esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the Human Gut Microbiota
### Factors Influencing the Gut Microbiota
#### 2.3.3 Other Factors  
Other factors that have been reported to also influence the gut microbiota include genetic factors, smoking, and physical exercise [101; 102; 103].  
The host's genetic composition is a determining factor in how certain bacteria influence the metabolism and functions of the microbiota. Members of the same family, by sharing similar genetics, tend to have a more similar microbial community compared to unrelated individuals. For example, hosts carrying the rs651821 variant of the APOA5 gene are more likely to be colonised by the genera _Lactobacillus_, _Sutterella_, and _Methanobreviacter_, which have been associated with a higher risk of developing metabolic diseases [101].  
Smoking is another relevant factor. Cigarette smoke is a complex chemical mixture, with nicotine as its main active substance. Nicotine is inhaled into the lungs, primarily absorbed by the pulmonary alveoli, but also by the GIT. Consequently, it can influence the composition of the gut microbiota by increasing the abundance of _Bacteroidota_ while decreasing _Bacillota_ and _Pseudomonadota_. On the other hand, it increases intestinal pH, which promotes the development of some pathogenic microorganisms, such as _Peptococcaceae_[102].  
Finally, regular physical exercise, particularly moderate-intensity aerobic activities performed for 30-90 min 3-5 times per week, is also positively correlated with the composition and functions of the gut microbiota. Such exercise regimens promote increased microbial diversity and a balance between beneficial commensal microorganisms and pathogens, including the growth of beneficial bacteria such as _A. muciniphila_, _Oscillospira_, _Faccalibacterium prausnitzii_, _Methanobreviacter_, _Veillonella atypica_, and _Preotella_ spp. Mechanistically, physical exercise enhances the production of SCFAs like butyrate, modulates immune responses by increasing IgA levels and reducing Toll-like receptor signalling, and decreases intestinal transit time, collectively contributing to improved intestinal barrier function and reduced inflammation [103; 104; 105].
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 4 Experiment and Analysis
### Storage Performance Analysis  
This study evaluated the efficacy of the proposed storage architecture in reducing blockchain storage requirements under various simulated environmental stresses. Comparative analyses were conducted to assess the impact of integrating the off-chain platform versus scenarios without it. We employed a distributed intrusion detection system that leveraged multiple hosts to provide both raw traffic data and preprocessed training data for analysis, with the raw data being of substantial size and the training data of reduced size. In our experiments, we used the CIC-IDS-2017 dataset, in which the traffic data were 8.2 GB, 10 GB, 7.7 GB, 10 G, and 12 GB, and the sizes of the training data were 173 MB, 12.6 MB, 50.4 MB, 14.2 MB, and 56.9 MB, respectively. Table 3 presents a concise comparison of the blockchain storage space required for shared data across simulated application scenarios, highlighting the differences between systems with and without the implementation of an off-chain storage solution.  
As depicted in Table 3, storing the entirety of the shared data directly on the blockchain, devoid of an on-chain/off-chain separation architecture, enlarges block sizes proportionally to the data size, notably hampering blockchain operational efficiency. Conversely, our proposed scheme maintains a constant 825-byte storage requirement on-chain by sequestering only the ciphertext of the shared data's hash value and essential account details, while the original data resides off-chain in IPFS. This segregation of data from proofs of ownership effectively mitigates blockchain storage overhead.
## 5 Experimental Results
### The Qualitative Experiments Results  
To verify the validity of the error compensation method proposed in this paper, an object-picking task was performed using the robot used in the experiment. The procedure for the object picking task is described in Figure 2. Using C#, control software was developed and Raspberry board was used for communication with the robot. Communication was carried out using the RS-232 standard. First, the robot was moved to the fine search position in the field-of-view area of the camera. Then, the camera was used to calculate the 3D pose of an object and then combined with the information of the hand-eye calibration. Then, the position and the normal vector of the object was calculated. Next, the position/orientation value of the end effector was estimated using a neural network. Finally, the robot's trajectory was modified, and the robot's gripper reached the target object. The photo of the robot's picking task is shown in Figures 12 and 13.  
The results of the experiment confirmed that the proposed method reduced the absolute position error in the workspace with 200 random positions by 50.3% on average, which is sufficiently applicable to the object-picking task. We are confident that applying the method proposed in this paper to robots will also allow for tasks requiring higher accuracy.  
Figure 12: Trajectory that the robot was taught.  
Figure 13: Object picking result.
# Study on Mechanical Properties of Multi-Cavity Steel-Concrete Composite Floor
## 2 Structure and Prefabrication  
### Structure  
Multi-cavity steel-concrete composite floor is composed of a multi-cavity steel plate framework and self-compacting concrete. As shown in Figure 2, the framework consists of the inset porous plates and the nonporous plates at the outside surface as the shell. The inset porous plates communicate with each other and thus form a continuous framework for concreting, which can strengthen the bonding performance of self-compacting concrete and improve the integrity of the composite floor [16; 17; 18].  
Figure 2: Structure diagram of multi-cavity steel–concrete composite floor.
## 3 Results and Discussion
### Essential Oil Composition
The scores of the samples in Figure 2 show wide overlapping of the samples from the three sites, with most KEM samples scoring with a negative component 1 value. Samples from the DEF site having a lower within population variability were rather clustered in the center. Outliers S4, S8, and K7 in the lower right quadrant had the highest \(\beta\)-phellandrene percentages. Outlier S2 had relatively higher amounts of oxidized monoterpenes. Samples K3 and K4, with the lowest component 1 scorings, had relatively high amounts of the minor essential oil compounds \(\delta\)-cadinene, bicyclogermacrene, and \(\gamma\)-cadinene. Figure 1: Essential oil compounds loading on the first two axes of a principal component analysis of _P. cembra_ essential oils. Figure 2: PCA scoring plot showing the essential oil samples from the individual _P. cembra_ trees. ABisab: \(\alpha\)-bisabolol, Ahum: \(\alpha\)-humulene, Amuu: \(\alpha\)-muurolene, Apin: \(\alpha\)-pinene, Bcar: \(\beta\)-caryophyllene, BiGer: bicyclogermacre, Bisab: \(\beta\) bisabolene, Bor: borneol, BorAc: borny lactate, Bphel: \(\beta\)-phellandrene, Bpin: \(\beta\)-pinene, CadOl: \(\alpha\)-cadinol, Cam: camphene, Cryp: cryptone, D3c: \(\delta\)-3-carene, Dcad: \(\delta\)-cadinene, Gcad: \(\gamma\)-cadinene, GerD: germacre D, GerD4 ol: germacre D-4-ol, Gmuu: \(\gamma\)-muurolene, Myrr: myrcene, MyrrAl: myrrtalen, Picar: trans-pinocarveol, Spath: spathulenol, T4 Ol: terpinen-4-ol, Ter: terpinolene, TerOl: \(\alpha\)-terpinel, Tether: thymolmethylther, Tmuu: \(\tau\)-muurolol, and Verb: verbenone  
A great variability of essential oils within a given population has also been reported in other conifers. In _Pinus sylvestris_, individuals rich in \(\alpha\)-pinene or \(\delta\)-3-carene occurred together within a stand [20]. High diversity of terpen might be important to increase the resistance against herbivory [21].
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
###### Abstract  
Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.  
underwater sensor networks; remaining energy; node degree; AUV; dynamic relay node IEEEexample:BSTcontrol  
2020  
24246661
## 3 Proposed Procedure to Develop Predictive Models in Healthcare
### Stage 6: Interpretation of Results and Model Selection
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Logistic** \\ **Regression** \\ \end{tabular} & **SVM** & \begin{tabular}{c} **Decision** \\ **Tree** \\ \end{tabular} & \begin{tabular}{c} **Random** \\ **Forest** \\ \end{tabular} & **MLP Neural** & **KNN** & **GBA** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Parameter** \\ **Selection** \\ \end{tabular} } & \multirow{2}{*}{Ridge: \(10^{-8}\)} & C: 4; & \begin{tabular}{c} Instances \\ per leaf: 15; \\ Conf.: 0.15 \\ \end{tabular} & \begin{tabular}{c} Trees: 200 \\ Depth: 5 \\ \end{tabular} & \begin{tabular}{c} LR: 0.5; \\ Mom.: 0.3 \\ \end{tabular} & \
\begin{tabular}{c} K: 20; \\ Euclidean \\ \end{tabular} &
\begin{tabular}{c} Iterat.: 50 \\ W.T.:100 \\ \end{tabular} \\
**Accuracy** & 86.6\% & 95.4\% & 83.7\% & 87.7\% & 91.8\% & 83.4\% & 87.1\% \\
**Precision** & 88.6\% & 95.4\% & 86.9\% & 86.1\% & 93\% & 83.5\% & 87.1\% \\
**Recall/Sensitivity** & 89.6\% & 97.8\% & 84.1\% & 92.3\% & 92.5\% & 91.2\% & 92.9\% \\
**Specificity** & 82.5\% & 91.7\% & 80.8\% & 76.6\% & 90.5\% & 71.7\% & 78.3\% \\
**F1 Score** & 89.1\% & 95.3\% & 85.5\% & 88.9\% & 92.8\% & 83.2\% & 86.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics considered for algorithm selection and the results obtained for each algorithm. Recall (\(>\)90%): SVM, random forest, MLP neural network, kNN, and GBA. According to the results, the four algorithms that fulfil the requirements are SVM, random forest, the MLP neural network, and GBA. From all these algorithms, SVM achieves the best results in all the metrics considered, and therefore it could be the model selected for production.
## 3 Motivating Scenario
### Disaster Model  
In this subsection, which sums up the familiar characteristics in many disasters, a simplified disaster model is shown as follows: suppose a disaster event \(i\) is a tuple of four parameters (\(C_{i}\), \(K_{i}\), \(l_{i}\), and \(A_{i}\)), where \(C_{i}\) denotes the central position of the area that the disaster event happens, as described by two-dimensional coordinates (\(x_{i}\), \(y_{i}\)); \(K_{i}\) is disaster intensity, which is used to express the risk level of the disaster event at \(C_{i}\); \(l_{i}\) represents the attenuation coefficient of this event; and \(Ai\) is the affected area of this disaster event. As described in [31], a disaster is described by a group of disaster events (devastating events). For each devastating event \(i\), \(l_{i}\) denotes the change of the risk level (intensity) in the affected area of event \(i\). Apparently, the closer a location is to the center point of the devastating event, the greater its disaster intensity. We assume \(I_{i}\) decreases linearly from the central position of the event. For location \(j\), the attenuation coefficient is calculated as follows:  
\[I_{i,j}=\frac{l_{j}}{L} \tag{1}\]  
where \(L\) is the farthest distance from \(C_{i}\) to the location affected by the devastating event in the monitoring area, and \(l_{j}\) is the distance from \(C_{i}\) to location \(j\). For parked vehicle \(n\) located at location \(j\), its survival probability can be expressed as follows:  
\[p(n)=\frac{I_{i,j}\times K_{i}}{K_{\max}} \tag{2}\]  
where \(K_{\max}\) denotes the highest recorded intensity of such devastating events. For example, for earthquake disasters, the highest earthquake magnitude achieves 12. As shown in Figure 2, based on the survival probabilities of the parked vehicles, we categorize ground parked vehicles into different states: safe, sub-safe, and dangerous. We assume there are two thresholds, \(T_{s}\) and \(T_{d}\) (\(T_{s}<\) = \(T_{d}\)), which can be predefined in terms of the disaster scene. Let \(rank(n)\) denote the state of parked vehicle \(n\), we then obtain the following:  
\[rank(n)=\left\{\begin{array}{c}safe\ p(n)<T_{s}\\ sub\ safe\ T_{s}\leq p(n)\leq T_{d}\\ dangerous\ T_{d}\leq p(n)\end{array}\right. \tag{3}\]  
Figure 2: Disaster model.  
Compared to vehicles in a dangerous state, ground vehicles in a safe state and a sub-safe state are more suitable for offloaded task execution because they are relatively far away from the center of the disaster event and have less damage. Suppose the cluster head periodically sends beacon messages to the UAV, from which the UAV learns the current state of each member node, its ID number, and the total number of surviving parked cars in the cluster.
## 2 Background  
### Physically Unclonable Functions  
PUFs are physical devices that act as one-way functions by mapping challenges to responses. The term \"PUF\" was coined in 2002 [4]; however, the use of physical devices' intrinsic features as security components dates back as far as 1984 [5]. PUFs have been widely used since their introduction, with the most popular PUFs being memory PUFs.  
A challenge-response pair (CRP) is defined as a PUF's challenge and subsequent response. A PUF is considered stronger if it has a larger number of CRPs. The threshold between _weak_ and _strong_ PUFs is determined by how the number of CRPs scales with size [6]. If a PUF's CRPs scale linearly, the PUF is considered _weak_; however, if the PUF's CRPs scale exponentially or quadratically, the PUF is considered _strong_.  
Memory-based PUFs are a category of PUFs that leverage the inherent cell-to-cell variations of memory devices. They derive their responses from the diverse electrical characteristics of individual memory cells. A variety of memory PUFs have been proposed. The first and most popular are static random-access memory (SRAM) [7; 8; 9; 10] PUFs. However, emerging memory technologies, such as resistive random-access memory (ReRAM) and MRAM PUFs, have also been proposed in [11; 12; 13] and [14; 15; 16; 17], respectively.  
A PUF's response variability might be affected by the environment or aging, making certain responses unstable. In binary addressable PUFs that consist of responses of only 0 s and 1 s, this can become an issue, as some responses might flip between 0 and 1, making them unreliable.
###### Abstract  
This work aimed to develop novel alternative gluten-free premixes for use in the production of cookies by optimizing the formulation of three different starches (corn, potato, and tapioca starch) in a rice/corn formula and then enriching the optimized formula with various natural flours (acom, soy, chestnut, chickpea, millet, and quinoa). An extreme vertices mixture design was used to optimize the mixing sub-part consisting of 30% (_w_/_w_) of starches. The enriched gluten-free cookies were characterized by physical, rheological, antioxidant, color, and sensory properties. An optimum was obtained by mixing 18.5084 g of corn starch, 6 g of potato starch, and 5.4916 g of tapioca starch. The enrichment results indicated that soy-enriched gluten-free cookies have a high specific volume (2.428 cm\({}^{3}\)/g) with the highest spread ratio (12.25) compared to other cookies. The rheological properties of enriched gluten-free doughs indicated a higher value of the consistency coefficient (K) of soy-enriched gluten-free cookies. The antioxidant characteristics were enhanced by replacing corn with several types of flours, with higher TPC for acom (2.83 mg GAE/g dw) and soy-enriched cookies (2.49 mg GAE/g dw) with better antioxidant activities (DPPH, ABTS, and RED). The stasters gave all the cookies favorable ratings for overall acceptability. With an average rating of 7.09, cookies enhanced with chickpea flour have a high acceptability compared to the other cookies. Considering all the tested parameters, PCA analysis clustered millet, corn, quinoa, and chestnut gluten-free enriched cookies into the same group. However, acom and soy cookies were placed in a separate group, and chickpea-enriched cookies were classified separately.  
gluten-free premix cookies; natural flours; extreme vertices design; technological properties; antioxidant properties +
Footnote †: journal: Applied sciences
# Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed
## 2 Methodology and Data Source
### Long Short Term Memory  
Long short-term memory networks are a type of recurrent neural network (RNN) designed to avoid the problem of long-term dependence, where each neuron contains a memory cell capable of storing the previous information used by the RNN or forgetting it if necessary [19]. Currently, it is widely used with success in time series prediction problems. LSTM-RNN was designed from a memory cell that stores long-term dependencies. In addition to the memory cell, the LSTM cell contains an input gate, output gate and forget gate. Each gate in the cell receives the current input \(x_{t}\), the hidden state \(h_{t-1}\) at the previous moment and the state information \(C_{t-1}\) of the cell's internal memory to perform various operations and determine whether to activate using a logic function. The state \(h_{t}\) of the unit,  
Figure 1: Time series of the wind speed in Halifax, Canada.  
the output at time \(t\) and the input hidden state at time \(t_{1}\) are determined by non-linearly activating \(tanh\) ( ) and the information of the output gate.  
The processing of a time point inside a LSTM cell could be described as below:  
1. The unwanted information in the LSTM is identified and thrown away from the cell state through the sigmoid layer called forget gate layer defined by \[f_{t}=\sigma\left[w_{f}(h_{t-1},x_{t})+b_{f}\right]\] (2) where, \(w_{f}\) is the weight, \(h_{t-1}\) is the output from the previous time stamp, \(x_{t}\) is the new input, \(b_{f}\) is the bias.
2. The new information that will be stored in the cell state is determined and updated by the sigmoid layer called the input gate Layer. Next, a \(tanh\) layer creates a vector of new candidate values that could be added to the state \[i_{t}=\sigma[w_{i}(h_{t-1},\ x_{t})+b_{i}]\] (3) \[\hat{c}_{i}=tanh[w_{c}(h_{t-1},x_{t})+b_{i}]\] (4)
3. The old cell state, \(c_{t-1}\), is updated into the new cell state \(c_{t}\). The cell state is updated as follows \[c_{t}=f_{t}*c_{t-1}+i_{t}*\hat{c}_{t}\] (5)
4. A sigmoid layer will be run to decide what parts of the cell state are going to the output. It is expressed by \[o_{t}=\sigma[w_{o}(h_{t-1},x_{t})+b_{i}]\] (6) \[h_{t}=o_{t}*tanh(c_{t})\] (7) The LSTM structure is illustrated in Figure 3.
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 3 Experiments
### Experimental Evaluation of the Proposed Model  
#### 3.3.1 Comparison with Diverse Benchmark Models  
To verify the superiority of the TCN, GRU, and ESN deep networks in crude oil price forecasting, we compared the three predictors with six different algorithms, which were the BPNN, RBFNN, GRNN, LSTM, CNN, and GMDH algorithms. Table 4 visually shows the evaluation indicators of different predictors. Figure 3 shows the prediction errors of different predictors in crude oil price series #1. Figure 4 presents the forecasting results for different predictors in series #1. The following conclusions can be drawn from Table 4, Figures 3 and 4:  
* The forecasting precision of the deep networks was superior to that of other traditional algorithms in all cases. This shows that the deep networks can better identify the volatility shape of raw data. The probable reason is that deep networks have rich hidden layers, which improves their performance to deal with non-stationary data and could mine the deep information more effectively.
* The TCN, GRU, and ESN showed better prediction performance than the LSTM and CNN on three datasets. Possible reasons are as follows. First, the TCN incorporates dilated convolution and residual structures. These features enable a large receptive field in a relatively shallow network and maintain stable gradient propagation while capturing long-range dependencies. Second, the GRU employs a simplified gating mechanism that lowers model complexity. Hence, it achieves better training efficiency and faster convergence when handling random fluctuations and long-term dependencies. Third, the ESN utilizes a reservoir with randomly sparse connections, which helps capture dynamic features in crude oil price data more effectively. It also maintains robust performance under highly random conditions. In contrast, the LSTM and CNN have more complex structures or limited receptive fields. When they face high noise and strong non-stationarity, they often encounter unstable training or inadequate long-term dependence capture. Therefore, the TCN, GRU, and ESN offer stronger adaptability and prediction ability for non-stationary and random crude oil price data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Series** & **Predictors** & **MAE (USD)** & **MAPE (\%)** & **RMSE (USD)** \\ \hline \multirow{8}{*}{\#1} & TCN & 0.7462 & 1.1634 & 1.0092 \\  & GRU & 0.7680 & 1.1910 & 1.0048 \\  & ESN & 0.8262 & 1.2775 & 1.0821 \\  & BPNN & 2.7229 & 4.3405 & 2.9994 \\  & GRNN & 3.6410 & 5.6739 & 4.0624 \\  & RBFNN & 1.3925 & 2.1287 & 1.8051 \\  & LSTM & 0.9536 & 1.4394 & 1.1935 \\  & CNN & 0.8698 & 1.3729 & 1.1409 \\  & GMDH & 1.1592 & 1.8026 & 1.4410 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation indicators for different predictors across three WTI crude oil price series.