esa-sceva/satcom-chunk-collection huggingface dataset:
# End-of-Life Scenarios for Mass Timber: Assumptions, Limitations and Potentials--A Literature Review
## 3 Results
### End-of-Life Scenarios: Reuse, Recycling, Energy Recovery and Landfill
#### 3.5.2 Recycling  
Recycling is another favored EoL scenario by many scholars. Twenty-nine papers have discussed recycling when considering EoL scenarios. \"Recycling\" refers to any recovery process in which waste materials are reprocessed into products, materials, or substances, either for their original purpose or for new uses. This excludes energy recovery--a scenario that will be explored next--and the reprocessing of materials intended for use as fuels or for backfilling operations [41; 42]. In the recycling of mass timber, the material is usually chipped or shredded and turned into boards or animal bedding [48]. The stored carbon remains in the recycled product, though typically for a shorter duration compared to reuse, as recycled materials generally have a shorter secondary lifespan. According to [PERSON] and [PERSON] et al. [31; 48], \"leakage\" can occur during some recycling processes, involving both material losses and carbon costs. For instance, if a chipped product such as animal bedding or wood mulch begins to deteriorate, the decomposition process could mimic that of landfill disposal, potentially releasing methane into the atmosphere. This greenhouse gas poses a significantly greater threat to the environment, given that its GWP is commonly cited in the range of 28 to 36 times that of CO\({}_{2}\)[49]. Additionally, the decomposition of these products may occur more rapidly than in landfills, as they are exposed to air and often mixed with other organic materials such as soil and animal feces, factors that can accelerate the process. This results in a much shorter product lifespan compared to other recycled products. Further research could explore the environmental implications of this issue.
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 5 Related Work  
Extensive research has been conducted on optimizing the overall energy consumption of NB-IoT devices. Adjustable parameters such as cDRX, eDRX, PSM [8; 12], CP, and UP optimizations [34; 35] and the default RAP [13; 14] have been explored. Moreover, there are some previous studies on PURs [10; 36]; however, these studies mainly rely on theoretical models and analyses. Therefore, additional research is needed to improve our understanding of how PURs impact NB-IoT energy efficiency.  
The authors [PERSON] et al. [11] implemented NB-IoT in the LENA-NB ns-3 framework. In their evaluation of NB-IoT, they showed that EDT could offer up to 2.9 times less latency, 1.6 times longer battery life, and 3.7 times less spectral usage than the standard optimization of the NB-IoT control plane. We heavily rely on their simulation environment as a vital part of our research infrastructure.  
[PERSON] et al. [37] showed how EDT and PURs could reduce NB-IoT signaling overhead in industrial IoT systems. [PERSON] et al. [10] showed that PURs are the most effective way to reduce UE power consumption when frequent data transmissions with small payloads are involved. For instance, with a 30 s periodicity, a 50 byte payload, and a 43 min eDRX cycle, PURs yield gains ranging from 7% in deep coverage to 50% in good coverage compared to EDT. The authors conclude that PURs significantly reduce signaling exchanges for small-size data transmissions, especially when uplink data transmissions occur hourly or less frequently, and the payload is minimal. [PERSON] et al. also compare two PUR transmission schemes, dedicated and shared PURs. Their research shows that shared PURs provide a 100% increase in the utilization of PUSCH resources in low-SINR regimes, while dedicated PURs are more versatile across SINR regimes, making it the preferred choice in several other scenarios.  
[PERSON] et al. [36] introduced SmartPUR, a version of PURs that utilizes Machine Learning (ML) techniques to verify and predict TA for mobile CIoT devices. Their evaluation of SmartPUR demonstrates its capability to significantly minimize fallback rates and reduce power consumption for mobile CIoT devices.  
Finally, the paper by [PERSON] et al. [38] examines two methods for transmitting small amounts of data while the device is inactive. The first method uses RACH, and the second uses Configure Grant (CG). The study compared the two methods and examined their impact on packet delay, power consumption, and signaling overhead metrics. The results showed that the CG-based method is better, with improved power efficiency and reduced latency.  
Figure 18: Estimated battery life of an NB-IoT device configured with different optimization techniques.
###### Abstract  
InGaAs quantum well (QW) lasers have attracted significant attention owing to their considerable potential for applications in optical communications; however, the relationship between the misorientation of the substrates used to grow InGaAs QWs and the structural and optical properties of QWs is still ambiguous. In this study, In-rich InGaAs/GaAsP single QWs were grown in the same run via metal organic chemical vapor deposition on GaAs (001) substrates misoriented by 0\({}^{\circ}\), 2\({}^{\circ}\), and 15\({}^{\circ}\) toward (111). The effects of substrate misorientation on the crystal quality and structural properties of InGaAs/GaAsP were investigated by X-ray diffraction and Raman spectroscopy. The 0\({}^{\circ}\) substrate exhibited the least lattice relaxation, and with increasing misorientation, the degree of lattice relaxation increased. The optical properties of the InGaAs/GaAsP QWs were investigated using temperature-dependent photoluminescence. An abnormal S-shaped variation of the peak energy and inverse evolution of the spectral bandwidth were observed at low temperatures for the 2\({}^{\circ}\) substrate, caused by the localization potentials due to the In-rich clusters. Surface morphology observations revealed that the growth mode varied with different miscuts. Based on the experimental results obtained in this study, a mechanism elucidating the effect of substrate miscuts on the structural and optical properties of QWs was proposed and verified.  
misorientation; optical communication; InGaAs/GaAsP quantum well; optical properties; localization potential +
Footnote †: journal: Physics Letters B
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 3 Model and Problem Formulation
### QoS Model
#### 3.2.2 Communication Delay  
The communication delay occurs when the service is not placed in the user's area, which is determined by the data transmission and the network propagation. The network propagation is determined by the distance \(p_{u_{i},m_{j}}(t)\) between user \(u_{i}\) and service \(v_{i}\) placed on edge node \(m_{j}\), such as hops [23]. Let \(t_{u_{i},m_{j}}\) denote the maximum transmission rate, where  
\[t_{u_{i},m_{j}}(t)=b_{u_{i},m_{j}}(t)\cdot\log_{2}(1+\frac{\tau\cdot g(u_{i}, m_{j})}{N}) \tag{2}\]  
We use \(b_{u_{i},m_{j}}\) to denote the channel bandwidth of the physical link, and \(\tau\) denotes the transmission power of the local mobile device of \(u_{i}\). Let \(g(u_{i},m_{j})\) represent the channel gain between \(u_{i}\) and MEC \(m_{j}\), where \(g(u_{i},m_{j})=127+30\cdot\log p_{u_{i},m_{j}}(t)\)[24]. Let \(N\) represent the noise power. The data transmission is determined by the bandwidth of the physical link \(b_{u_{i},m_{j}}\) and the data size of the request \(d_{u_{i}}(t)\) when it passes through the network devices between the connected MEC node and the service provided one. Therefore, the communication delay is  
\[D_{u_{i}}^{l}(t)=\sum_{m_{j}\in M}x_{ih}^{j}(t)\cdot\frac{d_{u_{i}}(t)}{t_{u_{ i},m_{j}}(t)} \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(M\) & Set of MEC nodes, where \(M=\{M_{j}\}\). \\ \(U\) & Set of users, where \(U=\{u_{i}\}\). \\ \(V\) & Set of services, where \(V=\{v_{h}\}\). \\ \(\mathbb{V}_{m_{j}}\) & Set of services placed on edge server \(m_{j}\). \\ \(\mathbb{U}(\mathbb{V}_{m_{j}})\) & Set of users served by the services in set \(\mathbb{V}_{m_{j}}\). \\ \(x_{ih}^{j}(t)\) & A boolean variable that indicates \(v_{h}\) serving \(u_{i}\) \\ \(A_{i}(t)\) & on edge server \(m_{j}\) at time slot \(t\). \\ \(A_{i}(t)\) & The amount of required computing resource of \\ \(u_{i}\) at time slot \(t\). \\ \(D_{u_{i}}^{c}(t)\) & The computing delay of \(u_{i}\). \\ \(D_{u_{i}}^{l}(t)\) & The communication delay of \(u_{i}\). \\ \(D_{u_{i}}^{u}(t)\) & Updating delay of \(u_{i}\) during the \\ \(D_{u_{i}}^{u}(t)\) & dynamic migration. \\ \(t_{u_{i},m_{j}}(t)\) & Maximum transmission rate \\ \(b_{u_{i},m_{j}}(t)\) & between \(u_{i}\) and \(m_{j}\). \\ \(p_{u_{i},m_{j}}(t)\) & Channel bandwidth of link between \(u_{i}\) and \(m_{j}\). \\ \(p_{u_{i},m_{j}}(t)\) & Physical distance between \(u_{i}\) and \(m_{j}\). \\ \(R_{m_{j}}^{s}\) & The storage capacity of \(m_{j}\). \\ \(R_{m_{j}}^{c}\) & The computing capacity of \(m_{j}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of main notations.
## 5 System Engineering Analyses
### Link Budget
#### 5.2.2. Uplink Budget  
The calculated parameters necessary for conducting uplink budget analysis are shown in Tables 9-11.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline
**EIRP (dBW)** & 32.51 \\ \hline
**Rcvd. Frequency (GHz)** & 0.14 \\ \hline
**Rcvd. Iso. Power (dBW)** & \(-\)117.29 \\ \hline
**Flux Density (dBW/m\({}^{2}\))** & \(-\)112.61 \\ \hline
**g/T (dB/K)** & \(-\)25.9 \\ \hline
**C/No (dB\({}^{*}\)Hz)** & 85.27 \\ \hline
**Bandwidth (kHz)** & 3.00 \\ \hline
**C/N (dB)** & 50.5 \\ \hline
**Eb/No (dB)** & 45.45 \\ \hline BER & \(1.0\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10. Uplink budget of autonomous ground station connected with the sensor network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline
**g/T (dB/K)** & \(-\)7.1 \\ \hline
**C/No (dB\({}^{*}\)Hz)** & 72.07 \\ \hline
**Bandwidth (kHz)** & 19.2 \\ \hline
**C/N (dB)** & 29.24 \\ \hline
**Eb/No (dB)** & 9.6 \\ \hline
**G.S. Rcvr (dB)** & 20.1 \\ \hline
**S/N required (dB)** & 9.6 \\ \hline
**BER** & \(1.0\times 10^{-3}\) \\ \hline
**System Link Margin (dB)** & 10.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8. _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline
**EIRP (dBW)** & 34.9 \\ \hline
**Rcvd. Frequency (GHz)** & 0.32 \\ \hline
**Rcvd. Iso. Power (dBW)** & \(-\)117.56 \\ \hline
**Flux Density (dBW/m\({}^{2}\))** & \(-\)106.09 \\ \hline
**g/T (dB/K)** & \(-\)25.92 \\ \hline
**C/No (dB\({}^{*}\)Hz)** & 84.97 \\ \hline
**Bandwidth (kHz)** & 3.00 \\ \hline
**System Link Margin (dB)** & 30.4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Uplink budget of BUAA ground station.
## 2 Utilization of GNSS
### Multi-GNSS Algorithm (GPS and Galileo)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**State** & **Description** & **Symbol** \\ \hline
16 & Error of GPS receiver clock bias & \(\Delta\delta t_{GPS}\) \\
17 & Error of GNSS receiver clock drift & \(\Delta\delta t_{GNSS}\) \\
18 & Error of Gal. receiver clock bias & \(\Delta c\delta t_{Gal}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Receiver Clock States of the Multi-GNSS Algorithm. referenced to E5a-E1 and E5b-E1 combinations, the formation of these combinations is done according to (6) and no further correction terms are necessary. \[\rho_{IF}=\frac{\left(\rho_{\alpha}-\gamma_{\beta,\alpha}\rho_{\beta}\right)}{1- \gamma_{\beta,\alpha}} \tag{6}\]  
Since all Galileo satellites broadcast civil signals on E1, E5a and E5b, only 5 % of the processed code observations are not E5a-E1 combinations, compared to 67 % of GPS observations that are not L5-L1 C/A. Since the percentage of processed E5b-E1 and E1 Galileo pseudo-ranges is so small, they do not affect the positioning performance substantially. Consequently, I/NAV data and Galileo's ionospheric correction algorithm for single-frequency users are not included in the algorithm in order to prevent unnecessary complexity. Clock correction parameters and the _Broadcast Group Delay_ (BGD) from the F/NAV data as well as the GPS Klobuchar model are used instead. As it does for GPS L5, the JAVAD TRIUMPH-LS does not track the data and pilot components on Galileo E1, E5a and E5b separately. Instead, a combined tracking of E1-B + E1-C is used on E1 and a combined tracking of I + Q components is used on E5a and E5b. The resulting pseudo-ranges receive the observation codes C1X, C5X and C7X in RINEX 3.03, respectively.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 2 Proposed RMLP-18T Cell and Its Operations  
This section describes the structure and operating principles of the proposed RMLP-18T cell. The schematic and layout of proposed RMLP-18T cell are shown at Figures 1 and 2. The operating principle of the circuit in Figure 1 is explained in Section 2.1. The cell consists of 12 PMOS and 6 NMOS transistors, totaling 18 transistors. Regarding key transistor sizing, the width of the primary pull-down NMOS transistors (N1, N2, N3, N4) connected to the storage nodes (Q, QB) was set to twice the minimum design rule size (2 \(\times\) Wmin), specifically to enhance the Read Static Noise Margin (RSNM). The width of core PMOS transistors P3 and P4 was set to three times the minimum size (3 \(\times\) Wmin) to strengthen the SEU recovery mechanism. These values were obtained through simulations to enhance RSNM and the SEU recovery mechanism. All other transistors within the cell utilize the minimum feature size (Wmin) allowed by the 90 nm CMOS process technology.  
Figure 1: Schematic of the proposed RMLP-18T cell.  
Figure 2: Layout of proposed RMLP-18T cell.
# Optimal Management of High-Voltage Line Congestions Using Power Source Redispatching
###### Abstract  
The increasing integration of renewable energy sources (RESs) reduces dependence on conventional generators, thereby minimizing the negative environmental impact of fossil fuels. The distributed location of RESs also affects the voltage profiles (voltage values in network nodes) and reduces power losses. The growing number of RESs connected to the network increases the total installed power in the sources in the power system. This contributes to the periodic excess of generated power. It creates the need to limit generation in conventional power plants and to switch off some RESs. This article proposes an original methodology for optimally managing overloads of high-voltage power lines. The combination of the power flow tracking method and metaheuristic optimization allows for the effective elimination of line overloads. The aim of the calculations is to find the optimal power distribution in the selected sources, which provide minimal power limitation. As a result, this means a minimal reduction in the total generation in RESs. In this way, the effect of eliminating line overloads is achieved at the lowest possible cost of power redispatching. On the basis of the IEEE 118 bus test network, computational cases are considered, which are examples of emergency states.  
source power redispatching; metaheuristic optimization; line overload management; power balance; power system Article  
1  
P. 1
###### Abstract  
This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN) models in time series analysis, specifically focusing on their application in prediction tasks of critical infrastructures (CIs). To accomplish this, shallow models with nearly identical numbers of trainable parameters are constructed and examined. The dataset, which includes 120,884 hourly electricity consumption records, is divided into three subsets (25%, 50%, and the entire dataset) to examine the effect of increasing training data. Additionally, the same models are trained and evaluated for univariable and multivariable data to evaluate the impact of including more features. The case study specifically focuses on predicting electricity consumption using load information from Norway. The results of this study confirm that LSTM models emerge as the best-performed model, surpassing other models as data volume and feature increase. Notably, for training datasets ranging from 2000 to 22,000 instances, GRU exhibits superior accuracy, while in the 22,000 to 42,000 range, LSTM and BiLSTM are the best. When the training dataset is within 42,000 to 360,000, LSTM and ConvLSTM prove to be good choices in terms of accuracy. Convolutional-based models exhibit superior performance in terms of computational efficiency. The convolutional 1D univariable model emerges as a standout choice for scenarios where training time is critical, sacrificing only 0.000105 in accuracy while a threefold improvement in training time is gained. For training datasets lower than 22,000, feature inclusion does not enhance any of the ANN model's performance. In datasets exceeding 22,000 instances, ANN models display no consistent pattern regarding feature inclusion, though LSTM, Conv1D, Conv2D, ConvLSTM, and FCN tend to benefit. BiLSTM, GRU, and Transformer do not benefit from feature inclusion, regardless of the training dataset size. Moreover, Transformers exhibit inefficiency in time series forecasting due to their permutation-invariant self-attention mechanism, neglecting the crucial role of sequence order, as evidenced by their poor performance across all three datasets in this study. These results provide valuable insights into the capabilities of ANN models and their effective usage in the context of CI prediction tasks.  
critical infrastructures (CIs); deep learning (DL); intelligent networks; artificial neural networks (ANNs); load forecasting; LSTMs; CNNs; convolutional LSTM (ConvLSTM); GRU; bidirectional LSTM (BiLSTM); transformers; power load forecasting +
Footnote †: journal:  
0000-0001-5000-0001-5000][PERSON] _et al._
# Data Chunks Placement Optimization for Hybrid Storage Systems
## 4 Heuristics for Finding an Optimal Data Chunk Placement
### Testing Results  
To test the performance of the algorithm, we took 6 files with the same size and split them to 192 chunks with size 512 KB, as described in Table 1. As the initial step, we distributed chunks over the transmitter storages because OUS and traditional cloud storages have no Network Coding functionality.  
In the first experiment, we wanted to determine the best data uploading strategy based on the varying costs of the vendors for different regions and compare our result with the single-vendor solution (either AWS or GCP) and OUS performance. In this experiment, we assumed that there was no redundant storage in the system, and all storages were fully available for data transfer. Besides, we accepted that each request is returning a 512 KB single chunk, and all requests go parallel.  
For the first case, both of the algorithms, OUS and DCP, performed well, with a minor difference, compared to single cloud data transfer solutions. In contrast to manual data uploading/downloading, where the cost of the data transfer cannot always be tracked, OUS and DCP have better performance because both algorithms always seek the minimum cost. Figure 2 describes how the minimum cost increased in four different environments during object rebuilding.  
In the second phase of the test, we followed the same approach as in the first test but with some changes. Normally, all cloud storages have an availability probability of 99.99%, which we cannot change. For this reason, we created damaged data sets. This type of data set cannot be used for object rebuilding because of the lack of bits.  
After testing both of algorithms, we admitted that DCP had a better performance compared to OUS and single cloud vendor solutions. Even though, in OUS, we also calculated overall cost with using minimum data transfer cost, OUS has no option for rebuilding an object from broken point (in this case where OUS starts the object rebuilding process from the beginning again), thus increasing overall cost data object rebuilding process. The second test actually shows the benefits of using Network Coding versus traditional data transfer strategies to cloud storages. Figure 3 describes the second test cases for the evaluation of the algorithm performance.  
Figure 2: Data transfer cost change without loss in the system.  
From both of the experiments, it can be seen that overall performance of the DCP is much better than OUS and single vendor data uploading strategies. Furthermore, because of the Network Coding performance, DCP has significant benefits over the mentioned technics.
## 2 Materials and Methods
### Volatiles Composition  
The samples harvested in 2015 were subjected to volatile composition analysis. The identification of the volatile compounds was performed by a headspace solid phase microextraction technique (SPME) followed by gas chromatography/mass spectrometry (HS-SPME GC/MS). Four fresh samples for each tree (thirty-six samples total) were deprived of the violet tegument. Kernels (around 4.5 g each samples) were transferred into vials with pierceable silicone rubber septa coated with polytetrafluoroethylene (PTFE). n-esanolo-d13 was used as internal standard (Cambridge Isotope Laboratories, Inc. USA) at a concentration of 4.52 ppm. Vials were covered with aluminum foil to avoid the light degradation and stored in the oven at 50 \({}^{\circ}\)C over night. Then, the vials were transferred in water bath at 50 \({}^{\circ}\)C with the SPME fiber inserted in the head space and were exposed for two hours (adsorption time). To analyze the volatile composition of pistachios, we used the fiber DVB/CAR/PDMS 50/30 \(\upmu\)m (Supelco, Bellefonte, PA, USA) that has been reported as the best solution to extract the highest number of compounds in pistachios [34]. Then the absorption was performed by headspace mode. After two hours, the fiber was introduced in the gas chromatograph. Chromatograph separation was performed with a 30 m long and 0.25 mm inner diameter-fused silica Supelcowax column (Supelco, Bellafonte, PA, USA). The fiber was maintained for 2 min at 40 \({}^{\circ}\)C and then for 22 min at 220 \({}^{\circ}\)C. The instrumental parameters of GS/MS were as follows: sink temperature 250 \({}^{\circ}\)C, full scan, mass range 35-350 dalton, absorption time 20 min (1 min splitless), column flow 1 mL/min, transfer line 280 \({}^{\circ}\)C and ionization EI (+).  
Identification of the compounds was based on the comparison of the obtained spectra with those of the Wiley7 and NIST MS Search 2005 mass spectral libraries. Quantification was carried out calculating the area of peak of each component, using the method ICIS of Xcalibur V 1.4 software (thermo electron). The relative amounts of volatiles (semiquantitative analysis) were obtained by multiplying the area ratio of the target compound/internal standard by the concentration (\(\upmu\)g/L) of the internal standard [35].