esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, we evaluated the effects of narrow beam phased antenna arrays over the radio channel metrics, Doppler power spectrum, and coherence time, in a context of 5G operation frequency bands. A proper mathematical model was selected for both metrics, and the best geometric placement of antenna array elements was determined. The results show that the circular antenna arrays provide improvement over both metrics, by considering the geometric placement of antenna elements on radio link metrics. The numerical simulation and results should be essential for an adequate determination of antenna array systems immersed in a context of millimeter-wave (mmWave) frequencies and the spatial radio resource allocation.  
Conceptualization: B.J.S., D.H.C., and E.J.; theoretical analysis and simulations: B.J.S. and E.J.; validated the theoretical analysis and the simulation results:B.J.S., D.H.C., L.F.Y., and M.A.F.; analyzed the results and contributed to writing the manuscript: B.J.S., D.H.C., and L.F.Y. All authors have contributed to reviewing the manuscript. Moreover, all authors have read and agreed to the published version of the manuscript.  
This work was supported by CONACyT under grant no.2016-01-1680.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**Array**} & \multicolumn{2}{c}{**Models**} \\  & & _VaMod_ & _VaMod_ & \\  & & _(\(\mu_{r}=1^{\circ}\))_ & (\(\mu_{r}=90^{\circ}\)) & \\ \hline \multirow{2}{*}{700 MHz} & CA-21 & 170.3 ms & 14.51 ms & \multirow{2}{*}{12.27 ms} \\  & CA-47 & 843.31 ms & 32.66 ms & \\ \hline \multirow{2}{*}{3.5 GHz} & CA-21 & 34.06 ms & 3 ms & \multirow{2}{*}{2.48 ms} \\  & CA-47 & 168.66 ms & 6.53 ms & \\ \hline \multirow{2}{*}{26 GHz} & CA-21 & 4.6 ms & 0.4 ms & \multirow{2}{*}{0.33 ms} \\  & CA-47 & 22.7 ms & 0.88 ms & \\ \hline \multirow{2}{*}{48 GHz} & CA-21 & 2.5 ms & 0.21 ms & \multirow{2}{*}{0.18 ms} \\  & CA-47 & 12.3 ms & 0.47 ms & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coherence time values of VaMod (using CA-21 and CA-47) and IEEE simulated in Figure 8 at different carrier frequencies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Frequencies** & **Parameter** & **Tx / Rx Source** & **Comparison of** \\  & **Examined** & **Studied** & **Specifications** & **Different Sources** \\ \hline \multirow{2}{*}{This work} & 0.7, 3.5, & \multirow{2}{*}{DPS and TC} & Circular antenna array & Planar, circular \\  & 26. 48 GHz & & (full-wave simulated) & and concentric \\ \hline \multirow{2}{*}{[20]} & Not specified & \multirow{2}{*}{DPS} & Directional antenna & Height of the \\  & & & & base station \\ \hline \multirow{2}{*}{[21]} & 5.9 GHz & DS and TC & Not specified & Not specified \\ \hline \multirow{2}{*}{[22]} & 60 GHz & TC & Not specified & No \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between this work and cited works.
## 2 Methods
### TSCD-TSA Dynamic Task Scheduling Algorithm  
Taking into account the differences in the network performance of each task execution node, it has different speeds when performing public remote sensing data collection tasks. In order to realize the prediction of the execution speed of each node task and to provide the basis for the node task execution ability for dynamic task scheduling, TSCD-TSA, combining a node network transmission speed prediction algorithm and a task scheduling algorithm, is proposed. The principle of TSCD-TSA is as follows: Firstly, the BP neural network algorithm is used to assist the timer in dynamically predicting the network transmission capacity of the node, and then the prediction results are used in the dynamic FCFS_PSOalgorithm, dynamic SJF_PSO, dynamic MAX_MAX_PSO, and dynamic PSO algorithms to achieve dynamic task scheduling.  
The TSCD-TSA dynamic task scheduling algorithm has the following rules during execution:  
* The update frequency of the BP neural network algorithm and dynamic task scheduling algorithm needs to be consistent.
* The BP neural network algorithm is used to adopt a data incremental prediction strategy when predicting; that is, the training data of the algorithm are always taken from the dataset within the most recent period of time to avoid the excessively long training time of the algorithm when the historical collection of data of the node is too large.
* When the dynamic task scheduling algorithm is executed, the scheduling result is updated, and tasks are only reallocated if they have been allocated but the node has not yet started execution. (That is, the result of dynamic task scheduling is the dynamic reallocation of those tasks that have been pre-allocated but not executed.)
# A Monopole UWB Antenna for WIFI 7/Bluetooth and Satellite Communication
## 4 Surface Current Distributions  
Figure 6 shows the surface current distribution of the antenna in resonant modes. It can be seen from Figure 6a,c that the surface currents at 2.4 GHz and 6 GHz are uniformly distributed, indicating that the antenna has good radiation characteristics at this frequency point. The etched semi-circular slot resonates with the added rectangular patch at 5.1 GHz, creating a current distribution at the junction of the semi-circular and rectangular, with some currents also distributed at the edges of the feeder. At 10 GHz and 13.2 GHz, the currents are mainly distributed near the U slot and the inner L patch of the feed line, from which it can be inferred that slotting on the feed line can generate resonance and adding L patches on both sides of the feed line can improve the impedance-matching performance of the antenna.
## 4 Results  
As previously discussed in Section 3.2, the data collected from 48 hospital buildings and healthcare facilities were filtered and screened for similar age groups and condition levels. Discontinued and replaced systems' records were removed from the database as they can possibly impose misleading decisions. Upon filtering, a total of 112 systems were identified as having instances representing their respective installation date, first inspection condition level, second inspection condition level along with the years they were inspected in. In total, 90 of the records were used for the model training process, while the rest of the data collected were used for further verification and model performance validation testing.  
The percentages of the systems were calculated to extract the initial condition and final condition vectors as per the predefined methodology. The systems' age and condition levels were fuzzified asformerly explained and the deterioration rates were identified accordingly. These deterioration rates were fed into the Markov model as initial transition probabilities from higher to lower condition states to be further optimized as per the data collected for calibration purposes. Separate matrices were initiated for every age group available for the oxygen gas systems to allow for a distinction between their behavioral changes demonstrated by the transition probabilities of the system condition levels from each initial condition level to the next lower condition.
## 3 Results
### Effect of the BSA Step on the Morphology and Conductivity of ENMHs  
To evaluate the impact of the BSA step on Cu deposition, surface and cross-sectional SEM/EDS analyses were conducted for both ENMH and ENMH(-BSA step) specimens. In the top-view SEM images, the ENMH exhibited a densely packed and uniformly distributed Cu layer on the nanofiber surface (Figure 2a,a'). In contrast, the ENMH(-BSA step) displayed sparse and discontinuous Cu deposition, indicating incomplete surface metallization (Figure 2b,b').  
Cross-sectional analysis focused on the central region of the heater to assess the depth and uniformity of Cu penetration. In the ENMH, strong and continuous Cu signals were observed throughout the cross-section, confirming that the plating layer penetrated deeply and grew uniformly within the nanofiber matrix (Figure 2c,c'). Conversely, the ENMH(-BSA step) showed weak and uneven Cu signals in the same region, suggesting limited infiltration of the Cu layer (Figure 2d,d').  
Electrical properties were compared by measuring sheet resistance over a plating time range of 9-13 min (Figure 2e). The ENMH consistently exhibited low sheet resistance across this range, with a minimum value of 1.895 \(\pm\) 0.931 \(\Omega\)/sq at 11 min. In contrast, the ENMH(-BSA step) showed significantly higher resistance throughout the same period. Its lowest value, 328.809 \(\pm\) 268.666 \(\Omega\)/sq at 13 min, was approximately 13 times greater than that of the ENMH at the corresponding time point (25.255 \(\pm\) 21.680 \(\Omega\)/sq). Moreover, the ENMH(-BSA step) displayed large standard deviations across time points, indicating poor reproducibility.  
To assess the onset of electrical instability due to delamination or overgrowth of the Cu layer, sheet resistance was further monitored over an extended plating duration (Figure 2f). The ENMH showed its lowest resistance at 11 min (1.895 \(\pm\) 0.931 \(\Omega\)/sq), followed by a sharp increase, indicating possible delamination or structural irregularities from 12 min onward. This was accompanied by increased standard deviation, reflecting decreased uniformity in conductivity. In contrast, the ENMH(-BSA step) reached its minimum resistance at 25 min (127.390 \(\pm\) 203.106 \(\Omega\)/sq), approximately 67 times higher than the global minimum of the  
Figure 2: Morphological and electrical characteristics of Cu-deposited PVDF nanofiber webs. (**a,b**) Surface SEM images of ENMH and ENMH(\(-\)BSA step), respectively. (**a’,b’**) EDS mapping images corresponding to (**a,b**). (**c,d**) Cross-sectional SEM images of ENMH and ENMH(\(-\)BSA step), respectively. (**c’,d’**) EDS mapping images of the cross-sections shown in (**c,d**). (**e**) Comparison of electrical conductivity between ENMH and ENMH(\(-\)BSA step) as a function of electroless plating time. (**f**) Sheet resistance of (**Left**) ENMH and (**Right**) ENMH(\(-\)BSA step) as a function of electroless plating time. Error bars represent standard deviation (\(n\geq 5\)).  
ENMH. After 30 min, both resistance and standard deviation rose sharply, indicating Cu layer degradation and severe structural instability.
## 5 Results, and Discussion
### Results of Training and Validation  
#### 5.1.1 Evaluation of Underfitting and Overfitting  
The training and validation loss graphs for three YOLOv10 models (YOLOv10-ViT, YOLOv10-Swin Transformer, and YOLOv10-PVT) trained on the dataset are shown in Figure 8. All three models exhibited a consistent reduction in both training and validation losses over successive iterations, despite occasional fluctuations between epochs rather than a perfectly smooth decline. This pattern indicates that the models effectively learned from the training data, increasing their accuracy on the validation set, and converging toward the optimal loss function value.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Purpose**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Images** \\ \end{tabular} } & \multicolumn{6}{c}{**Class**} \\ \cline{3-8}  & & **Loader** & **Dump Truck** & **Excavator** & **Remicon** & **Crane** & **Total** \\ \hline Training & 2840 & 665 & 1937 & 2243 & 957 & 403 & 6205 \\ Augmentation & 17,040 & 3990 & 11,622 & 13,458 & 5742 & 2418 & 37,230 \\ Validation & 946 & 221 & 445 & 832 & 419 & 143 & 2060 \\ Test & 946 & 236 & 511 & 815 & 420 & 122 & 2104 \\ Total & 21,772 & 5112 & 14,515 & 17,348 & 7538 & 3086 & 47,599 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed distribution of datasets for construction equipment.  
Figure 8: Training and validation loss graphs.  
The steady decrease in training loss across iterations suggests that underfitting was not an issue. In addition, the concurrent decrease in and close alignment of training and validation losses imply that overfitting was unlikely. As a result, no models needed to be excluded on the basis of underfitting or overfitting concerns.
## 4 Game Theoretic Client Selection Model
### System Setup and Objective  
Let \(\mathcal{C}=\{1,2,\ldots,N\}\) denote the set of clients in a FL system. In each communication round \(t\), every client \(i\in\mathcal{C}\) chooses an action:  
* \(a_{i}^{t}=1\): participate in training.
* \(a_{i}^{t}=0\): skip training.  
Each client is characterized by the following parameters:  
* \(A_{i}^{t}\in[0,1]\): current local model accuracy.
* \(B_{i}^{t}\in[0,1]\): normalized battery level.
* \(P_{i}^{t}\in[0,1]\): privacy preference (higher values indicate stronger preferences).  
Figure 1: System architecture of the proposed RBPS using NE in FL environments.  
The global objective is to select a subset \(\mathcal{S}_{i}\subset\mathcal{C}\) such that the overall system utility is maximized while considering accuracy, battery sustainability, and privacy preservation. As each client acts selfishly, a game-theoretic formulation is essential.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 7 Characteristics of the Developed System
### Long Range Communication (433 MHz UHF Frequency)  
A low power transceiver operating at 433 MHz frequency is suggested for long range communication activities like node localization, synchronization and handshaking. For all these activities, we don't need high bandwidth, as only a few bits of data are needed. Let us assume a small radiated power (100 \(\mu\)W) is considered for transmission:  
\[\mathrm{PW}=100\ \mu\mathrm{W}=10\ log_{10}\ (\mathrm{PW\ mW})\mathrm{dBm}=10\ log_{10}(0.1) \mathrm{dBm}=-10\mathrm{dBm} \tag{15}\]  
Masking factor (by objects like vegetation or other) for UHF long distance is considered very small \(MF=5\) dB.  
If receiver sensitivity of a UAV is \(\xi=-130\) dBm and a signal to noise ratio necessary to demodulate is \(SNR=15\) dB.  
The maximal path loss constraint is calculated as:  
\[M_{\mathrm{PL}}=\mathrm{PW}-\ \xi-SNR-MF=-10+130-15-5=100\ \mathrm{dB}\]  
Based on Equation (14), we can calculate the maximum distance covered by UHF communication:  
\[20\ log_{10}(D_{1})=100-32.45-20\log(F_{1})=100-32.45-52.7=14.85\]  
\[D_{1}=10^{0.7425}\cong 5.527\ \mathrm{Km}\]  
The maximum range between the UAV and the cluster head node is \(D_{1}\ =\ 5.527\ \mathrm{Km}\).  
In our developed system, we don't need to communicate over a long distance of 5 Km. If we are considering a UAV that is flying at a height of 300 m and it wants to activate nodes in 500 m area then only 0.825 \(\mu\)W of power is required by the UAV to transmit the \(F_{1}\) signal to sensor nodes to communicate.
## 3 Method
### Instruction Preprocessor  
#### 3.1.1.1. Instruction Normalization  
The process of disassembling binary codes inherently generates a diverse set of tokens due to the inclusion of numerous memory addresses, immediate values, and address offsets. This diversity significantly exacerbates the out-of-vocabulary (OOV) problem, complicating the model's learning process. As shown in Figure 2a, the original assembly code contains a vast array of tokens that represent memory addresses (e.g., 'loc_61C9'), immediate values (e.g., '0'), or address offsets (e.g., '0B8h'). The exact values of these tokens carry limited semantic information. But their roles are semantically rich. To mitigate this issue, our method implements instruction normalization rules, as detailed in Table 1. These specific rules are designed to reduce the complexity of the code and improve the learnability of the code semantics by the model:  
* Immediate values are replaced with the token 'immv'.
* Memory addresses are replaced with the token 'addr'.
* Address offsets are replaced with the token 'offs'.
* Function names are replaced with the token 'func'.  
The above normalization process produces a normalized instruction corpus, as shown in Figure 2b. This normalization helps to homogenize the input data, reducing the number of unique tokens the model must handle and maximizing the retention of semantic information.  
Figure 1: Architecture of the GMN+ model.  
Figure 2: An example of instruction normalization and instruction type extraction. (**a**) Original assembly instructions; (**b**) Normalized instructions; (**c**) Instruction types
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 3 Statistical Model for the DAP
### DAP Relationship between Neighbors  
Consider any network in which each node routes traffic based on deadline-aware routing tables, whose input is the remaining TTD of the packet to be forwarded and output is the next hop neighbor. Consider representing these routing tables by functions \(h_{u}(\tau)\), which return the neighbor that node \(u\) chooses as next hop when the remaining TTD of a packet is \(\tau\). This representation encompasses many common cases. One particular case is when the forwarding node is the same neighbor regardless of the remaining TTD, such as in routing by the minimum hop count [33,34].  
Assuming that the routing tables of all nodes in the network have been configured to a known and valid state, denoted as \(\bar{I}_{u}(\tau)\) for any node \(u\), we aim to determine the DAP for traffic originating from or passing through node \(u\). To achieve this, assume that node \(u\) possesses the following statistics for each of its neighbors \(v_{i}\):  
1. The PDF \(f_{uv_{i}}(\tau)\) of the link delay from \(u\) to \(v_{i}\).
2. The DAP-CDF \(\bar{F}_{v_{i}}(\tau)\) of \(v_{i}\) corresponding to the routing tables set up in the network.  
Then, the DAP-CDF seen from \(u\) when forwarding a packet via neighbor \(v_{i}\) is given by the convolution of the above two probability functions:  
\[\bar{F}_{u\to v_{i}}(\tau)=\bar{F}_{v_{i}}(\tau)*f_{uv_{i}}(\tau). \tag{1}\]  
In the above equation, \(u\to v_{i}\) denotes \"forwarding from \(u\) via \(v_{i}\)\", and \(*\) denotes the convolution operation. Given the routing table \(\bar{I}_{u}(\tau)\) of node \(u\), the DAP-CDF of node \(u\) for a remaining TTD \(\tau\) satisfies the following equation:  
\[\bar{F}_{u}(\tau)=\bar{F}_{u\to\bar{I}_{u}(\tau)}(\tau). \tag{2}\]  
In the next section, we provide an algorithmic description of how the DAP-CDFs \(\bar{F}_{u}(\tau)\) can be determined in a distributed fashion for the entire network utilizing the insights discussed above.
## 3 Results and Discussion
### Thermogravimetric Analysis  
Figure 14 shows TG analysis results. As the CNT content increased, the weight change caused by TG also increased. This appears to be because the addition of CNTs accelerated early age hydration and thereby increased hydrates, as can be seen from Figures 7 and 8. In the graph, the weight began to change after 50 \({}^{\circ}\)C, and the weight change increased as the CNT content increased. The decomposition of C-S-H, a main hydrate of cement, occurs between 50 and 200 \({}^{\circ}\)C, and it appears that the generation of C-S-H was accelerated. Portlandite, another main hydrate of OPC, decomposes between 400 and 580 \({}^{\circ}\)C. As its contents were 13.05, 12.30, 12.00, and 12.11% in CRC00, CRC01, CRC03, and CRC05, respectively, the effect of CNT addition on it was found to be insignificant. [PERSON] et al. [57] also reported that decomposition increased at temperatures below 200 \({}^{\circ}\)C as the addition of CNT increased. CNTs accelerate the early age hydration of OPC, but they appear not to have an impact large enough to rapidly increase hydration products.
## 2 Materials and Methods
### Sensory Evaluation  
Triangle tests [33] were carried out to determine whether plain consumers could detect any difference between the CTRL and VE 200 or CTRL and EcoE 200 dietary groups. The highest concentration of antioxidant was selected because it was thought that any effect on the sensory response would be more discernible. Moreover, in this way, at each detection time, it was possible to halve the number of consumers required to implement a \"proper\" triangle test, i.e., using a brand-new set of consumers with no previous experience with rabbit meat supplemented with antioxidants. In detail, on six distinct occasions (at 15, 30, 60, 120 and 240 days from the date of pouch preparation), six different groups of 18-20 consumers, as detailed in Section 3.3, all regular consumers of rabbit meat and all volunteers, convened in three separate shifts in a rather large room. That room was both olfactorily and visually neutral and temporarily arranged with 6-7 individual booths kept in dim light. Each panel was composed of students and staff at the Department of Veterinary Medical Sciences - DIMEVET (overall gender ratio, male proportion = 0.474; age ranging from 24 to 67 years). Each consumer received a triangle test [32] arranged on a separate tray, prepared with meat patties presenting either the CTRL vs. VE200, or the CTRL vs. EcoE200 dietary groups to be compared, as detailed below.  
Ground meat from each dietary group had been prepared as 30 g patties, each of which was put into an uncovered glass baby food jar and microwaved at 750 W (cooking time = 120 + 120 + 60 s) in a 2450 MHz, 1000 W variable power oven Mod. MT 243/486, (Whirlpool Europe, S.r.l., Comerio, Italy). Each jar had been covered with its own lid to keep its contents as warm as possible and then randomly assigned a three-digit number. Each triangle test consisted of three jars (the contents of two of which were identical while the third was different) placed in a line on the same tray. The panellists were asked to identify the odd sample, having been previously instructed to cleanse their palate between sampleswith the soft part of sliced unsalted Tuscan bread and still water at room temperature. In addition, as suggested by the relevant International Organization for Standardization (ISO) norm [33], the consumers were asked to briefly comment as to why their choice of the odd sample had been made.