esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Underlying Hindrance Groupings
#### 4.2.2 Lack of Continuous Involvement and Capabilities  
This grouping explained about 6% of the total information of the 21 critical hindrances and included six of them. Among these, two significant hindrances (H12 and H32) were closely related to the continuous involvement of the major stakeholders. \"Need for all key stakeholders to be on board to exchange information\" (H12) obtained the highest factor loading. The early and continuous involvement of the owner and the key designers and contractors from early design through project completion has been advocated for BIM implementation [53]. This would pave the way for the project-wide collaboration in the subsequent stages of the project. Another high-loading hindrance was \"assignment of responsibility/risk to constant updating for broadly accessible BIM information\" (H32). In the collaborative project team, the processes of creating digital models in the design stage and using, updating, and managing the models in the construction and operations and maintenance stages would assign liabilities to different participants as the project proceeds.  
Four significant hindrances (H07, H14, H31, and H42) were associated with relevant capabilities that are necessary for successful implementation of BIM. As recommended by the BIM Project Execution Planning Guide, the capabilities of each party can be defined as resources (personnel, tools and their training, and information technology support), competencies, and experience [57]. The second highest factor loading hindrance \"entrenchment in two-dimensional (2D) drafting and unfamiliarity to use BIM\" (H07) revealed that in Singapore many firms lacked competencies and experience to use BIM [32]. For example, the upfront BIM operators tend to lack enough field knowledge to know what they are modeling and its constraint in the actual construction (H14); consequently, the digital models may not be developed correctly. In addition, capital investment should also be included in the resources [42]. Thus, \"firms' unwillingness to invest in training\" (H31) and \"costly investment in BIM hardware and software solutions\" (H42) could also be related to the capabilities of the participants. Compared with the biggest firms in Singapore that can make most use of BIM, a huge number of SMEs face adoption challenges such as lacking the capital investment to build up BIM competencies [8]. The experts involved in the post-survey interviews also stressed the importance of the BIM implementers' financial capabilities, especially for the SMEs and foreign firms based in Singapore.
## 4 Simulation Results
### Complexity  
The corresponding application complexity in terms of the amount of floating-point operations (FLOPs), convergence cost, and average training time consumption in each epoch is shown in Table 1. Notice that the FLOPs were measured based on the frozen  
Figure 8: BER performance comparison of the proposed scheme and the other approaches.  
Figure 7: PSD performance comparison of the proposed scheme with the ideal case.  
graph and different platforms may cost different training times. In addition, the corresponding complexity of the DNN scheme is not demonstrated because it converges at an unacceptable training accuracy. As the results show, although the LSTM costs less FLOPs, the learning ability is limited since it achieves the worst accuracy as compared with the other two schemes. The CNN converges with relatively more epochs and costs much training time, because all input features must be analyzed by the convolution procedure and the corresponding input features are directly obtained from the original received signal without any transformation. Under memory nonlinearity scenarios, the CNN has higher structural complexity and introduces more convolution and pooling layers to achieve the equivalent target accuracy as that of the proposed scheme. By comparison, since the special customized input patterns based on the Volterra model were employed, the proposed scheme exhibits better learning efficiency and only requires 1750 epochs to achieve convergence. Moreover, it only needs 755.6 million FLOPs to achieve the equivalent BER performance, nearly one-third of the CNN. Note that the amount of FLOPs is also related to the number of input signals. Therefore, the proposed Volterra-aided DL scheme can effectively balance the performance and application complexity as compared with the original CNN scheme.
A characterization result that established significance in this study was the 1.5% Na content detected in CaE, which can be consequential to detrimental growth in salt-sensitive plants. Physiologically, salinity imposes, firstly, a water-deficit resulting from relatively high solute concentrations in the soil, and secondly, it causes ion-specific stresses resulting from altered Na\({}^{+}\) (or K\({}^{+}\)) ratios [46]. AN and AW had negligible Na, similar to that of other nutrients such as P and K. CaE, on the other hand, being the evaporator residue, was comprised of 1% K.  
Biomass streams are known to contain pathogens, and several factors influence the survival of pathogens during the treatment processes [47; 48; 49]. For example, in biogas plants, parameters influencing pathogen survival in AD processes include temperature and time, pH, and the initial number of pathogens, to name a few [50]. The current EU FPR (EU) 2019/1009 as set out by European Parliament outlines the standards for pathogens in fertilizers, stating that _Salmonella_ spp. should be absent per 25 g/25 mL of fertilizer tested, and _Escherichia coli_ or _Enterococcaceae_ should not exceed 1000 CFUs per g mL\({}^{-1}\) of fertilizer [51]. The determination of microbial loads revealed that some types of RDFs have high concentrations of bacteria present, particularly those that contain OM or OC, such as CaE, AN + CaE, and AW + CaE. Though bacteria were abundant in these products, tests performed for the detection of the pathogens _Salmonella_ spp. and _Listeria_ spp. confirmed their absence per 25 g or 25 mL of sample material, and enumeration methods employed for pathogens _Escherichia coli_ and _Campylobacter_ spp. exhibited \(<\)10 CFUs per 1 g or 1 mL of all RDF samples. With the exception of the blend AN + CaE, viable mesophilic fungal cells were few to non-existent. Results confirmed that the RDF products would conform with the EU FPR 2019/1009 [51], suggesting that these RDF products are safe for application as a substitute to synthetic N fertilizer products. Furthermore, JRC's REUNURE material criteria requirement of \(<\)10 CFUs g\({}^{-1}\)_Escherichia coli_ was met by AN ([PERSON] et al., 2020). However, it is important to highlight that RDFs need to be tested immediately after production and on different batches of product for complete confidence due to the loss of microbial viability associated with long storage periods.
## 3 Invalid Action Masking
### Action-Level Masking  
Different from logit-level masking, action-level masking works by replacing the action probability of invalid actions to be masked by zero and re-normalizing the action probability distribution (see Figure 2).  
Figure 1: The overview of logit-level masking. Logit-level masking calculates the action probability distribution by applying a state-dependent masking function \(m(\cdot|s)\) to the output logits of the policy network. \(m(\cdot|s)\) is a mapping that replaces the logits of invalid actions with a large negative number.  
Figure 2: The overview of action-level masking. The action-level masking function \(\varpi(\cdot|s)\) is a binary function where the values corresponding to invalid actions are zero. Action-level masking calculates the action probability by replacing invalid actions’ probabilities with zero and re-normalizing the probability distribution.  
Taking any state \(s\in\mathcal{S}\) as input, the original policy \(\pi_{\theta}\) outputs the action probability distribution \([\tau_{\theta}(a_{1}|s),\tau_{\theta}(a_{2}|s),\cdots,\pi_{\theta}(a_{n}|s)]\), where  
\[\pi_{\theta}(a_{i}|s)=\frac{\exp(l_{\theta}(a_{i}|s))}{\sum_{a_{j}\in\mathcal{ A}}\exp(l_{\theta}(a_{j}|s))}. \tag{6}\]  
The action-level masking function \(\varpi(\cdot|s)\) is a state-dependent binary function  
\[\varpi(\cdot|s) =[\varpi(a_{1}|s),\varpi(a_{2}|s),\cdots,\varpi(a_{n}|s)], \tag{7}\] \[\text{where }\varpi(a_{i}|s) =\begin{cases}1,&\text{ if }a_{i}\text{ is valid in }s,\\ 0,&\text{ if }a_{i}\text{ is invalid in }s.\end{cases}\]  
Then, we can re-normalize the Hadamard product of the original policy and action-level masking function and obtain the masked policy  
\[\pi_{\theta}^{\varpi}(\cdot|s) =\text{Normalize}(\pi_{\theta}(\cdot|s)\circ\varpi(\cdot|s)) \tag{8}\] \[=\begin{bmatrix}\pi_{\theta}(a_{1}|s)\varpi(a_{1}|s)\\ \frac{\pi_{\theta}(a_{1}|s)\varpi(a_{1}|s)}{\sum\limits_{a_{j}\in\mathcal{A}} \pi_{\theta}(a_{i}|s)\varpi(a_{i}|s)},\cdots,\frac{\pi_{\theta}(a_{n}|s)\varpi (a_{n}|s)}{\sum\limits_{a_{j}\in\mathcal{A}}\pi_{\theta}(a_{i}|s)\varpi(a_{i}| s)}\end{bmatrix}.\]  
We assume that for any state \(s\in\mathcal{S}\), at least one action in the action space should be valid (i.e., \(\sum_{a_{j}\in\mathcal{A}}(\varpi(a_{i}|s))\
eq 0,\forall s\in\mathcal{S}\)). However, it is important to note that this assumption may not hold true in certain scenarios, such as when a passive unit is surrounded from all sides. To address this, we propose incorporating a no-operation action into the action space so that at least one action is always legal in any given state. The sample probability of any invalid action \(a_{\text{inv}}\) is zero since the corresponding value of the action-level masking function \(\varpi(a_{\text{inv}}|s)\) is equal to zero. We design action-level masking because it is conducive to the theoretical analysis of the policy gradient algorithm.
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
## 2 Materials and Methods
### Proteins and DNA for Transcription In Vitro
#### 2.8.2 Protein Purification  
Wild type RNAP, containing a His10x-tagged \(\beta\)' subunit was purified from LK1723 as described [26].  
The SigA subunit of RNAP (LK22) was overproduced a purified as described [27].  
\(\sigma^{\text{B}}\), \(\sigma^{\text{E}}\), \(\sigma^{\text{F}}\), \(\sigma^{\text{H}}\) expression strains were grown to OD\({}_{600}\) -0.5 when IPTG was added to a final concentration of 0.8 mM. Cells were allowed to grow for 3 h at room temperature, cells were harvested, washed and resuspended in P buffer (300 mM NaCl, 50 mM Na\({}_{2}\)HPO\({}_{4}\), 3 mM \(\beta\)-mercaptoethanol, 5% glycerol). Cells were then disrupted by sonication and the supernatant was mixed with 1 mL Ni-NTA agarose (QIAGEN, Hilden, Germany) and incubated for 1 h at 4 \({}^{\circ}\)C with gentle shaking. Ni-NTA agarose with the bound protein was loaded on a Poly-Prep(r) Chromatography Column (Bio-Rad, Berkeley, CA, USA), washed with P buffer and subsequently with the P buffer with the 30 mM imidazole. The protein was eluted with P buffer containing 400 mM imidazole and fractions containing \(\sigma\) factor were pooled together and dialyzed against storage buffer (50 mM Tris-HCl, pH 8.0, 100 mM NaCl, 50% glycerol and 3 mM \(\beta\)-ME). The proteins were stored at \(-20\)\({}^{\circ}\)C.  
\(\sigma^{\text{D}}\) was purified from inclusion bodies as described in [28].  
Cells containing the plasmid for overproduction of \(\sigma^{\text{N}}\) were grown to OD\({}_{600}\) -0.5 and IPTG was added to final concentration 0.3 mM. Cells were then allowed to grow for 3 h at room temperature; afterwards the cells were harvested, washed, and resuspended in P buffer. All purification steps were done in P2 buffer (the same composition as P buffer, but pH 9.5). Cells were then disrupted by sonication and the supernatant was mixed with 1 mL Ni-NTA agarose (QIAGEN) and incubated for 1 h at 4 \({}^{\circ}\)C with gentle shaking. Ni-NTA agarose with the bound His-SUMO-\(\sigma^{\text{N}}\) was loaded on a Poly-Prep(r) Chromatography Column (Bio-Rad), washed with P2 buffer and subsequently with the P2 buffer with the 30 mM imidazole. The protein was eluted with P2 buffer containing 400 mM imidazole and fractions containing His-SUMO-\(\sigma^{\text{N}}\) were pooled together and dialyzed against P2 buffer.  
The SUMO tag was subsequently removed by using SUMO protease (Invitrogen). The cleavage reaction mixture was again mixed with the 1 mL Ni-NTA agarose and allowed to bind for 1 h at 4 \({}^{\circ}\)C and centrifuged to pellet the resin. Supernatant was removed, the resin was washed once more with P2 buffer with 3 mM \(\beta\)-ME. The supernatants (containing \(\sigma^{\text{N}}\)) were pooled together and dialysed against storage P2 buffer (P2 buffer and 50% glycerol). The protein was stored at \(-20\)\({}^{\circ}\)C.  
The purity of all proteins was checked by SDS-PAGE.
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
### IoT Security Challenges
#### 1.1.7 Botnet Attacks  
A significant security issue with IoT pertains directly to the devices themselves. Their inherent security vulnerabilities make them prime targets for botnet infiltrations.  
Essentially, a botnet is an ensemble of machines compromised by malware. Attackers harness these compromised machines to flood targets with overwhelming request traffic. Unlike conventional computers, IoT devices often lack regular security updates, heightening their susceptibility to malware exploits. Consequently, malicious actors can swiftly transform these devices into botnets, becoming conduits for vast request traffic [29,30].
## Results and Discussion
### Metabolite Network Analysis in Diatom C. calcitrans
Acetone and chloroform extracts had more (\(P\leq 0.001\)) cholesterol respectively, compared to the other solvent extracts. Photosynthetic pigments in diatoms are classified into two classes i.e., carotenoids and chlorophylls. Chlorophylls are derived from geranyldig phosphate (GPP), whereas geranylgeranyl pyrophosphate (GGPP) led to formation of lycopene and onwards to carotenes and xanthophylls [28]. In this study, the six carotenoids, fucoxanthin, astaxanthin, canthaxanthin, lutein, violaxanthin and zeaxanthin, were significantly more abundant (\(P\leq 0.001\)) in the acetone, chloroform and hexane extracts. The carotenoids in diatoms are responsible as health promoters through their capability to prevent inflammation, excessive oxidation and inhibit cancerous cells from proliferating [39; 30]. The presence of Chl \(a\) in diatoms has been reported to act as a mediator for photochemical conversion of solar energy, whereas Chl \(c\)1 in this diatom may serve as accessory pigment in photosynthesis processes [30]. More recently, the finding on Chls-_a_ esterified with geranylgeranyl (GG), dihydropyearanyl (DHGG), and tetrahydrogeranyl (THGG) found in cells of diatom _C. calcitrans_, was also confirmed by [PERSON] et al. [40] using \({}^{1}\)H NMR and \({}^{13}\)C NMR. The accumulation of these Chls-_a_ in _C. calcitrans_ might be attributed to high light stress of the organism. Methanol and hexane extracts had significantly (\(0.010\geq P>0.001\)) higher amounts of Chl \(a\) and Chl \(c\)1 compared to the other extracts.
## 7 Open Research Areas
### Applying MIMO and OFDM in Physical Layer  
Over the last decade, multiple antenna and Multiple Input Multiple Output (MIMO) techniques have been widely discussed to increase the reliability and throughput of various wireless systems. As discussed in Section 4.1.1, the antenna diversity can also improve reliability by achieving multiple different realizations of channel. So, if one antenna gets an interfered/distorted signal, which is non-recoverable due to signal propagation through a deep faded channel, another antenna may receive a copy of the signal which is suitable for decoding. This can significantly improve link reliability. MIMO can increase the system throughput without increasing the bandwidth. Several antennas to transmit/receive a portion of a signal with spatial diversity make this possible. Orthogonal Frequency Division Multiplexing (OFDM) takes this to the next step by using orthogonal sub-carriers in a frequency band. OFDM not only increases the throughput of a system, but also enables the facility to use different levels of modulation for different sub-carriers, based on the channel state information (CSI). Such techniques have been applied in the recently developed IEEE 802.11n standard to increase WiFi throughput to a next level [64]. However, the complexity of the receivers also increases to facilitate this technology, which makes it challenging to implement in WSN applications. Building simple MIMO transceivers with a low power consumption is still in a research phase [71].
## 4 Rationale for Each TACE Technique
### Therapeutic Effects of Bland Embolization with Particles  
In the recent RCT conducted by [PERSON] et al. [45], TACE using doxorubicin-loaded DEB presented a better local response, fewer recurrences, and a longer time to progression than bland embolization with same-sized microspheres (\(p=0.008\)). However, no significant benefit to survival of DEB-TACE was found, partly because of a short follow-up. On the other hand, the latest RCT conducted by [PERSON] et al. [46] showed no significant differences in tumor response (\(p=0.48\)), median progression-free survival (\(p=0.11\)), and overall survival (\(p=0.64\)) between the bland embolization and doxorubicin-loaded DEB-TACE groups.  
It is obvious that bland embolization has lesser damage to the liver than cTACE and DEB-TACE because non-drug-loaded microspheres can occlude the vessels with mild foreign body reaction [47], mostly non-necrotic vascular changes without hepatic necrosis [29]. However, the role of bland embolization with microspheres has not yet been established in the treatment strategy for HCC. In a report by [PERSON] et al. [48], the 1- and 2-year survival rates of 59 naive HCC patients with a mean largest tumor diameter of 4 cm (range, 1.2-14.7 cm) treated with bland embolization using HepaSphere (Merit Medical, Jordan, UT, USA) of 53 to 212 \(\upmu\)m in a dry state were 100% and 83%, respectively, and the median survival time (MST) was 30 months. [PERSON] et al. [32] reported the outcomes of bland embolization with small particles (50-\(\upmu\)m polyvinyl alcohol particles and 40-120-\(\upmu\)m Embosphere (Merit Medical) for 322 HCC patients. Among them, 109 patients without portal vein invasion or extrahepatic disease had an MST of 40 months and 1-, 2-, and 3-year survival rates of 84%, 66%, and 51%, respectively. However, four patients died of acute hepatic failure and two died of pulmonary embolism. Small particles have strong therapeutic effects because these can impart terminal vessel blockage within the tumor; however, the risk of unexpected complications caused by migrated particles may increase [31; 32]. Therefore, a superselective approach and careful observation of pulmonary shunting is recommended in the use of small-sized particles. In a report by [PERSON] et al. [49], 40- or 100-\(\upmu\)m Embozene (Varian, Austin, TX, USA) could be safely used in the superselective bland embolization procedure.  
Bland embolization with gelatin sponge particles can also be effective for achieving hemostasis of ruptured HCC in patients in a hemodynamically unstable condition with minimal invasiveness [50].
# Estimation of Thermal Radiation in Bed Mattresses
## 2 Full-Scale Fire Test
### Measurement Item  
The experimental measurements of the HRR, the flame height, and the heat flux in the mattresses of different heights were compared and analyzed.  
The oxygen concentration, flow rate, and temperature of exhaust gas in the duct were measured using a furniture calorimeter, and the heat release rate was calculated using Equation (1), based on the oxygen-consumption method [18].  
\[\dot{Q}=\frac{E^{1}\dot{m}\ \frac{M_{\mathrm{O_{2}}}}{M_{\mathrm{air}}}\ \left(1-X_{H_{2}O}^{0}\right)}{\frac{\dot{Q}-1}{X_{O_{2}}}+ \frac{\dot{X}_{O_{2}}}{1-\dot{X}_{O_{2}}}} \tag{1}\]  
where \(\dot{Q}\) is heat release rate (kW), \(E^{1}\) is the amount of energy developed per consumed kilogram of oxygen (kJ/kg); \(\dot{m}\) is the mass flow in the exhaust duct (kg/s); \(M_{\mathrm{O_{2}}}\) is the molecular weight of oxygen (g/mol); \(M_{\mathrm{air}}\) is the molecular weight of air (actually the molar weight of the gas flow in the duct) (g/mol); \(\alpha\) is the ratio between the number of moles of combustion products including nitrogen and the number of moles of reactants including nitrogen (expansion factor); \(X_{O_{2}}^{0}\) is the mole fraction of \(O_{2}\) in ambient air, measured on dry gases (\(-\)); \(X_{CO_{2}}^{0}\) is the mole fraction of \(CO_{2}\) in ambient air, measured on dry gases (\(-\)); \(X_{H_{2}O}^{0}\) is the mole fraction of \(H_{2}O\) in ambient air; \(X_{O_{2}}\) is the mole fraction of \(O_{2}\) in flue gases,  
Figure 1: Experimental setup of open space test: (**a**) arrangement of the test body (unit: mm); (**b**) photo in mattress test (HB = 515 mm).  
measured on dry gases (\(-\)); and \(X_{CO_{2}}\) is the mole fraction of \(CO_{2}\) in flue gases, measured on dry gases (\(-\)).  
The flame height was measured from the recordings of a video camera installed at the cross-section of the mattress. To measure the height before the experiment, a reference was set using a 5 m long rod, and continuous flames and intermittent flames were extracted from the video footage to obtain the average flame (Figure 2).  
A heat flux meter was set at the same height as the upper surface of the mattress, the measurement direction was upward, and the meter was installed at a horizontal distance of 0.2 m and 1 m from the side of the mattress (opposite to the burner), for the measurement of thermal radiation (Figure 3).
## 5 Simulation
The normalized bandwidth is maintained below the target normalized bandwidth value (i.e., 0.3) most of the time, except for the high dynamics region due to the unavoidable limitation of the integration time by the lower bound condition. Figure 10.1 Carrier tracking error at the low SNR region (210-270 s): (**a**) Proposed algorithm; (**b**) Fixed bandwidth (\(\rm B_{n}=15\) Hz, T = 20 ms). The fixed-bandwidth loop loses lock at approximately 253 s (C/N\({}_{0}=17\) dB-Hz) while the proposed algorithm maintains its lock. Figure 10.2 Carrier tracking results of the proposed table-based adaptive DPLL algorithm: (**a**) Carrier phase; (**b**) Doppler frequency; (**c**) Doppler rate. The proposed algorithm stably tracks the signal components for the simulation scenario.