esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### XRD Analysis  
XRD analysis was conducted to evaluate the crystallinity of the materials synthesized. Biochar, biochar/rGO, and biochar/rGO/DES-CTAB X-ray diffractograms (Figure 3) demonstrated that the biochar had been modified. The results showed that Biochar is an amorphous form of carbon with broad peaks, indicating a largely poorly crystalline structure. However, the diffractogram for biochar/rGO/DES-CTAB shows sharper peaks at the same 20 values as the primary biochar material. Deep eutectic solvent, which has a more crystalline structure than biochar, is responsible for the sharpness of the peaks. The XRD profile of biochar shows a small diffraction peak at two thetas equal to 29\({}^{\circ}\).  
Figure 2: FTIR spectra of biochar, biochar/rGO and biochar/rGO/DES-CTAB, and biochar/rGO/DES-glycerol.  
At exactly two thetas equal to 29\({}^{\circ}\), the biochar/rGO composite showed a more intense peak. This sharp diffraction peak is due to the introduction of the rGO, causing a structural ordering of carbons and improved material carbon content. Furthermore, the biochar/rGO composite showed a smoother baseline than the other synthesized materials. The XRD spectra for the DES-CTAB-modified composite (biochar/rGO/DES-CTAB) resulted in a slight shift to the left of the diffraction peak previously observed in biochar and biochar/rGO composites at two thetas equal to 29\({}^{\circ}\) to 28\({}^{\circ}\) (Figure 3). A new and more intense peak is observed at two thetas equal to 24\({}^{\circ}\). This could be due to the introduction of the DES-CTAB. The spectrum of the DES-CTAB-modified material was almost similar to that of the starting material, with three less intense bands observed at two thetas equal to 15\({}^{\circ}\), 25\({}^{\circ}\), and 45\({}^{\circ}\), respectively.  
X-ray diffractograms of biochar, biochar/rGO, and biochar/rGO/DES-glycerol showed a similar structural morphology. The broad diffraction peak at two thetas equal to 20\({}^{\circ}\) and 35\({}^{\circ}\) indexed as (C002) indicates an amorphous carbon structure with randomly aligned aromatic sheets [26]. The diffractogram for biochar/rGO/DES-glycerol, on the other hand, exhibits a sharper peak at the same two theta values as both materials. The diffraction peaks observed at two thetas equal to 25\({}^{\circ}\), 40\({}^{\circ}\), and 51\({}^{\circ}\) correlate to the structure's many inorganic components from the materials, most of which are quartz [27]. The peak at 11.5\({}^{\circ}\) (20) of all the indexed materials (001) [28], is most likely connected to graphene oxide-like materials. Furthermore, the sharpness of the peaks is due to the formation of the graphene oxide-like structure upon the modification of biochar with rGO. The development of graphene oxide, which can be used as a direct indicator of the degree of oxidation of the carbonaceous structure in the biochar's core, is particularly important for planar and extended molecules.
## 2 Materials and Methods
### Langmuir Adsorption Analysis  
The Langmuir model is an analytical technique to evaluate the resin's sorption capacity for the molybdenum ion recovery from aqueous solutions as adsorbent. This model's basic assumption model is the formation of a monolayer throughout the adsorbent's surface, assuming that only one molecule could be adsorbed on one adsorption site and the intermolecular forces decrease with the distance. The adsorbent surface is homogeneous  
Figure 4: Sulfate speciation in the copper PLS using the MEDUSA v.1 software.  
in character and possesses identical and energetically equivalent adsorption sites. The Langmuir equation is presented as Equation (4) [48; 49; 50]:  
\[q=q_{MAX}*\frac{C*K_{L}}{1+C_{e}K_{L}} \tag{4}\]  
\"C\" is the molybdenum concentration at equilibrium, and \"q\" is the quantity of total ions adsorbed at the resin's surface divided by the total mass of resin. \"qMAX\" is the resin's maximum adsorption capacity to form a single layer and K\({}_{\text{L}}\) is the Langmuir adsorption isotherm's equilibrium constant. All the Langmuir adsorption parameters can be calculated from the slopes and intercept of the linear plot of 1/q versus 1/C, according to Equation (5).  
\[\frac{1}{q}=\frac{1}{q_{MAX}}+\frac{1}{K_{L}*q_{MAX}}*\frac{1}{C} \tag{5}\]  
The trend on the adsorption process, whether it is favorable or not, can be determined using the dimensionless constant \"R\({}_{\text{L}}\)\", given by Equation (6).  
\[R_{L}=\frac{1}{1+K_{L}C_{0}} \tag{6}\]  
where C\({}_{0}\) is the initial molybdenum concentration. Typical values of R\({}_{\text{L}}\) are located within the range 0-1. This parameter indicates whether the adsorption process is unfavorable (R\({}_{\text{L}}\) > 1), favorable (0 < R\({}_{\text{L}}\) < 1), or irreversible (R\({}_{\text{L}}\) = 0). Both resins were studied using the lineal regression from Equation (5) under static conditions.
## 8 Result Analysis
### Authentication Latency
In comparison, the total authentication latencies of schemes [4; 10; 17; 44; 45] are \(m(2T_{mul}+4T_{h})+m(2T_{mul}+5T_{c})+m(T_{DE}+T_{BQ})\), \(m(3T_{mul}+3T_{h})+m(4T_{h}+5T_{mul}+3T_{add})+m(T_{DE}+T_{BQ})\), \(m(9T_{mul}+5T_{h}+4T_{add})+m(7T_{mul}+5T_{h}+3T_{add})+m(2T_{DE}+T_{BQ})\), \(6 mT_{h}+m(4T_{mul}+12T_{h}+T_{add})+m(T_{DE}+T_{BQ})\), and \(6T_{p}+(4m-1)T_{mul}+(m+6)T_{h}+m(T_{DE}+T_{BQ})\), respectively. As illustrated in Figure 7c, our proposed scheme achieves the lowest overall authentication latency,  
Figure 7: Performance evaluation and comparison of the authentication latency with schemes [PERSON], J 2024 [4], [PERSON], J 2024 [10], [PERSON], Y 2022 [16], [PERSON], S 2022 [45], and [PERSON], A 2020 [44]. benefiting from the use of lightweight Chebyshev chaotic map operations in place of computationally expensive elliptic curve operations.
## 2 Materials and Methods  
We collected 36 early- and delayed-phase PET/CT images from suspected lung cancer patients. The used cohort included 18 benign and 18 malignant cases. The histology of the benign cases consisted of five cases of pneumonia (organizing pneumonia (OP), cryptococcus, and pneumocystis pneumonia (PCP)), one case of granuloma, fungal infection, hamartoma, tuberculosis, nontuberculous mycobacterial infection (NTM), and eight other cases. Of the 18 malignant cases, one, four, and thirteen cases were small cell carcinoma, squamous carcinomas, and adenocarcinoma, respectively [18]. All these cases could not be determined by image diagnosis alone, thus a lung biopsy was performed for final diagnosis.  
The PET/CT imaging studies were performed using a Siemens True Point mCT (Siemens). Both types of the images were obtained using 200 \(\times\) 200 pixel matrices (voxel volume, 4.07 \(\times\) 4.07 \(\times\) 2.00 mm\({}^{3}\); scan time, 2.0 min/table), and were obtained with free breathing. Early- and delayed-phase PET imaging was performed 60 min and 120 min, respectively, after the administration of 3.7 MBq/kg of fluorodeoxyglucose (FDG).  
In addition, conventional CT images at maximal inspiration were also collected. These conventional CT images were acquired using Aquilion ONE (Canon Medical Systems) with 512 \(\times\) 512 pixel matrices (voxel volume, 0.625 \(\times\) 0.625 \(\times\) 0.500 mm\({}^{3}\)) with the lung kernel. In the cases of more than one CT exam per patient, we selected images that were acquired at the time closest to the PET/CT examination.  
We also collected patient information corresponding to the collected PET/CT images. In this study, we collected age, gender, smoking status, and laboratory test results as patient information. Seventeen types (personal information: 3, laboratory test results: 14) of patient information were collected. For the laboratory test results, the results closest to the PET/CT examination were collected. Details of the collected information are shown in the Table 1. The number within the brackets ( ) in Table 1 refers to the number of features in that category.  
This study was approved by the institutional review board, and patient agreements were obtained under the condition that all data were anonymized (No. HM17-002).
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 2 System Model
### OFDM Signaling  
Figure 1 shows how OFDM can be combined with the precoder. Let \(T=T_{F}+T_{c}\) be the OFDM blocklength with \(T_{F}\) symbols for the DFT and \(T_{c}\) symbols for the cyclic prefix. We assume that \(T_{F}\geq L\) and \(T_{c}\geq L-1\). For simplicity, all \(T_{F}\) subcarriers carry data and we do not include the cyclic prefix overhead in our rate calculations below, i.e., the rates in bits per channel use (bpcu) are computed by normalizing by \(T_{F}\).  
Consider the frequency-domain modulation alphabet \(\hat{\mathcal{U}}\) that has a finite number of elements, e.g., QPSK has \(\hat{\mathcal{U}}=\{\hat{u}:\hat{u}=(\pm 1\pm\mathrm{j})/\sqrt{2}\}\). Messages are mapped to the frequency-domain vectors \(\hat{\mathbf{u}}[m]=(\hat{u}_{1}[m],\ldots,\hat{u}_{K}[m])^{T}\) for subcarriers \(m=0,\ldots,T_{F}-1\) that are converted to time-domain vectors \(\mathbf{u}[t]\) by IDFTs  
\[u_{k}[t]=\frac{1}{T_{F}}\sum_{m=0}^{T_{F}-1}\hat{u}_{k}[m]\mathrm{e}^{\mathrm{ j}2\pi mt/T_{F}} \tag{4}\]  
for times \(t=0,\ldots,T_{F}-1\) and UEs \(k=1,\ldots,K\). For the simulations below, we generated the \(\hat{u}_{k}[m]\) uniformly from finite constellations such as 16-QAM or 64-QAM. We assume that \(\mathrm{E}[\hat{u}_{k}[m]]=0\) for all \(k\) and \(m\). Each UE \(k\) uses a DFT to convert its time-domain symbols \(y_{k}[t]\) to the frequency-domain symbols  
\[\hat{y}_{k}[m]=\sum_{t=0}^{T_{F}-1}y_{k}[t]\mathrm{e}^{-\mathrm{j}2\pi mt/T_{F}}. \tag{5}\]
## 1 Introduction  
Non-linear analyses are the most accurate approach for assessing the seismic behavior of civil structures, since the current codes highly encourage post-elastic dissipative behavior under strong earthquakes. Non-linear static analysis based on horizontal static loads with gradually increasing intensity (pushover analysis) is very commonly applied to ascertain the inelastic capacity of a structure until its collapse [1, 2]. On the other hand, non-linear time-history analysis under spectrum-consistent earthquakes may be very useful for studying the evolution of the seismic demand on a given structure [3, 4, 5]. Stemming from the concept of incremental seismic actions typical of pushover analysis, incremental dynamic analysis (IDA) is a type of non-linear analysis technique under scaled ground accelerations that is currently perceived as the most advanced tool for assessing the seismic response of structures. In the guidelines published by the Federal Emergency Management Agency (FEMA) [6, 7], IDA is presented as the fundamental basis with which to determine the performance factors of new types of structures, as well as to obtain the collapse thresholds of existing structures while considering the variability of seismic records. Many authors have carried out IDA to obtain the performance of different kinds of new and existing structures [8, 9, 10, 11, 12, 13, 14, 15, 16, 17]--even structures equipped with dissipation or isolation devices [18].  
However, some disadvantages still affect the accuracy of IDA, particularly the high record-to-record variability of the curves representing the maximum demand on a given building at different scaled seismic intensities. While the engineering demand parameter is usually taken as the maximum interstory drift ratio of a building, how to choose the most suitable measure of the scaled seismic intensity remains highly debated and is crucial for ensuring that the IDA procedure is sufficiently effective [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29].
# Energy-Efficient Hardware Implementation of an LR-Aided K-Best MIMO Decoder for 5G Networks
## 4 System Architecture Design and Verification  
We have developed and implemented a hardware architecture for an LR-aided K-Best detector targeting a 2 \(\times\) 2 MIMO system with quadrature amplitude modulation (QAM) spatial multiplexing and K = 2. The word length is chosen to be 16 bits for all design variables after analyzing their expected range in the software model. More details are provided in the following sub-sections.
## 2 Related Research
### Multi-label Text Classification Task Based on Deep Learning Technology  
In this section, we present a multi-label text classification task based on multi-intention recognition task. The multi-label text classification task is similar to the multi-label text classification task. For multi-label text classification tasks, with the rapid development of deep learning technology in recent years, using deep learning methods to solve multi-label classification tasks has become a research hotspot. Deep learning methods automatically extract features through neural network structures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which reduces labor costs and enhances feature expression ability [9]. Convolutional Neural Networks (CNN) [10] were initially applied in the field of computer vision. Later, [PERSON] [11] proposed the TextCNN model, which is suitable for text classification tasks and was obtained by [PERSON] through some modifications to the input layer of a CNN. It only has one layer of convolution and one layer of maximum pooling, so it has advantages such as a simple network structure and a fast training speed; it is widely used in multi-label text classification tasks [12; 13]. Its characteristic is that it can effectively extract the local features of sentences, but its disadvantage is that it requires the use of fixed windows. Therefore, when facing long texts, it is not suitable for capturing the long-distance dependencies of text sequences and is prone to losing key information. Recurrent Neural Networks (RNN) [14] can capture long-distance dependencies and are therefore applied to multi-label text classification tasks [15]. However, RNNs may experience gradient vanishing or exploding when processing text. To address this issue, the RNN variant BiLSTM [16] has emerged, which can effectively capture contextual information, extract the global features of sentences, and improve the classification accuracy of multi-label texts [17].  
In text classification tasks, in order to extract key information that is more effective for classification, [PERSON] et al. [18] proposed using the BiLSTM model to extract key features globally by introducing an attention mechanism to assign higher weights to key information, thereby improving the effectiveness of text classification. Some scholars have combined a CNN with BiLSTM for use. Some scholars have first obtained the local features of text through a CNN and then input them into LSTM for classification [19; 20], while others have been used to extract key information that is more effective for classification tasks.
## 5 Analysis of Coal Gangue Image Processing and Recognition Results
### Feedback on Defogging Effect and Recognition  
The image recognition results of coal and gangue under varying dust concentrations are presented in Figure 13. After defogging, the average recognition accuracy of coal in coal gangue images under five dust gradients reached 95.7%, 94.2%, 93.3%, 88.1%, and 82.4%, respectively, while the average recognition accuracy of gangue reached 97.4%, 95.3%, 91.2%, 86.8%, and 84.7%.  
Figure 13: Comparison of accuracy improvement of coal gangue image recognition before and after defogging. The star indicates the recognition accuracy before de-fogging, the square indicates the recognition accuracy after de-fogging, the line indicates the trend of the accuracy with the dust concentration, and the arrow indicates the effect of the recognition accuracy improvement before and after defogging of the same dust concentration.  
With the influence of five different dust concentration gradients, the recognition accuracy of coal before and after dehazing improved by 6.8%, 11.7%, 21.8%, 56.8%, and 71.8%, respectively. Similarly, the recognition accuracy of gangue showed improvements of 5.8%, 10.1%, 17.9%, 29.3%, and 45.8%, respectively. These results indicate a significant enhancement in recognition performance following dehazing under different dust concentration conditions.  
The recognition accuracy of both coal and gangue increases with the elevation of dust concentration, suggesting that the dehazing algorithm significantly enhances recognition performance within an effective range of dust concentration. Notably, the improvement in the recognition accuracy of coal surpasses that of gangue during the dehazing process. This discrepancy can be attributed to the interference caused by dust, which hinders the model's ability to effectively extract coal features, resulting in some coal being misclassified as gangue and consequently reducing the overall recognition accuracy.
# A Human Health Toxicity Assessment of Biogas Engines Regulated and Unregulated Emissions
## Appendix A  
### Italian and German Regulatory Frameworks  
To highlight the differences among tracked substances that can be considered \"regulated emissions\", the Italian standard is, firstly, analysed and, then, compared with the German regulatory framework. Germany is the sector leader and the most advanced country in biogas technologies. So, the most appropriate country to compare with.  
As shown in Figure 11, the Italian standard (Legislative Decree No. 118 of May 19 th, 2016 [36]) distinguishes the limits based on fuel type and, then, according to the engine nameplate thermal power.  
NO\({}_{\text{x}}\) is included among the regulated emissions for both fuels, despite different values fixed as regulatory limits. CO, HCl and VOCs are included among regulated substances for ICEs fed by biogas, but not for natural gas. Conversely, SO\({}_{\text{x}}\) and PM need to be tracked in the case of NG ICEs but not in biogas engines.  
Note that, in the updated legislation, it is clearly stated that: (i) the volatile organic compounds are expressed as Total Organic Carbon (TOC) and their legal limit includes only non-methane volatile organic compounds (NMVOCs); (ii) SO\({}_{\text{x}}\) are expressed as SO\({}_{2}\). An clarification necessary to solve the misunderstanding introduced by the previous legislation [55].  
Contrary to the Italian standard, the German legislation differentiates based on fuel type but sets different limits depending on the engine type and, in some cases, also based on the ICE thermal power as clearly summarized in Figure 12.  
Figure 11: Italian regulatory limits for biogas and natural gas plants [36].  
Both legislations set limits for NO\({}_{\rm x}\) and CO, while HCl and VOCs are considered less important than SO\({}_{\rm x}\) and formaldehyde by the German regulation. Formaldehyde is an organic substance known for its carcinogenicity. So, Germany is working to limit toxic and volatile compounds which are known to be human carcinogens, not only setting limits but also establishing supports to boost their reduction. In fact, to encourage formaldehyde reduction, the German government granted a bonus of 0.01 EUR per kWh to biogas plants able to simultaneously respect the legal limits set for the pollutants and maintain formaldehyde levels under 40 mg Nm\({}^{-3}\)[63].
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Advantages of Using DDPM in Biomedical Image Segmentation
#### 3.4.6 Addressing Challenges of Complex Anatomy  
Biomedical images frequently involve complex anatomical structures with intricate boundaries and varying intensity profiles. DDPM's ability to model complex probability distributions and handle diverse intensities makes it well-suited for segmenting structures with intricate anatomies. This is particularly advantageous in applications like neuroimaging, where detailed structures demand precise segmentation [33; 54].
## 5 Experimental Verification
The curves of theory and simulation seem to be basically kept constant; the reason for this is that the length of the leaf spring is much larger than the compression amount, and the frame displacement hardly affects its compression amount, so the negative stiffness value basically remains unchanged. Figure 18: Experimental model: (**a**) 3D model, (**b**) physical object, (**c**) FEM model. Each set of leaf springs is subjected to at least two experiments, and the data from multiple experiments need to be compared to confirm whether the overall stiffness and displacement curves are close, and, if there is a more similar result, the last measurement data are retained. Because the measuring tool is a digital caliper, there will be some errors in the measured values, resulting in fluctuations in the curves. The final data shown in Figure 19 focus on the overall stiffness and displacement curves during the comparison process and the local fluctuations are not dealt with for the time being. Therefore, the experimental data curve in the figure is the original data obtained from the measurement and has not been processed by other calculations. In addition to the static experiments, we also verified the vibration isolation performance using the model. As shown in Figure 20, swept-frequency vibration experiments were carried out using a leaf spring with a \(b\) of 80, \(l_{0}\) of 70, \(h\) of 0.25, and \(u_{x}\) of 0.4, and acceleration amplitudes were measured using an accelerometer for the basic and periphery frames, respectively. The signals in both the time and frequency domains indicate that the device has a significant vibration isolation effect. Due to the test equipment and environment, a more pronounced natural frequency was not obtained, but this does not affect the conclusion that the device has a vibration isolation effect. The reason may be that the vibration isolator filtered out too much vibration excitation, resulting in the response amplitude of the vibration-isolated part being too small to be measured. From the time domain signal, the response amplitude of the periphery frame is close to the noise amplitude. Because of the low signal-to-noise ratio of the signals, the frequency domain signals and the transfer rates demonstrated in Figure 20b,c were smoothed using a shifting smoothing process with a window width of 20, and the resolution of the spectra was 0.0047 Hz, so the smoothing process had no effect on the display of the natural frequencies. Figure 20b shows the presence of 100 Hz, 150 Hz, and 200 Hz peaks due to electromagnetic interference in the internal circuitry of the measuring instrument. The 50 Hz industrial frequency interference peaks were eliminated by grounding the sensor during the experiment, but the industrial frequency interference inside the instrument still exists. Figure 20: \(Cont.\)