esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Heuristic Scheme
### Path Mapping Algorithm  
This algorithm tries to map \(P_{\ell 2e}\) to its candidate physical path group, for better understanding see Algorithm 3. Given that the end-to-end paths in \(P_{\ell 2e}\) have shared virtual nodes because the service graph \(G_{s}\) is connected. Thus, a simple method is used to check if candidate paths have shared physical nodes too. This method compares the number of physical nodes to the number of virtual nodes; If the physical is less or equal to virtual nodes, the algorithm will continue, otherwise, it returns a negative response; as in line 2 of Algorithm 3. This algorithm saves the physical network state to a save point in order to rollback if the mapping fails. Then, it checks the virtualization technique type and the available resources to map \(f\) on \(N_{u}\) using a validation function, namely **CheckNodeValidation**. Whenever a new function is processed for mapping the links among the mapped nodes, another validation function is used to check the available bandwidth and end-to-end delay of the links, where this function is named **CheckLinksValidation**. In fact, checking of available resources, bandwidth, and delay is implemented in a similar way to that in Reference [11].  
```
Data:\(P_{\alpha z}\): a set of end-to-end paths of the service request, \(CandidatePathsGroup\): a list of candidate paths. Result:\(True/False\) begin ifNo of unique nodes in \(CandidatePathsGroup>No\) of nodes in \(G_{s}\)then Return\(False\) end /* saves copy of the physical network. */ Create a SavePoint\(mappedNodes\leftarrow\phi\)\(mappedEdges\leftarrow\phi\)for\(P_{uv}\in CandidatePathsGroup\)do for\(p\in P_{\rho 2e}\)do for\(N_{u}\in P_{uv}\)do for\(f\in p\)do if\(f\
otin mappedNodes\)then checkNode\(=\) CheckNodeValidation\((f,N_{u})\)if\(checkNode=False\)then Rebleback to SavePoint Return\(False\)  end  checkLinks\(=\) CheckLinksValidation\((f,N_{u},\)\(mappedNodes,mappedEdges)\)if\(checkLinks=False\)then Rebleback to SavePoint Return\(False\)  end \(mappedNodes\bigcup(f,N_{u})\)  end if  end if  end for  end for Return\(True\)
```  
**Algorithm 3**Path Mapping Algorithm  
This proposed heuristic scheme tests the possibility of mapping end-to-end paths to simple candidate paths of the same path identification. This means that the path length constraints in Equation (16) will not be taken into account, which will negatively affect the acceptance ratio. For this reason, we suggest to retrieve the physical paths based on the path identification of virtual links instead of end-to-end paths in order to improve the acceptance ratio. We also reported the path mapping based on the end-to-end path identification to proof the concept of end-to-end path mapping where the results of this work shows that the performance of the proposed heuristic scheme in large-scale can overcome its weakness in small-scale.
###### Abstract  
It is not yet clear how adding silicon foliar fertilisation affects olive leaf (OL) phenolics and their potential to impact different cancer cells. Thus, we conducted a field trial to study the effect of foliar Si biostimulant fertilisation on the OL phenolic content of the 'Lecino' (LE) and 'Istarska Bjelica' (IB) cultivars. The experiment compared untreated Control (C) and three distinct levels of silicon (Si1, Si2, Si3) with Si concentrations of 0.55 g/L, 1.1 g/L, and 2.2 g/L, respectively. Si3 application resulted in the highest levels of oleuropein, apigenin-7-O-glucose, luteolin-4-O-glucose, rutin, and tyrosol compared to the C treatment. The polyphenols showed high cytotoxic activity in three cancer cell lines tested: cervical adenocarcinoma (HeLa), colon cancer (HCT116), and osteosarcoma (U2 OS). The strongest inhibition of cell growth was observed in the HCT116 cell line. All cancer cells tested were more sensitive to treatment with polyphenols isolated from plants with added Si than those without added Si. The cytotoxic activity of the extracts on the healthy cell line RPE1 was similar to that on the cancer cell line HCT116 and U2 OS.  
silicon; olive leaf phenolics; Leccino; Istarska Bjelica; human cervical adenocarcinoma (HeLa); colon cancer (HCT116); osteosarcoma (U2 OS); human pigment epithelial-1 (RPE1)
# Thallium Poisoning: Case Report and Scoping Review on Diagnostic Delay and Therapeutic Outcome
## 1 Introduction
According to the Environmental Protection Agency (EPA) and the Occupational Safety and Health Administration (OSHA), thallium poses significant health risks through occupational and environmental exposure and has been identified at multiple hazardous waste sites nationwide. OSHA has set workplace exposure limits (PEL-TWA) at 0.1 mg of soluble thallium compounds per cubic meter of air for an 8 h workday, underscoring the need for strict control measures to limit skin and respiratory exposure [30]. The National Institute for Occupational Safety and Health (NIOSH) has indicated the same limit value (REL-TWA = 0.1 mg/m\({}^{3}\)) for work, up to 10 h. The Association of American Industrial Hygienists (ACGIH) set a much lower limit in 2009 (TLV-TWA = 0.02 mg/m\({}^{3}\)), confirming the notations \"skin\" (skin passage), \"stel\" (short time exposure limit, limit valid even for a short period), and \"ceiling\" (limit not to be exceeded). This brief review of the effects of thallium indicates how important it is for the clinician to consider this form of intoxication among the many possible etiological diagnoses of caseswith neurological symptoms. In fact, even in Western countries, a person can be intoxicated by inadvertent exposure through alternative drugs, uncontrolled food products, and contaminated water. Even intentional ingestion for suicidal purposes must be considered on account of the possibility of purchasing it online [31]. The rarity of thallium poisoning and the non-specific clinical signs of presentation may cause a delay in treatment and admission. Because intoxication often appears to be analogous to other diseases, ill patients may go unnoticed. If left untreated, thallium poisoning can cause irreversible harm to the digestive and neurological systems, or, in extreme situations, result in paralysis, coma, and death. In this article, we report on a case of unintentional thallium poisoning which had not been correctly identified in two previous hospital centers and had presented to our Center with symptoms compatible with thallium poisoning. In thallium poisoning, the therapeutic goal is to lower the levels of circulating metal as quickly as possible, before it is deposited in the tissues, by means of Prussian Blue (PB). We asked ourselves whether it was correct to start treatment with PB before having toxicological confirmation of the poisoning. For this reason, we undertook a scoping review to assess the relationship between the onset of treatment and the outcome.
# A Reconfigurable SRAM CRP PUF with High Reliability and Randomness
## 5 Measurement Result
### Performance Comparison  
Table 1 presents the comparison results with state-of-the-art strong PUFs. This design achieves a small area of 1.2 \(\times\) 10\({}^{4}\) F\({}^{2}\)/bit and power consumption of 7 pJ/bit. For a fair evaluation, the table includes normalized core area and power consumption metrics. The normalized area is calculated by linearly normalizing to the challenge bit width. The normalized power consumption is calculated by dividing by the minimum gate length of the CMOS process raised to the power of three and the challenge bit width. In Table 1, our proposed PUF consumes 4 times more power than [22] and 3.7 times more power than [32]. However, when considering the same operating frequency of 20 MHz, the proposed PUF's power consumption compared to [22] is nearly equivalent. Ref. [33] uses transistors operating in the subthreshold region, so it consumes 2.06 times less power than the proposed structure. Additionally, the power consumption of the proposed structure is also 2.05 times better than [33]. The area per bit is significantly smaller than [33;34], less than half the size of [22] but larger than [32], as more transistors were used. This design achieves the lowest worst-case BER of 4.86%. The Inter-HD value of 0.4949 is close to the ideal value of 0.5 when compared to most other PUFs except [33;34] and the intra-HD is the most comparable with those of other PUFs. Overall, the proposed PUF exhibits a good balance between the inter-HD value, intra-HD value, and area efficiency. A direct comparison with conventional reconfigurable SRAM CRP PUF demonstrates that our design presents the reliability and randomness, since it incorporates a larger number of transistors. Furthermore, our design achieves a smaller area per bit when compared to the conventional method by adopting a symmetric layout technique.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **This Work** & [22] & [32] & [33] & [34] \\ \hline Technology (nm) & **180** & 65 & 65 & 65 & 130 \\ \hline Bit-Width of & & & & & \\ Challenge (bit) & **32** & 32 & 32 & 60 & 65 \\ \hline Type of PUF & **Reconf. SRAM** & Reconf. SRAM & SRAM & Voltage Array & SCA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with state-of-the-art strong PUFs.  
Figure 8: ACF measured with 120,000-bit of (**a**) Worst case of delay mismatch (**b**) conventional SRAM CRP PUF and (**c**) Proposed SRAM CRP PUF.
## 4 Experiments and Results  
### Training and Validation Performance of the Classifier  
The training and validation performance of both the teacher (EfficientNet-B0) and student (DeiT-Tiny) models were precisely tracked for 50 epochs. The training loss consistently decreased, indicating effective learning, while the validation loss plateaued after approximately 30 epochs, suggesting convergence. Both models exhibited a steady increase in training accuracy, reaching up to 98% towards the final epochs. Similarly, the validation accuracy showed substantial improvement, stabilizing around 99% in the later stages of training. Notably, the teacher model demonstrated a slightly higher convergence speed than the student model, attributed to its more complex architecture.  
In Figure 4, the loss curves illustrate the decline in training and validation loss over the epochs for both the teacher and student models. This indicates that both models effectively minimize classification errors during training. Similarly, the accuracy curve depicts the improvement in training and validation accuracy, highlighting the models' ability to correctly classify chickens and non-chickens with high precisions.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 4 Optimal Path Discovery Phase
### Optimal Path Discovery
#### 4.2.4 Scenario 4  
In this instance, we take into consideration a scenario shown in Figure 4 with the following requirements for the source and destination nodes:* Source node C3: G is the front node.
* Destination node C4: K is the rear node.
* Source and destination nodes travel in opposite directions, away from each other.  
Regarding where the destination and source nodes are located on the road, this scenario is similar to Scenario 3. However, the issue is still difficult to handle because, in this scenario, the source and destination nodes' directions are opposite. Here, assume that there are two possible paths to reach the destination node C4: K from Node C3: G--CH >> C6: CH >> C5: CH >> C4: CH >> K and C3: G >> CH >> C3: CH >> C1: CH >> C4: CH >> S. Again, the traditional greedy protocols will choose Node C2: CH as \(O_{f}\), but as the nodes move, the chosen path will result in more hops than the other path. This is due to the fact that the destination node has left the communication range of the warning message by the time it reaches Node C1: CH. Therefore, a path reconstruction occurs, and the packets are routed on the new path. The situation is more complicated when Node C cannot find a node to forward the message. As a result, our suggested protocol prioritizes Node C6: CH by employing Equation (8). When \(H\) (\(S,D\)) = 0 and the destination and source move apart, the WM is transmitted through the second feasible path.
## 3 Preliminaries
### FCM Clustering Scheme in Routing Layer
Nodes with an FR value lower than or equal to the sender forward the packet after providing an ACK, whereas higher FR nodes discard it, reducing routing inefficiencies. To mitigate redundant transmissions, while a node is waiting to forward a packet or ACK, if it overhears any retransmission of the same data packet that it saved or overhears ACK for the same packet that has a smaller FR value than itself, the packet is instantly discarded by the node. The clustering algorithm is triggered only when node density surpasses a threshold, minimizing computational overhead. Additionally, each node maintains a list of broadcasted packet IDs, ensuring efficient duplicate suppression and optimizing network performance. The overall operation of the proposed OR protocol is structured into two distinct phases:  
1. The setup phase;
2. The data-Forwarding phase. Each of these phases has its step-by-step procedures, which are explained in Figure 4.
###### Abstract  
Clay mineral content and diagenetic processes are vital factors that affect reservoir quality, especially in tight elastic reservoirs, which are crucial for industrial and scientific purposes. The presence of clay minerals poses one of the most significant challenges in evaluating sandstone reservoirs' quality. Even though wireline logs may provide a good indication of the reservoir quality, there have been cases where they have failed. This work focuses on the clay minerals' impact on the porosity and permeability of a clastic reservoir. Typical outcrops from Labuan Island, Brunei-Sabah Basin, were chosen as a case study to investigate the petrophysical and petrographic parameters together with clay mineralogy profiling. The effects of the clays on the petrophysical parameters of the sandstone reservoir were evaluated through air permeability, spectral gamma ray measurements, a petrographic thin section analysis, a visual porosity estimation, and a grain size analysis. Field and petrographic studies revealed that Belait and Temburong formations contain massive, interbedded, laminated, and cross-bedded sandstones. Using an image analysis of the thin sections, porosity values ranged from 7.3% to 23.5%, with different macro and micro porosity distributions. According to the spectral gamma-ray investigation and air permeability, permeability reduction is strongly associated with clay minerals. The microporosity and permeability of the analyzed samples showed a unique pattern influenced by the grain size distribution. It was found that the textures dominated by mud grain size had a more significant impact on the air permeability and visual porosity, with coefficient of determination values of 0.83 and 0.70, respectively. The Belait Formation displayed a higher porosity and permeability compared to the Temburong Formation. This research provides new insight into the potential reservoir of Stage IV (the Belait Formation offshore equivalent) compared to Stage II (the Temburong Formation offshore equivalent).  
reservoir quality; clay minerals; porosity; permeability; Labuan Island
## Appendix A Implementation Specific Details of the Main Radar Blocks
### Receiver Chain Details  
The Receiver first component after the receiver antenna is a 20 dB low-noise amplifier (LNA). The amplified signal is multiplied with the transmitted waveform by a double balanced frequency-mixer. The output of the mixer (IF--10 MHz) goes through a chain of amplifiers with a gain of 64 dB, being terminated with a matched load of 50 \(\Omega\).  
The chain of amplifiers after the IF output brings the signal to a level that better uses the dynamic range of the analog-to-digital-converter (ADC) of the Data Acquisition System (DAS). The maximum output power of the last IF amplifier avoids overcoming the maximum input voltage of the DAS.  
The matched load termination allows sampling the IF signal with a high input impedance DAS. The analog bandwidth of the DAS (17 MHz) acts as a low-pass filter, reducing the noise power of the receiver.
## 3 Results and Discussion
### Statistical Characteristics
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Cases of Wall Surface**} & \(\mathbf{U_{\infty}}\) & \multirow{2}{*}{**F**} & \multirow{2}{*}{**Re\({}_{\tau}\)**} & \(\mathbf{u_{\tau}}\) & \(\mathbf{\tau_{w}}\) & \multirow{2}{*}{\(C_{f}\)} & \(DR\) \\  & (\(\cdot\cdot\cdot\cdot\)s\({}^{-1}\)) & & & & & & (\%) \\ \hline clean water smooth wall & 0.210 & 0 & 435.435 & 0.0094 & 0.08806 & 0.00401 & – \\ clean water riblet-\(0^{\circ}\) wall & 0.211 & 1.2 & 427.107 & 0.0089 & 0.07894 & 0.00356 & 11.20 \\ clean water DL-\(30^{\circ}\) wall & 0.212 & 2.6 & 378.263 & 0.0084 & 0.07032 & 0.00314 & 21.70 \\ clean water CL-\(30^{\circ}\) wall & 0.209 & \(-6.5\) & 655.472 & 0.0139 & 0.19257 & 0.00885 & \(-120.70\) \\ two-phase smooth wall & 0.212 & 0.6 & 730.970 & 0.0092 & 0.08436 & 0.00377 & 5.99 \\ two-phase riblet-\(0^{\circ}\) wall & 0.213 & 1.2 & 681.169 & 0.0088 & 0.07718 & 0.00341 & 14.96 \\ two-phase DL-\(30^{\circ}\) wall & 0.213 & 2.8 & 631.058 & 0.0082 & 0.06701 & 0.00296 & 26.18 \\ two-phase CL-\(30^{\circ}\) wall & 0.214 & \(-5.9\) & 1088.991 & 0.0135 & 0.18165 & 0.00796 & \(-98.50\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Flow parameters over the smooth and riblets surfaces in clean water. to a slower decrease in the intensity of the wake region, resulting in some regions having higher intensity than in the clear water flow field.
## 2 Materials and Methods
### Statistical Analysis  
Descriptive characteristics were calculated, and the results were presented as means and standard deviations (SD). All the force-time data were analyzed using a custom spreadsheet. G*Power software (v3.1.9.7) was used to calculate the statistical power post hoc. The correlation: the point biserial model statistical test was applied, with a coefficient  
Figure 2: Isometric F-T curve representative of a participant. The purple line represents the vGRF of the left leg, the orange line represents the vGRF of the right leg, and the blue line represents the sum of both vGRFs.  
of determination (r2) of 0.69 for the peak force during the IMTP. The alpha error was set at 0.05, and the sample consisted of 21 subjects. The calculations indicated that the statistical power of the analysis was 0.98. The Shapiro-Wilk test was performed to assess data normality. [PERSON]'s test was used to verify the homogeneity of the analyzed variables. ICC (3.1 model), CV, and 95% CIs were calculated to assess the intra- and inter-session reliability of both tests using a custom Excel spreadsheet [35]. Reliability was categorized as follows: ICC \(\leq\) 0.5 indicated poor reliability, values \(\leq\) 0.75 indicated moderate reliability, values \(\leq\) 0.9 indicated good reliability, and values > 0.90 indicated excellent reliability [36]. The acceptable CV threshold was set at \(\leq\)10% [37]. Paired \(t\)-tests with a significance level of \(p\)\(\leq\) 0.05 were used to determine if there were differences in PF, RPF, RFD 0-50; 0-100; 0-150; 0-200; 0-250, Force at 50; 100; 150; 200; 250 ms, and impulse 0-50; 0-100; 0-150; 0-200; 0-250 produced by IMTP and IBSqT across sessions. Hedges' g was used to determine the effect size of group differences in test-retest scores, using [PERSON]'s scale as trivial (g < 0.2), small (0.2 \(\leq\) g < 0.5), moderate (0.5 \(\leq\) g < 0.8), and large (g \(\geq\) 0.8) [38]. The standard error of measurement (SEM) provided an indication of score precision [39] and was used to understand whether differences in measurements were real or due to error [37]. The smallest worthwhile change (SWC) was determined by multiplying the between-subject SD by 0.2 (SWC0.2), which represents a typical small effect, or 0.5 (SWC0.5) for a medium effect [40]. SWC/SEM was calculated to determine whether a test could detect changes greater than the error, with values below 1.0 indicating that it was unlikely for the measure to detect changes larger than the error [41]. The limits of agreement corresponding to 1.96 SD of the bias (LoA) and 95% CI between the test-retest evaluations were determined from Bland-Altman plots. All statistical analyses were performed using IBM SPSS v26 statistical software (SPSS Inc., Chicago, IL, USA) and GraphpadPrism v9.
###### Abstract  
For harvest automation of sweet pepper, image recognition algorithms for differentiating each part of a sweet pepper plant were developed and performances of these algorithms were compared. An imaging system consisting of two cameras and six halogen lamps was built for sweet pepper image acquisition. For image analysis using the normalized difference vegetation index (NDVI), a band-pass filter in the range of 435 to 950 nm with a broad spectrum from visible light to infrared was used. K-means clustering and morphological skeletonization were used to classify sweet pepper parts to which the NDVI was applied. Scale-invariant feature transform (SIFT) and speeded-up robust features (SURFs) were used to figure out local features. Classification performances of a support vector machine (SVM) using the radial basis function kernel and backpropagation (BP) algorithm were compared to classify local SURFs of fruits, nodes, leaves, and suckers. Accuracies of the BP algorithm and the SVM for classifying local features were 95.96 and 63.75%, respectively. When the BP algorithm was used for classification of plant parts, the recognition success rate was 94.44% for fruits, 84.73% for nodes, 69.97% for leaves, and 84.34% for suckers. When CNN was used for classifying plant parts, the recognition success rate was 99.50% for fruits, 87.75% for nodes, 90.50% for leaves, and 87.25% for suckers.  
NDVI; image processing; SURF; SIFT; SVM; BP algorithm; performance; sweet pepper; deep neural network +
Footnote †: journal: Medical Imaging
# An Adaptive Model Predictive Voltage Control for LC-Filtered Voltage Source Inverters
## 2 System Dynamics on an LC-Filtered VSI  
A single-line diagram of the studied three-phase LC-filtered VSI is shown in Figure 1, which consists of a three-phase VSI connected to loads through an LC output filter. Based on Figure 1 and Kirchhoff's Laws, the system equation can be written as:  
\[\left\{\begin{array}{l}L_{f}\frac{d\dot{i_{f}}}{dt}=v_{inv}-v_{o}\\ C_{f}\frac{d\dot{i_{o}}}{dt}=i_{f}-i_{o}\end{array}\right. \tag{1}\]  
where \(v_{inv}\), and \(v_{o}\) are output voltage of the inverter and capacitor voltage, \(i_{f}\) and \(i_{o}\) are the inductor and load currents. \(L_{f}\) and \(C_{f}\) are the filter inductance and capacitance to filter the PWM (pulse-width modulation) harmonics. Therefore, system state-space equation can be written as:  
\[\begin{array}{l}\frac{dx(t)}{dt}=Ax(t)+Bv_{inv}(t)+Di_{o}(t)\\ x=\left[\begin{array}{cc}i_{f}\\ v_{o}\end{array}\right],A=\left[\begin{array}{cc}0&\frac{-1}{L_{f}}\\ \frac{1}{C_{f}}&0\end{array}\right],B=\left[\begin{array}{c}\frac{1}{L_{f}} \\ 0\end{array}\right],D=\left[\begin{array}{c}0\\ \frac{-1}{C_{f}}\end{array}\right].\end{array} \tag{2}\]  
where, \(x\), \(v_{inv}\) and \(i_{o}\) are system states, control and disturbance inputs, and \(A\), \(B\), and \(D\) are the state and input matrices. The practical implementation of MPC is based on the discrete state-space model of the system dynamics. This discrete state-space model can be obtained by discretizing (2) with the sampling period \(T_{S}\):  
\[\left\{\begin{array}{l}x(k+1)=A_{d}x(k)+B_{d}v_{inv}(k)+D_{d}i_{o}(k)\\ A_{d}=e^{AT_{S}}=L^{-1}\Big{\{}(s1-