esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Training, Hyperparameter Optimization, and Evaluation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Package Version** & **Parameter** & **Search Space** \\ \hline \multirow{4}{*}{LR} & & solver & ’newton-cg’, ’lbfgs’, ’lblinear’, ’sag’, ’saga’ \\  & & penalty & \multicolumn{2}{c}{‘none’, ’11’, ‘12’, ‘elasticnet’} \\  & & C & \(1\times 10^{-5}\), \(1\times 10^{-4}\), \(1\times 10^{-3}\), \(1\times 10^{-2}\), \(1\times 10^{-1}\), \(1\), \(10\), \(100\) \\  & & l1\_ratio & 0.1, 0.3, 0.5, 0.7, 0.9 \\  & & class\_weight & None, ’balanced’ \\ \hline \multirow{4}{*}{DT} & & criterion & ’gini’, ’entropy’ \\  & & splitter & ’best’, ’random’ \\  & & max depth & 2, 3, 5, 8, 12, 20, None \\  & & min\_samples\_split & 2, 3, 4, 5, 6, 7, 8 \\  & & min\_samples\_leaf & 1, 5, 10, 20, 30, 40, 50 \\  & & & 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, \\  & & max\_leaf\_nodes & 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, \\  & & max\_features & ’sqqr’, ’log2’, None \\ \hline \multirow{4}{*}{GB} & & min\_samples\_split & 0.1, 0.3, 0.5, 0.7, 0.9, 1 \\  & & min\_samples\_leaf & 0.1, 0.2, 0.3, 0.4, 0.5, 1 \\  & & max\_features & ’auto’, ’sqqr’, ’log2’, None \\  & & max\_leaf\_nodes & 8, 16, 64, 100, None \\  & & learning\_rate & 0.01, 0.05, 0.1, 0.25 \\  & & n\_estimators & 8, 16, 32, 64, 100, 200 \\  & & max\_depth & 2, 3, 5, 8, 12, 20 \\ \hline \multirow{4}{*}{XGB} & & n\_estimators & 35, 50, 65, 80, 100, 115, 130, 150, 300 \\  & & learning\_rate & 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, 0.3 \\  & & max\_depth & 4, 6, 8, 10 \\  & & min\_child\_weight & 1, 4, 6, 8 \\  & & subsample & 0.5, 0.8, 1.0 \\  & & colsample\_bytes & 0.5, 0.8, 1.0 \\  & & gamma & 0, 0.01, 0.25, 0.5, 1 \\  & & scale\_pos\_weight & 1, 4, 7 \\  & & reg\_alpha & 0, 0.001, 0.01, 0.1, 0.5, 1, 2, 5, 10 \\  & & reg\_lambda & 0, 0.001, 0.1, 0.5, 1, 2, 5, 10 \\ \hline \multirow{4}{*}{DNN} & & number hidden layer & 1, 2, 3 \\  & & epochs & 10, 20, 30, 40, 50, 70, 90, 110, 150 \\  & & batch\_size & 1, 16, 32, 64 \\  & & dropout & 0.0, 0.25, 0.5 \\  & & units & 40, 80, 100 \\  & & ‘uniform’, ’lecun\_uniform’, ’normal’, ’zero’, 'glorot\_normal’, ’glorot\_uniform’, ’he\_uniform’, ’he\_uniform’ \\  & & activation & ’softmax’, ’softplus’, ’softplus’, ’relu’, ’tanh’, ‘sigmoid’, ’hard\_sigmoid’, ’linear’ \\  & & kernel\_constraint & 1, 2, 3, 4, 5 \\  & learning\_rate & 0.001, 0.01, 0.1, 0.2, 0.3 \\  & & optimizer & SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optimized parameters and search space. the optimized models on the test hold-out, providing an unbiased assessment using the data that had not been previously seen in both the training and optimization phases. Performance evaluation was conducted using the area under the ROC curve (AUC) as the primary outcome, and precision, recall, and F1 measure as the secondary outcomes.
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 3 Low-Power-Density Rectenna  
The antenna is a crucial component in a rectenna, harvesting propagating RF energy in free space and providing impedance transformation between free space and the transmission line. Properly designing the receiving antenna can also improve rectification efficiency under low-power-density conditions. Designing high-gain and high-efficiency antennas can increase the power delivered to the rectifier; multiband and wideband antennas can broaden the range of available energy sources; dual-polarized and circularly polarized antennas can capture more polarization components; and multi-beam or beam-steerable antennas can extend the energy harvesting range.
## 5 Computational Experiments
### Mathematical Model
Additionally, the summation over \(d^{\prime}\in D\) considers the feasible arrival days, ensuring that any vehicle arriving at \(f^{\prime}\) on day \(d^{\prime}\) must have departed earlier, respecting the required travel time \(TT_{f,f^{\prime}}\). During this interval, the vehicle remains in transit and cannot be allocated to any other trip, preventing scheduling conflicts. The summation over \(f^{\prime}\in F_{f}\) includes all potential destination factories that receive products from \(f\). Equation (8) updates the stock in the CD, and the demand response constraint is Equation (10). Equation (9) determines that all products will have their demands satisfied sometime within the planning horizon, guaranteeing a feasible solution. Equations (10) and (11) define the value of \(Tmax\). Equation (10) determines when each product meets its demand, and Equation (11) indicates when all products meet the demand, i.e., the maximum amount of time to satisfy the demand. Equation (12) defines each factory's initial stock. Equations (13)-(21) define the variables' domain. Equations (15) and (16) represent the capacity constraints in the model, establishing the relationship between production variables and factory capacity. These equations ensure that the quantity of manufactured products does not exceed production capacity, which, in turn, implicitly limits the supply to the DC, reflecting the real constraints of the supply chain. The variables expressed in Equations (19) and (20) are binary, representing discrete decisions related to vehicle allocation for transportation and demand fulfillment on a given day. The variable \(Tmax\) is defined within the domain of non-negative real numbers, as established by constraint (21). However, since the objective function is formulated as a minimization problem and in conjunction with constraint (11), \(T_{\text{max}}\) assumes only integer valuesin the optimal solution. The remaining variables (see Equations (13), (14), (17), and (18)) are continuous because demand is expressed in tons, with a precision of up to four decimal places, ensuring an accurate approximation in the modeling of production, inventory, and transportation.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 6 Simulation Results
### Simulation Environment  
All nodes in the network are randomly distributed in a rectangle area of 100 m \(\times\) 100 m. Each node generates data packets with a fixed length according to a Poisson distribution with the arrival rate \(\lambda\). The default value of \(\lambda\) is 1 packet/s. The recipient of each data packet is randomly selected from the neighbor nodes of the sender. Each node has initial energy of 1 J. The wireless channel is a Rayleigh fading channel and the path loss exponent is 3. All protocols operate in a single rate mode. In order to guarantee that the transmission rates from the sender to its recipient are the same in different protocols,  
Figure 8: Successful data packet transmission after multiple retransmissions.  
Figure 7: Successful data packet transmission after one-time retransmission.  
if the direct transmission is used, the bandwidth utilization is \(R\), and if the cooperative transmission is used, the bandwidth utilization is \(2R\). The default value of \(R\) is 2 bit/s/Hz. In the simulation, the energy consumption of each data packet transmission includes not only the energy to transmit the data packet but also the energy to transmit the corresponding control packets. Let \(L_{\text{PHY}}\), \(L_{\text{MAC}}\), \(L_{\text{CRTS}}\), \(L_{\text{NRTS}}\), \(L_{\text{CCTS}}\), \(L_{\text{HITS}}\), \(L_{\text{ACK}}\), \(L_{\text{NACK}}\), \(L_{\text{OPD}}\) and \(L_{\text{DATA}}\) be the lengths of PHY header, MAC header, CRTS, NRTS, CCTS, HTS, ACK, NACK, OPD and data packets, respectively. Table 1 gives the default parameter settings used in the simulation.  
In WSNs, the traffic load distribution is an important influence factor on network lifetime. Therefore, we investigate the network performance at both uniform traffic load and nonuniform traffic load.
## 3 Materials and Methods
### Metrics and Validation
#### 3.6.3 Average Precision and Mean Average Precision  
From the precision x recall curve, the most common metric for evaluating detection models is extracted, called Average Precision (AP), which refers to the area below the curve of the maximum precision points interpolated at all recall levels, defined mathematically in Equations (4) and (5).  
\[AP=\sum_{n}(R_{n+1}-R_{n})P_{\text{interp}}(R_{n+1}), \tag{4}\]  
where  
Figure 6: YOLOv11 architecture.  
\[P_{\text{interp}}(R_{n+1})=\max_{\tilde{R}:R>R_{n+1}}P(\tilde{R}). \tag{5}\]  
Mean Average Precision (mAP) is a metric used to measure the average precision of object detectors across all classes in a given database. The mAP is the average AP across all classes.
## 3 Results and Discussion
### Effects of PdNPs-PVP-Q on the Cell Viability and Apoptosis of Cancer Colorectal Cells  
A colorectal adenocarcinoma HCT-15 cell line was employed to evaluate the capacity of PdNPs-PVP-Q to inhibit cell proliferation. After 36 h of incubation, PdNPs-PVP and PdNPs-PVP-Q treatments resulted in a significant cell growth inhibition in comparison with the untreated control. Differences between both PdNPs-PVP and PdNPs-PVP-Q treatments were found (\(p<0.05\); Figure 8A). Afterward, the apoptotic percentage of cells exposed to PdNPs-PVP and PdNPs-PVP-Q was evaluated. The exposure of HCT-15 cells to PdNPs-PVP did not result in significant differences in cell death in comparison to the untreated control. In contrast, the incubation of cells with PdNPs-PVP-Q resulted in a slight but significant (\(p<0.05\)) increase in apoptosis (Figure 8B). As a control, we used quercetin (40 uM) alone and observed a considerable increase in cell death, as has been reported [74]. Additionally, as a positive control of cell death, we used cisplatin (50 uM), which induced extensive cell death, as expected. It has been proved that PdNPs generate oxidative stress, induce mitochondrial dysfunction, and induce apoptosis increasing caspase 3 in SKOV3 cells [75]. Additionally, porous palladium transferrin-conjugated nanoparticles have been reported to be effective in chemo-phototherapy with regard to MC-7 human breast adenocarcinoma cell lines [15]. Concave PdNPs have been used with ascorbate for the inhibition of colorectal adenocarcinoma cell viability. The addition of concave PdNPs to the ascorbate promoted dose-dependent PdNP toxicity and increased ascorbate activity [76]. Quercetin has been reported as a very potent antioxidant molecule [77] and as a chemosensitizer and cancer chemotherapy [78]. Quercetin triggers apoptotic mechanisms as a growth control of colorectal cancer [15]. Our data showed that PdNPs-PVP-Q induces a small increase (8%) in apoptosis relative to control. This slight effect may be due to (i) the low effective concentration of quercetin delivered in the cellular cytoplasm, (ii) the effect of temperature in the release of quercetin from PdNPs in the cellular microenvironment, and (iii) the potential agglomeration of PdNPs-PVP-Q in the culture media. Thus, further measurements of PdNPs-PVP-Q and quercetin in cellular compartments are required.  
Figure 7: Quercetin liberation at different pH. (**a**) pH 6.5 and (**b**) pH 7.5.
# Topology and Polarization of Optical Vortex Fields from Atomic Phased Arrays
## 6 Summary  
Let us summarize the obtained results. We considered a circular array of \(N\) atoms excited into a \(P\)-state with a definite magnetic quantum number \(m_{z}\) and analyzed the topological properties of their coherent radiation, provided that it is controlled with a  
Figure 7: Lattices of polarization singularities are formed for a minimal number of emitters, **(a)**\(N=3\) and for **(b)**\(N=6\). Close-up near \(\rho=0\) is shown for **(c)**\(N=3\) and **(d)**\(N=6\). Here, \(l=1\), \(m_{z}=-1\), and \(z=2z_{R}\). The 3D plots are obtained by substituting the expressions (26) into Equation (30).  
phase parameter \(l\). We found that for \(N\to\infty\), the resulting radiation near the array axis is described by a superposition of circularly polarized Bessel beams with different topological charges. Next, we derived a result for the total vector potential of a phased atomic array with \(N\) atoms and a phase parameter \(l\) expressed in terms of an infinite series of Bessel vortices. We obtained the necessary conditions for the least number \(N\) required to generate vortex states with a given topological charge \(l\) for all components of the vector field: \(N\geq 2(l+m_{z})+3\).  
In analyzing the resulting vortex field properties, we applied a formalism of 3D-polarization [22] with a spin-density matrix of the twisted photons defined in terms of eight polarization parameters. We demonstrated that if the spin and orbital angular momentum of the radiation are anti-aligned, then the spatial variation in the radiation's polarization in the plane parallel to the atomic array near the vortex axis is independent of the distance to the source. The presence of a longitudinal component in the field plays a significant role in this behavior. In order to fulfill [PERSON]'s equations, optical vortex beams have to include non-zero longitudinal components [26], with the field ratios showing propagation-independent spatial features. Here, we found similar propagation-invariant polarization features in radiation from atomic arrays.  
The developed formalism can be extended and applied in several areas, from conventional polarized phased antenna arrays to complex geometries of multi-atom Dicke states to the transfer of quantum information between atomic ensembles.  
All authors contributed equally to this research. In addition, A.A. provided funding acquisition and supervision. All authors have read and agreed to the published version of the manuscript.  
We thank the US Army Research Office for support under Grants W911 NF-19-1-0022 and W911 NF-23-1-0085.  
Not applicable.  
Data availability StatementData is available upon request from the authors.  
A.A. thanks [PERSON] for useful discussions and hospitality at IQOQI.  
The authors declare no conflict of interest.
# The Importance of Gram-Negative Rods in Chronic Rhinosinusitis
## 3 Results
### Bacterial Microbiota
\begin{table}
\begin{tabular}{c c c c c} \hline
**Bacteria** & \begin{tabular}{c} **Primary CRS** \\ **(n = 90 Strains)** \\ \end{tabular} & \begin{tabular}{c} **Secondary CRS** \\ **(n = 35 Strains)** \\ \end{tabular} & \
\begin{tabular}{c} **Control** \\ **(n = 73 Strains)** \\ \end{tabular} &
\begin{tabular}{c} **\(p\)-Value * \\ \end{tabular} \\ \hline \multicolumn{5}{c}{Anaerobes (obligate)} \\ \hline _Cutibacterium acnes_ & 2 (2.2\%) & 1 (2.9\%) & 2 (2.7\%) & 0.9690 \\ _Peptostretococcus_ spp. & 1 (1.1\%) & - & 1 (1.4\%) & 0.7942 \\ _Fusobacterium_ spp. & 1 (1.1\%) & - & - & 0.5471 \\ Mixed anaerobic component & 4 (4.4\%) & 2 (5.7\%) & 6 (8.2\%) & 0.6012 \\ of the URT microbiota & & & & \\ \hline \multicolumn{5}{c}{Gram-positive Aerobes} \\ \hline _Staphylococcus epidermidis_ & 33 (36.7\%) & 13 (37.1\%) & 16 (22.0\%) & 0.0931 \\ _Staphylococcus aureus_ & 14 (15.6\%) & 4 (11.4\%) & 6 (8.2\%) & 0.3578 \\ CNS & 8 (8.9\%) & 2 (5.7\%) & 3 (4.1\%) & 0.4605 \\ _Corynebacterium_ spp. & 3 (3.3\%) & - & 4 (5.5\%) & 0.3495 \\ _Streptococcus pneumoniae_ & 1 (1.1\%) & 1 (2.9\%) & - & 0.3775 \\ _Streptococcus_ spp. & 3 (3.3\%) & 2 (5.7\%) & 1 (1.4\%) & 0.4559 \\ _Enterococcus faecalis_ & - & - & 1 (1.4\%) & 0.4229 \\ _Enterococcus_ spp. & 2 (2.2\%) & - & - & 0.2975 \\ \hline \multicolumn{5}{c}{Gram-negative Aerobes} \\ \hline _Escherichia coli_ & 7 (7.8\%) & 4 (11.4\%) & 1 (1.4\%) & 0.0797 \\ _Klebsiella oxytoca_ & 2 (2.2\%) & 1 (2.9\%) & - & 0.3971 \\ _Serratia marcescens_ & 1 (1.1\%) & - & - & 0.5471 \\ _Citrobacter koseri_ & - & 1 (2.9\%) & 3 (4.1\%) & 0.1662 \\ _Citrobacter freundii_ & - & 1 (2.9\%) & - & 0.0963 \\ _Proteins mitrabilis_ & 1 (1.1\%) & - & 1 (1.4\%) & 0.7942 \\ _Enterobacter cloace_ & 2 (2.2\%) & - & - & 0.2975 \\ _Morganella morgani_ & 2 (2.2\%) & - & - & 0.2975 \\ Mixed aerobic component & 3 (3.3\%) & 3 (8.5\%) & 28 (38.4\%) & 0.00000001 \\ of the URT microbiota & & & & \\ \hline \multicolumn{5}{c}{URT-Upper Respiratory Tract, CNS-Coagulase-Negative Staphylococcusocic other than _S. epidermidis._ * \(\chi^{2}\) Pearson} \\ \end{tabular}
\end{table} 
Table 2: Microbiota of patients with CRS. the control group, three strains of _Citrobacter koseri_, one strain of _E. coli_, and one strain of _Proteus mirabilis_ were isolated.
## 3 Materials and Methods
### The Generic Planning Problem (GPP) for the GIS and Its Instances in PDDL
#### 3.2.3 PDDL Problem for an Instance of the Generic Planning Problem (IGPP)
The _at_ grounded predicates describe the fixed relations between the _thingposes_ and the _agents_ or the _agentposes_ (lines 2-3). Further predicates encode the properties of the things (lines 4-7), while special predicates are used to define the process step configurations (lines 8-10). As well as the constants, the initial state of the IGPP also contains the description of a possible scene. The set \(s_{0,scene}\) contains mainly _at_ predicates that are used to describe the location of the _agents_ and _things_ in the environment (lines 12-14 in Listing 9). Lastly, a possible status of the process steps \(s_{0,ps}\) is set up (line 16 in Listing 9). Excerpt from the PDDL problem file for the custom grippers IGPP with the initial state. ```
1;Constants
2(atrobot1_thingpose1 robot1)...
3(atworkbench1_thingpose1 workbench1)...
4(thing_moveablebase)...
5(thing_placeabletool_for_humantoolsbench1)...
6(thing_for_agentbaserobot1)
7(thing_for_agentcorner1 human1)...
8(process_step_connectbasestep1 workbench2_thingpose1)
9(process_step_precedence_type1 basestep1 profile_big1 step1)
10(process_step_precedence_type1 basestep1 profile_big2 step1)...
11;Scene
12(atrobot1 workbench1)
13(atnothingrobot1_thingpose1)
14(atbasepallet2_thingpose1)...
15;Processstepstatus
16(process_step_todobasestep1)
```  
**Listing 9**Excerpt from the PDDL problem file for the custom grippers IGPP with the initial state. While the definition of the initial state \(s_{0}\) is achieved through a long list of grounded predicates, especially because many static properties and relations must be defined, the goals \(g\) are usually declared only with a few grounded predicates. These predicates describe which process steps must be finalized. An example of the list of goals for the custom grippers IGPP is given in Listing 10. ```
1(process_step_doneprofile_big1 step1)
2(process_step_doneprofile_big2 step1)
3(process_step_donecorneristep1)...
```  
**Listing 10**Excerpt from the PDDL problem file for the custom grippers IGPP with the goals. With a corresponding PDDL domain derived as presented in Section 3.2.2, the PDDL formulation of an IGPP for the custom grippers IGIS is complete. This section is concluded with an important remark. Usually, for each IGIS, a main IGPP is formulated. The main IGPP contains an initial state where no process steps have yet been executed and a goals set with all process steps that must be completed at the end. However, during the real execution, new situations occur, when new plans must be generated from new initial states or for new goals. In these cases, IGPs are derived for situations of the considered IGIS. It must be pointed out that the formulations of the situational IGPs differ from the formulation of the main IGPP only in the PDDL problem file. The PDDL domain file remains the same for them all. Due to the fact that a large number of such situational IGPs (e.g., planning problems) are possible, a validation methodology for any main IGPP is introduced.
## 4 Experimental Results and Security Analysis
### Statistical Random Number Tests
#### 4.3.3 Random Test Result under SP800-22
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Longest Runs of Ones} & 0.495722 & 95\% \\ Rank & 0.465132 & 96\% \\ Discrete Fourier Transform & 0.734127 & 96\% \\ Non-Overlapping Templates Matching & 0.695132 & 96\% \\ Overlapping Templates Matching & 0.896535 & 95\% \\ Universal Statistical & 0.648275 & 97\% \\ Linear Complexity & 0.574414 & 99\% \\ Serial & 0.894725 & 98\% \\ Approximate Entropy & 0.245157 & 98\% \\ Cumulative Sums & 0.445712 & 95\% \\ Random Excursions & 0.729454 & 95\% \\ Random Excursions Variant & 0.655182 & 95\% \\
**(c) Improved Compound CML, Combining both the Logistic Map and Tent Map in** & **a Single Stage** & \\ \hline \hline \multirow{2}{*}{**Statistical Tests**} & \multirow{2}{*}{\(p\) Value**} & **Pass Rate under \(10^{7}\)** \\  & & **Bits/Sample** \\ \hline Frequency & 0.524101 & 97\% \\ Block Frequency & 0.801100 & 98\% \\ Runs & 0.721200 & 98\% \\ Longest Runs of Ones & 0.520152 & 98\% \\ Rank & 0.624262 & 99\% \\ Discrete Fourier Transform & 0.524242 & 99\% \\ Non-Overlapping Templates Matching & 0.626452 & 97\% \\ Overlapping Templates Matching & 0.641242 & 99\% \\ Universal Statistical & 0.742565 & 99\% \\ Linear Complexity & 0.805249 & 99\% \\ Serial & 0.712120 & 98\% \\ Approximate Entropy & 0.645206 & 99\% \\ Cumulative Sums & 0.601125 & 97\% \\ Random Excursions & 0.754527 & 97\% \\ Random Excursions Variant & 0.742442 & 97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._proposed stream cipher based on the improved compound CML, combining the logistic map and tent map in an alternate stage. The experimental result shows the pass rate is at least about 97%. We can conclude that the pass rates of the compound systems are better. Table 5 shows a comparison with recent works of the methodology and its applications. The applications are divided into two scopes, i.e., image encryption and stream cipher. The schemes in the applications of image encryption require more complicated computation and increase the computational load. These are not the applications in real time communications. The other applications in stream ciphers need some nonlinear functions to overcome the weakness of the original CML and relative complex transformation between the binary sequence and real number. Our proposed scheme is endowed with a counter to eliminate the weakness of the CML and the transformation between the binary sequence and real number is also easier. It suits to real-time communications in the resource constrained IoT environment.