esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 5 Process Flow
### Clustering  
The clustering module reads the results of the previously calculated Euclidean distance measurements and computes which sectors should belong to a cluster based on this information. During clustering, there is no need for pretrained curves, so the curves are assigned to the appropriate groups through a chosen clustering procedure \"on their own\". The choice among clustering procedures was made by the spectral clustering method [20] due to its speed, efficiency, and accuracy. During the clustering process, it can be configured to use a precomputed distance matrix. When the module starts, the program prompts the user to specify the number of clusters into which they wish to divide the distance matrix of the time series. As a result, the program assigns a cluster value to each sector.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results
### Integration of \(\Delta SM_{SMAP}\) with Seismic Records
#### 3.3.1 Petrinja, Croatia  
The 2020 Petrinja earthquake on 29 December with an M 6.4 mainshock resulted in pervasive liquefaction with large surface manifestations that led to structural infrastructure damage [72]. Figure 8d shows that almost all of the ground failures, identified by field reconnaissance teams, were located in grids with an \(MDI>0\). In addition, grids for which the \(MDI\) was higher than 0.08 cm\({}^{3}\)/cm\({}^{3}\) (Figure 8b) contained some of the reported liquefied zones and areas characterized by a high probability of liquefaction by the USGS.  
Figure 7: \(\Delta SM_{SMAP}\) (9 km \(\times\) 9 km) for Petrinja, Croatia 2020 earthquake in isolated grids for which (**a**) \(MDI>0.08\), (**b**) \(MDI>0.04\), (**c**) \(MDI>0.02\), and (**d**) \(MDI>0\) cm\({}^{3}\)/cm\({}^{3}\).
## 2 Results and Discussion
### Effect of TBAF Concentration on AVICEL(r) Chromatograms
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{
\begin{tabular}{c} **(TBAF)** \\ **(mg/mL)** \\ \end{tabular} } & \multirow{2}{*}{\%Excl.} & \multirow{2}{*}{**Dispersity**} & \multicolumn{2}{c}{**Mn**} & \multicolumn{2}{c}{**Mn**} & \multicolumn{2}{c}{**Mn** (kDa)**} & \multicolumn{2}{c}{**Supplier MM (kDa)**} \\ \cline{6-9}  & & & **(kDa)** & **(kDa)** & **(Lu)** & **(Lu)** & **(Lu)** & **(Lu)** & **(Lu)** & **(Lu)** \\ \hline \multirow{6}{*}{Avicel\({}^{\circledR}\)} & \multirow{2}{*}{100} & \multirow{2}{*}{2.4} & 2.7 & 82.1 & 221.3 & \multirow{6}{*}{36.6} & \multirow{6}{*}{47.8} & \multirow{6}{*}{24.3} & \multirow{6}{*}{56.7} \\ \cline{1-1} \cline{5-10}  & & & 1.1 & 12.6 & 14.2 & & & & \\ \cline{1-1} \cline{5-10}  & 50 & 3.5 & 2.1 & 110.0 & 233.4 & & & & \\ \cline{1-1} \cline{5-10}  & 20 & 0.9 & 1.1 & 43.6 & 48.4 & & & & \\ \cline{1-1} \cline{5-10}  & 10 & 0.8 & 1.1 & 43.8 & 46.9 & & & & \\ \cline{1-1} \cline{5-10}  & 5 & 1.3 & 1.3 & 68.2 & 87.0 & & & & \\ \cline{1-1} \cline{5-10}  & 2.5 & 64.5 & nd & nd & nd & & & \\ \hline \hline \end{tabular}  
* %Excl. is the no-retention percent of the sample;* After dissolution in DMSO/TBAF as described in our previous work [26]; nd: not determined.  
\end{table} 
Table 1: Influence of TBAF concentrations on MM distribution analyses of Avicel\({}^{\circledR}\) samples. Figure 3: **(A)** Chromatograms of Avicel\({}^{\circledR}\) samples in different TBAF/DMSO solutions (DMSO; 60 \({}^{\circ}\)C; 0.5 mL/min) and (**B**) Representation of different TBAF concentration related phenomena affecting the elution volume, among to TBAF concentration. Avicel(r) sample dissolved with a 10 mg/mL TBAF concentration, the average molar masses calculated using two different calibration curves (CC DT 0 and CC DT 1) were compared (Table 2). Some caution is required when considering these values, because pullulan were used as standards as no cellulose standards were available on the market. As described by [PERSON] et al. [35], molar mass of cellulose samples determined by conventional calibration should be corrected by a factor determined by an absolute method based on light scattering. Nonetheless, the similarity of the obtained values supports the suitability of the CC DT 0 calibration curve for all samples dissolved in DMSO/TBAF system.
## 3 Efficient LFSR Implementation
### Grouped Operations  
Once the \(n\) binary LFSRs have been constructed, it is time to generate the sequence. Instead of running \(n\) independent instances of the binary LFSR, which require 1-bit operations with an \(n\)-bit processor, we propose to group the \(n\) LFSRs into a unique LFSR with connection polynomial \(p(x)\) but using \(n\)-bit cells. The result is not an LFSR over \(GF(2^{n})\) but a parallel implementation of \(n\) LFSR over \(GF(2)\) using only one processor. Since the addition and multiplication in the binary LFSRs correspond to the XOR and AND bitwise operations, respectively, instead of applying the XOR to 1-bit values, we apply it over \(n\)-bit values. The processor takes the same time to perform the XOR operation with 1-bit values than with \(n\)-bit values because the word length is \(n\), thus saving a lot of execution time. Hence, Equation (4) can be redefined as follows:  
\[\begin{bmatrix}d_{i,0}\\ \vdots\\ d_{i,n-1}\end{bmatrix}=c_{1}\begin{bmatrix}d_{i-1,0}\\ \vdots\\ d_{i-1,n-1}\end{bmatrix}+c_{2}\begin{bmatrix}d_{i-2,0}\\ \vdots\\ d_{i-2,n-1}\end{bmatrix}+\cdots+c_{L}\begin{bmatrix}d_{i-Ln,0}\\ \vdots\\ d_{i-Ln,n-1}\end{bmatrix}, \tag{17}\]  
in such a way that the \(n\) sequences stated in Equation (7) are simultaneously generated using a unique polynomial \(p(x)\) (see Equation (5)).  
From the practical implementation perspective, there is no difference at all with respect to preforming a classical binary LFSR, which includes coding and execution, since the 1-bit XOR operation is actually performed taking \(n\)-bit operands. Hence, the \(n\)-bit grouped operation proposed in this paper is a way of not wasting the capacity of the operations of the n-bit processors. As a consequence, this implementation method increases the bit generation net rate by \(n\) because the generation of a new element of a 32-bit \(m\)-sequence takes the same amount of time as a new element of a 1-bit \(m\)-sequence.
## 4 General Multi-Stage ASA Switch Architecture
### Evaluation Results  
In this section, we discuss the power loss and the maximum number of \(N\times N\) space switches used in a general multi-stage \(N^{t}\times N^{t}\) ASA switch.  
Recall that a general multi-stage \(N^{t}\times N^{t}\) ASA switch consists of two stages of \(N^{t-1}\)\(N\times N\) AWGs and \(2t-3\) stages of \(N^{t-1}\)\(N\times N\) space switches, where \(N\) is odd and \(t\) is an integer. In addition, the typical insertion loss of an AWG is 7 dB [21] if we use the conventional star-coupler structure [31] for the \(N\times N\) AWGs, and the typical insertion loss of a space switch is 2 dB [32]. Thus, the total loss of the general multi-stage \(N^{t}\times N^{t}\) ASA switch is around \(4t+8\) (=\(2\times 7+(2t-3)\times 2\)) dB. Note that there are \((2t-3)\times N^{t-1}\)\(N\times N\) space switches used in the general multi-stage \(N^{t}\times N^{t}\) ASA switch. Note also that currently \(32\times 32\) AWGs are commercially available [21], which implies that the reasonable maximum odd \(N\) for the general multi-stage \(N^{t}\times N^{t}\) ASA switch is 31. Thus, the reasonable maximum number of \(N\times N\) space switches used in the general multi-stage \(N^{t}\times N^{t}\) ASA switch with odd \(N\) is \((2t-3)\times 31^{t-1}\). Different values of power losses and maximum number of space switches used in a general multi-stage \(31^{t}\times 31^{t}\) ASA switch for \(2\leq t\leq 6\) are given in Table 1.
# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
## 3 Results
### Attention Focus and Visual Engagement in Training  
Eye-tracking data provided insights into participants' fixation duration on safety-critical areas, such as control panels, warning indicators, and procedural instructions. The VR + eye-tracking group showed a 20% numerical increase in fixation duration compared to the VR-only group; however, this difference did not reach statistical significance (t(58) = 3.87, \(p\) = 0.7292, [PERSON]'s d = 0.037), suggesting that while a trend may exist, the current findings are inconclusive.  
Inexperienced operators in the VR-only condition generally exhibited lower fixation durations, which may correlate with lower accuracy and higher error rates observed elsewhere in the study. Although eye-tracking feedback appeared to improve their engagement, the changes in fixation behavior were not statistically supported.
# Performance Evaluation of Bluetooth Low Energy: A Systematic Review
## 3 BLE Performance  
In this section, we examine the main performances of BLE and how they have been analyzed in literature. The main characteristics, examined in the following sections, are: throughput, the number of devices that may be connected to a single _master_, power consumption, connection and transmission latency and the maximum reachable range.  
In Table 1 we summarize the main parameters introduced in Section 2, in order to better understand the BLE performance analysis made in the following sections.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Key-Parameters** & **Description** & **Characteristic Addressed** \\ \hline _connEvent_ Section 2.2.2 & The time in which two devices exchange packets & Throughput Section 3.1, Latency Section 3.4, Power consumption Section 3.3, Piconet size Section 3.2 \\ \hline _connInterval_ Section 2.2.2 & The time between two consecutive _connEvents_ & Throughput Section 3.1, Latency Section 3.4, Power consumption Section 3.3, Piconet size Section 3.2 \\ \hline _Radio Idle_ Section 2.2.2 & The period between two consecutive _connEvents_, when the communication is off & Throughput Section 3.1, Latency Section 3.4, Power consumption Section 3.3, Piconet size Section 3.2 \\ \hline _connSlaveLatency_ Section 2.2.2 & Amount of _comEvents_ which can be skipped avoiding the risk of disconnections & Piconet size Section 3.2, Latency Section 3.4 \\ \hline _advInterval_ Section 2.2.1 & The rate at which advertising packets are sent & Power consumption Section 3.3, Latency Section 3.4 \\ \hline _scanInterval_ Section 2.2.1 & The rate at which the server’s radio turns on & Power consumption Section 3.3, Latency Section 3.4 \\ \hline _scanWindow_ Section 2.2.1 & The amount of time the radio keeps on scanning & Latency Section 3.4 \\ \hline _round-trip_ Section 2.2.2 & The time to send a data packet and an ACK. & Throughput Section 3.1, Latency Section 3.4 \\ \hline _one-way_ Section 2.2.2 & The time to send a packet in _notification_ mode & Throughput Section 3.1, Latency Section 3.4 \\ \hline _payload_ Section 2.3 & Number of bytes per each data packet, that is 20 bytes & Throughput Section 3.1 \\ \hline _piconet_ Section 2.4 & A basic BLE network, composed by a _master_ and a _slave_ & Piconet size Section 3.2 \\ \hline _scatternet_ Section 2.4 & A network where a device performs _master_ and _slave_ simultaneously. & Piconet size Section 3.2 \\ \hline \end{tabular}
\end{table}
Table 1: This table resumes all the parameters introduced in Section 2, which are responsible for the BLE performance analyzed in Section 3. The first column contains the name of the parameter and the subsection where it is defined; the second describes this parameter; the third contains the performances influenced by this specific parameter and the subsection where they are described in detail.
## 3 SWIPT-Based Multi-Hop Uneven Clustering Routing Protocol Design for RF EH-CRSNs
### Network Model
Cluster construction sub-stage: Ordinary nodes that are not yet clustered look for the CH within their own transmission range that has common available channels and the highest CHs selection weight, to which they send a join request, thereby marking themselves as clustered. CHs acknowledge these join requests from the ordinary nodes and list them as their CMs. Ordinary nodes that fail to identify a CH become CHs by default. CHs that do not receive any join requests form clusters independently. Once all ordinary nodes have identified their CHs, cluster construction is completed, and the process moves to the route selection sub-stage. 3. Route selection sub-stage: Specifically, nodes in the first ring can reach the sink in a single hop, enabling them to send packets directly to the sink. Due to communication range limitations, all CHs beyond the first ring must select appropriate relay nodes to assist in forwarding data packets until they reach the sink. CH \(j\), located in the third ring and beyond, selects two inner-ring CHs, \(a\) and \(b\), that maximize the competition value \(\mathit{Compet}(j)\) and record them; if no suitable nodes are discovered, CH \(j\) seeks assistance from its CM \(k\) to locate the subsequent hops and eventually determines two relay nodes, \(a\) and \(b\), that maximize \(\mathit{Compet}(j)\). CH \(j\) in the second ring selects the first-ring CH \(a\) that maximizes \(\mathit{Compet}(j)\) as the next-hop relay; if none are found, then CH \(j\) uses its CM \(k\) to search for the next hop, and finally records two-hop relays, \(k\) and \(a\), that maximize \(\mathit{Compet}(j)\), completing the route selection. The expression for the competition value \(\mathit{Compet}(j)\) is as shown in Equation (10). \[\mathit{Compet}(j)=\left\{ \begin{array}{ll}\mathit{E}H\_W(a)&\mathit{if}\ \mathit{r}(j)=2,\ \mathit{CM}\ k\
otin\mathbf{relay}\\ \mathit{E}H\_W(a)\times\mathit{E}H\_W(k)&\mathit{if}\ \mathit{r}(j)=2,\ \mathit{CM}\ k\in \mathbf{relay}\\ \mathit{E}H\_W(a)\times\mathit{E}H\_W(b)&\mathit{if}\ \mathit{r}(j)\geq 3,\ \mathit{CM}\ k\
otin \mathbf{relay}\\ \mathit{E}H\_W(a)\times\mathit{E}H\_W(b)\times\mathit{E}H\_W(k)&\mathit{if}\ \mathit{r}(j)\geq 3,\ \mathit{CM}\ k\in \mathbf{relay}\end{array} \right.\] (10) where \(\mathbf{relay}\) denotes the relay set composed of CMs.
# A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap
## 5 Empirical Analysis
### A Real Example: The Boston Housing Dataset  
The Bayesian nonparametric algorithm introduced in this paper is also evaluated and compared to other models on a well known real dataset: the Boston housing dataset from the UCI repository [28]. The dataset is composed of 506 samples and 13 variables. The objective of this regression problem is to predict the value of prices of houses using variables at hand.  
The training set is made up of the 70% of the observations (\(N\)) and the remaining part is taken as validation set on which mean squared error, bias and variance are evaluated. The model is built using a 10 fold cross validation.  
The Uniform distribution \(\mathcal{U}(min_{x_{j}},max_{x_{j}})\) is chosen as prior distribution \(F_{0}\) for each covariate \(x_{j}\), and a k nearest neighbour with \(\hat{k}=5\) is used for the generation of the pseudo-samples in the Proper Bayesian bootstrap, as explained in previous sections.  
Results in terms of nonparametric confidence intervals evaluated on MSE, squared bias and variance of the different models are shown in Figure 6.  
Observing the mean squared error on the validation set and the cross validation error, it can be noticed that the model involving Proper Bayesian bootstrap with low \(w\) performs as well as other ensemble models. While when \(w\) is high, i.e., prior is over weighted, the performances are influenced by the fact that in the dataset too many pseudo-samples are introduced.  
As expected, observing the models variance in Figure 6d, it can be noticed that ensemble models are in general more stable than single decision tree and the models involving Proper Bayesian bootstrap are the most stable ones, as obtained in the simulated example. This is due to the higher independence among single trees given by the external pseudo-data introduced thanks to the prior distribution. We can conclude that also on real dataset the proposed model is more stable with respect to other ones employing different bootstrap techniques, maintaining a competitive level of accuracy.  
Figure 5: Nonparametric confidence intervals for the variance on the validation set varying \(N\), number of observations in the training set.
## 2 Material and Methods
### Knockout of SRD5A1 by CRISPR Interference  
Plasmid vectors containing pLenti-U6-sgRNA-SFFV-Cas9-2A-Puro (#454421110502; SRD5A1 sgRNA CRISPR All-in-One Lentivirus set (Human); Applied Biological Materials Inc., Richmond, BC, Canada) was used to knockout SRD5A1 in cells by CRISPR interference. Three SRD5A1-specific single-guide RNAs (sgRNAs) designed using the genetic perturbation platform (GPP) sgRNA Designer tool were synthesized and separately cloned into lenti-dCas9-2A. Lentiviruses were packaged and transfected into PC-3 cells. Stably transfected monoclonal PC-3 cells were selected by 2 \(\mu\)g/mL puromycin, as recommended by the manufacturer. The knockout of SRD5A1 in cells was verified by genomic sequencing and quantitative real-time PCR. The sgRNA sequences for SRD5A1 are as follows: sgSRD5A1#1 5'-GTCCCGCACGTGCGAGCTC-3', sgSRD5A1#2 5'-GACTCCGGTAGCCGGCCCTC-3', and sgSRD5A1#3 5'-GCTACCGGAGGGCCGCTAC-3'.
## 3 Future Work
### Prosumer Relationships
#### Socio-Economic-Technological Aspect  
Prosumer management covers social, economic, and technological dimensions. This section outlines different areas for future research concerning these three dimensions.  
A significant topic for future research is the emerging forms of mutuality in upcoming energy initiatives where digital platforms enable energy exchanges [86]. For example, this could include exploring how Facebook and other social media platforms can act as an enabling platform for energy sharing initiatives such as Vandebron in The Netherlands and Brooklyn Micro-grid in the USA [86]. A future study could also cover the different challenges encountered when using these digital platforms.  
Another future work could be to investigate energy management methods based on the consumer-centered energy system architecture aimed at achieving various optimization targets in an electric vehicle integrated smart grid [89].  
Being a major stakeholder in the energy sharing process, the role of utilities is also essential. Future research direction could be to investigate the different functionalities of utilities further as these would significantly affect business decisions [15; 91]. The developed game-theoretic framework could be enhanced to consider uncertainties such as customer behaviors which are becoming more difficult to predict [91]. Another line for future research would be to examine the optimal sizing of energy cells for strategic planning. Furthermore, the framework could be extended to apply to large smart grids that could operate in real-world scenarios [91].  
Analyzing the socio-economic setting is essential to gain a deeper level of acceptability for smart grid development among consumers [92]. Therefore, the socio-economic investigations are an essential aspect of future research. Resolving the engineering and the socio-economic research gap could also be another research direction. Another area would be business applications, security, policy, and organizational approaches [92]. Even with the rapid advancement of technology, the development of new technologies alone does not guarantee acceptance from consumers because consumers perceive that this comes at high costs.  
Instead of investigating the various elements from the technical or social science perspectives separately, it is recommended to focus on bringing together all these contributions from different perspectives and adopt a holistic approach [83]. The suitable business model would incorporate features such as a combination of technologies to optimize system implementation, cost structures to enhance funding and ongoing operations, defined management and governance structures to allow meaningful collaboration, and necessary relationships to provide a convincing value proposition for the adoption of PCGs [83].
## 3 Results
### Between-Group Comparison for Last Fixation Duration  
Finally, within-group analysis showed that the last fixation duration for the U16 group was affected by the shooting angles (\(F_{(4;495)}=3.207\); \(p=0.013\); \(\eta^{2}=0.025\); no effect size, \(\pi=0.827\)), and we found longer times of last fixation when comparing between angle \(45^{\circ}\) and angle \(90^{\circ}\) (\(p=0.017\)) (Table 6).  
Between-group analysis showed significant differences in the last fixation duration (\(t_{(998)}=-5.629\); \(p=0.001\); \(d=0.356\); moderate effect size). Regarding the influence of shooting positions and angles in the last fixation duration, significant differences were found for positions 2, 3, 4 and 7 and in angles \(0^{\circ}\), \(45^{\circ}\), \(135^{\circ}\) and \(180^{\circ}\).  
\begin{table}
\begin{tabular}{r r r r r r} \hline  & & \multicolumn{2}{c}{\(\bar{\mathbf{x}}\)\(\pm\)**SD**} & \multicolumn{2}{c}{**t**} & \multicolumn{1}{c}{\(\boldsymbol{p}\)} & \multicolumn{1}{c}{\(\boldsymbol{d}\)} \\  & & **U16** & **Pro** & & & \\ \hline  & 1 & 326.22 \(\pm\) 261.29 & 343.15 \(\pm\) 224.05 & \(-\)0.348 & 0.729 & 0.070 \\  & 2 & 253.54 \(\pm\) 217.28 & 311.31 \(\pm\) 230.08 & \(-\)1.291 & 0.200 & 0.258 \\  & 3 & 339.16 \(\pm\) 236.54 & 387.28 \(\pm\) 291.78 & \(-\)0.906 & 0.367 & 0.181 \\  & 4 & 304.35 \(\pm\) 221.64 & 328.90 \(\pm\) 261.12 & \(-\)0.507 & 0.613 & 0.101 \\ positions & 5 & 404.28 \(\pm\) 260.63 & 396.63 \(\pm\) 320.27 & 0.131 & 0.896 & 0.026 \\  & 6 & 389.29 \(\pm\) 244.26 & 353.14 \(\pm\) 258.72 & 0.719 & 0.474 & 0.143 \\  & 7 & 296.34 \(\pm\) 221.23 & 436.73 \(\pm\) 300.87 & \(-\)2.658 & 0.009 \({}^{*}\) & 0.532 \\  & 8 & 347.17 \(\pm\) 282.23 & 417.52 \(\pm\) 252.34 & \(-\)1.314 & 0.192 & 0.263 \\  & 9 & 329.90 \(\pm\) 214.20 & 355.81 \(\pm\) 254.62 & \(-\)0.551 & 0.583 & 0.110 \\  & 10 & 292.79 \(\pm\) 198.39 & 338.88 \(\pm\) 304.72 & \(-\)0.896 & 0.372 & 0.179 \\ \hline  & \(0^{\circ}\) & 289.88 \(\pm\) 241.85 \({}^{\mathrm{a}}\) & 327.23 \(\pm\) 226.51 & \(-\)1.127 & 0.261 & 0.159 \\  & \(45^{\circ}\) & 321.75 \(\pm\) 228.72 & 358.09 \(\pm\) 277.03 & \(-\)1.012 & 0.313 & 0.143 \\ angles & \(90^{\circ}\) & 396.79 \(\pm\) 251.41 \({}^{\mathrm{a}}\) & 374.88 \(\pm\) 290.47 & 0.570 & 0.569 & 0.081 \\  & \(135^{\circ}\) & 321.76 \(\pm\) 253.58 & 427.13 \(\pm\) 276.42 & \(-\)2.809 & 0.005 \({}^{*}\) & 0.397 \\  & \(180^{\circ}\) & 311.34 \(\pm\) 206.24 & 347.35 \(\pm\) 279.49 & \(-\)1.036 & 0.301 & 0.147 \\ \hline \end{tabular} \({}^{*}\)Statistically significant differences (\(p<0.05\)). (\({}^{*}\)) Angle \(0^{\circ}\) vs. angle \(90^{\circ}\).  
\end{table}
Table 5: Between-group comparison for first fixation duration, according to position and angle (times in milliseconds—ms).