additionally, HE-MPC hybrid frameworks require converting between HE ciphertexts and MPC values.  
On the other hand, our framework, HeFUN, is an MPC-free interactive SNNI framework where we use HE only as the cryptographic primitive. Compared to HE-MPC hybrid frameworks, HeFUN is simpler and requires fewer communication rounds, as HeFUN escapes from the main bottleneck block, i.e., GC/ABY. We detail this in Section 5.1.  
Our protocol, HeFUN, overcomes the shortcomings of previous approaches by introducing two novel protocols for evaluating the ReLU function (HeReLU) and refreshing ciphertext (HeRefresh). Table 1 shows a comparison between HeFUN and existing approaches. HeFUN satisfies security, accuracy, and efficiency criteria. Notably, HeFUN is the first framework that meets all criteria only using LHE. The idea is that we allow the client and the server to interact _solely_ based on HE (without the need for any MPC protocols). We call this approach interactive LHE (iLHE).
# On the Complexity of the Bipartite Polarization Problem:
## 4 Solving the Bipartite Polarization Problem  
To solve the bipartite polarization problem, we use two approaches: one based on a complete algorithm to find the exact polarization, and another one based on a local search algorithm that finds an approximate solution.  
For the complete algorithm, we can find the polarization of a debate by solving the integer nonlinear programming formulation (MINLP) defined in [25]. Thus, we can find the bipartite polarization of a User Debate Graph \(\mathcal{G}=\langle\mathcal{C},\mathcal{E},\mathcal{S},\mathcal{W}\rangle\) by solving the following MINLP of it, where each node \(C_{i}\) from \(\mathcal{C}\) is associated with an integer variable \(x_{i}\), such that \(x_{i}=-1\) represents that \(C_{i}\) is in the \(L\) partition and \(x_{i}=+1\) represents that \(C_{i}\) is in \(R\):  
\[\begin{array}{l}max_{x}\left(\frac{1}{|\mathcal{C}|^{2}}\sum_{ \begin{subarray}{c}(x_{i}x_{j})\text{ {with}}\\ S(C_{i})\leq 0,S(C_{i})>0\end{subarray}}-S(C_{i})S(C_{j})(1-x_{i})(1+x_{j})/4.0 \right)*\\ \left(2+\frac{1}{|\mathcal{E}|}\sum_{(i,j)\in\mathcal{E}}-c(p(i,j))\cdot w(i, j)*(1-x_{i}*x_{j})/2.0\right)\\ \text{subject to: }x_{i}^{2}=1\ \ \forall C_{i}\in\mathcal{C}\end{array} \tag{1}\]  
Observe that the first summatory in the objective function represents the term \(SC(L,R,\mathcal{G})\), and the second one the term \(SWeight(L,R,\mathcal{G})\). Then, we use the branch-and-bound solver [29] of the SCIP Optimization suite (version 8.0) [30] to optimally solve problem instances with this MINLP formulation.  
For the approximate approach, we use the local search solver developed in [24], which is inspired by an algorithm for the maxcut problem [31]. The solver uses a greedy approach based on a steepest ascent hill climbing strategy plus restarts to escape from local minima. The number of steps of the local search algorithm is bounded by the number of nodes, and the number of restarts is set to 10. So, the worst case running time of the local search approach is linear in the number of nodes.  
The initial bipartition \((L,R)\) in the local search solver is randomly generated as follows. We place each user's opinion \(C_{i}\) in \(L\) with probability \(P_{L}=(1-\mathcal{S}(C_{i}))/2\) and in \(R\) with probability \(1-P_{L}\). That is, the closer \(\mathcal{S}(C_{i})\) is to \(-1\), the higher the probability to be placed initially in \(L\). As we will see in Section 6, this initial bipartition can be very close to the optimum solution for at least the polynomial time case of the problem we present in that section.
## 3 Results  
After implementing the VA system, various tests were conducted. The most representative test involved launching a query, searching the Internet for it, and storing the response in a cache. Subsequently, the same or a similar question was posed. The algorithm determines the similarity with the information stored in the cache and verifies if it is the same query. Depending on this, the response is delivered to the user either from cache or by searching the Internet. Additionally, the algorithm calculates the time elapsed in each part of the process performed, achieving millisecond-level accuracy. Figure 6 shows a screenshot of the prototype displaying the time results obtained with the algorithm during a test. This test demonstrates the difference in response times between cloud-based AI and responses retrieved from the local cache. In this exercise, for the tests (queries to the VAs), a bank of questions was created, classified into 5 categories: Weather (Q1), Places (Q2), History (Q3), Mathematics (Q4), and Characters (Q5). These queries showed that response times and energy consumption might depend on the type of topic or the size of the query itself. However, the relationship and differences between the results from the cache and those from the cloud remained consistent; in other words, response times were always higher when an internet connection was required. Table 3 presents 10 questions per defined category, each with their respective results in terms of measured time and power. The system's power consumption does not vary significantly across different queries or categories, with the average measured operating power of the virtual assistant remaining around 3.15 W.  
Subsequently, the averages for each category are calculated and presented in Table 4. In this case, query time, cache time, and cloud time are distinguished for each query. The standard deviation of response times for cache queries is 0.24 s, and for cloud tests, it is 1.95 s.  
Figure 7 illustrates the average times measured for each stage of the process, the time the VA takes to pose a question, the time to retrieve a response from the cache, and finally, the time required to obtain a response from the cloud. Figure 6: Direct image from the prototype display showing a test of elapsed times for cloud and local cache queries. The blue boxes highlight the performing times. Figure 7: Average time for query/cache/cloud.
## 2 Simulation and Observations Related to Gas Pollution
Figure 1: The flow scan and rate scan results: (**a**) the flow scan of two models, at a fixed rate of 30 kHz/cm\({}^{2}\); (**b**) the rate scan of two models, at a fixed gas flow of 60 mL/min. The simulation has been through iterations to a high precision, so the error bars are contained within the symbols. Figure 2: (**a**) The noise of MTD and TOF counters. Red shades mark the response operated with the beam off on that day; (**b**) The noise of MTD counters after blocking the extra gas volume. Figures are reproduced from [18]. In the laboratory, high-rate MRPCs [19; 20] were being tested using an X-ray source when unexpected current growth, recorded by the High-Voltage (HV) power source, was observed. As shown in Figure 3, the current of the SoLID prototype grows linearly in a stable counting rate which was monitored by a Time-to-digital Converter (TDC). The geometric information of the detectors mentioned in this paper can be found in Table 1, and we also report the X-ray tests performed on them in Table 2. The current behavior in a longer term, with the negative impacts, still needs investigations, but it can be inferred that noise and aging might become problems due to their relations to the MRPC current. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detector** & **CBM-MRPC2** [19] & **SoLID Prototype** [20] & **(High-Rate) Sealed MRPC** [12] \\ \hline Active area (cm\({}^{2}\)) & 32 \(\times\) 27 & 25.8 \(\times\) 8 & 32 \(\times\) 27 \\ Gas gaps & 4 \(\times\) 2 stacks & 8 \(\times\) 4 stacks & 4 \(\times\) 2 stacks \\ Gas gap width (mm) & 0.25 & 0.128 & 0.25 \\ Glass type & Low resistive & Low resistive & Low resistive/float \\ Strip pitch (mm) & 10 & 10 & 10 \\ Strip length (cm) & 27 & 25 & 27 \\ Gas box volume (cm\({}^{3}\)) & 60 \(\times\) 60 \(\times\) 10 & 35 \(\times\) 50 \(\times\) 10 & No box \\ Net gas volume (L) & 33.6 & 16.5 & 0.17 \\ Gas flow (mL/min) & 50 & 100 & 30/5 \\ \hline \end{tabular}
\end{table} 
Table 1: The geometry parameters of the MRPC prototypes discussed in the paper. Figure 3: The current response of a SoLID prototype in full X-ray irradiation, in an estimated counting rate of 1 kHz/cm\({}^{2}\). For CBM-MRPC2, it is estimated to run in a flux at 2-8 kHz/cm\({}^{2}\), and the current design of an MRPC2 module can be translated to a 19 L gas volume per counter. Thus, the results above have brought the concern of pollutant concentration in future operations. For this reason, the development and examination on the high-rate sealed MRPC have been encouraged. It is hoped that a better gas exchange and stable response to the high luminosity will be realized, while maintaining its good timing performance.
## 5 Results and Discussion
### Spectral Fatigue Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Revision** & **(i) Operation and Static Storm** & **(ii) Dynamic** & **(iii) Seismic** & **(iv) Seismic** & **(iv) Seismic** & **(v) Ultimate** & **(vi) Spectral** & **Status** \\  & **Static Storm** & **Storm** & **Resistance** & **Ductility** & **Wave Resistance** & **Fatigue** & **Fatigue** & **Fatigue** \\ \hline Reference value & UC \(<\) 1.00 & UC \(<\) 1.00 & UC \(<\) 1.00 & Cr \(>\) 2.0 & RSR \(>\) 2.20 & Life \(>\) 20 years & \\  & TOPSIDE: & TOPSIDE: & TOPSIDE: & & & & & \\ Unity check (UC) ratio & UC \(=\) 0.81 & UC \(=\) 0.64 & \({}^{*}\) UC \(=\) 1.001 & — & — & — & OK \\ in structural members & JAKCE: & JAKCE: & JAKCE: & JAKCE: & — & — & — & OK \\  & UC \(=\) 0.93 & UC \(=\) 0.99 & UC \(=\) 0.631 & — & — & — & OK \\  & PLLES: & PILLES: & PILLES & & & & & \\  & UP \(=\) 0.77 & UP \(=\) 0.81 & UP \(=\) 0.530 & — & — & — & OK \\  & Down \(=\) 0.75 & Down \(=\) 0.79 & Down \(=\) 0.504 & — & — & — & OK \\ Unity check (UC) ratio & LOAD \(=\) 0.783 & LOAD \(=\) 0.777 & LOAD \(=\) 0.447 & — & — & — & OK \\ in tubular members & STR \(=\) 0.968 & STRN \(=\) 0.969 & \({}^{*}\) STRN \(=\) 1.048 & — & — & — & OK \\ Safety factor & 2.18 \(>\) 2.00 & Storm \(=\) 1.582 \(>\) 1.50 & 2.04\(>\) 2.00 & — & — & — & OK \\  & Storm \(=\) 1.58 \(>\) 1.50 & & & & & & \\  & \({}^{*}\)TES & \({}^{*}\)YES & \({}^{*}\)YES & — & — & — & Reinforce with \\  & (strengthen) & (strengthen) & (strengthen) & (strengthen) & & & Rings \\  & RSR & & & & & & & \\ Cr (Seismic) & — & — & — & & & & \\ Minimum life in years & — & — & — & — & — & & \\ Node 370 & — & — & — & — & — & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary results of the structural analysis.