esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### MTBE-Soluble and i-PrOH Fractions  
The lignin precipitated from the black liquor was fractionated to MTBE insoluble lignin (BLN) and MTBE soluble lignin (see Scheme 1). The MTBE soluble fraction, \(\sim\)15 w% of the total amount isolated, contained low molar mass compounds, mainly wood extractives and a complex mixture of phenolic compounds. Some polymeric or oligomeric lignin was also detected from the characteristic signals of the aromatic- and methoxyl groups in the HSQC spectrum (Figure 4). Large amounts of aliphatic, aromatic, and olefinic correlation signals was seen, origination from various lignin degradation products and wood extractives. The HSQC spectrum lacks all the traditional lignin structures as seen from the oxygenated aliphatic area. Large amounts of carboxylic groups and small amounts of aliphatic hydroxyl groups was detected in this fraction based on the \({}^{31}\)P-NMR analysis (Table 2), which is consistent with wood extractives and lignin degradation products. The MTBE insoluble lignin (BLN lignin) was separated to a high and medium molar mass portion by \(i\)-PrOH solvent fractionation (see Scheme 1 and Table 3). The method allows us to prepare lignin with a more narrow molar mass.  
Figure 3: Multiplicity edited HSQC of the oxygenated aliphatic area of (**a**) Pine BLN lignin; (**b**) selectively methylated Pine BLN lignin and (**c**) fully methylated Pine BLN lignin. (CH/CH\({}_{3}\) shown as positive (blue) and CH\({}_{2}\) as negative (red).  
Solvent fractionation is a common method to separate lignin based on size and a wide array of solvents, or solvent mixtures, has been used for this purpose [35; 36].  
Structurally the \(i\)-PrOH insoluble portion (\(\sim\)70 w%) had a larger molecular mass than the \(i\)-PrOH soluble portion (\(\sim\)30 w%). It also contained a smaller amount of carboxylic groups and free phenolic G-units compared to the \(i\)-PrOH soluble fraction. The majority of low molar mass compounds were removed from the starting lignin during the MTBE extraction, but small amounts was still detected in the \(i\)-PrOH soluble fraction. All of the fractions had a narrower PDI value than the BLN lignin. Low PDI values in lignin are a favorable parameter in producing functional materials as it reduces the complexity of the polymer [19].  
Figure 4: _Cont._
## 3 Experimental Set-Up
### Data Acquisition and Preprocessing  
#### 3.4.1 Data Acquisition  
The collected data include driving trajectory data of the subject and its surrounding vehicles, their speeds and road environment parameters. According to the following method, the useful driving trajectory data of each driving decision are extracted and classified into the driving decision data set:  
1. Lane changing: The driving trajectory data of 10 s before implementing lane changing are recorded in lane changing data set.
2. Car following: The driving trajectory data within the 50 m gaps between the subject and its leading vehicle are recorded in car following data set.
3. Free driving: The driving trajectory data beyond 50 m gaps between the subject and its leading vehicle, and the driving trajectory data output when the subject vehicle with the desired speed are recorded in free driving data set.  
After data classification and statistics, a total of 3211 groups of free driving data, 5312 groups of car following data, and 1009 groups of lane changing data are obtained. Each group of driving decision data includes one group of the driving trajectory data, together with their corresponding speeds and the road environment parameters.
# Thermal Evaluation of a Hybrid Dryer with Solar and Geothermal Energy for Agroindustry Application
###### Abstract  
The process of drying at the agroindustry level is considered the most important stage for the storage and conservation of food, but an inadequate sizing of the systems can generate an excessive consumption of fuel and energy. In the case of the agricultural sector, especially in small and medium producers in Ecuador, they tend to dry cereals outdoors, spreading the product over black plastics, asphalt floors, or cements called \"tendal\". This process depends on the weather conditions of the area and can take between one or four days of drying, tending to damage the product by fungi. On the other hand, certain producers build dryers empirically, using as the main fuel gas for domestic use, diesel, or firewood. Among the problems presented for these types of dryers are heat losses in the drying chamber as it is not thermally insolated, a lack of temperature control (above 60 \({}^{\circ}\)C) and drying speed, excessive fuel use, and 10% burned product, which are essential to control to obtain a quality product and savings in the energy consumption of the dryer. With regard to the problem raised above, the design and construction of a hybrid dryer that worked with 80% of thermal solar and low enthalpy aerogeothermal energy and 20% with electric power was conducted to optimize the drying process and ensure a quality dry product. The work proposes the dimensioning of each component of the dryer. As a result, it was found that the efficiency of the equipment was around 60%, reaching a maximum operating temperature of 52 \({}^{\circ}\)C, with a stabilization time of the system in the first hour within 40 min. It should be noted that the design of the prototype is easy to adapt and replicate.  
hybrid dryer; aerogeothermal; agroindustry +
Footnote †: journal: Applied Physics Letters
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 3 Analytical Solution of the Time-Harmonic Electromagnetic Field around One Current-Carrying Coil
### Current Density of a Current-Carrying Coil  
Due to the circular structures of the primary and secondary coils in the IPT system, a cylindrical coordinate was adopted to analyze the system. The three directions of the cylindrical coordinate system are denoted as \(\mathbf{e}_{\rho}\), \(\mathbf{e}_{\phi}\) and \(\mathbf{e}_{z}\), as shown in Figure 4. The radius of the coil is \(a\) and the height is \(h\). Fields 1 and 2 are the regions above and below plane \(\Gamma\), respectively.  
Figure 4: Schematic of a coil.  
Because the direction of the free current density is the same as \(\mathbf{e_{\phi}}\) and current only exists in the coil, the current density at any point \(P(\rho,\phi,z)\) can be expressed as follows:  
\[\mathbf{J}_{S}(P)=C_{2}\delta(\rho-a)\delta(z-h)\mathbf{e_{\phi}}, \tag{13}\]  
where \(C_{2}\) is an undetermined coefficient affected by the external current.  
Therefore, with a directional micro-section \(d\mathbf{S}=d\rho dze_{\phi}\) as shown in Figure 4, the integral form of current \(l\) of the coil is shown as follows:  
\[I=\int\limits_{S}\mathbf{J}_{S}\cdot d\mathbf{S}=C_{2}\int\limits_{S}\delta(\rho-a) \delta(z-h)d\rho dz=C_{2}, \tag{14}\]  
where \(S\) is a hypothetical surface through which the current density of a circular coil passes vertically.  
Thus, the current density of the coil is \(\mathbf{J}_{S}=l\delta(\rho-a)\delta(z-h)\mathbf{e_{\phi}}\). When the number of coil turns are \(N\), ignoring the radius of the wire, the current flowing through the directional micro-section \(d\mathbf{S}\) is \(N\!I\), and the current density at the corresponding location is \(\mathbf{J}_{S}(P)=N\!I\delta(\rho-a)\delta(z-h)\mathbf{e_{\phi}}\).
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 3 Push-Based Approaches in ICN
### Push Communication via Interest Notifications  
The use of special Interest packets to implement push-based communication is the solution considered in the original design of CCN. In this case, producers start the transmission via special Interest packets, in a similar way to the call-back approach. These _Interest Notification_ packets carry small data in association to the announced Name Prefix[13; 14; 15]. For instance, for the case exemplified in Figure 3.  
P1 triggers an Interest Notification packet holding a Name Prefix _/homenumber123/room/temperature/valueroom = A/valuetemp = 22_. Therefore, in addition to polling consumers, these packets already carry updates for consumers.  
Although being simple to implement, _Interest notifications_ do not support reliability. To ensure reliability, consumers explicitly confirm the reception of an Interest notification, e.g., with a dummy  
Figure 3: IoT example, publish-subscribe NDN approach. P1, P2 and P3 are temperature sensors placed in different rooms. C1, C2 and C3 are consumers.  
acknowledgement packet such as the (_aData_ packet) [15]. As data is not cached in reply to these Interest notifications, routing loops may occur, since Interest Notification packets are forwarded based on information stored in the database that stores routes, i.e., the _Forwarding Information Base (FIB)_ and not in the cache where pending interests expressed by consumers are stored, the _Pending Interests Table (PIT)_. A way to circumvent this problem is to rely on timestamps sent with Name Prefixes [13; 14].
# Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
## 3 Experiment
### Deep Learning Approach
#### 3.4.2 Convolution Neural Network (CNN) for Text Classification  
To make the sentences in text into an image, we need to encode words with numeric values. Word embedding is employed to express words as numeric values. The method that assigns a unique integer value to each word would cause the dimension explosion. Word embedding set the dimensionality, k, and expresses the word using k-dimensional values. For example, when k is set to 5, \"hospital\" is expressed as {0.1,0.2,0.3,0.8,0}. This embedding method dramatically reduces the dimensions. However, this method does not consider the relationship between words when placing a word in a matrix. The advanced technique is to consider the relationship between words when transforming the input data into an input matrix like an image. Word2 Vec considers that words that appear in a similar context have similarities in semantic meaning. We adopted Google's word2 Vec [23], which is a pretrained model from the GoogleNews dataset.  
We performed the experiment using word-embedding method and Word2 Vec method as well.  
The architecture of the text classification is shown in Figure 3. We transformed words into a k-dimensional vector. We set the number of words to make an input matrix, denoted as \(n\). A sentence is tokenized and \(n\) words organize the input data. At the end of the sentence, there could not be enough words to fill the input. In this case, we pad the rows with zeros until the number \(n\) is filled.  
To derive abstract features from input data and make the algorithm derive useful features automatically, we employ a hidden layer and a convolutional layer to the network.  
In a convolution layer, we feed the input matrix to the filter of k*h size, then the input of h*k size is mapped to a cell in the convolutional layer. In Figure 3, the rectangle with a red line is a 3*k filter. The filter works as an aggregator of h words. The filter needs to keep the word meaning when  
Figure 3: Model architecture with an example sentence.  
aggregating words, so the horizontal size of the filter is set to k. As a result, the convolutional layer is one-dimensional. The number of nodes in a convolution layer is calculated as n-h + 1.  
The vertical size of the filter can be varied. In this work, we test two sizes of filter, 3 (in red) and 8 (in yellow). The number of filters depends on the user's decision. More filters are needed to enrich features. The max pooling layer is also employed to avoid overfitting a result of too much features. The max pooling layer adopts a filter with the size of (n-h + 1)*1 and finds the maximum value for all values in a precedent convolutional layer. The max pooling layer generates one-dimensional m rows. Finally, m rows are fully connected to the output. The output layer has two nodes to express bi-class, gender in our case. Two nodes are filled with 1 or 0, it indicates male or female respectively.
## 4 Result Analysis of Detection Model
### Comparison with State-of-the-Art Models  
In this section, we conducted a performance comparison of our proposed model with several state-of-the-art object-detection models, including Single Shot Detectors (SSDs), faster regions with convolutional neural networks (R-CNN), YOLOv3, YOLOv4, YOLOv5-M, YOLOv5-L, YOLOv5-X, YOLOv7-L, YOLOv7-X and our optimized version of YOLOv8-L. We evaluated their mAP performance, with higher values indicating more precise object detection. The objective was to identify each model's strengths and limitations and highlight our proposed model's exceptional performance. Figure 13 illustrates the comparison of our models with other object-detection models.  
Figure 12: Runtime analysis of the optimized YOLOv8 model.  
SSD emerged with the lowest mAP score of 74.3% among the models we evaluated, indicating its relatively inferior performance in endangered-animal-detection tasks. The next fastest R-CNN had a modest mAP of 78.8%, a marginal improvement. The mAP scores of YOLOv3 and YOLOv4 were 79.3% and 81.9%, respectively, reflecting their moderate effectiveness in this context. The YOLOv5 family of models (YOLOv5-M, YOLOv5-L and YOLOv5-X) achieved mAP scores ranging from 83.2% to 85.7%, demonstrating their capacity to improve object-detection precision. This trend was reinforced by YOLOv7-L and YOLOv7-X, which had mAP values of 85.9% and 86.6%, respectively. With a mAP score of 90.2%, YOLOv8-L performed better in our evaluation than its predecessors and the majority of other models. Nonetheless, the \"Optimized YOLOv8-L\" model attained the most outstanding mAP score (92.44%), representing the pinnacle of our investigation. This significant margin of improvement over other models demonstrates our optimization efforts' efficacy, highlighting our proposed model's robustness in detecting objects within images.  
Therefore, our performance comparison revealed that our optimized YOLOv8-L model emerged as the top performer, outperforming all baseline models with an exceptional mAP score of 92.44%, indicating the robustness of our model.
## 2 Background
### Energy Harvesting
Solar is the most obviously available cheap method; it is also easy to integrate and deploy due to its simplicity and the availability of the components. Wind energy harvesting is the second simplest method. The efficiency of this method largely depends on wind-speed, which is proportional to the resulting rotational speed of the turbine;this limits its potential of being deployed in certain environments. Solar inverters use an algorithm to maximize harvested power, whereas wind inverters must have a maximum power characteristic curve, unique to each wind turbine model. In essence, this means that the ability of a wind harvester to harvest energy in conditions where wind-speed is low arise from wind inverters with low threshold. Vibration and thermal energy are not commonly found techniques, but they are viable options, and even better options when it comes to renewable energy. They are expensive and difficult to fabricate as they contain complexities not found in other methods. This limits their potential in many situations. Table 1 summarizes the energy-harvesting techniques as well as the strengths and weaknesses of the discussed methods. Looking at the nature of the proposed model, and the environment within which the model is to be deployed, that is, smart hydroponics available at farms located in remote areas, many of these farms are not connected to the national power grids because of their location. Therefore, an alternative energy source is needed. There are many ambient sources of energy in the environment where the proposed model will operate. Any of the discussed techniques can be integrated with the proposed model to solve the problem of power; therefore, we adopt the solar technique.
# Evaluation of the Digital Data Performance Transmission on a Fiber-Radio System
## 3 Simulation Results
### Optical Transmission Results  
These results correspond to the electro-optic arrangement of Figure 3. Figure 5a shows an example of a proposed sequence to validate our proposal (010110111001010101). This digital waveform is generated and visualized on the virtual generator and oscilloscope of the VPI Photonics software interface, respectively. To avoid bit losses, a space of three \"0\" values and a start value of \"1\" bit are added at the beginning of the sequence to maintain its integrity. This signal is optically modulated by the MZ-IM and transmitted through the optical fiber. Finally, the PD delivers the corresponding electrical signal shown in Figure 5b where the presence of some spurious peaks attributed to the high frequency at which the system operates is observable. The procedure to cancel these peaks is discussed in Section 3.2.  
Table 2 tabulates the width values of the pulses sent and recovered to quantify the widening that occurs as a consequence of the influence of the chromatic dispersion exhibited by the SM-SF. As was previously mentioned, these data are exported in a file with a.txt extension.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Simulation Parameters**} \\ \hline \(\mu\) **factor** & 4 (adimensional value) \\ \hline Slow fading \(\zeta\) mean & 0 dB \\ \hline Slow fading \(\sigma\) & 8 dB \\ \hline Fast fading mean & 0 dB \\ \hline Fast fading \(\Delta\) & 8 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters considered for an urban zone [13; 14; 15].  
Figure 5: (**a**) The waveform of the transmitted bits. (**b**) Electrical data delivered by the photodetector.
## 2 Materials and Methods
### Synchronization  
The LabVIEW-based THz-TDS control program stored spectra at a varying rate from 9 spectra per second to 16 spectra per second in an unsynchronized manner. The THz-TDS controller hardware did not allow direct access to the internal timestamps. Consequently, every spectrum was tagged with a software (LabVIEW) based timestamp, which is derived from the computer's CPU clock and can be assumed to be highly correlated with the effective measuring time. The Python control program of the robot ran on the same computer as the THz-TDS control software and used timestamps derived from the same CPU clock. Due to the hardware abstraction layers and implementation details of the LabVIEW and Python frameworks, offsets may have occurred due to drifting and latencies, which are varying over time. In practice, it was hard to quantify the resulting time scales. A generous, educated guess for the upper error bound is 40 ms, which for the scanning speeds used in this paper results in an error smaller than the expected experimental resolution limit. This allowed the usage of the two timestamps for the synchronization of each measured THz-TDS spectrum with a sample position. However, we still had to consider a small, constant offset originating from the measurement procedure itself, which was determined for a specific scanning speed setting from two consecutive scan lines of the resolution target. Figure 2 shows a typical mapping of the scan positions of the spectra with the robot movement.  
For data collection times above around 7 min, short sequences of spectra are missing and impair reliable data collection. As a solution, the scan was split into several parts, storing the data to disk in chunks. This is illustrated in Figure 2, where the spectra were collected in two parts, with scanning times of 10 min each. Image data were calculated using a regular grid with a pixel distance of half of the line spacing and attributing the spectrum of the scan position with the shortest Euclidean distance. A \(5\times 5\) Gaussian kernel with a width of 1 pixel was then applied to the image for smoothing.
## 1 Introduction  
Eutrophication, a consequence of human activities, involves the excessive input of nutrients like nitrogen and phosphorus into slow-moving water bodies, such as lakes, estuaries, and bays. This nutrient influx triggers the rapid proliferation of algae and zooplankton, depleting dissolved oxygen levels and causing a decline in water quality, ultimately leading to mass fish mortality [1]. Additionally, eutrophication has been linked to increased greenhouse gas emissions and global warming [2, 3]. This phenomenon has evolved into a global environmental challenge [4, 5, 6, 7], necessitating a comprehensive understanding of its causes, the exploration of remediation strategies, and the development of innovative treatment technologies for the sustainable management of eutrophic water bodies.  
Phosphorus, which is predominantly present in water as orthophosphate, metaphosphate, and organic phosphorus, is widely recognized as the primary driver of eutrophication, with elevated concentrations being particularly problematic [8, 9, 10, 11, 12]. The critical threshold for eutrophication in lake water is often defined as a total phosphorus (TP) concentration exceeding 0.02 mg/L, accompanied by a total nitrogen (TN) concentration surpassing 0.2 mg/L. Rapid plankton and algae growths become evident when the TP exceeds 0.1 mg/L [13].  
Commonly used methods of phosphorus removal are physical, chemical, and biological methods, adsorption, and so on [14, 15, 16, 17]. Among the various phosphorus removal methods, adsorption stands out due to its low energy consumption, high capacity, and minimal environmental impact [18; 19]. This study delves into a comparative assessment of adsorption and phosphorus removal capabilities of a previously developed efficient phosphorus removal material based on fly ash and steel slag, which has been further modified with zirconium oxychloride octahydrate. The investigation explores different modification techniques and their influences on the material's decolorization and phosphorus removal performance.  
Following our group's previous research findings, the optimal EPRC substrate ratio was determined to be 12:2:1 (fly ash/steel slag/cement), with a particle size of 5 mm demonstrating the most effective phosphorus removal [20; 21]. Consequently, this study adheres to the optimal composition and particle size, employing three distinct modification methods to produce Zr-EPRC phosphorus removal materials with enhanced performance. Subsequently, a comprehensive analysis of the phosphorus removal properties, stability, and safety of Zr-EPRC materials is conducted.  
The contribution of this study lies in the novel use of zirconium oxychloride octahydrate-modified materials to prepare Zr-EPRC. This innovative approach enhances the adsorption and phosphorus removal efficiency of the material, providing a valuable advancement in the field.