esa-sceva/satcom-chunk-collection huggingface dataset:
# Manipulator Control of the Robotized TMS System with Incurved TMS Coil Case
## 4 Force and Torque Control for Adhering Between the TMS Coil and the Head  
If the TMS coil is controlled only by its position, then it may not adhere to the head of the subject. In this case, the position of the brain targeted by TMS can vary significantly due to the head's movement, even if it is small. In addition, it is not possible to compensate for a relatively small error without considering the interaction between the coil and the head. In this study, a force/torque control strategy is proposed to ensure the coil remains attached to the  
\(\mathbf{u}_{2}\) is the position controller which compensates the error between the coil's current and target positions. Even if \(\mathbf{u}_{2}\) does not consider the component of the error parallel to the orientation of the coil, it does not matter because \(\mathbf{u}_{1}\) will compensate for that by adjusting the coefficients \(|\cos\theta|\) and \(|\sin\theta|\) from (9). Meanwhile, if the target point estimated by the navigation system is located above the head, the coefficient \(|\cos\theta|\) makes the error larger. This is because while the force control is trying to reduce the magnitude of the error, which is parallel to \(\mathbf{u}_{1}\), the error component parallel to \(\mathbf{u}_{2}\) becomes larger owing to both orientations being opposite in this situation. In other words, \(\theta\) can be near \(180^{\circ}\). However, adhering the coil to the head rather than compensating for the error is more important if the coil is not too far from the estimated target point. The force control described above is shown in Figure 7.  
SInce the incurred coil case contacts the head with a larger area than the flat one, a larger force is required to rapidly reduce the coil's position error. However, transferring a larger force to the head for a longer time makes the subject uncomfortable. To solve this problem, \(F\) is scheduled based on the coile position error, as shown in Figure 8. The maximum force \(F_{\text{max}}\) is set for the safety and hardware limitations. The minimum force \(F_{\text{min}}\) is set to maintain contact between the coil and head. \(e_{o}\) is the coil error when the manipulator begins the force control. \(e\) is the coil's current error. \(r_{1}\) and \(r_{2}\) are the scaling factors for determining the error interval where \(F\) varies linearly with \(e\).  
Figure 6: Vectors \(\mathbf{u}_{1}\) and \(\mathbf{u}_{2}\), which affect the orientation of the force control.  
Figure 7: Block diagram describing the force control.
## 3 EDAS Prototype Specifications  
We have developed an evaluation prototype to investigate the feasibility of the EDAS architecture as a real-world implementation. The test settings were designed to reflect a working IoT-eHealth environment where a remote patient with wearable body sensors is monitored from a hospital site. The test environment consists of the following hardware and software components.  
At the remote patient domain:  
* Libelium open source eHealth Sensor Shield V.2.0
* Arduino Uno R2: A 16-MHz 32 K micro-controller for the eHealth Shield
* RN-XV 171 IEEE 802.11 b/g-compatible Wifi module
* XBee Communication Shield for communicating Arduino serial data over Wifi
* Samsung Galaxy SIII-Mini as a relay device  
At the hospital-controlled domain:  
* MySQL 5.6.12 as a storage for primitive medical and security events
* Apache 2.4.4 to display real-time patient vital signs enabled with HighCharts API* Sixty four-bit OSSIM V.4.6.1 as the EDAS monitoring and analysis platform
* A Jena-SPARQL-enabled JAR as an adaptation engine for accessing, modifying and storing the RDF /XML adaptation ontology
# A Critical Review of the Status of Current Greenhouse Technology in China and Development Prospects
## 2 The Current Situation of Greenhouse Development in China  
### China Greenhouse Market Development Overview  
China's greenhouse development started late, and over the course of time, its greenhouse development experienced three stages (Figure 1). The first stage began in the 1960s with the gradual emergence of agricultural greenhouses in China, which were structurally simple, single-function plastic greenhouses; the second stage began during the 1970s following the \"Eleventh Plenary Session of the Third Plenary Session of the Third Central Committee\". In the second stage in the 1970s, the greenhouse sector in China received special attention, and the area of greenhouses grew considerably, with the first solar greenhouses (also named solarium) beginning to appear. In the third stage, the 1990s to the present, the continuous efforts of China's researchers, combined with advanced greenhouse control equipment from abroad and the introduction of advanced crop production technologies, resulted in further modernization of China's greenhouse structure and crop production quality and also increased productivity. Today, with the introduction of the \"double carbon\" policy, today's greenhouses are moving in low-energy-consumption, high-efficiency, and high-yield directions.  
Structurally, China's greenhouse development has gone through four main stages: from simple plastic sheds, bamboo- and wood-structure sheds, and steel-structure sheds to the intelligent sheds currently used. From the initial simple plastic tunnels to bamboo and wood structure greenhouses to steel structure greenhouses. Intelligent greenhouses  
Figure 1: The development of facility-based agriculture in China.  
are based on steel structure greenhouses with automated equipment, such as room temperature control through thermostat sensors that monitor the air temperature. When the temperature is lower than the set temperature, the heating system will be automatically switched on, and when the temperature reaches the set value, the heating system will be automatically switched off. Advanced automation in greenhouses can not only control the room temperature but also automatically control irrigation, fertilizer supply, lighting, ventilation, etc.
## 3 Measurements  
Based on our previous experiences with the deployment of repeaters in PLC networks and their impact on network performance, we found out that repeaters in specific cases had a large impact on throughput and latency. Compared to results published in [62], we experienced more significant drop, especially in terms of throughput of the whole network. This outcome lead us to further research the impact of repeaters.  
For purposes of testing powerline communication, we decided to perform laboratory and on-field tests of the impact of repeaters used in such networks to observe its impact on network performance. For the acquisition of exact results, a pair of network testers together were used to analyze the behavior of a PLC network. Devices fully supported benchmarking methodology RFC 2544, RFC 6349 methodology and the Transmission Control Protocol (TCP) throughput tool. Depending on the type of data transmitted through the network, different demands on the protocol side were recognized.  
On the level of transport layer of the ISO/OSI hierarchy system, two common protocols prevail: User Data Protocol (UDP) and Transmission Control Protocol (TCP). For the real-time transmission of connection-less messages and transaction-oriented communication, UDP is used. This protocol is typical by its lightweight structure, which does not rely on confirmation of sent and received packets. Verification of successful transmission of all packets should be covered by higher layers, if necessary. In its simple form, this protocol tries to deliver information in the shortest amount of time, otherwise it might be discarded due to its discontinuous character.  
On the other hand, for some communication tasks direct confirmation on specific numbers of sent/received packets might be required. Transport protocol, called TCP, is designed for this purpose with its connection-oriented reliable type of communication. For enabling such communications, a three-way handshake is required. After that, bidirectional communication can be performed. Communication protocols adopted by electric utilities such as IEC 60870-5-104 or Modbus TCP/IP are based on principles of TCP/IP networks.  
Regarding the types of data which are transmitted in Smart Grid networks, and due to communication requirements, TCP is usually preferred. Further in this article, testing methods based on TCP communication are going to be used.
# ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing
## 4 Experiments and Results
### Enhancement Processing  
As described in Section 3.1, the original chromosome images (430 in total) were processed for quality and quantity enhancement. A sample chromosome image is shown in Figure 5a. First, we convert the original RGB image to a grayscale image with a size of 516 \(\times\) 516, as shown in Figure 5b. It can be observed that the image well remains its structural information after the conversion. Second, the image contrast stretching operation is performed to stretch the image contrast to the range of [0, 255]. As shown in Figure 5c, we can see that the contours of the chromosomes in the image become clearer, and some impurities in the background are eliminated or attenuated. Finally, CLAHE is performed to further enhance chromosome structural features without amplifying background noise, especially the strip feature. Our proposed method includes the above steps, and the final result is shown shown in Figure 5d. The originally blurred band features in the chromosome image become clearer and more distinguishable, with distinct outline features for each chromosome instance. At the same time, the background noise is well suppressed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Details** \\ \hline Epoch & 100 \\ Batch size & 4 \\ Optimizer & Adam \\ Learning rate & 0.00001 \\ Threads number & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter setting.  
The data after image enhancement is sent to our proposed data augmentation step, which mix the various methods including random translation, random brightness adjustment, random scaling, etc. to process the input data and generate new generated data. In the first stage, the data augmentation is carried out on 430 pieces of data, and 30 pieces of generated data are generated for each pieces of data. At this time, a total of 12,900 new data are generated. In the second stage, we manually screened the new data generated, removed the generated data with low quality (excessive noise generation, excessive translation, etc.), and finally obtained 12,666 generated data. The generated data and the original data constitute the chromosome segmentation dataset containing 13,096 pieces of data. The generated data includes generating images and corresponding label files.The new sample obtained by data augmentation is shown in Figure 6.
## 2 System Design Overview  
Figure 1a shows the schematic of the proposed WDM silicon photonic transmitter with surface-normal optical interfaces for optical input and output. Eight silicon Mach-Zehnder optical modulators based on bidirectional grating couplers are utilized to convert the electrical data to the optical domain with eight different wavelengths. For wavelength division multiplexing, an interleaved AMMI is utilized which is comprised of two \(1\times 4\) AMMIs and a bidirectional grating based MZI optical interleaver. As a key component, the bidirectional grating couplers are designed for perfectly vertical coupling and the structure is carefully optimized by the techniques of grating apodization and subwavelength grating engineering. Thus, the eight optical signals with different wavelengths are launched in-plane and coupled out-of-plane by the perfectly vertical grating couplers. The surface-normal optical interfaces make this silicon photonic transmitter chip very promising for low-cost fiber packaging.
# Mobility-Multihoming Duality
## 1 Introduction  
A Future Internet would enable Ubiquitous Computing, allowing for flexible communication with any and all connectivity available, through multiple different network technologies. However, the addressing architecture of the current Internet Protocol (IP) does not enable this directly at the network layer [1].  
Nevertheless, in the current Internet, mobile devices with multiple connectivity are becoming increasingly common for example smartphones and tablets. Multiple connectivity and mobility are enabled through retrospective enhancements to the IP architecture, but they do not integrate well. Currently, the user can suffer from disruption to communication on a host as the physical network connectivity changes. This can be to move between different network technologies (e.g., from 4G or 5G to IEEE 802.11/WiFi--also known as _vertical handover_). It can also be to enable and disable individual connectivity to different networks at the same time, e.g., the simultaneous use of 5G and IEEE 802.11/WiFi. This is because the IP address changes when the point of attachment (PoA) to a network changes, breaking the transport layer end-to-end state. Furthermore, while a device might be connected to multiple networks simultaneously, the use of IP addresses prevents a host from leveraging multiple network interfaces easily--true _host multihoming_--to improve its communication throughput or reliability. While solutions exist separately for mobility and multihoming, it is not possible to use them as a harmonised solution for a host.  
Harmonised host mobility-multihoming is when _both_ mobility and multihoming are occurring simultaneously. The host with harmonised mobility and multihoming capabilitywill be able to add or remove network connectivity dynamically, and they will be able to make use of more than one network actively for both sending and receiving. Such a mechanism can enable a much more flexible use of network connectivity, especially in the mobile context. For example, in situations where a mobile host is moving through an area with public IEEE 802.11 networks, it may be able to increase its available network capacity dynamically by making use of multiple connectivity points, as well as using existing cellular connectivity. If the cellular network happens to be congested, unlike the current single-homed mechanism, a host will not lose connectivity, and it will still be able to use the IEEE 802.11 connectivity in parallel. Once congestion has decreased, the host will be able to benefit from either the added capacity of the additional network or choose to connect only to a single network.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 4 Evaluation
### Assumptions  
We assume that a normal user from the source node \(Src\) is communicating with the destination node \(Dst\). We assume that the usual latency or loss rate of the default routing path is lower than other alternative paths. For a multi-path test, we assume that an attacker consumes the bandwidth of the path that the victim is currently using, making it difficult for the victim to communicate by degrading the network performance, such as through latency increase or packet loss. In this case, we assume that the attacker knows the path setting of the router because in a legacy router protocol, the path setting is pre-configured with a single path. In addition, we assume that an attacker is a passive attacker that cannot recognize when the victim has changed its path.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 5 Performance Analysis  
### Simulation Design  
In order to analyze the actual performance of the algorithm, we use OPNET to build a simulation platform of the LEO satellite network to simulate the algorithm. The Walker parameter of the constellation is 128/8/3, the orbital height is 1050 km, and the orbital inclination is 89\({}^{\circ}\). There is a seam between the orbits in the opposite direction, and the satellites on both sides of the seam are not linked.  
We use the satellite communication equipment coverage density as the simulation input, and the traffic demand between any two satellites node \(i,j\) meets [20]:  
\[TR_{i,j}=\frac{\left(SD_{i}*SD_{j}\right)^{\theta}}{d_{i,j}{}^{\varphi}}. \tag{7}\]  
Among them, \(SD_{i}\) and \(SD_{j}\) represent the equipment coverage density of the area served by satellite node i and satellite node j, \(d_{i,j}\) represent the distance between the two satellite nodes, \(\theta=0.5,\ \varphi=1.5\), and the data flow of each node obeys the Poisson distribution.  
We compared the disruption tolerant distributed routing algorithm (DTDR) based on local link information in this article with the DRA algorithm [6], OPSPF algorithm [15], and DODR algorithm [13]. The DRA algorithm statically calculates the route in each virtual node switching cycle, and when the link fails, the satellite directly connected to it switches to the backup path. OPSPF forms the network topology of the space segment according to the position of the local satellite and the relative position of satellites during topology switching. When the link fails, it advertises the failure information globally, and uses the shortest path algorithm to calculate the route. The DODR algorithm is based on the topology discovery strategy of the virtual nodes. In the process of path discovery, the routing information overhead is saved by restricting the access area. When the link fails, the area is expanded to establish a new path. The path selection follows the shortest propagation delay criterion. Common algorithm performance indicators include end-to-end delay, throughput, packet loss rate, link utilization, etc. Here we use end-to-end delay and packet loss rate to compare the performance of different algorithms [21].
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 3 Related Work
### Classification of Available Systems  
Previously-proposed advert delivery systems incorporate various combinations of architectures, as well as privacy mechanisms and can be arranged in three general categories.  
* Trusted proxy-based anonymity.
* Selection from pool of adverts.
* Anonymous direct download.
## 3 Data and Methods
### Color Fusion of Frequency Decomposition Data
#### 3.4.3 Main Frequency Analysis and Single-Frequency Body Optimization  
Different thicknesses of sand bodies exhibit different tuning frequencies, i.e., sand bodies with specific thicknesses have specific response frequencies. Stratal slices at these frequencies provide a clear depiction of the boundaries and internal structures of sand bodies. In this study, a frequency interval of 5 Hz is employed to analyze the relationship between seismic events and sand bodies. On the original seismic profile, the composite sand body of the S31 layer corresponds to a set of composite peaks, while the composite sand body of the S32 layer corresponds to a set of composite troughs. Following frequency decomposition at 45 Hz, the relationship between the event and medium-thickness sand body (single sand body) is enhanced, highlighting the fact that sand bodies of varying thicknesses possess different tuning frequencies. Considering that the thickness of the sand body in the target area is 8-25 m, three seismic cubes with different frequencies (20, 45, and 70 Hz) are selected based on the effective frequency width and seismic velocity of the seismic data in the study area. The tuning thickness corresponding to these frequencies can enable the characterization of the thickness envelope of the target sand body. The amplitudes of seismic bodies at different frequencies are color-mapped to red, green, and blue, which represent thick-, medium-, and thin-layer sand bodies, respectively. RGB fusion display is performed on the three single-frequency bodies, and the obtained seismic profile (c) shows the thickness information through bright and dark tones. Taking the S32 layer as an example, the (c) results indicate that the high-brightness white area is consistent with the thick sand body (Figure 4). As the shale content increases, the brightness gradually decreases.  
This study also employs frequency decomposition body slices for RGB fusion. Compared to the conventional individual frequency stratal slices, each frequency division data will strengthen the thickness of the sand body corresponding to the main frequency tuning, RGB fusion can show the sand body corresponding to the thickness of the three frequency division data through the change of three different colors and brightness, which is the biggest advantage of this technology. For the characterization of sedimentary facies, this method can be used to display sedimentary units of different thicknesses at the same time, and the pronounced strong contrast between bright and dark colors highlights the distribution of sand bodies with varying thicknesses on the plane, which can offer improved characterization of fan boundary, showing a more complete distribution of sedimentary facies. Therefore, it compensates for the limitations of traditional individual frequency slices that fail to depict sand body thickness, thus reducing the multiplicity of sedimentary facies interpretation.
###### Abstract  
In recent years, the use of image fusion method has received increasing attention in remote sensing, vegetation cover changes, vegetation indices (VIs) mapping, etc. For making high-resolution and good quality (with low-cost) VI mapping from a fused image, its quality and underlying factors need to be identified properly. For example, same-sensor image fusion generally has a higher spatial resolution ratio (SRR) (1:3 to 1:5) but multi-sensor fusion has a lower SRR (1:8 to 1:10). In addition to SRR, there might be other factors affecting the fused vegetation index (FVI) result which have not been investigated in detail before. In this research, we used a strategy on image fusion and quality assessment to find the effect of image fusion for VI quality using Gaofen-1 (GF1), Gaofen-2 (GF2), Gaofen-4 (GF4), Landsat-8 OLI, and MODIS imagery with their panchromatic (PAN) and multispectral (MS) bands in low SRR (1:6 to 1:15). For this research, we acquired a total of nine images (4 PAN+5 MS) on the same (almost) date (GF1, GF2, GF4 and MODIS images were acquired on 2017/07/13 and the Landsat-8 OLI image was acquired on 2017/07/17). The results show that image fusion has the least impact on Green Normalized Vegetation Index (GNDVI) and Atmospherically Resistant Vegetation Index (ARVI) compared to other VIs. The quality of VI is mostly insensitive with image fusion except for the high-pass filter (HPF) algorithm. The subjective and objective quality evaluation shows that Gram-Schmidt (GS) fusion has the least impact on FVI quality, and with decreasing SRR, the FVI quality is decreasing at a slow rate. FVI quality varies with types image fusion algorithms and SRR along with spectral response function (SRF) and signal-to-noise ratio (SNR). However, the FVI quality seems good even for small SRR (1:6 to 1:15 or lower) as long as they have good SNR and minimum SRF effect. The findings of this study could be cost-effective and highly applicable for high-quality VI mapping even in small SRR (1:15 or even lower).  
image fusion; quality assessment; vegetation index; spatial resolution ratio; spectral response function; signal-to-noise ratio 2020 IEEE Transactions on  
2020, 12, 1550; doi:10.3390/rs12101550