esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Proximate Composition  
The proximate composition results (Table 1) showed that carbohydrates and fiber were the most important compounds in the flours analyzed. For the concentrates, the most important compounds were proteins; they had a lower carbohydrate and fiber content than the flours. The percentage of fat ranged between 9.93% and 12.31%. Although hemp seed has a fat content of 30% fat [35]; however, the production of flours and protein concentrates from hemp occurs after defatting the seed, which is why a low percentage of fat was found in the samples analyzed. The starch content was between 0.82% and 1.11%, with one of the flour samples showing contents below the detection limit. The ash content was higher in the concentrates than in the flours with mean values of 6.68% for the flours and 8.48% for the concentrates. In terms of energy (kcal/100 g), there are no significant differences between flours and concentrates. No statistically significant differences in moisture content were found between the different samples analyzed with a mean value of 6.26%.  
The nutritional composition obtained in our samples was similar to that described in the literature for hemp flours [16] and for hemp protein concentrates [36]. The two commercial brands analyzed showed differences between them; in the case of the flours, the differences were significant for all the parameters analyzed. Organic flour had higher contents of carbohydrates, fiber, and ash, but lower contents of fat, protein, and starch. Furthermore, [PERSON] et al. [15] showed that the variety of hemp also influences the composition of the flour obtained, especially its macro- and microelement content and the content of individual carbohydrates (sucrose, fructose, and glucose). In the literature, flours with whole fat content and defatted flours can be found. Non-fat flours have lower concentrations of protein, starch, fiber, and ash [15]. This fact can lead to confusion among consumers when comparing different flours, as not all commercial brands indicate whether the flour is defatted or not.  
In the case of the concentrates, although the carbohydrate, fiber, and fat contents showed no significant differences between the two brands; however, the contents of protein and starch were significantly lower in the organic concentrate. This was also observed in the organic flours. The nutritional composition of protein concentrates can be highly variable depending on the process used to extract them. Thus, the application of an isoelectric precipitation process causes a reduction in the ash content [37], a dry fractionation process affects the carbohydrate contents, and a combined treatment of alkaline extraction and isoelectric precipitation influences the protein content [36]. This highlights the great variability that consumers may find within hemp protein concentrate.
# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 4 Time-Series Analysis to Predict EtEQ
### Prediction of Some Steps Ahead  
This analysis was performed to explore if time-series analysis and prediction can be used to predict the value of EtEQ some time-steps ahead into the future.  
Figure 15 shows the average MAE of paths. It shows the results of the RBR algorithm using the same setup that the baseline experiment (a lag window size of 12 instances, a training dataset of 2016 instances, and a test dataset of 288 instances), and then predicting 1-10 time steps into the future. The results obtained from the majority of the tests were satisfying. As we can observe, the average MAE grows very slowly. It seems possible to conclude that we could successfully predict the EtEQ several steps ahead in time.  
Once more, we analyzed the variability of errors for each number of steps ahead using a box plot, as shown in Figure 16. Although the values for the median and the first quartile are similar for all steps ahead considered, the values of third quartile and outliers (not depicted) grow with the number of steps. These differences in the variability of errors lead to the differences in the average Mean Absolute Error (MAE).
# Screening of Some Romanian Raw Honeys and Their Probiotic Potential Evaluation
## 3 Results
### Physico-Chemical Parameters of Raw Honey Samples
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Honey** & **Ca** & **Mg** & **Cu** & **Fe** & **Mn** & **K** & **Ni** & **Se** & **Zn** & **Cr** \\
**Samples** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** \\ \hline P1 & 83.0 & 69.7 & 0.31 & 7.96 & 2.23 & 375.30 & nd & 0.79 & 0.39 & 0.37 \\  & (4.26) a & (3.59) a & (0.01) b & (0.38) b & (0.11) b & (19.3) a & & (0.04) a & (0.02) c & (0.02) a \\  & 33.6 & nd & 0.21 & 3.58 & 7.26 & 282.64 & & 0.40 & 0.33 & 0.20 \\ P2 & (1.73) d & nd & (0.01) dc & (0.18) c & (0.38) a & (14.6) b & nd & (0.02) b & (0.01) d & (0.01) c \\  & 24.5 & 46.3 & 0.19 & 3.5 (0.17) & 0.30 & 147.75 & 0.50 & 0.31 & 0.50 & 0.16 \\  & (1.20) e & (2.35) cb & (0.01) ed & c & (0.01) fe & (7.35) e & (0.02) a & (0.01) c & (0.02) a & (0.01) d \\ P4 & 35.8 & 40.9 & 0.74 & 2.99 & 0.68 & 66.89 & & 0.22 & 0.26 \\  & (1.72) d & (1.99) c & (0.03) cd & (1.05) dc & (0.03) cd & (3.38) f & nd & (0.01) d & nd & (0.01) b \\  & 35.2 & 27.4 & 0.14 & 1.70 & 1.52 & nd & nd & 0.16 & 0.41 & 0.29 \\  & (1.67) d & (1.33) d & (0.00) e & (0.08) e & (0.07) c & nd & nd & (0.00) e & (0.02) cb & (0.02) b \\  & 30.3 & 46.1 & & 1.84 & 0.09 & 242.38 & & 0.15 & 0.32 & 0.18 \\  & (1.59) ed & (2.30) cb & nd & (0.08) e & (0.00) f & (11.8) c & nd & (0.00) e & (0.01) d & (0.01) dc \\ P7 & 36.9 & nd & 0.35 & 1.21 & 0.07 & 195.10 & & 0.13 & 0.36 & 0.19 \\  & (1.76) d & & (0.02) b & (0.06) e & (0.00) f & (9.97) d & nd & (0.00) e & (0.02) dc & (0.01) d \\ P8 & 66.9 & nd & 0.25 & 9.00 & 0.93 & & 0.14 & 0.35 & 0.26 \\  & (3.40) b & nd & (0.01) c & (0.46) a & (0.05) d & nd & nd & (0.01) e & (0.02) dc & (0.01) b \\  & 32.2 & 41.3 & 0.56 & 2.74 & 0.59 & 130.35 & nd & 0.16 & 0.36 & 0.20 \\  & (1.59) d & (2.01) b & (0.03) a & (0.13) d & (0.03) ed & (6.76) e & & (0.01) e & (0.02) dc & (0.01) c \\ P10 & 46.8 & & 0.15 & 9.05 & 1.51 & 60.73 & & 0.14 & 0.46 & 0.29 \\  & (2.22) c & nd & (0.01) e & (0.44) a & (0.07) c & (3.02) f & nd & (0.00) e & (0.02) ba & (0.01) b \\ \hline \end{tabular} The values are mean of three samples (n = 3, standard deviation in parentheses), analyzed individually in triplicate. Different letters within a column denote significant differences (\(p<0.05\)); nd = not detectable.  
\end{table} 
Table 3: Minerals results from raw honey samples. Figure 5: Hierarchical clustering of the honey samples based on physico-chemical parameters (Euclidean distance, \(r=0.76\)).
# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 5 Synthetic Data Manipulation
### Data Acquisition  
The critical failure, which is the broken rotor bar modeled by increasing the rotor resistance of an IM, occurs within several days under constant operating conditions. The fault propagation over time and the observed torque with load, stator current, and speed signals are shown in Figure 4. Forty days are considered for each raw data recording, and 1829 samples are recorded every day.  
As can be seen from Figure 3(b), the amplitude of the torque signals increases following the fault characteristic over time. On the other hand, from Figure 3(c,d), it can be seen that it is tough to track the fault characteristic from the stator current and speed signals only by looking at them. Therefore, the torque, stator current, and speed signals should be preprocessed to acquire more meaningful information and to create fault-related CIs aiming to make decisions.  
Figure 4: The fault propagation over time and the raw collected signals.
# The Perspective of Large-Scale Production of Algae Biodiesel
## 4 Results and Discussion
### Weaknesses
#### 4.2.3 Environmental  
The use of biodiesel from algae instead of conventional diesel has a positive impact on the environment as GHG emissions are reduced. However, the production of biofuels from lignocellulosic material has a better effect in this regard. Furthermore, lignocellulosic materials reduce the potential conflict between land use for food production and energy. Lignocellulosic raw material requires less intake of fertilizers, pesticides, and energy and is cheaper than crops. Biofuels from lignocellulosic have a favorable impact on the environment because they have a very small CO\({}_{2}\) footprint. Production of diesel from forest residues by gasification and Fischer-Tropsch synthesis releases a CO\({}_{2}\) emission of only 3.6 kgCO\({}_{2}\)/GJ fuel, and the price of the produced fuel is $18/GJ fuel. Production of diesel from the agricultural residue by hydrolysis and aqueous phase reforming of sugars releases a CO\({}_{2}\) emission of 25.2 kgCO\({}_{2}\)/GJ fuel, and the price of the produced fuel is $45-$86/GJ fuel.  
Production of algae diesel via transesterification releases a CO\({}_{2}\) emission of 32.8 kgCO\({}_{2}\)/GJ fuel, and the fuel price is $24-$318/GJ fuel [14]. Algae seem to be the most expensive feedstock, with the highest CO\({}_{2}\) emission.  
Water footprint in the production of algal biodiesel, without the water recycling, is as much as 3726 kg of water/kg of biodiesel [96].
## 4 Performance Evaluation
### Simulation Environment
#### 4.1.4 Validation of Output  
To evaluate the location feature of the number of users in the randomly selected sampled data \(C_{n}\) (data partition made through bootstrapping), we first recognize that location is considered the initial feature. However, it is essential to note that since features are selected in a random sequence, the day of the week or time might be chosen as the first feature in some decision trees. This randomness in feature selection contributes to the uncorrelation of different trees in the forest, which is a fundamental characteristic of random forests. In this context, \(u_{j}\) represents one such randomly selected feature from the set. Additionally, \((U,E)\) denotes the time information regarding the user's presence at the chosen location, while \(O_{j}\) refers to the output of the tree.
## 4 Application of HS to VM Consolidation
### Harmony Search  
The HS is an algorithm that imitates the process of developing chords by improvised music players and is relatively simple compared to other algorithms; however, it shows high performance in optimal search in many engineering fields [38]. The algorithm is a population-based metaheuristic in which a solution is represented by the harmony memory (HM), and the number of HMs is determined by the harmony memory size (HMS). HS usesthree operators to develop from dissonance to harmony. The operators comprise memory consideration (MC) that extracts the sound played in the past, pitch adjustment (PA) that allows adjustments to the extracted sound within the range of the bandwidth (BW), and randomization that allows random plays. The process of generating solutions for each operator is shown in Equations (10) and (11).  
\[x_{i}^{New}=\ \left\{\begin{array}{ll}x_{i}\in\left[x_{i}^{1},\ x_{i}^{2},\ \ldots\ x_{i}^{HMS}\right]&\text{IF }rnd1\leq\text{HMCR}\\ x_{i}\in\left[x_{i}^{Lower},\ x_{i}^{Upper}\right]&\text{IF }rnd1>\text{HMCR} \end{array}\right. \tag{10}\]  
\[x_{i}^{New}=\ \left\{\begin{array}{ll}x_{i}^{New}+BW&\text{IF }rnd2\leq\text{ PAR}\\ x_{i}^{New}&\text{IF }rnd2>\text{PAR}\end{array}\right. \tag{11}\]  
HS generates the new HM \(x_{i}^{New}\) in order starting at index \(i\). First, it generates first random number between 0 and 1 to determine whether to generate a solution using the HMC or randomly. If it is smaller than or equal to the HMCR, one of the HMs is selected to generate \(x_{i}^{New}\). The second random number is generated, and if it is smaller than the PAR, a neighborhood solution that is calculated by adding a random value to \(x_{i}^{New}\) within the appropriate range (BW) is generated as \(x_{i}^{New}\). If the first random number is greater than the HMCR, the solution is generated randomly in a feasible range. If a newly generated HM \(x_{i}^{New}\) is a better solution than the worst HM from the existing HMs, the newly generated HM replaces the existing worst HM. The process is then repeated a certain number of times to obtain the best HM.
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 4 Experimental Results and Analysis  
### Datasets and Experimental Settings  
The ACDC dataset [34] is a publicly available dataset containing cardiac MRI images from 100 patients. Each patient's data include two-dimensional image sequences capturing the entire cardiac cycle at end-systole (ES) and end-diastole (ED). Each image sequence includes three segmentation targets: the right ventricle (RV), the left ventricle (LV), and the myocardium (MYO). The dataset was selected due to its prevalence in cardiac image segmentation studies, thus facilitating comparisons with other methods. To assess the efficacy of semi-supervised training, we employed the segmentation of three-dimensional volumetric images into two-dimensional slices. The training process was conducted on an NVIDIA Tesla V100 GPU. We used a crop size of 256 \(\times\) 256 and a batch size of 8 per GPU. The model was trained for 300 epochs using a learning rate of 0.01 with an SGD optimizer. The optimizer had a momentum of 0.9 and a weight decay of 0.0001. A confidence threshold of 0.95 was applied during training to filter out uncertain predictions.
## 3 Overview of Physical Unclonable Functions  
### PUF Definition and Functionality  
PUFs are physical, random functions that offer particular physical outputs that are easy to generate but difficult to construct without gaining access to the object. Figure 3 shows the PUF functionality. PUFs work by implementing challenge-response authentication. The input to the PUF module serves as a challenge, and, based on the input and transfer function of the PUF, a digital fingerprint is produced, which is termed as the response. In  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Definition** & **Example** & **Major Disadvantages** \\ \hline \multirow{4}{*}{User} & Checks a user’s identity by allowing human-to-machine transfer. & Signal sign-on (SSO), multi-factor, three-way authentication. & \multirow{2}{*}{Vulnerable to spoofing attacks} \\ \cline{3-4}  & Detects and checks users & & \\ \multirow{2}{*}{Device} & accessing a device. This method of authentication uses a device’s metadata or an app. & PUF, mobile. & No standardization of IoT device authentication \\ \hline \multirow{4}{*}{Software/protocol} & Users or devices are authenticated through a software application that verifies access to objects. & Challenged response; mutual authentication; out-of-band management; one-, two-, and three-way authentication; zero-knowledge authentication. & Limited cryptographic authentication \\ \hline \hline \end{tabular}
\end{table}
Table 3: Types of authentication, definitions, and examples.  
Figure 2: Taxonomy of user authentication methods.  
other words, for a given PUF, there is a specific input known as a \"challenge\" that produces an output response that is distinctive to the particular PUF and, hence, unclonable.
## 4 Experimental Results and Analysis  
### Experimental Setup  
The proposed SkinSwinViT framework is instantiated within the Anaconda environment, utilizing Python 3.9, with the installation of essential libraries such as Pytorch, Scikit-Learn, Matplotlib, and Numpy on the Linux operating system. The system configuration encompasses an Intel Platinum-8350C processor operating at 2.6 GHz, complemented by 32 GB DDR4 RAM (4 modules) and four NVIDIA RTX 3090 graphics processing units. Training of the SkinSwinViT framework is conducted on the ISIC2018 dataset, augmented as outlined in the data enhancement section to forestall overfitting, bolster classifier efficiency for unobserved images, and mitigate sample imbalance. Furthermore, three optimization strategies, namely Adam, AdamW, and SGD, are employed, with the optimal strategy selected to update SkinSwinViT parameters during training iterations. Concluding specifications entail an epoch count set to 100, with batch size values explored within the range [4; 8; 16; 32], with superior performance observed when the batch size is set to 4 and a default learning rate of 0.00001.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm  
Localization has drawn a lot of attention from the researchers in UWSN research fields because the localization algorithms of WSN are not suitable for use in UWSNs directly. Hence, new methods or improved techniques must be put forward, and many research results have been reported. To make a better comparison of these papers, we classify them into five aspects, namely, computation algorithm, spatial coverage, range measurement, the state of the nodes and communication between nodes. We show the taxonomy figure in Figure 1. In the next section, we will introduce each part of the taxonomy in detail.
# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 2 Method
### Construction of Object Semantic Enhancement Model
#### 2.1.1 Extraction of Multi-Layer Fusion Features
These two layers provide a level of abstraction between low-level features and high-level semantic features, which are highly effective in capturing the shape, texture, and partial structural details of an object. Moreover, it excludes both fine-grained low-level features (e.g., Layer1 edge features) and overly broad high-level semantics (e.g., Layer4 global features), since they lack stability under fast motion and struggle to capture detail in complex environments. Therefore, choosing Layer2 and Layer3 can ensure sufficient information while avoiding unnecessary computational costs. Figure 2: \(Cont\). Figure 2: ResNet50 output heat maps for different layers.
## 4 Results and Discussion
### Weibull Parameter  
Due to the Weibull characteristic of material properties, the stress distribution played nonlinear and uniform changes when the perforation pressure affected the shale rock. Due to different geological sedimentary environments and the impact of different geological structures, the rock generated different priorities and compositions, which played different material properties on macro-level. The process occurred on the Weibull model for the shale rock with different Weibull parameters. Under both the perforation pressure and the in situ stress, the stress distributions exhibited obvious differences for different \(\alpha\) and \(\beta\) values, as shown in Figure 36. From Figure 36a, it can be seen that, at the root position (\(z=0\) mm), when \(\alpha\) was 4, largest tensile stress was observed in the middle of the perforation wall. On the other hand, the maximum stress at the root was not very high as compared with that in the first case (when \(\alpha=4\)) (Figure 37a). However, the best value for \(\beta\) was found to be 1.2, because it resulted in the highest value of tensile stress, which varied from 100 to 300 mm at the middle of the perforation wall (Figure 36b). In addition, this case did not generate large stress at the root, which was beneficial to the cement's safety (Figure 37b). The results reveal that the selection of Weibull parameters played an important role for the stress distribution. Before the simulation, the material parameters were obtained through experiments, and appropriate Weibull parameters were then selected for further analysis.  
Figure 37: \(z\)-directional stress distribution at the root of the perforation. (**a**) The influence of \(\alpha\); (**b**) The influence of \(\beta\).  
Figure 36: \(z\)-directional stress distribution along the wall. (**a**) The influence of \(\alpha\); (**b**) The influence of \(\beta\).  
In order to further study the influence of Weibull parameters, the numbers of failure elements were compared. As shown in Figure 38a, the results indicated that, with the increase in Weibull parameters (\(\alpha\) and \(\beta\)), the number of failure elements sharply decreased. In particular, when the value of \(\alpha\) was 2, there was no element of failure, even under the load of high perforation pressure. This means that \(\alpha\) determines the initial number of weak elements. Under the perforation pressure, the weak elements tended to fail easily, and cracks were thus initiated. The results also showed that the effect of \(\alpha\) on the number of failure elements was stronger than that of \(\beta\) (Figure 38b). Mostly, \(\beta\) reflected the average number of weak elements. The results showed that the nature of properties of inhomogeneous rock was a key factor and affected crack initiation and propagation.
# Resonant Coupling Effects in a Double-Layer THz Bandpass Filter
###### Abstract  
Cascading similar frequency selective surfaces (FSSs) improves the roll-off rate and frequency selectivity of THz bandpass filters. However, resonant coupling between FSS layers causes anomalous transmission peaks. In this work, we have employed an equivalent circuit model to analyze a double-layer FSS structure with periodic cross apertures at different interlayer spacing. We found that the anomalous peaks can be attributed to the resonant coupling between the upper and lower layer FSSs, and their periodic appearance with changing interlayer spacing is related to the half-wavelength repeatability of the circuit. We have fabricated a double-layer FSS sample using femtosecond laser micromachining and measured it using time-domain THz spectroscopy. The results using the equivalent circuit model agree with the Finite-Difference Time-Domain (FDTD) and experimental results.  
bandpass filters; equivalent circuit model; FSS; time-domain THz spectroscopy +
Footnote †: journal: Applied Sciences
###### Abstract  
Mitigation of channel unfavorable circumstances during data routing in underwater wireless sensor networks (UWSNs) has utmost significance. It guarantees saving packet corruption along unfavorable channels so that vital data is not lost or become meaningless. This paper proposes two routing protocols for UWSNs: localization free energy efficient routing (LEFER) and its improved version, localization free energy efficient cooperative routing (Co-LFEER). The LFEER makes decision of choosing a relay based on its maximum residual energy, number of hops and the bit error rate of the link over which packets are transmitted. These metrics are chosen to save packets from corruption to the maximum limit and maintain stable paths (where nodes do not die soon). Since a single link is used in the LFEER for packets forwarding, the link may become worse with changing circumstances of the channel. To deal with this issue, cooperative routing is added to the LFER to construct the Co-LFEER protocol, in which some copies of packets are received by destination to decide about packets quality. Converse to some prevalent protocols, both LFEER and Co-LFEER are independent of knowing the sensor nodes' positions, which increases computational complexity and wasteful utilization of resources. Based on extensive simulations, the proposed schemes are better than Co-DBR in reducing energy utilization and advancing packets to the desired destination.  
UWSNs; LFEER; Co-LFEER; routing +
Footnote †: journal: Computer Networks