esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In conclusion, in PDE, NGALds values were coherent with the quantitative NGAL performed by the central laboratory and with the biomarkers implicated in the diagnosis of peritonitis (specifically WBC in PDE). Finally, it was an accessible method with real-time results. NGALds could be an extra novel biomarker of peritonitis, helpful at the patient's bedside or when/if the laboratory-based NGAL test is not available.  
In the future, we can speculate that this point-of-care test could be integrated into a remote monitoring program, improving medical outcomes in PD patients, thereby lowering emergency visits and hospitalizations [20].  
The NGALds might be an accurate triage tool at the bedside of the patient, shortening the length of diagnosis and distance between the patient at home and the clinical staff in the hospital.  
G.M.V.: conception of the study, drafting of the article, and laboratory analysis, S.M.M.: conception of the study, interpretation of data, and drafting of the article, M.M.: collection of data and drafting of the article, E.C. and S.L.: collection of data and statistical analysis, D.M.: statistical analysis, I.T.: management of patients, V.C. and M.D.C. and M.C.: laboratory analysis, F.K.M.: final approval of the draft, A.G.: management of patients, D.G.: conception of the study, C.R. and M.Z.: providing intellectual content of critical importance to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. NGALds kits were donated by BioPorto Diagnostics. BioPorto Diagnostics did not participate in the protocol development, analysis, or interpretation of the results.  
All procedures were in accordance with the Helsinki Declaration. The protocol and consent form were approved by the Ethics Committee of San BortoloHospital (Code 08/2014). All patients were informed about the experimental protocol, and the objectives of the study, and they all gave written informed consent. None of the patients denied the written consent.  
All patients were informed about the experimental protocol and the objectives of the study before providing informed consent and biological samples.  
Datasets may be requested from email to the correspondent author.  
We express our gratefulness to the medical, nursing, and secretarial staff from the Nephrology Department, St. Bortolo Hospital, Vicenza. We thank \"Associazione Amici del Rene Vicenza (A.A.R.VI) ONLUS\".  
The authors declare that there is no conflict of interest. [PERSON] in the last three years has been consultant, medical advisor or part of the speaker bureau receiving fees from the following companies: Asahi Medical, Aferetica, Baxter, B.Braun, Biomerieux, Bioporto, Cytosorbents, ESTOR, Fresenius Medical Care, GE Healthcare, Kaneka, Medica, Medtronic-Bellco, Nipro, Spectral, Toray, Jafron.
# Current Status, Issues and Future Prospects of Personalized Medicine for Each Disease
## 2 Rheumatid Arthritis
### Challenges for Personalized Medicine  
Although the efficacy of biologics in RA is higher than that of conventional drugs [27; 28], the response to treatment differs from patient to patient, as not all patients achieved remission during induction therapy. Even if remission induction is achieved it is difficult for all patients to maintain remission. In addition, only a limited number of patients can maintain remission even after maintaining remission followed by drug withdrawal [29; 30].  
There are many factors involved in immunity and inflammation, such as numerous immunocompetent cells and molecules including various cytokines, and no clear direction for personalized medicine has been determined at this point. In the case of biologics, there have been several reports investigating the genetic association of therapeutic drug effects, but no genetic mutations or polymorphisms have been found to provide a clear indicator. For TNF\(\alpha\) inhibitors, studies examining genetic biomarkers have not found any indicator markers [31; 32].  
Some degree of genetic background has been identified for RA, and it is considered to be an other-factorial disease in which the interaction of multiple genetic and environmental factors leads to the development of the disease. Of the disease susceptibility genes identified to date, HLA is the most reliably associated across populations, and althoughthe association between the shared epitope of HLA-DRB1 and the disease susceptibility, severity, and clinical form of RA has been established, the molecular mechanisms remain to be elucidated [33]. Furthermore, there are many reports that HLA-DRB1 correlates with ACPA-positive patients and it is desired to be used as a clinical biomarker [34].  
According to reports of a GWAS meta-analysis on rheumatic diseases, most of the genetic polymorphisms associated with RA are mutations occurring in molecules related to the targets of existing rheumatic drugs and their pathways, and meta-analysis is expected to be a useful form of analysis in terms of developing new drugs and identifying new biomarkers for the treatment of rheumatic diseases [35; 36].
## 3 Model Evaluation
### Hyperparameter Optimization  
In this work, the HDP-HMM hyperparameters (\(\alpha\), \(\gamma\), \(s\)) were optimized using a global optimization technique, i.e., Bayesian optimization. The optimization predetermines the hyperparameter range before model training. The log-likelihood function is considered as the convergence metric. The Mann-Kendall entropy [37] of log-likelihood at each iteration is calculated as the objective function, as given in Equations (20) and (21). The Gamma distribution is chosen as the prior to \(\alpha\) (lower-level DRP concentration parameter) and \(\gamma\) (upper-level DRP concentration parameter). The sticky parameter, \(s\), has a significant impact on the model convergence time and convergence effect. The objective function and Mann-Kendall entropy are expressed as  
\[\text{argmax}f(\alpha,\gamma,\ s) \tag{20}\]  
Figure 4: State-space diagram representation of the HDP-HMM with the DRP emissions.  
\[f(\alpha,\gamma,s)=\sum_{i=1}^{d}\sum_{j=1,j>i}^{d}(t_{j}-t_{i})*\frac{sgn(L_{j}-L_{ i})}{C} \tag{21}\]  
\[C = d*\frac{d-1}{2}\] \[sgn(x) = \left\{\begin{array}{c}-1,x<0\\ 0,\ x=0\\ 1,\ x\geq 0\end{array}\right. \tag{22}\]  
where \(d\) is the log-likelihood length.  
The hyperparameters are then used to train the HDP-HMM on the degradation dataset. Here, we consider a multivariate Gaussian distribution for the emission distributions and an individual Gaussian distribution for the prior over the mean and the variance parameter. The weak limit parameter, \(L\), i.e., the state truncation level, is set to 20. The hyperparameter search range is set to (1,10), (1,10), and (1,50) for \(\alpha\), \(\beta\), and \(\gamma\), respectively, with 300 iterations, and the log-likelihood convergence is shown in Figure 5.
# Leaky Wave Generation Through a Phased-Patch Array
## 5 Conclusions  
This research demonstrates the feasibility of generating a field using a patch array that emulates the behaviour of a leaky-wave antenna (LWA). This finding allowed us to also propose an alternative approach to antenna design for hyperthermia treatment, with a prototype that produces a field that can be approximated with an inhomogeneous wave using a small phased-array antenna. The phased array obtained through the sampling method demonstrated a few challenges, especially in the application at 2.4 GHz, where we needed to control the coupling between the patches, which partially impacted the quality of the approximation to the LWA. This technique also demonstrated advantages, such as enhanced flexibility, that may allow easier adaptation for the amplitudes of the phase and attenuation vectors to specific tissues, and the elimination of both the need to re-use power at the end of the LWA to enhance antenna efficiency and the need to suppress unwanted modes. These advantages would come at the cost of a more challenging implementation, as an array is inherently more complex than an LWA structure. Additionally, our study highlighted significant differences in the scanning behaviour of the leaky-wave and phased-array antennas. Further research is required, to determine whether this aspect of an LWA can also be replicated with a patch array. Future research may focus on approaching the open challenges highlighted in this paper, to improve the design of this promising type of structure.  
Conceptualisation, A.C., P.S., M.D.M., and F.F.; methodology, A.C. and P.S.; software and simulation, A.C.; validation, M.D.M. and F.F.; formal analysis, P.S. and A.C.; data curation, A.C.; writing--original draft preparation, A.C. and P.S.; writing--review and editing, A.C., P.S., M.D.M., and F.F.; supervision, M.D.M., and F.F. All authors have read and agreed to the published version of the manuscript.
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 4 Multi-User Detection of TTMMA via Message-Passing
### Tone-Space Expansion  
Suppose that the tone-paths of two UEs are merged at a certain node and separated in the next step, as shown in Figure 6a. In this case, four tone-paths should be taken account of in the receiving UE, as shown in (_a-c-d_), (_a-c-e_), (_b-c-d_) and (_b-c-e_). The number of candidate codewords increases exponentially as a separation occurs after a merger, which greatly affects the demodulation complexity.  
We use a method called tone-space expansion in order to reduce the demodulation complexity. When two or more tone-paths overlap, the message-passing process can detect the values are superposed at each position. Therefore, by separating overlapping symbols and running a message-passing process on the next symbol, it is possible to prevent an increase in the number of candidate codewords due to the separation. For example, if the values at \(a\) and \(b\) of the \((k-1)\)-th symbol are superposed on the \(k\)-th symbol \(c\) as shown in Figure 6b, they are separated into \(c_{1}\) and \(c_{2}\), respectively. Then, we connect \(c_{1}\) and \(c_{2}\) to the \((k+1)\)-th symbol, similar to the existing \(c\), and perform the next message-passing. In other words, the tone-space expansion method adjusts the trellis diagram when superposition is checked during message-passing.  
The tone-space expansion is very simple, but greatly reduces the demodulation complexity and improves performance. As shown in Figure 6c, the tone-space expansion can be used to distinguish the tone-path, even if separation occurs after superposition, so that the number of candidate codewords does not increase. Tone-space expansion also improves demodulation performance by decomposing the complex-path, which is two or more tone-transitions in which superposition and separation occur at the same time.  
Figure 6: (**a**) Example of merger and separation occurrence of the tone-path; (**b**) Tone-space expansion with merged paths; (**c**) Performing message-passing and routing according to tone-space expansion.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 4 Results and Discussion
### Discussion
#### 4.3.4 BiLSTM Model  
Figure 10 shows that the accuracy achieved with BiLSTM at 20 epochs in the training set is (98.47%), while in the validation set, it is (99.32%). This increase in accuracy is accompanied by a steady decrease in loss, indicating a continuous improvement in the model's ability to fit the data. These results suggest successful training, with the model achieving high accuracy and low loss on both datasets.  
The F1-score showed varying performance across the different classes, with values ranging from (77.00%) to (85.00%). Classes 0 and 4 showed the highest F1-score, with (84.00%) and (85.00%), respectively, while classes 1 and 2 had the lowest F1-score, with (80.00%) and (77.00%), respectively. The precision by class also varied, ranging from  
Figure 9: LSTM accuracy.  
(91.00%) to (99.00%). Class 0 had the highest precision (99.00%), followed by classes 3 and 2. Class 1 had the lowest precision (93.00%). As for the recall per class, the values varied between (64.00%) and (80.00%). Class 4 had the highest recall (80.00%), while class 2 had the lowest recall (64.00%). These metrics show that the model has generally acceptable performance, although there may be room for improvement in some specific classes, especially in classes 1 and 2, where the F1-score was lower.  
The total model training time was 646 s. During this process, (48.00%) of the GPU was utilized, indicating a considerable use of available hardware resources to accelerate model training; this may result in a faster and more efficient training process than using only the CPU.
## 7 Performance Evaluation
### Simulation Results and Discussions
#### 7.3.1 Gps, DRR, and DRRF  
We show system throughput, percentage of overhead, and fairness index of GPS, DRR, and DRRF. The results show that the throughput is similar to that obtained by our numerical analysis (GPS and DRRF). Note that the actual throughput varies because of the variation of the actual number of burst, Downlink Channel Descriptor (DCD), Uplink Channel Descriptor (UCD), and other management messages being transmitted as well.  
For the first scenario, since the number of slots is derived from the queue length, and its MCS level is used, the results show that all three algorithms can achieve perfect _throughput fairness_ (Jain Fairness Index is 1.) The average throughput of GPS and DRRF is around 1.78 Mbps. For DRR, the average throughput is around 1.47 Mbps due to the frame underutilization.  
In the second scenario (See also Table 6), the overhead _versus_ system utilization, since DRR only sends a full packet; the percentage of the overhead is the lowest (0.37%). In other words, there are no fragmented packets. Table 6 shows that the overhead of GPS is the highest because GPS has no consideration of the packet size (an allocated portion of the packet is transmitted to each MS in each frame.) The overhead of DRRF is close to that of DRR because it favors full packet transmission with fragmentation allowed.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **System Throughput (kbps)** & **Percentage of Overhead** & **Jain Fairness Index** \\ \hline GPS & 4,658 & 4.62 & 1.00 \\ DRR & 2,418 & 0.37 & 1.00 \\ DRRF & 4,651 & 0.71 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 6: System Throughput _versus_ Percentage of Overheads [10].  
In terms of system throughput, since the fragmentation is not allowed for DRR, the system throughput of DRR is the lowest, _i.e._, 2.42 Mbps _versus_ 4.65 Mbps for GPS and DRRF. To sum up, _DRRF provides higher system throughput than DRR, and has less overhead than GPS._
## 4 Evaluation
### Error Analysis
#### 4.4.1 Incomplete Matches  
We observed that the occurrence metrics increase when we allow for partially incomplete patterns to be discovered (hallucination), however, the exact \(F_{1}\) scores do not always increase. After observing the pattern occurrences which do not contribute to the rise in \(F_{1}\) score, we discovered that these patterns do not completely match the reference annotations, as shown in Figure 3.  
The difference between a reference annotation and a model's proposed pattern usually presents itself at the edges of an occurrence, where the model assumes that one or more preceding or succeeding events belong to the pattern. These events frequently occur at the same locations (relative to the pattern), with similar time and pitch offsets. Thus, the model adds these events to the pattern occurrence, causing mismatch with the reference annotation. Such errors could be resolved by incorporating theoretical rules governing the beginnings and endings of patterns, e.g., gap rule ([46], p. 68) into the pattern selection algorithm.
# A Compact Circular Rectenna for RF-Energy Harvesting at ISM Band
## 3 Rectenna Simulation Results and Discussion
### Rectifier Results and Discussion  
To assess the performance of the rectifying antenna, the RF-DC transformation efficiency is one of the most vital parameters, including antenna performance, power loss in  
Figure 11: (**a**) Photo of the measurement setup for CMCP antenna (**b**) S\({}_{11}\) comparison of the measured results with the simulated results for the fabricated antenna.  
Figure 10: Two-dimensional polar plot radiation patterns at 2.45 GHz (**a**) E-plane (**b**) H-plane.  
the rectifying component, and impedance pairing between the RF source and the rectifier. RF AC-DC conversion efficiency is the ratio of the load power delivered to the amount of rectifier input power. The rectifier network with IMN is optimized for the resonating frequency of 2.45 GHz. The RF-DC transformation efficiency (power conversion efficiency (PCE)) can be acquired by using Equation (3).  
\[\eta=\frac{P_{DCout}}{P_{ACin}}\times 100 \tag{3}\]  
\[P_{DCout}=\frac{\left(V_{DCout}\right)^{2}}{R_{L}} \tag{4}\]  
where \(P_{DCout}\) represents the output load DC power, \(P_{ACin}\) represents the rectifier input power, \(V_{DCout}\) represents the DC output load voltage, and \(R_{L}\) represents the resistance load.  
Simulated plots are obtained for the rectifier PCE, output DC voltage, and output DC power versus RF-input power sweep from \(-30\) dBm to \(20\) dBm with an optimum load resistance of \(10\) K\(\Omega\). The plots are depicted in Figure 12.  
From Figure 12a, it is found that in the absence of IMN the rectifier, efficiency is very low, i.e., \(8\%\) at \(0\) dBm power input, and in the presence of IMN, the rectifier efficiency is drastically improved, i.e., \(60.6\%\) at \(0\) dBm power input. The maximum efficiency of \(63\%\) is obtained at \(-2.4\) dBm RF input power. The conversion efficiency slowly increases with a rise in the RF input power and reaches its peak before gradually reducing due to the nonlinear nature of the LC-IMN which was designed to operate at a resonating frequency according to the rectifying component (diode). From Figure 12b, it is noted that the output DC voltage is \(2.46\) V, and the output DC power is \(0.6\) mW at an input RF power of \(0\) dBm. The highest output DC voltage and power observed are \(3.65\) V and \(1.34\) mW at \(10\) dBm input RF power, but the rectifier efficiency is very low because the Schottky diode HSMS \(2852\) almost reaches its breakdown voltage. Based on the output DC power obtained at \(0\) dBm, it is clear that the proposed rectenna is useful to power up some wireless sensor that consumes less power than \(0.6\) mW, such as wearable sensor-\(60\)\(\mu\)W, smoke detector-\(55\)\(\mu\)W, and Wi-Fi flash memory-\(210\)\(\mu\)W.  
Figure 12: Simulated plots of the proposed rectifying network at \(2.45\) GHz with RL of \(10\) K\(\Omega\) (**a**) Efficiency vs. \(\mathrm{P_{in}}\) (**b**) \(\mathrm{V_{out}}\), \(\mathrm{P_{out}}\) vs. \(\mathrm{P_{in}}\).
# The Effect of Rainfall on Economic Growth in Thailand: A Blessing for Poor Provinces
## 4 Results and Discussion
### Economic Sector Results  
Table 3 examines how precipitation affects the GPP per capita growth at the sector level (agriculture, industry, and service). Precipitation was found to have significant negative effects on the agricultural and service sectors while the industrial sector was positively affected by precipitation although this was not statistically significant. For the overall economic sector, temperature and population growth were conducted as the control variable. We found that the relationship between average precipitation and GPP growth was very robust since the precipitation coefficient remained unchanged across all \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Dependent Variable Is the**} & \multicolumn{3}{c}{**Feasible Generalised Least Squares (FGLS)**} & \multicolumn{2}{c}{**Ordinary Least Squares (OLS)**} \\ \cline{2-6}  & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** \\ \hline Precipitation (100 mm/year) & \(-0.0120\)* & \(-0.0238\)*** & \(-0.0231\)*** & \(-0.0234\)*** & \(-0.0234\)** \\  & (0.0063) & (0.0076) & (0.0077) & (0.0077) & (0.0090) \\ Precipitation interact with... & & & & & \\ Poor province dummy & & 0.0337*** & 0.0322 *** & 0.0322 *** & 0.0322 ** \\  & & (0.0121) & (0.0125) & (0.0125) & (0.0130) \\ Temperature (Celsius) & & & 0.00383 & 0.00321 & 0.00321 \\  & & & (0.0072) & (0.0072) & (0.0081) \\ Temperature interact with... & & & & & \\ Poor province dummy & & & \(-0.00321\) & \(-0.00247\) & \(-0.00247\) \\  & & & (0.0070) & (0.0070) & (0.0079) \\ Population growth & & & & \(-0.421\)** & \(-0.421\)** \\  & & & & (0.1866) & (0.2109) \\ Constant & 0.000445 & 0.0320 & \(-0.0731\) & 0.0443 & 0.0443 \\  & (0.0153) & (0.0187) & (0.1107) & (0.1069) & (0.0778) \\ Observations & 1596 & 1596 & 1596 & 1596 & 1596 \\ Log likelihood & 2188.786 & 2192.647 & 2192.790 & 2195.334 & \\ \(R^{2}\) & & & & & 0.2706 \\ \hline \hline \end{tabular}  
* Notes: 1. Standard errors in parentheses; 2. Time and provincial fixed effects were included (coefficient not reported);  
* Significant at 10%, *
* Significant at 5%, **
* Significant at 1%; 4. For all OLS models refer to Table 3.  
\end{table} 
Table 2: Main economic growth results. Sample: 76 provinces (1995–2015). panels (Column 4). In considering the economic sector and subsector, the present study had three interesting findings as follows. Firstly, it was surprising to find that precipitation had a negative effect on the agricultural sector although it remained positive for poorer regions (refer to Table 3, Panel A). Thailand's main agricultural products come from the country's poorer regions (the Northeast and North), which are very dry areas.
# An Optimal and Stable Algorithm for Clustering Numerical Data
## 2 Preliminaries
### k-AMH Clustering Framework  
Similar to k-Means, the k-AMH [17] uses the same clustering framework, which begins with random seeds and updating the centers of the clusters and ends with optimizing an objective function. Finally, the objects closer to the centers of the clusters are grouped together into those clusters. The k-AMH algorithm differs from the k-means algorithm in terms of how the centers are updated. The k-AMH algorithm exploits the objects themselves to update the cluster centers, whereas the fuzzy c-means algorithm uses the mean, as the name implies. In general, the k-AMH algorithm has two extensions: the k-AMH algorithm for categorical clustering, which is the original version previously introduced for clustering Y-STR data, and the recent k-AMH algorithm for numerical clustering. In addition, there are two extended versions of numerical clustering [27]. The first one follows the original k-AMH categorical algorithm exactly except that the numerical distance, e.g., Euclidean distance (previously reported as k-AMH numeric I), is used, whereas the second version has its objective function substituted with the objective function of the fuzzy c-means algorithm (previously reported as k-AMH numeric II).