esa-sceva/satcom-chunk-collection huggingface dataset:
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 5 Comparative Analysis  
Table 5 compares the middleware solutions reviewed in Section 4 by use case, real-time capability, scalability, and key limitations. The middleware with \"\" is built based on a standard. It includes standardized platforms (e.g., Apache Kafka, DDS, MQTT) and custom-developed solutions (e.g., COPSS, CaaS, ITL), offering a functional overview of middleware performance across various ITS domains. The table reveals trade-offs in middleware design, such as between real-time responsiveness and scalability or between implementation complexity and adaptability to constrained environments. The table shows that custom middleware dominates in specialized contexts like VANETs, where domain-specific communication models are necessary to address safety and coordination challenges.  
However, standard middleware platforms like Apache Kafka and DDS demonstrate high scalability and find applications in IoT and railway systems, where throughput and reliability are a priority. However, these often come with increased computational or integration overheads. Lightweight protocols such as MQTT and XMPP are favored in low-bandwidth or user-contributed environments such as EDR systems and public transit apps, even though they may lack the robustness or security features needed for safety-critical ITS applications.  
Finally, the table highlights real-time capability as a differentiator: while most middleware reviewed supports some form of real-time data exchange, only a few, such as DDS, COPSS, and OPC UA with TSN, offer deterministic or bounded-latency performance suitable for safety-critical operations like autonomous driving and train control. Conversely, scalability is limited in middleware solutions that depend on central brokers (e.g., MQTT in the EDR system) or rely heavily on human input (e.g., XMPP-based crowd-sensing).
## 2 Materials and Methods
### Validation of Management Zones  
According to PA theory, after a target crop field has been partitioned into \(n\) sub-field units, the appropriateness of MZ needs to be evaluated to determine whether there are real differences between them (or not) in terms of the agricultural phenomenon to be managed (e.g., soil properties, yield). Historically, this task has been accomplished by implementing strategies as simple as ANOVA models or as complex as mixed linear models (MLM). However, no standard method has been described to this date [10; 11].  
Since in our case MZ are expected to show individual potential to host pest populations, their represented environments are also likely to be differentiated from one another. This condition can be evaluated with SDM background tests, which are tools initially designed to measure the level of environmental overlap between SDM models generated for two species (pairwise comparisons) using Schoener's \(D\). This index (0-1) is sensitive to ecological similarities (between geographic entities) given by diet and microhabitat variables [57; 58].  
In this work, the generation of zonal SDM models and the implementation of background tests was based on the \"ENMTools\" package for R [59], which estimates the spatial distribution of compared species based on the Maxent (i.e., maximum entropy) algorithm [60] and estimates [PERSON]'s \(D\) with the following equation:  
\[D=1-\frac{1}{2}\left(\sum_{ij}\lvert Z_{1 ij}-Z_{2 ij}\rvert\right), \tag{3}\]  
where \(Z_{1 ij}\) and \(Z_{2 ij}\) represent the occupancy of entities 1 and 2, respectively.  
In practice, environmental and geographic samples (i.e., environmental values, geographic coordinates; \(n=500\)) of management zones included in preliminary field partition models were used to implement pairwise background tests that generated individual matrices of between-zone overlaps (one for each pest). Such matrices were used to feed a set of visual networks that represented management zones as labeled nodes, the environmental similarity between zones as numbers next to each link (i.e., \(D\)), and the statistical significance of a particular nexus value as the link's width. In cases where preliminary field partition models considered more than one absence zones, these were fused into a single absence cluster before MZ networks were computed.  
The threshold used to determine similarities between MZ was an environmental overlap equal to or greater than 10% (\(D\geq 0.1\)). Regardless of the observed similarity between zones, statistically significant environmental relationships (\(\alpha=0.05\)) were represented as \"thick\" links between nodes. In contrast, statistically insignificant ones were drawn as \"slim\" (low similarity values below the alpha level) and \"normal\" (high similarity values below the alpha level) links between nodes.  
The results of this exercise (i.e., environmental relationships between MZ) were used to generate a series of final field partition models that were regarded as the best possible options to facilitate pest management practices within the experimental orchard from an eSSPM perspective.
## 3 Results and Discussion
### Correct Calculation of Blank  
The _AWCD_ values measured on the 10\({}^{-5}\) dilutions at 96 h were calculated subtracting from the raw data (OD\({}_{590}\) - OD\({}_{750}\)) the blank well OD reading from the OD value of each substrate well (\(i_{bc}\)) for each well and successively subtracting the blank-corrected OD reading at time 0 from subsequent blank-corrected readings at \(\pm\)12\({}_{Xh}\) (\(c_{i}\)) for both 0-h and 96-h incubation times. The same methodology was carried out (a) without subtracting the blank well OD reading, (b) without subtracting the blank-corrected OD reading at time 0 or (c) without subtracting both. The results, depicted in Figure 1, were quite different on the basis of the type of calculation done, particularly for some classes of carbon substrates (e.g., phosphorylated compounds), showing that it is necessary to apply the  
Figure 2: Mean values (\(n=5\)) \(\pm\) SD of (**A**) _AWCD_, (**B**) \(H^{\prime}\) and (**C**) \(E\) measured every 12 h during a 144-h incubation in different dilutions (\(\sim\)10\({}^{5}\) CFUs mL\({}^{-1}\), dashed line; \(\sim\)10\({}^{4}\) CFUs mL\({}^{-1}\), continuous line; \(\sim\)10\({}^{3}\) CFUs mL\({}^{-1}\), dotted line) of soils from the experimental olive orchard. Na\({}_{4}\)P\({}_{2}\)O\(\blacktriangledown\) 10 H\({}_{2}\)O concentration was 1.8% (\(w/v\)). The R\({}^{2}\) values and the respective trend lines (dotted lines in grey) were calculated on the basis of a polynomial function of degree three.  
sequential subtractions and that non-specific OD values must be taken into account for obtaining reliable _AWCD_ data.
## 4 Results
### Alternate Cloud Mask Evaluation  
#### 4.3.1 Testing Cloud Score+ Thresholds  
Similar to the preliminary phase described above, we used the CSLAP 2019 dataset to explore Cloud Score+ thresholds. As an example, Table 5 summarizes R\({}^{2}\) for the MPH model based on different Cloud Score+ thresholds for phycocyanin concentration. Based on the results shown in Table 5, the most effective threshold appears to be 50%. Applying lower thresholds (30% or 40%) decreased the R\({}^{2}\) of the model while applying higher thresholds (60%) decreased the available data as well as the R\({}^{2}\) of the model.  
Figure 7 illustrates the scatterplots for the correlation between in situ and Sentinel-2 data with the application of the 50% Cloud Score+ threshold for cloud masking. The application of Cloud Score+ for cloud masking improved the R\({}^{2}\) values for the correlation between in situ and Sentinel-2 data. Based on the training dataset, the phycocyanin MPH model using the Cloud Score+ approach achieved an R\({}^{2}\) value of 0.87, whereas the corresponding MPH model using the QA60 bitmask approach had an R\({}^{2}\) value of 0.76.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Cloud Score+ Threshold**} \\ \cline{2-5}  & **30** & **40** & **50** & **60** \\ \hline All data & 0.62 (n = 359) & 0.67 (n = 321) & 0.68 (n = 263) & 0.62 (n = 196) \\ \hline \(>\)8 \(\mathrm{\SIUnitSymbolMicro g}\)/L & 0.81 (n = 42) & 0.86 (n = 40) & 0.87 (n = 32) & 0.80 (n = 27) \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of R\({}^{2}\) for MPH models based on different thresholds for Cloud Score+ (CSLAP phylocyocyanin 2019—concurrent data).  
Figure 7: _Cont._
# Spintronic RF-Direct on-off Keying Modulation Using a Frequency Division Multiplex
## 5 Measurement Results
STNOs have low power consumption behavior characteristics, which are more than 10 times less than other published results [23; 24; 25] designed for lower milliwatt operation, as shown in Table 2. Notably, the wireless technology of spintronic modulation could be designed with much smaller size, as shown in Table 2, than even a ring VCO which is designed without an inductor [25], as it is operated independently as a nano-scaled resonator. The STNO and a ring VCO has a wider tuning range than LC VCO, whose frequency range is limited by the resonance of the inductor and capacitor. A ring VCO has broadband operating characteristics, but generally has low frequency oscillation characteristics, and it is difficult to generate RF frequencies. However, STNOs can obtain frequencies up to RF and millimeter waves. This allows design as a communication structure with direct conversion. The characteristics of a low power consumption, wide tuning range, and small size provide flexibility of design for array structure. In spite of these characteristics of STNO, the spectral purity of the STNO is much worse than others. We thus proposed a wireless communication with STNO-adopted OOK modulation which can overcome the problems of poor spectral purity.
## 2 Methodology
### Orbital Configuration and Sensor Type
#### 2.2.5 Constellations  
In addition, the performance of four hypothetical constellations (i.e., mixtures of different sensors) is also considered. Four specific constellations were selected from a near-infinite number of possible configurations as the focus of the paper to make the analysis tractable. The combinations explored here include (see Table 2):  
* sensors of PMW radiometer, two C-band SARs, Narrow LiDAR;
* sensors of PMW radiometer, Ku-band SAR, narrow LiDAR;
* sensor IDs PMW radiometer, Ku-band SAR, wide LiDAR; and
* sensor IDs PMW radiometer, Ku-band SAR, two C-band SARs, wide LiDAR, narrow LiDAR.  
The different constellations represent: (a) currently available techniques for snow remote sensing (C1), e.g., the two C-band SARs represents the Sentinel-1 A/B constellation; (b) proxies of sensors feasibly applied in the near future (C2); (c) incorporate the additive value of a wide swath LiDAR (C3); and (d) represent what could be achieved if all sensors are simultaneously spaceborne (C4). The selection of these different sensors within each constellation does not consider a likely cost cap to sensor deployment. Rather, the selection of these different sensors aims to explore what could be viewed assuming the financial resources were available to deploy such a configuration.
# Noise Prediction Using Machine Learning with Measurements Analysis
## 4 Conclusions  
In this study, we found that as frequencies increased, the average Leq error values between morning and night were greater, with noise in the morning returning higher and greater values than those at night. This may be due to the fact that more people work in the morning and more noise is generated. Moreover, the human voice has a high noise frequency, whereas machine pumps have a noise lower frequency, indicating significant differences in noise sources.  
This study focused on the prediction results for the noise frequency for one of twelve sensors (sensor 8) at 125 Hz. This sensor was chosen because its static Leq value (>70 dBA) reached the threshold of damaging human hearing, which affects physical and mental health. Based on this finding,  
Figure 16: Different sensor prediction effects at different frequencies.  
we used the GBM model to predict future noise data. The Leg prediction results for sensor 8 at 125 Hz showed an error rate of less than 1 dBA and a R\({}^{2}\) value greater than 0.7, which is a favorable prediction performance result. The poorer prediction results of the other sensors were between 2 to 1 dBA with a R\({}^{2}\) value that was generally below 0.7.  
The results indicate that the prediction model worked well in most regions and frequencies and particularly for sensor 8 (125 Hz), which is a serious noise zone. The results also indicate that this working environment produced good noise prediction performance using the proposed method. This enables the notification of laborers to prevent long-term exposure while predicting future noise pollution. In fact, we are now collecting more characteristic data for several months for this purpose. We believe that it would be better to have a longer observation duration to predict detailed noise location. This would keep employees healthy for avoiding a harmful noise position to prevent people from working in that environment. In the future, we will analyze the data structures of the noise frequencies of more sensors, discuss noise types, and analyze the possibilities of noise-related physical harm. We will also attempt to add new features to improve noise prediction performance.  
[PERSON]-[PERSON] wrote and conducted the experiments, analyzed the results, and organized the layout of the paper, figures, and tables. [PERSON] conceived and corrected the article and was responsible for making clear and understandable content. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Synchrotron Radiation Research Center (NSRRC), grant number 10811 SA001.  
The authors acknowledge the financial support and equipment provided by the National Synchrotron Radiation Research Center (NSRRC), Taiwan. We gratefully acknowledge the software support of PTCOM Technology Co. Ltd.  
The authors declare no conflict of interest.
## 5 IETF ROLL Working Group
### Research Challenges
#### 5.4.5 Scalability of the Non-Storing RPL Approach  
As already mentioned the non-storing mode is a good mechanism to limit the usage of memory for nodes in dense networks, but it also has a negative effect on the depth of routing tree. In non-storing mode, the addresses of all the intermediate nodes are added to the packet. This includes the danger that these messages will get fragmented and limits the available payload [62]. This is definitely a challenge that has not been investigated thoroughly.  
**6. IETF CoRE Working Group**  
More recent, in 2010, an IETF working group, called Constrained RESTful Environments (CoRE), was founded specifically to work on the standardization of a framework for resource-oriented applications, allowing realization of RESTful embedded web services in a similar way as traditional web services, but suitable for the most constrained nodes and networks. Their work resulted in the Constrained Application Protocol (CoAP), a specialized RESTful web transfer protocol for use with constrained networks and nodes.  
_6.1. Key Protocols_  
CoAP is defined in draft-ietf-core-coap [83] in conjunction with a number of additional specifications. At the time of writing this article, there were three CoRE Internet-Drafts that are currently nearing completion and one completed RFC. In addition there were one more group Draft and 35 other individual Internet-Drafts listed on the CoRE website [5]. In this subsection we will describe briefly the (almost) completed CoRE Internet-Drafts and the published RFC. In the next section we will provide a brief summary of the topics covered by the individual Internet-Drafts in the CoRE working group.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 1 Introduction  
Proportional-integral-derivative (PID) [1; 2; 3; 4; 5; 6; 7; 8; 9] control has few tuning parameters: proportional gain, integral time, and derivative time, and its structure is simple. Hence, PID control has been widely used in industry, and numerous tuning methods have been proposed.  
In the model-based approach, optimal tracking and robust stability are achieved. However, the control system must be redesigned whenever the controlled system is changed. As a simple model-based optimal design method, [PERSON] and [PERSON] proposed the step response method (ZN method) [10]. Using the ZN method, the PID parameters are decided based on the step response trajectory so that the tracking performance is optimized. However, the robust stability is not taken into account [11; 12]. Therefore, robust control designs have been proposed [13; 14]. Using robust design methods, robust stability is obtained, but the tracking performance is not optimized. In [15], \(H_{2}\) optimal design was proposed using internal model control (IMC) design, and hence, stability was assured. Although robust PID control systems are also designed in the discrete time domain [16; 17], the stability margin cannot be prescribed.  
The relationship between the tracking performance and the robust stability is a trade-off relationship [18]. Therefore, trade-off PID design methods have been proposed [19; 20]. In the trade-off design methods, the PID parameters are decided such that the tracking performance is optimized subject to the prescribed robust stability, where servo or regulator mode is selected for performance optimization. A trade-off optimization approach has been designed in the continuous time domain that consists of a first-order plus dead-time (FOPDT) system [21; 22], a second-order plus dead-time (SOPDT) system [23; 24], and a two degrees-of-freedom (2 DOF) system [25; 26].  
For a discrete time system, trade-off approaches have been proposed [27; 28]. Discrete-time methods are useful for controlling discrete time systems, in which the controller is implemented with a digital computer. However, the robust stability was not assigned in [27], and the controlled plant was restricted to a non-zero system [28]. Therefore, in the present study, a new discrete time domain approach is proposed, in which the constraint is relaxed and the prescribed robust stability is accomplished. Specifically, the FOPDT model with a zero is normalized, and a universal design method is obtained. As a result, the trade-off design strategy is available for a large class of discrete time systems. Finally, the effectiveness of the proposed method is demonstrated through numerical examples.
## 5 Summary and Conclusions  
This paper presents the derivation, application, and discussions of the stability stiffness matrix of axial-loaded Timoshenko beam-columns. The main conclusions are summarized as follows:  
1. Equilibrium analysis of an axial-loaded Timoshenko beam-column is conducted based on the equilibrium conditions of shear force and bending moment of an element short segment, and a fourth-order governing differential equation is established. The solution of the differential equation can be used to analyze the element deformations and forces. Then, the element stability stiffness matrix is formulated, showing the relationship between the element-end flexural deformations and the corresponding forces.
2. Based on the element stability stiffness matrix, a matrix structural analysis approach for the elastic buckling analysis of axial-loaded Timoshenko beam-columns is established. First, the structural stiffness matrix is assembled from the element stiffness matrix to show global structural responses. The eigenproblem for an elastic buckling analysis is then solved by setting the determinant of the structural stiffness matrix to zero. The established matrix analysis approach is demonstrated using classical elastic buckling analysis examples of the Timoshenko column with typical element-end boundary conditions.
3. The simplified form for the element stability stiffness matrix of Timoshenko beam-columns by [PERSON] et al. [18] could lead to large errors because it is defined considering the conditions that it gives the same result as the exact solution for zero shear deformation effect or zero axial force effect. However, for the condition of a relatively large axial force factor, the exact element stability stiffness matrix derived in this paper should be used.  
All three authors have contributed to the idea discussion, equation derivation, and manuscript drafting of this paper.  
This research has received no external funding.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Deflection Analysis  
Taking PD-40-1 as an example, the variation in deflection during the loading process was analyzed. The center deflection of the specimen was calculated by the measured results from the 1# and 2# dial indicators. The deflection of other measuring points 3-8# can be measured directly. The load deflection F-U curve of the specimen PD-40-1 during loading process is shown in Figure 11a,b.  
The center and mid-span of the wallboard were bent and deformed downward, and the maximum deflection of the center point was about 36 mm. The deflection of the support point increased inversely with downward direction, indicating that the corner of the wallboard at the support point warped upward to a maximum of 4.5 mm. In order to analyze the effects of the concrete material, reinforcement ratio and reinforcement form on the center deflection of the specimen, the load deflection F-U curve of specimen is given.  
Figure 12 show that the deflection values of ordinary FAC specimen was larger than the RAC specimen, therefore the deformation capacity of the FAC specimens was relatively stronger. The main reason is that the elastic modulus of the recycled aggregate concrete is lower than that of the FAC. The deformation capacity of specimens with a high reinforcement ratio is poor, which indicates that  
Figure 11: Load deflection F-U curve of specimen PD-40-1. (**a**) Deflection of the support point; (**b**) Deflection of the center and mid-span point.  
Figure 10: Influence of reinforcement ratio on bearing capacity of specimens. (**a**) Ordinary concrete wallboard with different reinforced form; (**b**) Recycled concrete wallboard with different reinforced form.  
the reinforcement ratio of specimens in practical engineering should not be too high. In the case of equivalent reinforcement, the specimens with smaller bars and smaller spacing have better capacity to deform, rather than fail, under stress.