esa-sceva/satcom-chunk-collection huggingface dataset:
# Benefits of Probiotics--Biodetoxification
## 2. Probiotics--Biodetoxification of Food
### The Role of Probiotics in Food Biodetoxification
#### 2.2.6 Nitrite and Nitrate  
Nitrite and nitrate are commonly used in meat preservation. They are responsible for carcinogenic N-nitrosamine formation in food matrices, as well as endogenously, after ingestion [56]. Nitrites (most commonly sodium nitrite--E250) are preservatives widely used in the food industry, particularly in processed meat products. Nitrates and nitrites can occur naturally in food products [57; 58; 59], as well as be added as preservatives or in the form of plant extracts. They extend the shelf life of food items, inhibit microbial growth, delay rancidity, and improve the flavor and color of meat products. Long-term exposure to N-nitroosamines may increase the risk of developing cancer [60]. LAB is naturally present in meat as well as strains added as starter cultures or additive cultures. Many reports have confirmed the ability of LAB (i.e., _L. pentosus_, _L. curatatus_, and _L. sakei_, _L. plantarum_) to break down and reduce nitrate and nitrosamine content [18]. LAB produces organic acids, especially lactic acid, leading to acidification of the meat, which has an impact on reduced N-nitrosamine generation [61]. Moreover, LAB strains can be used as meat preservatives, avoiding the use of nitrates in meat preservation [62]. The new EU regulations aim to reduce the permissible levels of nitrates and nitrites by approximately 20 percent. This decision follows an in-depth scientific assessment conducted by the European authorities. The updated limits take into account the diverse range of products and production conditions across the European Union. By sending a clear signal to both the food industry and smaller producers, the Commission intends to address the challenges posed by the presence of nitrites and nitrates throughout the EU food supply chain [63].
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## 2 FTN Signaling with Turbo Equalization
### BCJR Detection and Turbo Equalization  
The BCJR algorithm computes the probability of states and paths in trellises. It performs forward and backward recursion among the state transition map. Suppose the channel observation \(\mathbf{y}=y_{1},y_{2},\ldots,y_{N}\), and the a priori symbol probability of each transmitted symbol is known. The joint probability can therefore be calculated as follows:  
\[P(s_{n}=i,s_{n+1}=j,\mathbf{y})=\alpha_{n}[i]\Gamma_{n}(i,j)\beta_{n+1}[j], \tag{7}\]  
where \(\alpha_{n}[i]\) is called the forward path metric for state \(i\) at stage \(n\), \(\beta_{n+1}[j]\) is called the backward path metric for state \(j\) at stage \(n+1\), and \(\Gamma_{n}(i,j)\) is the branch metric connecting states \(i\) and \(j\) at stage \(n\). Usually, the transmitted symbols start from (and terminate at) an initial all-zero state, so that  
\[\alpha_{n+1}[i]=\sum_{i\in S}\alpha_{n}[i]\Gamma_{n}(i,j) \tag{8}\]  
is initialized as \(\mathbf{\alpha}_{0}=(1,0,\ldots,0)\), where \(S\) is the set of states that can reach state \(j\) at stage \(n+1\). Regarding binary modulation, there are two elements in set \(S\). Backward recursion is similar in that \(\mathbf{\beta}_{N}=(1,0,\ldots,0)\). It starts from the last symbol (stage) then proceeds backward to the beginning, computing the backward path metric as follows:  
\[\beta_{n}[i]=\sum_{j\in S}\beta_{n+1}[i]\Gamma_{n}(i,j) \tag{9}\]  
where \(S\) is the set of states that can reach state \(i\) at stage \(n\). The branch metric is  
\[\Gamma_{n}[i]=[P(a^{\prime})/\sqrt{\pi N_{0}}]\exp[-(y_{n}-l_{i,j})^{2}/N_{0} )], \tag{10}\]  
where \(l_{i,j}\) is a label on the branch from state \(i\) to \(j\), and \(P(a^{\prime})\) is the a priori probability of symbol \(a^{\prime}\), which causes the transition (from state \(i\) to \(j\)). Let \(\lambda_{n}[j]=\alpha_{n}[j]\beta_{n}[j]\), and let \(j\) be a state at stage \(n\). Then, we calculate LLRs via  
\[LLR(a_{n})=\ln\frac{\mathrm{P}[a_{n}=+1]}{\mathrm{P}[a_{n}=-1]}=\ln\frac{ \sum_{j\in\mathcal{L}_{+1}}\lambda_{n}[j]}{\sum_{j\in\mathcal{L}_{-1}}\lambda_ {n}[j]}, \tag{11}\]  
and \(\mathcal{L}_{\pm 1}\) are sets of states reached by \(a_{n}=\pm 1\). The M-BCJR algorithm proposed by [2] utilized the reduced search of trees and trellises. It proceeds breadth-first through a tree structure of metric values, keeping only the dominant \(M\) paths at each tree stage, and it also deals with empty values of \(\alpha_{n}[j]\), \(\beta_{n}[j]\). More details can be found in [2]. In coded FTN signaling, turbo equalization is usually adopted, whereby the BCJR detector and channel decoder exchange soft information iteratively. Turbo equalization enhances detector and decoder performance but increases complexity and latency proportional to turbo iterations. The coded FTN signaling diagram is depicted in Figure 1, where the dashed box represents turbo equalization.
## 6 Conclusions  
### Research Conclusions  
S&T innovation ability is the primary productive force, and it can promote the comprehensive and sustainable development of the economy and society. This paper investigates the impact of GPSTPS policy on regional S&T innovation ability and aims to promote the sustainable development in China. Based on the panel data of 30 provinces from 2008 to 2017 in China, this paper constructs the PMC index model and spatial econometric model of GPSTPS policy, investigating the relationship between GPSTPS policy and regional S&T innovation ability. Results show that: (1) There is a positive and significant spatial correlation in regional S&T innovation ability between 30 provinces in China. (2) On the whole, the GPSTPS policy does not play a vital role in improving the regional S&T innovation ability. (3) From the perspective of subregions, the effect of GPSTPS policy on the improvement of S&T innovation ability is not obvious in the central and western regions. However, GPSTPS policy obviously promotes the regional S&T innovation ability in the eastern region of China. (4) From the perspective of spatial spillover effect, GPSTPS policy has a positive spatial spillover effect on the improvement of regional S&T innovation ability in the eastern region, while the spatial spillover effect of the policy on S&T innovation ability is not obvious in central and western regions.  
Research on regional S&T innovation development in Europe shows that public S&T services supported by governments, such as S&T parks and technology research and development centers established by governments, can effectively overcome the shortcomings of small and medium-sized enterprises' innovation and promote regional S&T innovation in the process of the European integration. Compared with the results of this study, it is found that the public S&T services supported by European governments can effectively promote regional S&T innovation ability.
# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
## 3 Proposed Flying Watchdog Architecture  
The drone system is comprised of two distinct components: an aerial surveillance module designed to augment security patrols and a ground-based checkpoint platform serving as a check-in station for security personnel. The ground checkpoint, or PGP, has Wi-Fi connectivity to transmit pertinent information about patrol schedules, logging data, and alerts to web or mobile interfaces.  
This Internet of Things (IoT)-based framework offers functionalities such as scheduling security patrols, recording drone-generated maps with GPS coordinates, and triggering alarms during security breaches. It is worth noting that while IoT applications of this nature have found widespread adoption, the primary focus of this study does not center on this application domain. The proposed architectural configuration is illustrated in Figure 1. The drone can fly with a predetermined flight pattern between checkpoints. Upon reaching each checkpoint, the drone engages in a search mode to identify PGP by executing a circular flight path with a 1 m radius, and the camera is directed downward at a 45\({}^{\circ}\) angle. Subsequently, upon PGP detection, the drone activates a laser for 20 s to transmit a security code, effectively halting the checkpoint timer. The PGP is intricately designed to establish a network connection, facilitating the transmission of check-in time data to the IoT Hub platform. If a check-in is not performed within the specified time frame, an alarm at the monitoring station is triggered. Importantly, this research underscores the adaptability of the approach, allowing for flexibility in the number of PGPs employed for checkpoints and customization of consequences in case of untimely check-ins by the drone.
# Optimal Insertion Torque for Orthodontic Anchoring Screw Placement: A Comprehensive Review
###### Abstract  
The optimal insertion torque (IT) for orthodontic anchoring screws (OASs) was hypothesized to vary with OAS features and insertion methods. This review examines the indexed English literature, to determine the appropriate IT range for OAS success based on area of insertion and OAS features. Eleven original articles with OAS placement in humans including IT and success rate were selected and were used to evaluate the relationships among IT, success rates, screw design, and placement methods at different sites. The TIs and success rates ranged from \(6.0\pm 3.2\) to \(15.7\pm 2.3\) Ncm and from \(62.5\) to \(100.0\%\) in the upper and lower buccal alveolar areas, respectively. For the mid-palatal area, the range was \(14.5\pm 1.6\) to \(25.6\pm 5.5\) Ncm and \(83.0\) to \(100.0\%\), respectively. TIs of \(5\)-\(12\) and \(6\)-\(14\) Ncm were found to be optimal for the commonly used \(\varphi 1.5\)-\(1.7\) mm OASs in the upper and lower interproximal areas, respectively. In the mid-palatal suture area, TIs of \(11\)-\(16\) and \(20\)-\(25\) Ncm were considered suitable for tapered \(\varphi 1.5\) mm and \(\varphi 2.0\) mm OASs, respectively. Although identified optimal IT ranges deserve the recommendations, care must be taken to monitor the IT during placement constantly.  
orthodontic anchoring screw; orthodontic mini-implant; temporary anchorage device (TAD); placement torque; insertion torque; success rate +
Footnote †: journal: Applied
## 3 Results and Discussion
### Physicochemical Characteristics
Soil Dry} \\ S1 & 7.63 & 430.3 & 320.8 & 1.29 & 189.14 & 978.0 \\ S2 & 7.81 & 501.3 & 316.4 & 2.59 & 133.62 & 1038.6 \\ S4 & 7.76 & 754.7 & 306.5 & 2.59 & 205.92 & 1585.5 \\ S6 & 8.47 & 248.9 & 305.4 & 0.89 & 183.63 & 975.8 \\ S12 & 7.54 & 271.2 & 106.5 & 2.07 & 244.94 & 669.5 \\ S15 & 5.38 & 412.7 & 173.3 & 3.12 & 255.72 & 489.2 \\ S22 & 7.05 & 954.3 & 432.6 & 6.78 & 216.32 & 732.1 \\ S24 & 6.48 & 123.2 & 437.9 & 5.14 & 278.22 & 975.6 \\ \hline \multicolumn{6}{c}{b. Soil Rainy} \\ S1 & 7.80 & 589.7 & 338.2 & 11.48 & 16.69 & 856.1 \\ S2 & 7.81 & 363.7 & 335.2 & 2.87 & 16.71 & 1098.1 \\ S4 & 7.76 & 440.7 & 328.3 & 7.18 & 22.30 & 1484.8 \\ S6 & 7.54 & 161.0 & 309.2 & 1.36 & 44.56 & 855.6 \\ S12 & 6.17 & 122.6 & 484.5 & 0.78 & 389.78 & 790.1 \\ S15 & 7.52 & 379.7 & 435.8 & 2.85 & 105.71 & 1161.0 \\ S22 & 7.02 & 598.0 & 414.4 & 5.72 & 77.94 & 852.5 \\ S24 & 5.66 & 475.0 & 423.8 & 3.94 & 111.23 & 730.9 \\ \(p\)-value \({}^{\rm[b]}\) & 0.817 & 0.550 & 0.091 & 0.323 & 0.036 & 0.747 \\ (Bilateral) & & & & & & \\ \hline \multicolumn{6}{c}{c. Sediment Dry} \\ S1 & 7.96 & 598.3 & 316.7 & 1.94 & 27.90 & 794.0 \\ S2 & 7.84 & 792.7 & 237.4 & 3.89 & 116.34 & 1221.0 \\ S4 & 7.91 & 361.0 & 281.3 & 2.32 & 360.45 & 1037.9 \\ S6 & 8.09 & 206.6 & 188.4 & 0.13 & 111.35 & 975.8 \\ S12 & 7.17 & 455.3 & 380.9 & 5.01 & 492.96 & 1160.2 \\ S15 & 7.00 & 1045.3 & 185.4 & 8.35 & 206.24 & 996.2 \\ S22 & 7.11 & 887.7 & 248.1 & 5.54 & 306.33 & 916.0 \\ S24 & 6.74 & 842.3 & 1
## 1 Introduction
### Motivation and Contribution  
Figure 1 shows the motivation behind the proposed work in this paper. Although the effectiveness of NOMA has largely been explored in the sum throughput maximization, the identification of IDR still remained an unexplored area. Previously, it has been shown that RIS can lead to an overall improvement in the data rate by enhancing the effective channel gain of the downlink NOMA system. When time division multiple access (TDMA) is clubbed with multiple RISs in a distributed RIS-assisted NOMA (TDMA-RIS-NOMA) downlink system, a point-to-point communication model is created between the access point-to-RIS-to-user device. Due to this point-to-point communication model, the optimization of the phase shifts provided by meta-atoms of each RIS is facilitated. The optimized phase shifts of meta-atoms maximize the equivalent channel gain between the access point to the user. In this scenario, the channel becomes saturated and signal-to-interference plus noise ratio (SINR) becomes a function of power coefficients only. In this study, the power coefficients are calculated to maximize the SINR of each user belonging to a NOMA cluster  
using a geometric progression-based power allocation method such that IDR reaches its upper bound. Our findings set an upper limit for IDR and are useful for planning the resource allocation to achieve the desired quality of service (QoS) requirements in a NOMA downlink cluster.  
To the best of the authors' knowledge and belief, no resource allocation strategy tries to achieve the upper bound on the IDRs of lower CG users and simultaneously increases the IDR of the highest CG in stand-alone NOMA and RIS-NOMA systems. This IDR maximization becomes important when the access point (AP) is radiating low transmit power, as in the case of internet-of-things (IoT) networks [15].  
Motivated by the above-mentioned points, this paper presents the TDMA-RIS-NOMA downlink system, where exploiting the upper bound of IDRs is possible with easy beamforming and channel gain controlling. The main contributions from this paper are listed below:  
* A point-to-point beam-forming strategy is exploited in the TDMA-RIS-NOMA downlink cluster such that at any instant, one RIS is serving only one user device for downlink communication.
* A geometric progression (GP)-based power coefficient allocation scheme is derived to find out the power coefficients of all the users belonging to an RIS-assisted downlink NOMA cluster for maximizing the IDRs of the users.
* Distribution of data rate among the users of an RIS-NOMA downlink cluster is analyzed and an expression is derived to calculate the upper bound of IDRs.
* Based on the upper bound of IDRs and QoS requirements, a clustering algorithm is proposed where we decide the cluster bandwidth and cluster size in the downlink NOMA-RIS system for different applications.
## 1 Introduction
### Background
The considerations regarding the positioning and orientation payload for UAV systems are confounded by the payload weight and size restrictions, and a trade-off between accuracy and weight must often be made. This has resulted in the majority of UAV-borne LiDAR systems consisting of larger UAVs, capable of carrying heavier payloads [16, 17, 18], making them difficult to deploy in forested environments. For example, the platform outlined in [PERSON] _et al._[17] has a take-off weight of 330 kg. The mini-UAV outlined by [PERSON] _et al._[14] uses a combination of a tactical grade IMU and laser scanner. This combination, which weighs over 3 kg, pushes the payload limitations of most commercially available vertical take off and landing (VTOL) UAV's suitable for forest based research. Micro-Electromechanical System (MEMS) based IMUs offer an alternative option for positioning and orientation that is both lightweight and low-cost. These IMUs have been deployed for a variety of positioning and orientation tasks, including navigation, obstacle avoidance and land-based mapping [20]. This technology can be used as the primary orientation sensor within a GPS/IMU sensor framework to provide the high rate estimates of position and orientation required for LiDAR mapping. However, due to the high levels of error within MEMS IMUs and based on reported errors of sensor fusion algorithms using such sensors [21], it can be shown that estimates of orientation and position would be of an inadequate accuracy for use on-board a UAV-borne LiDAR system [22]. Several innovative algorithms fusing GPS and MEMS observations have been shown to improve the modelling of the large stochastic drifts within MEMS IMUs and as a consequence the accuracy of orientation estimates [23, 24, 25]. Furthermore, the augmentation of techniques developed within the fields of photogrammetry and computer vision have contributed to improving the accuracies of MEMS-based navigation systems when used for direct georeferencing [26, 27, 28]. These developments suggest that a UAV system consisting of a lightweight MEMS based IMU along with GPS and visual observations can provide estimates of position and orientation with the accuracy required for mapping forest metrics using UAV-borne LiDAR.
## 2 Simulation Description
The preliminarily carried-out simulations had shown that the absorption enhancement is observable for the texture widths in the range 50-500 nm, which was used as a reference for the dimension range in this work. The geometries of the model used in the simulations are presented in Figure 1.  
The top edge of the air domain was set-up with \"port\" boundary condition, which has the possibility to produce excitation waves. The excitation was set-up in the form of a vertically incident plane wave with the constant irradiation power, swept in the range of wavelengths from 400 to 1200 nm. The used \"port\" boundary condition has the properties of an absorbing boundary, suppressing the multiple reflections from the simulated device. The width of the model was set to match the width of the texture (\(d\)). Thus, the periodic Floquet boundary condition could be established on the corresponding left and right boundaries of the model. The power of the incident irradiation was kept constant while the model width was varied, in order to normalize the levels of absorption and reflection for the variable texture dimension. Thus, the results of models with different widths can be directly compared. Since the natural sunlight that reaches the Earth's surface is not polarized, the cases of both TE (transverse-electric) and TM (transverse-magnetic) polarized irradiation have been simulated and the average value has been calculated. Finally, the calculated absorption was scaled to AM1.5 standard solar irradiance spectrum [27] in order to assess the contribution of light-trapping to the absorption enhancement depending on the real-life spectral distribution of the incident light.
## 4 Simulations
### Effects of Repeaters and Bottlenecks on Data Throughput  
The results from Figure 11 show that estimated theoretical values by the authors are decently accurate to the measured values in real infrastructure. For this reason, we introduce here simulations with real values as input. This section focuses strictly on the effect of repeaters on data throughput when bottlenecks are present on the route. Topologies, shown in Figure 19, with 0 to 5 repeaters were used for simulation with 10 different values of bottleneck from 100 to 1000 kb/s. The bottleneck was always placed between the source and and router 1 (R1), except the topology with direct connection where the bottleneck was the direct link. The maximum throughput of all other links was set to 1000 kb/s. Significant throughput drop is expected in this simulation due to the delay caused by the repeaters during data re-transmission.  
The results from the simulation are shown in Figure 20. The most significant effect of repeater limitation is noticeable on higher bottleneck values. This means that calculating with repeater limitations is more important if the data throughput is significantly higher than the weakest point-to-point connection in a topology. The plot shows that maximum values do not reach the  
Figure 19: Simulation topology for discovering effects of repeaters and bottlenecks on data throughput.  
Figure 18: Balancing of two streams in a network with bottlenecks.  
link available throughput, which is caused by the fact that the shown values of throughput are obtained on application layer that calculates with overhead.  
The issue of influence of repeaters on data throughput was revealed via the measurements, which were after that used as an input for simulations. The reason is that the problems with bottleneck cannot be discovered via measurements on real topology because it has fixed parameters and usually only one particular bottleneck value. Through the simulations it was possible to simulate network throughput behavior with a small step of the bottleneck value. Lessons learned from the simulation provide information that can be used in another real location with different numbers of repeaters and bottlenecks. The results can provide utilities information about the suitability of the infrastructure for upgrade before implementing PLC/BPL technology. If the infrastructure proves to be unsuitable for the amount of traffic, it will prompt the utilities to upgrade connection points and power cables, which will ultimately provide higher performance of the PLC/BPL network.
## 3 Proposed Method  
### Time-Frequency Representation (TFR)  
Audio waveform is commonly transformed to time-frequency representations (TFRs) in which temporal and spectral information can be characterized simultaneously. In this study, we evaluated several TFRs, including Mel-spectrogram, MFCCs [2] and Constant-Q spectrogram [19]. We denote TFRs as \(S(f,t^{\prime})\) in Figure 2, where \(t^{\prime}\) is frame index.
## 3 Results
### The Optimal Temperature and pH for the Enzymatic Synthesis of 1-octen-3-ol from linoleic Acid  
For the effective production of 1-octen-3-ol by _Ab_LOX and _Ab_HPL, the optimal temperature and pH for the enzymatic reaction were studied. The optimal reaction temperature was studied by detecting the enzymatic reaction production of 1-octen-3-ol by SPME-GC after the reaction for 1 h over a temperature range of 4-45 \({}^{\circ}\)C and pH 7.2 with the substrate 3 mM linoleic acid. Briefly, 1-octen-3-ol was not detected as a product for the reaction conducted at 4 \({}^{\circ}\)C. The optimal reaction temperature was 35 \({}^{\circ}\)C, whereas the relative production yield sharply declined at 45 \({}^{\circ}\)C (Figure 2A). The optimal reaction pH was studied over a pH range of 5.0-9.0 at 35 \({}^{\circ}\)C with a substrate of 3 mM linoleic acid for 1 h. The optimal reaction pH was 7.0, which correlated with the maximum production yield (Figure 2B).  
Effect of Reduced _Ab_LOX Expression on Agaricus bisporus Mycelial Growth and the Production of 1-octen-3-ol  
To confirm that _Ab_LOX was also involved in the synthesis of 1-octen-3-ol in vivo, as_Ab_LOX transformantsants of _A. bisporus_ were constructed. After five generations of subculture, four transformants were obtained and named _Ab_LOX-1 to _Ab_LOX-4. The mycelial growth rate of the four transformants in nutrient-rich potato dextrose agar medium was not lower than that of the parent, except for _Ab_LOX-2 (Figure 3A). The expression of the _Ab_LOX in the four transformants was 34-82% lower than that of the parent (Figure 3B), and the production of 1-octen-3-ol was 70-80% lower than that of the parent (Figure 3C). In addition, the _Ab_LOX expression of all strains showed a significant positive correlation with their 1-octene-3-ol yield (\(p<0.01\)).  
Figure 3: The _Ab_LOX expression (**A**), mycelial growth rate (**B**) and mycelial 1-octen-3-ol yield (**C**) of the antisense _Ab_LOX transformantsants derived from _Agaricus bisporus_. WT, The parent strain of _A. bisporus_ As2796; as_Ab_LOX-1 to _as_Ab_LOX-4, The antisense _Ab_LOX transformantsants. Data marked with (**) were significantly different at \(p<0.01\) from WT based on one-way ANOVA.  
Figure 2: Effects of temperature (**A**) and pH (**B**) on the production of 1-octen-3-ol from linoleic acid by _Ab_LOX and _Ab_HPL.