esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Baselines in the Literature
### Baseline Methodologies
#### 2.2.7 Control Group  
With the control group or peer group methodology, the baseline is determined by taking the measurements from a control group similar to a flexibility source cluster. The control group is supposed to represent the behaviour of the flexibility source cluster. When no flexibility is activated in the control group, it can be used to establish the baseline for the flexibility source cluster.  
The control group methodology is applied by France [16], who identified two key advantages of the control group approach, namely:  
1. No large dataset with historic measurements is required. This method can therefore be applied immediately.
2. Manipulation effects of flexibility sources are avoided.  
However, [16] also identified a disadvantage of control group baselining, as participating in a control group would imply not providing any flexibility, making this less attractive.  
The control group methodology is insensitive to gaming and the user dilemma, since the flexibility on the one hand, and the data to determine the baseline on the other hand, are obtained from different groups of customers. This, however, results in an approach less simple to understand and implement, so that this is a less transparent baselining methodology. In practice, choosing the control group transparently in such a way that accurately represents the behaviour of the flexibility cluster in case no flexibility would be provided. One reason for this is that it is often unknown to a DSO what exact appliances are behind a connection.
# Reliability Assessment of Cyber-Physical Distribution Systems Considering Cyber Disturbances
## 5 Case Study
### Analysis of Results
#### 5.2.3 Impact of Access Network Structure on CPS Reliability of Distribution Network  
In addition, the cyber system network topology is also an important factor affecting the reliability of CPSs in distribution networks. Therefore, the impact of various different access network structures, such as bus-type, star-type, tree-type, and net-type, on the CPS reliability of the distribution network is studied in this paper. The schematic diagram of each access network structure is shown in Figure 20.  
The results of the effects of different access network structures on the system reliability indices are shown in Figure 21.  
Figure 21 shows that the net-type has the smallest reliability indices and the highest system reliability because more redundant communication lines are available as backups to improve the reliability of the system. Furthermore, the reliability indices of the star-type are slightly higher than those of the net-type, but the variation of the reliability indices is smaller compared with the rest of the structures. Therefore, a suitable access network structure needs to be selected according to the reliability requirements.  
Figure 20: Schematic diagram of access network structure.  
Figure 21: Reliability indices of different access network structures.  
In the calculation of reliability indices for different access network structures, the superiority of the combined method of fault tree and Petri net in this paper is also reflected, as only the correlation matrix of Petri net needs to be changed to continue the calculation, which greatly improves the efficiency of the reliability assessment.
## EMMNet: Sensor Networking for Electricity Meter Monitoring  
**[PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 3, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 4 and [PERSON] 1,2**  
1 School of Computer Science and Technology, University of Science and Technology of China,  
Hefei, Anhui 230027, China; E-Mails: [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (B.-H.Z.); [EMAIL_ADDRESS] (Y.-G.Q.); [EMAIL_ADDRESS] (X.-F.J.)  
2 The State Key Laboratory of Networking and Switching Technology, Beijing, 100876, China  
3 National Institute of Informatics, Tokyo, 101-8430, Japan; E-Mail: [EMAIL_ADDRESS] (Y.-S.J.)  
4 Hangao Electronics Co., Ltd. Anhui, China; E-Mail: [EMAIL_ADDRESS] (X.-D.H.)  
* Author to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS]; Tel.: +86-0551-03607462; Fax: +86-0551-03607462.  
_Received: 11 May 2010; in revised form: 27 May 2010 / Accepted: 6 June 2010 / Published: 24 June 2010_  
**Abstract:** Smart sensors are emerging as a promising technology for a large number of application domains. This paper presents a collection of requirements and guidelines that serve as a basis for a general smart sensor architecture to monitor electricity meters. It also presents an electricity meter monitoring network, named EMMNet, comprised of data collectors, data concentrators, hand-held devices, a centralized server, and clients. EMMNet provides long-distance communication capabilities, which make it suitable suitable for complex urban environments. In addition, the operational cost of EMMNet is low, compared with other existing remote meter monitoring systems based on GPRS. A new dynamic tree protocol based on the application requirements which can significantly improve the reliability of the network is also proposed. We are currently conducting tests on five networks and investigating network problems for further improvements. Evaluation results indicate that EMMNet enhances the efficiency and accuracy in the reading, recording, and calibration of electricity meters.  
**Keywords:** smart sensor; electricity meter monitoring; dynamic tree protocol; application
## 3 Results and Discussion
### Characteristics of Temporal Variation in WUE and TVDI under Different Land-Use Types  
As shown in Figure 3a, there are obvious differences in WUE under different land-use types caused by NPP and ET. However, the interannual variation in WUE showed a small fluctuation, and the dynamic process of WUE in each land-use type had a good consistency. For different land cover types during 2000-2020, the values of WUE descended in the order of wooded land (1.25 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) \(>\) shrub land (1.03 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) \(>\) open woodland (0.94 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) \(>\) grassland (0.88 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) \(>\) dryland (0.77 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) \(>\) other woodland (0.47 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O). The difference in WUE among the different vegetation types is remarkable, and is consistent with previous research [22; 31]. The main reason for this result is that, under the same water supply conditions, forest land can produce more biomass and store more organic substances [41]. Previous research also demonstrated that the WUE of evergreen plants was significantly lower than that of deciduous plants, and the WUE of arbor, shrub, herb, and liana was also different. Liana had the highest WUE, and there was little difference between the values of arbor and shrub [42]. The estimated TVDI in this work can be divided into three groups (Figure 3b), namely, the higher-value group (other woodland and dryland), the middle-value group (shrub land, open woodland, grassland), and the low-value group (wooded land). The TVDI under different land-use types presented small changes during 2000-2012, and then exhibited a slightly decreasing trend for all land-use types in the TMS. The obtained higher TVDI values for other woodland and dryland may be caused by the higher temperature because TVDI is more sensitive to the land surface temperature, and land surface temperature contains more drought information than NDVI [43]. By comparison, it has been reported that the main influential factor for the TVDI in northern and north-western regions was sunshine duration [44]. However, the response of TVDI to climate factors in different regions may differ, and needs to be further investigated and analyzed.  
Figure 2: Annual NPP (**a**), ET (**b**), WUE (**c**), and TVDI (**d**) in the TMS during 2000–2020.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Random Access Protocols
#### 3.1.5 QoS Provisioning MAC Protocol  
The QoS provisioning MAC protocol [70] was proposed by [PERSON] et al., to overlay the Time-Division Multiple Access (TDMA) to co-exist with single-hop CRAHN. It consists of four stages of the cognition cycle, namely observe stage, plan stage, decide stage and act stage. During the observing stage, a neighbor list foundation system is acquainted with a record of the spectrum use circumstance of the PU and SU. An SU uses this list to perceive the spectrum utilization in its neighborhood.  
The plan stage consist of two major functions. One is to make sure that an SU is not interfering with a licensed user, and the other one is to use the free spectrum efficiently. To achieve these functionalities a contention resolution system joined with gating, straight backoff and slow down shirking is proposed to upgrade the throughput and access deferral and decency of the protocol. To guarantee agreeable QoS on the built-up ad hoc connection, an invited reservation strategy is presented in the chosen stage; to advise users of the initial time of the Contention-Free Period (CFP) and a Contention Period (CP) in each round. The act stage includes the distributed frame synchronization mechanism for SU.
## 3 Results and Discussion
### Case-Study I: Lakes
It can be seen that the accuracy of the estimation using GPS only remains at about 80% for the two heights considered for the UAV, but increases to 90% when including Galileo. It further improves when GLONASS is added, and reaches 98% when Beidou is added. Notice that the curves for the different heights follow the same trend which supports our conclusion that the more satellites, the better the area estimation accuracy. Figure 9: Simulation results of multi-GNSS reflections: (**a**) the sensor trajectory considered, (**b**–**e**) the specular points lines using the different GNSS constellations, (**f**) the ratio between the estimated and the real area of the Northern Avigliana lake versus the number of satellites in view, two different heights of the sensor are considered. In fact, the high-end multi-constellation GNSS receivers, are already the state-of-the-art for the majority of the civil applications and their use on-board UAVs is growing. Indeed, the results presented in this sub-section refer to a particular case study (and thus they rely on the properties of this water body). Nevertheless, they demonstrate the expected advantages of processing reflected multi-GNSS signals. Processing multi-GNSS signals allows increasing the number of specular reflection points over a certain area. As an example, by, adding Galileo, we expect to double the number of visible satellites compared to what we could have if we were using GPS alone. At mid latitudes, the number of visible satellites can be greater than 40 when GLONASS and Beidou are considered too. This results in increasing the number of polygon vertices and therefore creates a more accurate estimation of the water surfaces. Undoubtedly, the method presented here is an effective and simple way for estimating surface water. It can be integrated with other sources of data, or even to be used when orthophotos or maps are not available. Surely any reduction or expansion in the water surface can be detected when comparing against historic data. Indeed, utilizing UAV-based GNSS-R sensors, combined with ad hoc processing routines, proves to be an innovative tool for water monitoring. The results shown here were obtained computing the specular points at a rate of 20 Hz, but finer resolutions are attainable with a moderate increase in the complexity.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results
### High Workload  
Human workload is the sum of the all task demands which are imposed on operators and the subjective response of the operators to those demands [33]. According to the organization of SCCs, one operator could be assigned to monitor more than one vessel (up to six vessels) [54]. In this situation, operators have a higher level of workload than operators onboard. This challenge is derived from human-machine interactions. The remote system constantly provides information to provide an optimal level of SA, which increases the risk that operators will experience information overload and a high level of workload in monitoring many different systems.  
[PERSON] et al. [22] state that an SCC can present all of the information operators need and would have if they were onboard, but note that if this is the case, then the observation of multiple screens and simulators would lead to information overload. Other research studies have highlighted that information overload has two main reasons: (1) a constant flow of information designed to maintain SA, and (2) the fact that operators might be overseeing several ships at the same time [7]. [PERSON] et al. [7] also point out that human errors may occur because of the _\"carry over effect\"_ when one operator monitors more than one vessel at the same time. These situations refer to higher mental workload as an important human factor challenge within remote control centers.  
Furthermore, one of the major predictors of procedural error in the operation of UAVs is operators' workload [16]. Increasing pilots' workload was a cause of the Nogales UAS accident [32]. [PERSON] et al. [33] found that automation alters workload by decreasing manual load and increasing mental workload. In addition, in the field of crane remote operation, one study found that the tasks can be different every time without any previous information about its operation. Hence, the operators must be able to reorient immediately on a received task [43]. This refers to operators performing multiple tasks in control centers, which creates a high level of mental workload.  
The research reviewed here suggests that SCC workers could be exposed to too much information, which would make it impossible for them to understand the conditions of a ship they are overseeing. On the other hand, [PERSON] et al. [50] discuss that insufficient and poorly designed automation leads to an erratic rise in operators' workload, which has to be considered in the design of SCCs. In this case, a poor design may provide unnecessary information which could lead to an erratic increase in operator's workload, and finally, leads to a higher level of mental workload.
## 2 Methods and Principle
### AR Model Order Determination and Parameter Estimation  
The smaller the variance of the error term, the more accurate the model, and the more it can simulate the relationship between the items in the sequence.  
According to formula (1), it follows that  
\[\rm{a(k)}=x(k)-\varphi_{1}x(k-1)-\varphi_{2}x(k-2)-\cdots-\varphi_{n}x(k-n) \tag{2}\]\[\sigma_{\rm a}^{2}=\frac{1}{\rm N-n}\sum_{\rm k=n+1}^{\rm N}\left({\rm x(k)-\sum_{ \rm i=1}^{n}\varphi_{\rm i}x(k-i)}\right)^{2} \tag{3}\]  
In formulas (2) and (3), \(\varphi_{\rm i}({\rm i=1,2,\cdots,n})\) is the parameter of the model. \(\sigma_{\rm a}^{2}\) is the variance.  
When the order n of the AR model is unknown, it needs to be determined in the recursive process. With the increase of model order, the variance of the model decreases gradually. When it reaches the minimum value or no longer changes, the order at this time is the correct order of the AR model.  
The model order is determined using final prediction error (FPE) criterion. The FPE criterion is the sum of the power of the unpredictable part of the AR(n) process and the error power caused by inaccurate AR parameter estimation.  
The definition formula is as follows:  
\[{\rm FPE(n)=\frac{N+n+1}{N-n+1}\sigma_{\rm a}^{2},\ n=0,1,\cdots,L} \tag{4}\]  
In formula (4), L is the highest order given in advance. N is the number of data samples, and the value in parentheses increases as n increases (towards N). It reflects that the inaccuracy of the estimation of the prediction error power is increasing. The minimum value will appear in the process that \(\sigma_{\rm a}^{2}\) decreases as the order increases. The order corresponding to the minimum value of FPE is the final order. When M is the minimum order, formula (5) is satisfied.  
\[{\rm FPE(M)=\min_{0\leq n\leq L}FPE(n)} \tag{5}\]  
According to the measurement data of the system, under certain criteria, the unknown parameters of the model can be identified.  
Recursive least squares (RLS) estimation algorithm can realize parameter recursive estimation. After the identified system obtains new observation data, it will use the new observation data recursively and modify the previous estimate in combination with the previous estimate, so that new parameter estimates can be obtained until the parameter estimates reach satisfactory accuracy [21].  
Formula (1) is written in vector form:  
\[{\rm x(k)=\eta^{T}(k)\theta+a(k)} \tag{6}\]  
\[\theta=\left[\varphi_{1},\varphi_{2},\cdots,\varphi_{n}\right]^{T}\
## 4 Discussion  
Recent development of highly performant advanced treatment processes, in addition to traditional AOPs, has opened new horizons in the field of water purification and production. Although not all the proposed processes will turn out to be industrially applicable in the end, some are well beyond the stage of laboratory testing and have already seen real-world application. Table 11 summarizes the pros and cons of some of the processes examined in this paper.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technology** & **Pros** & **Cons** & **Notes** \\ \hline AOPs & & & \\ \hline UV & Established disinfection technology. & Low energy transfer yields. UV exposure is mutagenic for bacteria and is suspected of antibiotic resistance buildup. Not highly efficient in contaminants removal. & UV penetration affected by turbidity and presence of nitrates. Cost is almost linear with flow capacity. & UV penetration affected by turbidity and presence of nitrates. Cost is almost linear with flow capacity. \\ \hline UV + & Higher degradation rates compared to individual processes. Higher yield of hydroxyl radicals. & Cost of chemicals. May require long process contact time. & Proven technology. Specific energy efficiency (EE/O) higher than ozomation alone but lower than ozomation alone but lower than other AOPs. Cost is directly proportional to treatment capacity. \\ \hline H\({}_{2}\)O\({}_{2}\) + O\({}_{3}\) & More effective than the individual processes. & High cost. Ozone may generate toxic byproducts. & pH and chemical ratios control required. \\ \hline UV + TiO\({}_{2}\) & Catalyst is costly but can be recycled. & No full-scale applications reported. & pH sensitivity. Oxygen sparging required. \\ \hline Sonication & Energy use similar to UV systems. & Need for supplemental oxidants. High cost. & Not commercially applicable. \\ \hline \hline \end{tabular}
\end{table}
Table 11: Pros and cons of ATP technologies.  
Despite many real -scale applications of some of these processes with real wastewaters, there is a need for a deeper understanding of their reaction kinetics in complex systems, to enable better design of combination treatment schemes. For all processes examined, an understanding of chemical reaction mechanism (in the form of reliable chemical models) is needed to address issues about contaminant decomposition and byproduct formation in complex solutions. It was shown, for example, that ionizing radiation together with oxidants, such as ozone or hydrogen peroxide, may further improve an individual process removal efficiency but may also induce secondary scavenging effects in solution.  
There appears to be a general lack of comparative investigations between consolidated AOP processes and innovative ARPs/AORPs, and of their combinations with other conventional processes. This prevents reaching conclusive evaluations about the most suitable and cost-effective solution for advanced treatment of water/wastewater for specific uses. It should also be considered that site-specific conditions may lead to different conclusions for different sites.
# A Back-to-Back Gap Waveguide-Based Packaging Structure for E-Band Radio Frequency Front-End
## 3 The Fabrication and Measurement of the Packaging Structure  
Due to the excellent isolation characteristics of the hybrid coupler and the absence of interference between its constituent units, it can obtain good simulated results without the necessity of intricate optimization.  
The final structure is depicted in Figures 3 and 15a. It has three metal layers, with the MS-GW transition ridges positioned on the top and bottom metal plates, and the quartz plates and periodic EBG structures are also on both sides of the middle metal plate. Moreover, the GW ports are gradually matched to the WR10 standard waveguide through waveguide tapers.  
Figure 14: Simulated results for the connecting structure.  
Figure 13: Structure and dimensions of the connecting structure. (_ln_1 = 0.94 mm, _ln_2 = 0.26 mm).  
Figure 15: (**a**) The fabricated packaging structure; (**b**) actual measurement environment.  
Subsequently, the cavity of the model is fabricated using computer numerical control (CNC) technology, with copper having been chosen as the metal material to ensure the mechanical strength of the encapsulated structure. Additionally, the transition structure, which is based on a quartz dielectric substrate, is processed using a laser etching technique. Then, the quartz dielectric plate is bonded to the metal cavity using conductive adhesive, completing the assembly of the packaging structure. The process, from design to fabrication, is shown in Table 3.  
The fabricated model was tested using a vector network analyzer (VNA) and extenders, as shown in Figure 15b. A VNA expands its frequency range through extenders, allowing it to measure the electrical characteristics of components at frequencies between 60 and 90 GHz. The extenders are then connected to the fabricated package structure by using a straight waveguide in order to measure its S-parameters. The simulated and measured results are shown in Figure 16, and are in good agreement with the results of the simulation. Specifically, the passbands span from 70.95 GHz to 76.2 GHz and from 80.92 GHz to 86.25 GHz, respectively, featuring maximum insertion losses of 2.26 dB and 2.42 dB within these passbands, thereby demonstrating commendable electrical performance.
## 4 Advancements in Industry 4.0 and Pandemic Management
### Comparative Analysis of AI, IoT, and Cloud Computing in Industry 4.0
#### 4.7.2 Internet of Things (IoT)  
IoT technologies provide real-time monitoring and control, enabling a network of interconnected devices to communicate and make decentralized decisions. This leads to significant improvements in operational efficiency and asset management. IoT's challenges include ensuring data security, managing the vast quantity of data from sensors, and integrating disparate systems across different standards and protocols.