esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Image Processing  
For each patient, all four [18F]FDG PET brain volumetric images were rigidly aligned (registered) with each other. Then, the average image was taken and registered to the Montreal Neurological Institute (MNI) space (ICBM 2009a Nonlinear Symmetric T1) using a suitable deformable model. The geometric deformation model found was then applied to all four images already aligned with each other, which were resampled with an isotropic voxel size of 2 mm afterwards. After this, the resampled images were smoothed with a Gaussian kernel with 12 mm full width at half maximum, and finally intensity normalized. This intensity normalization was carried out at the voxel level (voxelwise), independently, per image, using the pons as the reference region, i.e., each voxel standardized uptake value (SUV) was divided by the mean pons SUV, originating a voxelwise SUV ratio (SUVR). The SUV is a voxelwise semi-quantitative scale, usually automatically quantified by the PET scanners. In this work it was computed as:  
\[\text{SUV}=\frac{\text{activity concentration}\left(\frac{\text{Bq}}{\text{ mL}}\right)}{\text{injected activity}\left(\text{Bq}\right)}\times\text{body weight}\left(\text{g}\right) \tag{1}\]
# Are Strong Baselines Enough? False News Detection with Machine Learning
## 2 Materials and Methods
### Datasets
#### 2.2.1 Liar Dataset  
The LIAR dataset [4] is a benchmark dataset created for false news detection. It contains 12.8 thousand real-world manually labelled short statements that were collected from PolitiFact.com with various contexts. The dates for the statements are primarily from 2007-2016. The dataset also includes an analysis report and links to source documents for each statement, as well as information about the speaker, the speaker's job title, subject, political party affiliation, the credit history of the speaker, and the context for each statement.  
The dataset has six different labels to determine the truthfulness ratings. These labels are as follows: pants-fire, false, barely-true, half-true, mostly-true, and true. The pants-fire label represents a completely false statement, and the true label represents a completely true statement. The distribution of cases for each label is balanced, except for pants-fire which has significantly fewer cases compared to other labels. The pants-fire label has 1050 cases whereas the other labels have cases ranging from 2063 to 2638 cases.  
The statements have 732 different subject types ranging from various topics, with the most frequent subject being healthcare, and it appears in the dataset 5 times. The average statement is 17.9 tokens long. Most of the speakers of the statements are U.S. politicians, but other speaker types are also included such as journalists, social media users, and Internet newspapers. Overall, there are 2910 unique speakers, and each speaker appears 3.5 times on average in the dataset. The most common speaker in the dataset is [PERSON], who appears in the dataset 5 times. Table 5 shows an example of a statement from the LIAR dataset [4].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **All Samples** & **True Samples** & **False Samples** & **Information Type** \\ \hline LIAR & 12,851 & 7134 & 5707 & News related to politics. \\ \hline FakeNewsNet & 23,921 & 6480 & 17,441 & \
\begin{tabular}{c} News related to politics \\ and celebrity gossip. \\ \end{tabular} \\ \hline Twitter15 & 1490 & 372 & 370 & Rumors spread on Twitter. \\ \hline Novel ChatGPT & 300 & 100 & 200 &
\begin{tabular}{c} Automatically generated \\ false and real news \\ articles. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: A summary of all used datasets.
## 2 Related Work
### Experiment Setup for the Investigation of the Impact of Attenuation on the Packet Error Rate
#### 3.1.1 Packet Error Rate Measurement of Wake-Up Receiver  
The measurement setup was conceived using two nodes, WuRx and WuTx, with peer-to-peer communication. The SPIRIT1 transceiver was programmed to send Wupt with a power of 11 dBm and to wait for an acknowledgment from the receiver. The WuTx sent 100 consecutive packets and waited for the acknowledgment for each packet. Finally, the number of received and lost packets was measured.  
The transmitted Wupt was an 868 MHzon-off keying (OOK) signal at a specific data rate to match the operative frequency of the LF AS3933 receiver after passing by the envelope detector. We used a Schottky diode in the envelope detector to convert the RF signal into LF signals.  
When the AS3933 validated the Wupt passed by the envelope detector, it sent an interrupt to the MSP430G2553 to activate the main transceiver and turn it into Idle reception mode, waiting for the data packet. When the data were received, the SPIRIT1 transceiver sent back an acknowledgment (ACK) to inform the transmitter that the packet had arrived. A sequence diagram shows the communication protocol process in Figure 9.  
The experiment was repeated several times starting from 53 dB to 80 dB of attenuation with a step of 0.5 dB. The count of the lost packets is plotted in Figure 10 to demonstrate the impact of attenuation on the PER for AS3933.  
Figure 10: Variation in the PER according to the attenuation factor using WuRx communication.  
Figure 9: Communication sequence diagram.  
When the attenuation went from 66 dB to 69 dB, the PER rose to 100%. That showed the limit of the sensitivity in the WuRx. At this point, the transmission power was conserved, and the attenuation level was 66 dB. The SPIRIT1 transceiver was attached to a spectrum analyzer to measure the received output power, which was \(-58\) dBm. We had 11 dBm transmission power; the output level was meant to be \(-55\) dBm, but the 3 dB here was the result of the losses due to the transmission losses in the cable.
# Nanotechnology Solutions for Controlled Cytokine Delivery: An Applied Perspective
## 3 Nanoparticles as Cytokine Delivery Systems
### Types of Nanoparticles for Cytokine Delivery
#### 3.1.2 Liposomes  
Liposomes are well-studied carriers exploited for the delivery of numerous types of biomolecules, including cytokines [50; 51; 52; 53]. Vesicles composed of one or more lipid membranes are able to easily cross lipid bilayers and cell membranes [44], but can be rapidly eliminated in vivo; therefore, stabilization, for instance with PEG, is essential in these nanocarriers [54]. In addition, the conditions used to prepare liposomal formulations are of great importance: solvents such as chloroform are often used [55] as well as high temperatures or low pH that decrease nanoparticles' biosafety or induce cytokine denaturation [31]. [PERSON] and collaborators demonstrated that liposomes containing IFN-\(\gamma\) can be useful for tumor vaccination, providing a reservoir for cytokines at the site of antigen presentation. However, low but detectable cytokine/liposome amounts were found in blood, liver, spleen, kidneys and lymph nodes [38], probably due to the non-functionalization of these nanoparticles. Although the nanoparticles were completely cleared in 168 h, 70% of the injected dose was eliminated from the injection site in 8 h, thus exhibiting a release profile which does not seem to be fully suited for the application [38].  
In another study, [PERSON] and co-workers demonstrated that combined treatment with IL-2 and anti-CD137 caused anti-tumor immunity, but was accompanied by severe systemic toxicity. Thus, the authors developed PEGylated liposomes (with a lipid mixture of DOPC, cholesterol, DSPE-PEG, and DSPE-PEG-maleimide) containing IL-2 and anti-CD137 conjugated to the particles' surface. The produced liposomes provoked stronger anti-tumor activity and showed an absence of toxicity when compared to systemic delivery of free drug combination, by the evaluation of pro-inflammatory cytokine levels in the serum [42]. This is a useful approach for the delivery of cytokines, as these are available to bind to the respective receptors at the target cells' surface. Moreover, it allows the increment of cytokineactivity and the introduction of an additional drug to be delivered, potentiating the desired effect, usually termed as co-delivery. As an example, [PERSON] and co-workers confirmed that TNF-\(\alpha\) coupled to the outer surface of the liposome improved protection against _Plasmodium berghei_-induced experimental cerebral malaria in mice compared to liposomes encapsulating TNF-\(\alpha\), or even the free cytokine [56].
# The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning
## 3 Methodology
### PPO Algorithm
\[L_{\pi_{old}}(\pi)=\eta(\pi_{old})+\sum_{s}\rho_{\pi_{old}}(s)\sum_{a}\pi(a|s)A _{\pi_{old}}(s,\ a) \tag{3}\]  
where \(\rho_{\pi_{old}}(s)=P(s_{0}=s)+\gamma P(s_{1}=s)+\gamma^{2}P(s_{2}=s)+\cdots\) is the discount status distribution. \[\eta(\pi)\geq L_{\pi_{old}}(\pi)-CD_{KL}^{max}(\pi_{old},\ \pi) \tag{4}\]  
where \(C=\frac{4c\gamma}{(1-\gamma)^{2}}\), \(D_{KL}^{max}(\pi_{old},\ \pi)=max_{s}D_{KL}(\pi_{old}(\cdot|s)\ ||\ \pi(\cdot|s))\), \(D_{KL}\) is KL divergence. \[\underset{\pi}{\mathrm{argmax}}[L_{\pi_{i}}(\pi)-CD_{KL}^{max}(\pi_{i},\ \pi)] \tag{5}\]  
\[\underset{\theta}{\mathrm{argmax}}\mathrm{E}_{s\sim\rho_{\theta_{old}}},\ a \sim\pi_{\theta_{old}}\left[\frac{\pi_{\theta}(a|s)}{\pi_{old}(a|s)}Q_{\theta_{ old}}(s,\ a)\right]\] (6) subject to \(\mathrm{E}_{s\sim\rho_{\theta_{old}}}\left[D_{KL}(\pi_{old}(\cdot|s)\ ||\ \pi_{\theta}(\cdot|s))\right]\leq\delta\)(3)  
According to the analysis in the paper [27], the TRPO optimization method has two disadvantages. One is that the objective function with the average KL divergence constraint makes the smaller update step size, which results in slower learning efficiency, and the other is that the optimization problem with constraints requires higher calculated cost, and the solving process is cumbersome. For these reasons, the PPO method [27] is proposed to replace the constraint with the penalty, which can solve the problem of determining the universal penalty factor and obtain a more concise optimization form, as shown in Formula (7). Applying \(r_{t}(\theta)\) as a distance measure between the updated policy and the original policy, the application of clipping eliminates the driving force of \(r_{t}(\theta)\) exceeding \([1-\epsilon,1+\epsilon]\), which limit the policy update within a certain range. This concise form can realize the constraint effect of Formula (6) and have a more universal applicability hyperparameter \(\epsilon\). From the perspective of the gradient composition of the objective function, the gradient of Formula (7) eliminates the gradients from the data whose \(r_{t}(\theta)\) is out of the range \([1-\epsilon,1+\epsilon]\), compared with the case without clipping, which ensures the robustness of the policy update. \[L^{CLIP}=\mathrm{E}_{\pi_{\theta_{old}}}\left[\min\left(r_{t}(\theta)\hat{A} _{t},\mathrm{clip}(r_{t}(\theta),1-\epsilon,1+\epsilon)\hat{A}_{t}\right)\right] \tag{7}\]  
where \(r_{t}(\theta)=\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta_{old}}(a_{t}|s_{t})}\), \(\hat{A}_{t}=A_{\pi_{old}}(s_{t},\ a_{t})\) is the advantage function of \(\pi_{old}\).
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 3 Results
### Validation and Clay Fraction Mapping
As in the case of optical observations, the proposed radar technique would clearly be disturbed by irrigation, which affects the relationship between the average seasonal values of soil moisture and soil clay content. The RF algorithm is found to have the most accurate results, using different indicators. Nevertheless, similar results are obtained for both the RF and SVM techniques. In the present study, a relatively small number of features are used as inputs for the classification algorithm. It would be interesting to use a greater number of samples for the validation process. The computing time needed for the learning process is also an important criterion, when comparing different classification algorithms. The parametric configuration of the SVM often requires an optimization phase, which may take longer than in the case of the RF. Conversely, the RF is less sensitive to its parametric configuration, thus reducing the required training time. Furthermore, recent advance techniques have been proposed to increase the predictive power of models. [PERSON] et al. [58] have proposed an accurate estimation of soil texture components (clay, silt, and sand). They showed that with a combination of geographical proximity ad spectral similarity in regression analysis will increase the predictive power of a novel memory based on learning algorithm. They predicted clay content with an R\({}^{2}\) of 0.84 and an RMSE of 6.32%. Moreover, [PERSON] et al. [59] have designed a Geospatial Soil Sensing System (GEOS3) based on Landsat TM5 products to retrieve soil spectral reflectance from several years with creating a Temporal Synthetic Spectral Reflectance (TESS), which is a synthetic spectral fingerprint of the soil surface. The functionality of GEOS3 is evident, as it could be used to support soil surveys, digital soil mapping (e.g., mapping of clay contents), precision agriculture, soil sampling allocation, soil management zones, soil conservation, and for improvements to existing soil maps. They also proposed the use of Normalized Burn Ratio 2 (NBR2), as an auxiliary index to decrease the similarities found on the multispectral patterns between sandy soils and straw cover.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Centralized Context Cloud Architecture
#### 3.1.4 Metadata  
With our approach, we propose to merge a set of sensors' data as one set of metadata. Every time a sensor's record changes, the information is sent to the C-ITS-C. A new metadata set is produced with the same id and a new sequence number. This new metadata set is then distributed to the routers (RSUs) of the static part in the context cloud. Grouping several data as one presents an asset: it benefits from a common name and a sequence number. For example, in a city context, if we want to get information on one hundred traffic lights, only one ICN interest is needed instead of one hundred. Oppositely, the data returned contain information about every traffic light, even the ones that did not change. Thus, we have to be sure that the metadata process will not be harmful to the overhead and this depends on the context, the number of sensors, and the data update frequency. This following inequation \(C*F_{CAM}*(Pi+Pv)*Nv>C*(Pi+Pv+Pd)*F_{d}\) can be used to determine if the metadata will be useful. The inequation's left side represents the weight of the interests generated every second by all the vehicles in the range of an RSU in the case that we do not build metadata. The right side gives the weight of the sent metadata on the G5 channel by one RSU at the frequency \(Fd\).  
For example, let us take \(Nv=100\), and \(C=100\), then:  
\[\left\{\begin{array}{rcl}C*F_{CAM}*(Pi+Pv)*Nv&=2000000\text{ bytes}\\ C*(Pi+Pv+Pd)*Fd&=&600\text{ bytes}\end{array}\right.\]  
In this system, it is preferred to build the metadata.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Designation** & **Description** & **Value** \\ \hline \(C\) & Number of sensors concerned & \\ \(P_{t}\) & Data’s id length (bytes) & 1 \\ \(P_{v}\) & Data’s sequence number & 1 \\ \(P_{d}\) & Data’s length (bytes) & 4 \\ \(F_{CAM}\) & CAM frequency (Hz) & 10 \\ \(F_{d}\) & Data’s update frequency (Hz) & 1 \\ \(N_{v}\) & Number of vehicle in range of & \\  & an RSU & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanisms’ parameters.  
If \(Pd=100\), \(Fd~{}=~{}10\) and \(Nv=10\), then:  
\[\left\{\begin{array}{c}C*F_{CAM}*(Pi+Pv)*Nv=~{}20000\text{ bytes}\\ C*(Pi+Pv+Pd)*Fd~{}=102000\text{ bytes}\end{array}\right.\]  
This time, it is better to not use metadata.
## 3 Proposed Framework
### Classification Algorithms
#### 3.5.2 Random Forest  
Classification and regression can be performed using the ensemble-classification algorithm. Combining the output from individual decision trees, the RF creates a class of results that are then classified. Random Forests are generally suitable for massive datasets with many input variables [35]. The optimal hyperparameter used in this classifier is n_estimators = 100.
# Static Analysis for ECMAScript String Manipulation Programs
## 5 Abstract Semantics of ECMAScript String Operations
### Abstract Semantics of ToLowerCase  
The abstract semantics of toLowerCase is defined by the function \(\texttt{LC}^{\sharp}:\texttt{Dfa}_{/\equiv}\rightarrow\texttt{Dfa}_{/\equiv}\) which returns as result an automaton that recognizes the same strings of the input automaton, where any upper-case symbol is replaced with the corresponding lower-case symbol. \(\texttt{LC}^{\sharp}\) is computed by Algorithm 3.  
```
Input :\(\texttt{A}=(Q,\Sigma,\delta,q_{0},F)\) Output :\(\texttt{LC}^{\sharp}(\texttt{A})\)
1\(\delta^{\prime}\leftarrow\varnothing\); foreach\((q,c,q^{\prime})\in\delta\)do
2\(\delta^{\prime}\leftarrow\delta^{\prime}\cup(q,\texttt{toLowerCase}(c),q^{ \prime})\)
3 end foreach\(\texttt{A}^{\prime}\leftarrow(Q,\Sigma,\delta^{\prime},q_{0},F)\); return\(\texttt{A}^{\prime}\)
```  
**Algorithm 3**\(\texttt{LC}^{\sharp}:\texttt{Dfa}_{/\equiv}\rightarrow\texttt{Dfa}_{/\equiv}\) algorithm  
Starting from an input automaton A, the idea is to return as result the automaton A, that is a copy of A with the exception that any upper-case symbol read by a transition is replaced by its corresponding lower-case symbol. Transitions that already read lower-case or special symbols are unaltered. An example is reported in Figure 10.  
**Theorem 7**.: \(\texttt{LC}^{\sharp}\) _is both sound and complete. Formally,_  
\[\forall\texttt{A}\in\texttt{Dfa}_{/\equiv}.\texttt{LC}(\mathcal{L}(\texttt{A }))=\mathcal{L}(\texttt{LC}^{\sharp}(\texttt{A}))\]
## 3 Simulations and Experiments on Brake Pressure Response
### The Experiment on the Brake Pressure Response  
In this experiment, we focused on verifying the mathematical model and the influence of the pipe, so we used a solenoid valve instead of the brake control valve to simplify the measurements. The orifices in Figure 2 are replaced by the solenoid valve and fittings. A brake-circuit test rig was built, as shown in Figure 9. It consists of an air source, a precision regulator (IR3020, SMC), a buffer tank (27 L), a solenoid valve (SY5100, SMC), two pressure sensors (PSE540, SMC), a pipe (T1075, SMC),  
Figure 8: Flow-chart of brake circuit simulation.  
Figure 10 indicates that the charge time in the brake chamber is 0.7 s without a pipe and 1.2 s with one. Thus, the time delay is increased by 0.5 s by the pipe restricting the air transmission. For a vehicle being driven at high speed, 0.5 s could cause a serious traffic accident if the brakes cannot be applied promptly. Therefore, the pipe effect must be considered carefully when designing a brake system, and the time delay should be reasonably compensated. In addition, the simulation results are consistent with the experimental ones, which also helps to validate the mathematical models developed in this paper. However, if we look at the results carefully, there is a little deviation at the beginning of the charge. This is because the parameters of the rubber piston are complex and difficult to measure correctly, and the deforming force in the extension process is complex too. Thus, we used an approximate model to describe the piston that causes the deviation.  
If we combine Figures 10 and 11, it is obvious that the location of the deviation is exactly coincident with the process of the rod extending. Once the rod has extended fully, the rubber deformation has no effect, and the simulation coincides well with the experiment. Furthermore, the pressure in the initial part is very small and it does not begin to brake, so the deviation has little effect on the predicted braking force. Thus, all of this verifies that the CIP method is a precision method for predicting the pressure in the pipe. It also demonstrates that the model and method proposed in this paper are sufficiently accurate to calculate the pressure response of the brake system.  
Now that the model has been verified experimentally, we turn our attention to modifying the values of various structural parameters in the simulation to study their effects.
## 2 Literature Review
### Research Hypothesis Development
#### 2.3.5 Green Procurement Practices Influenced by Electronic Informing  
E-informing, also known as E-noticing, is an E-procurement process that includes receiving and disseminating purchase information both from and to corporate and peripheral partners using web-based technologies, aided by online notification technologies. This improves the efficiency and efficacy of the tender process in state-owned enterprises, resulting in improved procurement processes and supply chain performance [7; 8; 76; 133]. A study by [131] discovered that E-informing has a positive significant influence on supply chain performance and therefore enhanced sustainability in her study of the impact of E-procurement on organizational performance in state corporations in Nairobi County. Moreover, Ref. [12] found that E-informing, which is a component of E-procurementdimensions, improved supply chain performance in their research of E-informing on organizational performance [11; 33]. As a result, companies must employ E-informing in the procurement process. Moreover, companies must electronically consult standards for products/services that take commodities' quality into account to improve the performance of the supply chain [50; 63; 77]. Therefore, it is hypothesized that:  
**Hypothesis 5 (H5).**_E-informing influences green procurement significantly._
## 3 BILSTM-Based Deep Neural Network Rock-Mass Classification Method
### Deep Neural Network Structure
#### 3.1.3 Window Selector and Voter  
The BILSTM network must consider the window size, i.e., the extent to which the rock mass at a certain depth to be predicted is affected by the pre- and post-sequence depth MWD data. In this study, the initial sample window size was set to 40 cm for each BILSTM cycle, while considering the influence of 20 depth-sequences for predicting the surrounding rock class.  
To avoid model overfitting, while improving the generalization of the neural network, we designed a window-selector module with random window selection within the default sample window, as shown in Figure 6. Under the initial sample window, small windows are randomly generated according to the depth-sequence for the BILSTM cycle, so that different information can be learned even if the samples are the same, thereby greatly improving the generalization.  
To avoid the data collected at a certain depth being affected by the random effects of site construction and special circumstances that may lead to misclassification, a 'Voter' module was designed for the hidden-state outputs at different depths; the voter is shown in Equation (11). The results of each BILSTM unit's hidden-layer output undergo fully connected Softmax activation, and the prediction results are mapped at the \([0,1]\) interval by the Softmax function to obtain the predicted rock-class probability. The rock class with the highest probability within the sample circulation window is taken as the final prediction result by the voter.  
\[\text{Vote}(n)=\left\{\begin{array}{ll}\text{I, \ if }n_{\text{I}}=max(n)\\ \text{II, \ if }n_{\text{II}}=max(n)\\ \text{III, \ if }n_{\text{III}}=max(n)\\ \text{IV, \ if }n_{\text{IV}}=max(n)\\ \text{V, \ if }n_{\text{V}}=max(n)\end{array}\right. \tag{11}\]  
Figure 5: Structure sketch of the fully connected (FC) layer; (**a**) The first two fully connected layers; (**b**) The last fully connected layer.  
Figure 6: Window selector.  
where \(n\) denotes the set of rock-mass class probabilities output by the BILSTM network in the selection window, and \(n_{\text{I}}\) to \(n_{\text{V}}\) represent the probabilities of different rock-mass classes.