esa-sceva/satcom-chunk-collection huggingface dataset:
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 2 Impedance Characteristics Analysis of D-PMSG
### Impedance Modeling of D-PMSG
#### 2.3.3 Impedance Analysis of D-PMSG  
Transforming (11) to frequency domain, at the voltage perturbation frequency \(f_{p}\):  
\[s\mathbf{L}\mathbf{I}_{a}(f_{p})=\mathbf{V}_{ga}[f_{p}]-\mathbf{V}_{a}[f_{p}] \tag{20}\]  
Positive-sequence impedance is defined as:  
\[Z_{p}(s)=-\mathbf{V}_{p}(s)/\mathbf{I}_{p}(s) \tag{21}\]  
Introducing (17), frequency-domain version of (19) and (20) in (21), the positive-sequence impedance of the grid-connected D-PMSG can be easily obtained:  
\[Z_{p}(s)=\frac{j\omega_{1}L-sL-H_{i}(s-j\omega_{1})}{H_{i}(s-j\omega_{1})_{1} \mathbf{I}_{T}(s)e^{j\omega_{1}}+V_{1}\mathbf{T}_{p}(s)-1} \tag{22}\]  
Of course, the impedance characteristics have also been verified by simulation. Directly apply voltage \(v_{a}(t)=565.3\mathrm{cos}(2\pi f_{1}t)+20\mathrm{cos}(2\pi f_{p}t+\pi/4),f_{p} =\)(0, 100 Hz) at PCC, and the other set up parameters remained the same as Appendix A. Voltage and current signals collected and analyzed by FFT tools, the calculated simulation results compared with impedance prediction are shown in Figure 8. From the phase characteristics we can find that the D-PMSG's equivalent inductance \(X_{PMSG}\) is positive at subsynchronous frequency while negative (capacitive) at subsynchronous frequency.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 4 Reflected Field Strength Calculation
### Reflected Field Strength at Receiving Point
#### 5.2.1 Relay Node and Receiving Point Location  
Figure 14 shows the variation in reflected field strength with increasing distance between the relay and receiving point when the height difference between the relay node and receiving point is 0, 1, and 1.5 m. Let the distance from the relay node to the reflection plane \(m_{5}\) be \(w_{3}\) = 2.5 m, the distance from the receiving point to the reflection plane \(m_{6}\) be \(w_{2}\) = 2.5 m, and the height of the relay node be \(v_{3}\) = 2 m. The receiving point starts to move along and parallel to the roadway wall 10 m above the second corner. It then passes through the second corner until it arrives at the end point 10 m away from the second corner, i.e., the distance between the relay node and the receiving point is the path passed by the receiving point length.  
Figure 14 shows that the overall effect of the height differences on the reflected field strength at the receiving point was small. When the relay node is at the same height as the receiving point, the reflected field strength at the receiving point is greater than when there is a height difference because the receiving point deviates from the maximum radiation direction of the relay node, resulting in a reduction in the number of valid reflection lines. When the distance between the relay node and receiving points exceeds 10m, the reflected field strength attenuation is high. This is due to the presence of corners, which reduces the number of valid reflection lines, consistent with the trend of the attenuation curves  
Figure 14: Reflected field strength at the receiving point as a function of the distance between the relay node and the receiving point for different height differences.  
obtained in the literature [27; 28] for mine roadways. At this time, the maximum attenuation of the reflection field strength is approximately 40 dB. Since the U-shaped roadway is a symmetrical structure, if the influence of electromagnetic wave at the relay node is not considered, the maximum attenuation of the reflected field strength at the receiving point is about 80 dB for the electromagnetic wave emitted by the transmitting point in the U-shaped roadway.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 3 Protocol Specification
### Modbus-UDP Packet Priority and Coexistence with Other Traffic  
Modbus-UDP message priority at L3 and L2, as well as the internal Linux protocol stack priority (SO_PRIORITY socket option), may be assigned in two different ways:  
1. IP Type of Service (ToS) Differentiated Services Code Point (DSCP) AF42, as originally specified in RFC 2474 [40], IEEE 802.1Q Priority Code Point (PCP) 5 [38] (generally used for voice traffic) in the VLAN tag, and internal SO_PRIORITY 6. This priority assignment does not require the process to be privileged.
2. IP ToS DSCP CS4, IEEE 802.1Q PCP 5 in the VLAN tag, and internal priority 7. The process requesting this priority assignment must be privileged due to the internal priority being \(\geq 7\). Both schemes are suitable for wired LANs. Design and testing of an appropriate priority assignment scheme for other kinds of networks, for instance, wireless LANs, has been left for future work. The experiments described and commented on in Section 4 were carried out using priority scheme 2, which leads to better performance.  
As outlined before, both the IP ToS and the SO_PRIORITY are set directly by the Modbus-UDP protocol stack software by means of the corresponding socket options: SOL_SOCKET level, SO_PRIORITY option, and IPPROTO_IP level, IP_TOS option, respectively. Handling the VLAN tag, instead, requires an interaction with the Linux protocol stack. For instance, the following sequence of privileged commands  
$ ip link add link $IF name $VLAN_IF type valind $VLAN_ID $ ip address add addDR brd $BRD dev $VLAN_IF $ ip link set dev $VLAN_IF type valn $ egress-qos-map 7:5 ingress-qos-map 5:7 $ ip link set dev $VLAN_IF up  
configures a virtual network interface called $VLAN_IF that uses physical interface $IF to send and transmit VLAN-tagged packets with VLAN identifier $VLAN_ID. The virtual interface has IP address $ADDR and broadcast address $BRD. Moreover, the third command asks the protocol stack to map outgoing packets at SO_PRIORITY 7 into PCP 5 (VO, voice traffic with \(\leq 10\) ms latency) and vice versa for incoming packets. All other outgoing traffic has PCP 0, and all other incoming traffic is given SO_PRIORITY 0, the lowest.
## 4 Research on Critical Technologies
### Atmospheric Channel Modeling  
Due to atmospheric turbulence effects, when a laser beam is transmitted into the atmosphere, the light intensity at the receiving end undergoes random undulation, i.e., a light intensity scintillation effect, which leads to signal attenuation and the deterioration of the received signal quality at the receiving end. Therefore, in order to improve the performance of the system, it is essential to establish channel models under different atmospheric turbulence conditions. Existing channel models include log-normal distributions, _gamma-gamma distributions_, negative exponential distributions, and so on[PERSON] These distribution channel models are generally represented by probability density functions.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 2 Background
### Adaptive Semi-Persistent Scheduling (SPS)  
The adaptive SPS is a series of PUSCH resources semi-persistently allocated to an LTE UE, where the allocations have a fixed period \(T_{SPS}\) in the time domain but the allocation size is variable in the frequency domain. The adaptation of the allocations is done in terms of the number of PRBs utilizing the BSR reported by the UE within its transmitted uplink data unit to vary the PRBs allocated for the next semi-persistent transmission. The BSR is piggybacked with the uplink data unit, hence does not require any explicit control channel resources. The uplink scheduler considers this BSR information for determining the future uplink resources according to a customized function and the UE can also determine the exact amount and time-frequency co-ordinates for the allocation using this mutual knowledge.  
Instead of signaling ahead of each uplink transmission separately, the following parameters are signaled via the PDCCH before the UE can initiate their adaptive SPS uplink transmissions: the adaptive SPS period, \(T_{SPS}\), uplink subframe number, \(i\), initial frame offset, \(j\), MCS index, \(I_{MCS}\), starting index of the contiguously allocated uplink PRBs, \(k\), the maximum number of allocated uplink PRBs \(N_{P,max}\) in a subframe and the adaptation function \(func(B)\) which is used to calculate the adaptively allocated number of PRBs for each new transmission based on the latest BSR information \(B\).  
The supported adaptive SPS periods are always integer multiples of the LTE frame duration. This constraint is required to avoid changes in the allocated uplink subframe number for the subsequent adaptive SPS transmissions from the same device. Initial frame offset indicates the number of frames the device has to wait until they can begin the adaptive SPS transmissions. More than one device can use the same uplink resources for adaptive SPS transmission if they have the same value of \(T_{SPS}\) yet different initial frame offsets (\(j\)) assigned to them which means they are multiplexed in time.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 3 The Proposed Protocol
### Detailed Design of APCDBRP  
This section presents an adaptive power-controlled depth-based routing protocol (APCDBRP). The routing protocol consists of three main parts: route establishment phase, data forwarding phase, and data protection and route reconstruction mechanism. All packets in the network, including data packets and control packets, record the transmitting power of the sending node. Each node maintains a routing table that stores the forwarding factor in its neighboring nodes, with the neighbor having the highest forwarding factor being selected as the optimal forwarding node. Furthermore, due to the influence of water currents, underwater nodes may shift, resulting in changes to the network topology. This necessitates frequent updates to the routing information, which could lead to data forwarding failures and routing voids. The faster the flow of water, the more pronounced this problem becomes. The implicit routing update mechanism and the data protection and route reconstruction mechanism mentioned later in the text are primarily employed to address this issue.
## 4 System Architecture  
We intend to employ NXT-Freedom to enforce bandwidth guarantees for VDCs fairly. It mainly has two objectives: one objective is to allocate the demanded bandwidth to each VDC dynamically and fairly; the other one is to ensure performance isolation among VDCs during enforcement of allocated bandwidth. To achieve those two objectives, we employ two modules, as shown in Figure 5. One module is a centralized bandwidth allocator module, and the other is a distributed bandwidth enforcer module running on the hypervisor of each compute server.
# A Systematic Review of Computer Vision and AI in Parking Space Allocation in a Seaport
###### Abstract  
Urban expansion has ushered in a landscape of opportunities and challenges across housing, transportation, education, health, and the economy. In response to these evolving dynamics, the application of artificial intelligence (AI) and computer vision (CV) technologies has emerged as a transformative solution. While smart traffic monitoring and advanced parking distribution systems have eased urban pressures, optimizing mobility remains pivotal in the context of burgeoning smart cities. However, the seaport industry grapples with formidable issues in the efficient transport of containers. In light of this, the fusion of AI and CV technology holds promise as a solution. This study pioneers a systematic review, representing a novel exploration, delving into a comprehensive evaluation of the existing literature. It scrutinizes the profound advantages AI and CV offer in constructing sustainable, efficient parking ecosystems within seaports. Our methodological approach encompasses data collection, rigorous quality assessment, and meticulous exploration of the application of CV and AI in the realm of smart parking management. The findings underscore the pivotal role of AI and CV technologies in the development of efficient, sustainable transportation systems, particularly for optimizing container movement within seaports. This research presents a comprehensive analysis of the literature in the area of the application of AI and CV technologies in optimizing parking management at seaports, shedding light on the potential for sustainable transportation solutions in this critical domain. As these technologies usher in enhancements in traffic management, parking space allocation, and container logistics within seaports, this study represents a vital and timely contribution to the field, serving as a cornerstone for future innovations in seaport management and the broader context of smart cities.  
port management; computer vision; deep learning; machine learning; artificial intelligence (AI); parking space allocation
## 6 Results and Discussion
### Evaluating the Flexibility of HPWH: Prosumers
Figure 15 depicts simulations for the daily average PV generation for two different months and seasons (November and July) representing different PV generation performances. With respect to demand, in November, the peak is similar, but it is delayed to 12:00, while in July, the peak is delayed to 10:30. In July, the WH end-use peak grows (Figure 15b, blue curve) due to a higher consumption of water. Nevertheless, the HPWH demand increase is balanced by the increase of PV generation and the commitment of DR (Figure 15, grey curves). Notice that DR target basically follows PV generation (see Figure 13). In this way, the net-demand decreases in both cases (Figure 15, blue curve minus grey curve). Figure 14: DR control of HPWH loads to meet PV target generation profile on an individual day: (**a**) Demand profiles; (**b**) Average temperatures inside the tanks. Figure 15: DR control of HPWH control group (QHCG) to meet PV monthly average generation profiles: (**a**) November; (**b**) July. Some data about the goodness of DR strategies for prosumers are presented in Table 6. Although the HPWH demand is increased when DR is applied, the net demand with DR is reduced. This benefits prosumers because it avoids problems with the injection of power into the network. For instance, the development of control strategies to demand increases self-consumption around 15-30%. Notice that this depends on the PV energy generated and the capacity of WH loads to increase their temperature (e.g., through a mixing valve or the use of an ancillary resistor). As stated before, the first option is marginal in the Spanish residential sector because these mixing valves require additional capital and installation costs (e.g., \(\xi\)100-200 is the capital cost of a valve) and periodic inspections to ensure a properly functioning system, especially in several areas in Spain with a hard water supply (i.e., high concentrations of calcium and magnesium ions, which affects the WH and ancillary resistors performance). Nevertheless, mixing valves have several benefits: first, they enable WH to be operated at higher temperatures (preventing the growth of Legionella and increasing storage capacity); second, they can reduce the size and power requirements to provide capacity during larger time periods, and finally, they assure constant hot water temperature to the customer that avoid water losses due to low or high temperatures during the service. This option will be considered in more detail in the authors' future research. Both options involve an additional cost to the user. For this reason, customers and aggregators should benefit from other DR revenues through their participation in complex products, services, and markets.
## 4 DUS-CS Hardware Design  
In the direct under-sampling system, the most vital component is data acquisition (DAQ) hardware. This is designed according to the receiving, collecting, and transmitting functions of the underwater acoustic signal. The hardware components include a preamplifier, band pass filtering, programmable amplifier, ADC (analog-to-digital conversion), the DUS-CS processor and the network interface (Figure 7). Figure 8 is an image of the signal collection card.  
Figure 6: Variation of the matching rate with the SNR. (**a**) the compression ratio is 100%; (**b**) the compression ratio is 50%.  
Figure 7: Data acquisition (DAQ) components.  
The preamplifier is the first stage of the collection card, which is controlled with a single resistor that meets the requirements of the system. The bandpass filter uses a fourth-order high-pass filter and a fourth-order low-pass filter in cascade implementation. The programmable amplifier is used for automatic gain control of the underwater acoustic receiver. According to the experimental environment, it is possible to adjust the amplification factor manually or automatically to improve the signal-to-noise ratio of the receiver in a better state. The ADC converts the signal from analog to digital and this function determines the quality of the acquired signal. The processor is the main component of signal acquisition and control, and it is also the main module of the direct under-sampling method. The network collection of the DAQ is used for data upload and network protocol conversion.
# Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network
## 2 Proposed E-LFDA Method for WLAN Indoor Positioning  
### Motivation  
Fingerprinting positioning can be considered as a multiclass classification problem. Each reference point can be labeled as one class by related RSS samples. The whole process of location estimation can be regarded as classifying the real-time RSS samples into the right classes. Therefore, compared with PCA, LDA performs better, since more discriminative features can be extracted. LDA maximizes the between-class distance while constraining the within-class distance into a certain extent. When RSS distributions are Gaussian or semi-Gaussian, LDA performs well [27]. However, in a realistic wireless propagation environment, non-Gaussian and multimodal distributions can be always observed. For example, due to the antenna diversity [28] or multipath effect, RSS distribution from one AP at a fixed location may show more than one peak or cluster, as seen in Figure 1. For multimodal RSS samples at some reference point, LDA may merge the multimodal data into a single cluster by constraining the within-class distance into a small value. This rigorous constraint may decrease the degree of freedom left for increasing feature separability, and, thus, degrade the classification ability of location features extracted by LDA. Compared with LDA, LFDA gives a less restrictive constraint, because faraway pairs of within-class samples are not imposed to be close. Thus, a larger degree of freedom is left for LFDA to maximize the between-class scatter and more discriminative features than LDA are extracted.  
Furthermore, due to the highly uncertainty and multimodality properties of RSS signal, a large number of RSS samples are required at reference points to characterize these properties [29; 30]. It is well known that the generalization ability is mainly determined by the ratio of training sample size to feature dimensions in machine learning and pattern recognition. For the deployed LFDA method, it is prone to data sampling density as well [26], especially when the dimension of input signal space is high. Therefore, in order to improve the performance of LFDA, it is necessary to increase the number of training RSS samples. To address this issue, an intuitive way is to increase the number of samples collected during offline phase. However, samples collection is a time-consuming work, especially for a large-scale target environment. Therefore, we propose a signal perturbation method to increase the number of training samples without adding offline data collection effort.
# Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing
## 3 Indoor Reconstruction Methodology
### Planar Primitive Extraction
Indeed, the floor can be designed as the horizontal plane having the lower height (Figure 3a,b). Conversely the ceiling is detected as the horizontal plane located at the highest level. In this way, the distance from floor to ceiling can be also worked out. To determine the floorplan, first the walls of the room need to be detected (Figure 3c). This problem can be difficult due to possible clutter and occlusions, resulting in the fact that walls may have not been scanned and thus are pending in the point cloud. For this reason a proper completion is necessary in order to reconstruct these walls in a plausible way (Figure 3d). A first rough floorplan can be obtained by projecting the points belonging to the ceiling onto a horizontal plane. Indeed, the acquisition of the ceiling surface, due to its location, is generally less influenced by clutter and occlusions than other surfaces of the room. Figure 3: Detection of room surface: (**a**) localization of the room addressed in this example; (**b**) planar elements classified as ceiling (purple) and floor (sand); (**c**) possible wall surfaces; (**d**) a wall portion is missing (red circle) due to occlusions, (**e**) occupancy map of the ceiling with real walls (green segments) and spurious boundaries (red segments) for Room 1, (**f**) detected primitives (green) and guessed ‘pending’ walls. Usually, indoor environments with a standard usage feature most of the floor surface free for walking and moving around. The horizontal plane is discretized into cells of size \(\beta_{1}\times\beta_{1}\) where \(\beta_{1}\) is set equal to the mean sampling resolution in the point cloud. Then an occupancy map is generated where one pixel represent elements where MLS data are available, and zero pixels are grid elements with no data. Starting from this binary rate map it is possible to derive pixels representing the boundary of 'occupied' cells which represent a first rough floorplan. Due to possible occlusions the obtained planes may contain some spurious boundaries, i.e., the ones not associated to a wall (Figure 3e). To validate the obtained boundaries, a check is done against the segmentation results. In particular, only vertical segments falling inside the cells labelled as boundary are considered as real wall surfaces. In indoor modeling applications, a single pending small wall may jeopardize the entire reconstruction of the floor plan. In the developed strategy, such gaps are filled by incorporating additional, unseen 'pending' walls [36]. In the indoor environment it is possible to observe that walls generally intersect orthogonally. For this reason 'pending' walls are guessed as orthogonal to the already detected and are added from the boundary of the recognized walls (Figure 3f).