esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Communication Overhead
#### 5.2.1 Spatial Communication Overhead Per Node  
To generate the spatial aggregation, every node sends the local aggregation to the GW after adding up the encrypted message from all \(k\) pairs. The data sent per node can be denoted as \(\{LS(i,d)\|t\}\), the size is \(|X|+k\cdot|R|+128\) bits (a partitioned part size is \(\frac{|x|}{k}\) bits, \(k\) partitions take \(|x|\) bits; a noise key takes \(|R|\) bits, and then \(k\) noise keys take \(k\cdot|R|\) bits), so the total packet size is \(|x|+k\cdot|R|+128\) bits.  
For the scheme in Reference [23], the spatial aggregation packet per node is in the form as \(\{E\parallel H\parallel R\parallel t\}\), its size is \(\{|E|+|X|+k\cdot|R|+|H|+128\}\) bits.  
Every user node in Reference [24] generates \(k\) results, the data is in the form of \(\{(y_{1}\|y_{2}\|\cdots\|y_{k})\|t\}\), in which \(y_{i}\) involves the computation of data sharing and hash random value, so its size is \(K\cdot(K\cdot|H|+|X|/k+128)\) bits.  
We provide the individual spatial communication overhead comparison in Table 4.  
We plot the individual communication overhead comparison between our scheme and the other two schemes [23,24] during spatial aggregation in the Figure 6. We can see clearly the three schemes' individual overhead all grow with the increasing of \(k\) value. The packet width per node in the scheme in Reference [24] grows significantly than the other two schemes, especially when \(k\) value is relatively higher, and communication overhead closes to O(\(k^{2}\)), due to the \(\mathrm{x}^{k}\) polynomial operation per  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scheme** & **Computation Overhead Per Smart Meter** \\ \hline Scheme in [23] & \(|E|+|X|+k\cdot|R|+|H|+128\) \\ Scheme in [24] & \(K\cdot(K\cdot|H|+|X|/k+128)\) \\ Our scheme & \(|X|+k\cdot|R|+128\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Individual spatial communication overhead comparison (bits).  
Figure 5: Variety of temporal computation overhead per node with the value k.  
node before the matrix multiplication operation. Our scheme's growth rate is close to the scheme in Reference [23], which is higher always slightly higher than ours, due to the relatively higher public key encryption width.
## 4 Discussion
### Importance of the Segmented Long Time Series Study and GEE  
As shown in Table 5, the trend of NPP was inconsistent over the two periods by Theil-Sen method. The different results and their significance between the two periods show that the change trend of grassland NPP over a short period of time might not be reliable. The change trend can be easily disturbed by one year of unusual increase or decrease and is determined by the selection of the start and end points. Furthermore, some short-term sudden changes may also be of interest, and should not be overlooked. The same situation also existed in the spatial distribution of NPP changes. For example, as shown in Figure 9, in west Hulun Buir, although there was no obvious decline in the area of grassland NPP at the scale of 21 years, different regions showed decreasing trends during the different periods (2000-2010 or 2010-2020). In other words, the grassland NPP decline in west Hulun Buir has not been stopped, but it has shifted within the region. However, it is difficult to find such a phenomenon in a long time series directly. Thus, it is of great importance to undertake trend research at an appropriate time scale, with both long and short time series, in order to find more comprehensive details of the NPP change trends. In this paper, not only was the long time series of 21 years (from 2000 to 2020) analyzed, but we also divided the long time series into two 11-year periods (from 2000 to 2010 and from 2010 to 2020) as short time series.  
A large amount of remote sensing data and computational power are required for the long time series and large-scale spatiotemporal monitoring. In this study, the quantity and quality of the grassland in Inner Mongolia were evaluated by LUCC and NPP based on the GEE platform. The high-performance computing power and rich image resources of the GEE platform can be used for the long-term research of NPP in Inner Mongolia. The observed data from meteorological stations were applied in this study. However, the observation data from meteorological stations cannot be accessed conveniently for a larger scale study (e.g., a continental or global scale). Thus, for better and extensive monitoring of ecosystem growth changes, it may be easier and quicker to adopt the massive amount of remote sensing data and meteorological images available on the GEE platform [52; 58].
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 2 Materials and Methods
### Autoformer Network Structure
By applying the inverse Fourier transform to Equation (4), we can convert the frequency-domain autocorrelation to the time-domain autocorrelation coefficient. The autocorrelation mechanism analyzes the autocorrelation under different time lags, identifies the periodic patterns of the sequence, and selects representative lags. These time lags are used to align similar subsequences and global information is extracted through weighted aggregation. This method reduces the computational load and improves the efficiency of information utilization through sequence aggregation. The time-series prediction method based on the autocorrelation mechanism can focus on the seasonal components in the time series, effectively enhancing the model's ability to extract data patterns and trends.
# Stabilization Time of Running Equivalent Level \(\mathbf{L_{Aeq}}\) for Urban Road Traffic Noise
## 2 Materials and Methods  
### Experimental Data Set  
Noise monitoring data collected in 97 sites in the city of Milan, Italy, were considered for the present study [28]. They consisted of 268 time series of 1 s short \(L_{Aeq,1s}\) level, each lasting continuously for 24 h. Road traffic noise was the predominant source in each site and, therefore, the reference time \(T\) for computing the stabilization time \(ST\) was set to \(T=1\) h, according to the current Italian legislation.  
The selected sites were representative of the urban road network in Milan, which includes different types of roads according to the Italian road functional classification, namely motorways (class \"A\"), thoroughface roads (class \"D\"), urban district roads (class \"E\") and urban local roads (class \"F\"). The distribution of the sites across the road types is given in Table 1, together with the number of 24 h time series of 1 s A-weighted short \(L_{Aeq,1s}\) level, monitored continuously by a class 1 sound level meter, set to time weighting Fast with the microphone placed at 4 m above the road. The unattended monitoring was carried out on weekdays only (Monday to Friday), and data collected with rain and wind speed \(>\)5 m/s were excluded from the study. In 62 sites, the monitoring lasted continuously for a few days. More details on the noise survey (e.g., locations of monitoring sites in the urban area) are available in [28].  
In addition, for each site the hourly traffic flow \(Q_{h}\) was provided by the Municipal Agency of Mobility, Environment, and Land of Milan (AMAT). The road traffic flows were calculated by a model based on origin-destination matrices applied on the entire city road network, reported in Figure 2. It has to be addressed that the Italian functional classification of roads is mainly based on the geometry of the roads, and therefore does not always correspond to the roads' actual use by urban traffic. In other words, roads of the same geometry and functional class could show quite different amounts of road traffic flow, depending on their actual function in the mobility road network. Such discrepancy has been already observed in previous investigations carried out on Milan road network [24; 25; 26].
## 3 Current WSNs Designed for Do nana
### ICARO 1 Network
#### 3.3.1 Animal Localization Project  
In addition to its use for flood level monitoring, the ICARO 1 network has also been used for other projects. For example, in 2011, it was used to deploy a localization prototype. In this prototype, the ICARO 1 infrastructure of the nodes was used for anchor nodes, which were used together with a newly developed low weight and low cost mobile device that had high autonomy, and it was used as a non-anchor node for tracking animals in the park.  
To obtain the localization of non-anchor devices, we proposed the use of an algorithm called _Localization based on an Intelligent System_ (LIS) [16]. LIS is a range-free localization technique. This algorithm is useful in applications that have high power consumption limitations. In applications such as wildlife localization in a natural environment, where the power availability and the weight are substantial restrictions, the use of energy-hungry devices that add extra weight, such as GPS's or hardware (for example, a mobile directional antenna) is not a good solution.  
For these reasons, for localization purposes, it would be better to use implicit characteristics in communications, such as connectivity, number of hops or RSSI (Received Signal Strength Indicator). The measurements that are related to these parameters are currently integrated in most radio devices. These measurement techniques are based on the beacons' transmissions between the devices. The LIS algorithm is designed by taking these characteristics into account. Figure 9 shows the graphics environment of the LIS algorithm, which is designed for the ICARO 1 network.  
LIS includes tracking with a distributed localization method that uses small mobile nodes (called tags), which have no additional hardware requirements, into a network of anchor nodes. LIS implements a hibernation protocol for the non-anchor nodes, which can save a large amount of energy on these devices.  
LIS obtains the position with a combination of two algorithms; one algorithm is based on a local node that uses a fuzzy system to obtain a partial solution, and the other algorithm is based on a centralized method, which is executed in a base station or a cluster-head that merges all of the partial solutions. Figure 10 shows the localization error of LIS compared with the Centroid Localization (CL) algorithm [17].  
Figure 8: Flood level estimation of the ICARO project.  
According to our results, this localization technique has been demonstrated to be useful in applications in which external devices such as GPS are not useful because of their high level of power consumption.
# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 8 Future Directions and Research Perspectives  
In addition to the challenges mentioned earlier, the field of using EEG signals to diagnose CVD is relatively new and lacks established procedures. This highlights the importance and necessity of further research and improvements. Certain issues were not covered in earlier studies in this field; thus, future studies must address these issues. According to [98; 99], EEG data are highly affected by factors such as emotions and age; thus, it is important to consider this while recording EEG data in upcoming studies. Another factor that may affect the EEG data, which has not been mentioned in the reviewed studies, is the screen brightness during data recording. This factor can influence EEG signals, as evidenced in [100], where an increase in screen brightness resulted in amplified responses, particularly in the areas most affected by visual stimulation, which are the parietal and occipital regions [101; 102; 103].  
Moreover, several aspects could be improved, including those discussed earlier concerning sample sizes, potential biases, and methodological constraints. It is well recognized that in EEG experiments, large datasets of EEG data should be utilized to achieve the best results; however, it is challenging to recruit a large number of suitable subjects due to the discomfort some volunteers may experience during certain procedures. This underscoresthe importance of simplifying EEG recording procedures in a manner that maintains the integrity of the study's objectives. This approach would enable researchers to address two limitations: the issue of the small sample sizes encountered in the previous studies and the methodological constraints. Moreover, while many studies have identified various ways to distinguish EEG responses to different colors, most have not utilized these methods for diagnostic purposes. Future studies could leverage and refine these methods to develop precise CVD diagnostic techniques, aiding individuals unable to undergo conventional tests and enhancing the reliability of diagnostic methods even for those who can undergo traditional testing.
###### Abstract
The heating unit heated the channel through electromagnetic induction so as to heat the medium flowing through the channel to cure the raw tire. Although the above studies can promote efficiency and reduce the energy cost of the curing process, the traditional heating mode of tire curing has not been changed, where heat was supplied by steam for the raw tire through medium channel and the heat conductivity of the bladder is poor. On the other hand, the traditional tire curing technology has the defect that accuracy of the produced tire is low. Firstly, variations in geometry and material often occur during components preparation and the raw tire building. When raw tire is moved to the curing process, due to a lack of sufficient rigidity, it is difficult for the high-elastic bladder that expands relying on steam to correct the uneven raw tire. What is more, the bladder itself is often asymmetric because of incomplete expansion, aging, adhesion, or unreasonable design, aggravating the problem. These situations would result in the unbalance and non-uniformity of the tire, which seriously affect its operation and driving safety [12; 13; 14]. Secondly, when the nitrogen gas with a pressure of 2.4-2.6 MPa is injected into the bladder through the inlet following the steam, the saturated steam with a pressure of about 1.8 MPa would naturally accumulate in the upper part of the bladder, because it has a lower density than the nitrogen. This accumulation of steam would consequently result in temperature difference between the top and bottom parts of the bladder. Due to heat exchange, the temperature difference increases as the condensate water deposited at the bottom of the bladder increases, leading to the inconsistency of SOC of the left and right sidewall, which affects the tire quality. Facing the drawback of the traditional curing method, this work presents a novel direct-pressure curing technology (DpCT) [15; 16] and develops a set of special equipment utilizing a radially expandable steel internal mold to replace the bladder. The internal mold generates heat through electromagnetic induction in conjunction with the external mold instead of steam and pipelines. Sample tires were trial-produced with the DPCT equipment. The energy-saving performance and high-precision characteristics of the new technology are also discussed.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 3 The Proposed Method
### Vicinity Region-based Communications and Query Processing
#### 3.3.2 Object-Side Tasks  
Each moving object \(o\) maintains a subspace \(N\) as its vicinity region and a set \(qs\_set\) of query segments inside \(N\). Whenever \(o\) moves, it checks (i) if it leaves \(N\) and (ii) it enters or leaves each query segment \(qs\inqs\_set\). If \(o\) leaves \(N\), it sends the RequestVR(\(o.p,o.cap,N_{old}\)) message to the server. In response, \(o\) receives the AssignVR(\(N_{new},qs\_set\)) message from the server. On the other hand, if \(o\) enters or leaves \(qs\), it sends the UpdateResult(\(o.p,qs\)) message to the server. In addition, \(o\) expects the following broadcast messages from the server and processes them as follows:  
* \(\mathsf{InsertQS}(qs)\): When \(o\) listens to the \(\mathsf{InsertQS}(qs)\) message, it first checks if \(o.p\) is inside \(qs\). If so, \(o\) sends UpdateResult(\(o.p,qs\)) message to the server to let the server insert \(o\) into the results of \(qs\)'s relevant queries. Then, \(o\) checks if \(qs\) is inside or partially intersects its vicinity region \(N\). If so, \(o\) inserts \(N\cap qs\) into \(qs\_set\). In case that the cardinality \(|qs\_set|\) of \(qs\_set\) is greater than \(o.cap\), i.e., \(|qs\_set|>o.cap\), \(o\) sends the RequestVR(\(o.p,o.cap,N_{old}\)) to the server.
* \(\mathsf{SplitRegion}(\hat{N},N_{left},N_{right})\): When \(o\) listens to the \(\mathsf{SplitRegion}(\hat{N},N_{left},N_{right})\) message, it first checks if its vicinity region \(N\) equals \(\hat{N}\). If this is the case, if \(o.p\) is inside \(N_{left}\), \(o\) sets \(N_{left}\) as its new vicinity region \(N_{new}\); otherwise, it sets \(N_{right}\) as \(N_{new}\). Then, for each query segment \(qs\) (\(\inqs\_set\)) that intersects \(N_{new}\), \(o\) replaces it with \(qs\cap N_{new}\).
* \(\mathsf{DeleteQS}(qs)\): When \(o\) listens to the \(\mathsf{DeleteQS}(qs)\) message, it just deletes \(qs\) from \(qs\_set\) if \(qs\) is inside its vicinity region \(N\).
* \(\mathsf{MergeRegion}(N_{left},N_{right},qs\_set)\): When \(o\) listens to the \(\mathsf{MergeRegion}(N_{left},N_{right},qs\_set({left+right}))\) message, it first checks if its vicinity region \(N\) equals \(N_{left}\) or \(N_{right}\). If so, \(o\) sets \(N_{left}\cup N_{right}\) as its new vicinity region \(N_{new}\) and replaces \(qs\_set\) with \(qs\_set({left+right})\).
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 2 Related Work  
### Object Detection  
#### 2.1.1 Thresholding  
According to [PERSON] [1], thresholding is one of the eldest, simplest and most popular techniques for image segmentation. In general, this task can be categorized into two schemes: global and local thresholding. On the one hand, if there is only one threshold used for the entire image it is called global thresholding. On the other hand, if there are several subregions in an image and for each of them a different threshold is used, it is referred to as local thresholding or adaptive thresholding.  
A prerequisite proposed by [PERSON] and [PERSON] [2] is that the image must include a bright object against a dark background or vice versa. All pixel values smaller or equal to the threshold value are assigned to one cluster and the remaining ones to the other group. A simple approach of a threshold selection can either be via user input, i.e., with a range slider or automatically determined by the system itself through analysing the image beforehand [3]. This can be done by determining the corresponding histogram of the image and placing the threshold in the valley between the two modes.  
As presented by [PERSON] [4] and [PERSON] [5], thresholding is often used as an initial segmentation step, e.g., for detecting vehicles in low-light conditions. Several approaches exists where thresholding is used to extract vertical edges at nighttime using stereo vision. Furthermore, it can also be employed for localizing taillights on grey-scale images. Hence, knowing the relations between a detected pair of tailllights and, with further assumptions, the typical width of a vehicle, a 3-D range information can be inferred.
## 3 Construction of Coherent Integration Time Optimization Model
### The Relationship between Model Parameters and Coherent Integration Time
#### 3.2.2 Effective Incoherent Average Number  
As discussed in Equation (15), the effective incoherent average at the SP is closely related to the waveform correlation [32]. Therefore, when the processing time of the incoherent average power waveform is \(T_{\text{L}}\), the variation of \(N_{\text{eff}}\) with the coherent integration time can be approximated following Appendix B as:\[\frac{1}{N_{\text{eff}}}=\frac{T_{\text{c}}\sum\limits_{\bar{n}=-(N_{\text{I}}-1)}^{ N_{\text{I}}-1}\left(2P_{\text{d}}\iint\frac{2P_{\text{d}}G_{\text{r}}(\overrightarrow{p}) \varphi_{0}}{4\pi R_{\text{r}}^{2}(\overrightarrow{p})R_{\text{r}}^{2}( \overrightarrow{p})}\Lambda^{2}(\Delta\tau)\text{sinc}^{2}(\Delta fT_{\text{c} })\exp^{-|2\pi\Delta f(\bar{n}T_{\text{c}})}d^{2}p+\frac{2P_{\text{d}}n_{0}^{ \text{d}}}{T_{\text{c}}}\text{tri}(\frac{\bar{n}T_{\text{c}}}{T_{\text{c}}})+ \frac{Bn_{0}^{\text{d}}n_{0}^{\text{u}}}{T_{\text{c}}}\text{tri}(\frac{\bar{n} T_{\text{c}}}{T_{\text{c}}})\right)}{T_{\text{I}}\left(2P_{\text{d}}\iint\frac{2P_{ \text{d}}G_{\text{r}}(\overrightarrow{p})\varphi_{0}}{4\pi R_{\text{r}}^{2}( \overrightarrow{p})R_{\text{r}}^{2}(\overrightarrow{p})}\Lambda^{2}(\Delta \tau)\text{sinc}^{2}(\Delta fT_{\text{c}})d^{2}p+\frac{2P_{\text{d}}n_{0}^{ \text{d}}}{T_{\text{c}}}+\frac{Bn_{0}^{\text{d}}n_{0}^{\text{u}}}{T_{\text{c }}}\right)} \tag{19}\]  
where \(n_{0}^{\text{d}}=2 kT_{\text{d}}\) and \(n_{0}^{\text{u}}=2 kT_{\text{u}}\) are the power spectral density of thermal noise from the up-looking and down-looking chains respectively, \(\text{tri}()\) is the triangular function, \(\Delta\tau\) is the difference between \(\tau_{n}\) and the scattering point delay \(\tau(\overrightarrow{p})\), and \(\Delta f\) is the difference between 0 Doppler and the scattering point Doppler \(f(\overrightarrow{p})\).  
Accordingly, the coherent integration time optimization model can be derived by simultaneously applying Equations (7) and (17)-(19). In accordance with Equation (17), a larger coherent integration time is needed to improve the altimetric sensitivity and precision. Nevertheless, the limitation of \(N_{\text{eff}}\) on the altimetric precision leads to a relatively smaller expected value of the coherent integration time. As a consequence, the best precision result is achieved by balancing the time required for the coherent integration of both aspects.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 1 Introduction
### Technologies  
LoRa is a technology mainly used for long-range communications [9; 10; 11] and is resistant to multipath because it makes use of the spread spectrum technique based on Chirp Spread Spectrum (CSS) modulation [12].  
Zigbee is a technology that uses DS-CDMA and FDMA multiplexing [13]. It is characterized by having a mesh network topology, being very low-cost and having lower power consumption compared to LoRa. Due to its lower power consumption, it has a shorter range than LoRa [14]. The characteristics of mesh networking, short-range communication and ultra-low-power consumption make Zigbee the ideal technology for home applications [6] in wireless personal area networks (WPAN).  
5G is the fifth-generation technology standard for broadband cellular networks. It is a technology that enables higher connection speeds compared to its predecessor, 4G. This technology is based on Orthogonal Frequency Division Multiplexing (OFDM) modulation, which offers high efficiency. 5G technology is characterized by providing a high connection speed, compared to traditional LTE networks, low latency and high bandwidth; it allows a large number of connected devices and achieves an efficient use of energy which increases the battery life of the devices [7].