esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Monolithic Photonic Integration for Optical Memories
The eye diagrams of the FF output signals feature a recovery time of 150 ps and an Extinction Ratio (ER) of 6 dB. Figure 4h-j depict synchronized time traces and the respective eye diagrams for the Read operation. For this evaluation, the FF was set to one logic state and the data were then read by transmitting the _Inverted Access signal_, shown in Figure 4h. Figure 4i,j illustrate the time traces of the RAM cell output signals, after the random access controlling operation of the AG for both of the FF logic states. A high output value is only obtained when there is no _Inverted Access_ signal pulse present, indicating that access is granted for the Read operation, while when there is a high optical power for the _Inverted Access_ signal, the RAM output of the RAM cell features a logical \"0\" and access to the memory is thus blocked. The eye diagram of the read output features an ER of 9 dB. Figure 4k shows the BER measurements obtained for both the Write and Read RAM cell functionalities, where, in all cases, error free operations are demonstrated. For the Write operation, the BER diagrams reveal a power penalty of 0.6 dB for the _Set_ and _Reset_ signals compared to the Back-to-Back measurements of the _bit_ and \(\overline{bit}\) signals, and in turn, 4.6 dB for the FFOut1 and FFOut2 signals of the data written to the RAM cell, while for the case of the Read operation, a power penalty of only 0.5 dB was obtained at the \(10^{-9}\) condition. The results were obtained with a current injection of around 250 mA for each SOA, while when relying on four SOAs operating at 5 Gb/s, the resulting energy efficiency per bit is 400 pJ/bit.
## 4 Discussion
The studies on machine learning applications in urban settings offer a parallel to the digital twin's use of machine learning models. The application of machine learning algorithms allows for a more sophisticated analysis of the vast dataset, enabling the identification of patterns that might not be immediately apparent. This resonates with the digital twin's use of a random forest classifier for predictive modeling. The literature showcases a combination of digital twin technology and machine learning for enhancing public services [5; 8; 12; 13; 14; 22]. In a similar vein, the digital twin in Patras leverages machine learning within a Flask-based framework to optimize urban planning. This integration signifies a synergy between the dynamic representation of the city (digital twin) and advanced analytical tools (machine learning) to improve public services. Spatial Clustering: The spatial clustering of reported issues on the interactive map is a pivotal feature. Clusters serve as a visual aid, quickly highlighting areas with a higher concentration of problems. This functionality streamlines decision-making processes, enabling city officials to prioritize interventions and allocate resources efficiently. Continuous Development and Adaptability: The commitment to continuous development, as evident in the frequent updates and additional features, underscores the adaptability of the digital twin. The inclusion of new filters and charts, as showcased in Figure 19, ensures that the platform remains responsive to the evolving needs of its operators. Thisadaptability positions the digital twin as a scalable solution capable of incorporating additional functionalities and datasets in the future. User Empowerment: The emphasis on user interaction and empowerment through the application's features is noteworthy. By providing users with the ability to filter issues, years, areas, forecasts and visualize data based on their preferences, the digital twin becomes a versatile tool for diverse stakeholders. This user-centric approach enhances engagement and ensures that the platform is accessible and beneficial to a wide range of users, from city officials to residents. The literature on the potential of digital twins for urban planning aligns with the broader implications of the digital twin project in Patras. The ability to simulate different urban planning scenarios corresponds to the digital twin's capability to predict and simulate future issues based on historical data. This reinforces the transformative potential of digital twins in revolutionizing urban planning methodologies. Potential for Real-Time Integration: The architecture's extensibility, particularly the incorporation of Flask, opens doors to future enhancements. The suggestion of integrating real-time data from various sensors and IoT devices positions the digital twin as a platform that can evolve to incorporate emerging technologies. This potential for real-time data integration holds promise for creating a more dynamic and responsive urban management system.
## 3 Results and Discussion
### Influencing Factors of Vibration Isolation Effect for the Composite Subgrade Structure  
The previous section has proven the great vibration isolation of the composite subgrade structure. In order to investigate the vibration isolation effect of the composite subgrade structure with different thicknesses of XPS plates or vibration intensity (corresponds to different falling heights of the hammer), the following analysis was conducted.  
In this study, the isolation coefficient was adopted to evaluate the vibration isolation effect of the composite subgrade structure, which is defined as:  
\[C=A_{i}/A_{0} \tag{1}\]  
where C is the isolation coefficient; \(A_{i}\) is the acceleration peak value of the composite subgrade structure under different test conditions; and \(A_{0}\) is the acceleration peak value without setting the composite subgrade structure (corresponds to the acceleration value of G1). When \(C<1\), it means that the vibration isolation effect is significant. The smaller the C value, the better the vibration isolation effect.
# MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation
## 3 Proposed Model
### Residual Block  
The current U-Net architecture has only a few layers. However, it can be substantially deeper to provide better performance [29]. Though adding more layers sometimes increases the network performance, it enlarges the number of parameters, leading to redundant computation and increasing the problem of gradient vanishing during training. Gradient vanishing is the decrease in the learning rate with forward propagation due to the presence of too many hidden layers, which degrades the network's performance [30]. To mitigate this problem, [PERSON] et al. [27] introduced residual blocks that share the same idea of concatenating the input (identity short-cut) and propagating the low fine details. This enhances the network performance without the need for going deeper or deploying a pre-trained network.  
Thus, residual blocks are confirmed to provide deeper networks and reduce the gradient vanishing problem. Moreover, residual connections make the model learning easier as they learn a function with reference to the input feature map, instead of an referenced function [27]. Accordingly, we replace the basic U-Net blocks shown in Figure 2a with residual blocks proposed in [27], shown in Figure 2b, where each block is constituted of two \(3\times 3\) convolution layers, followed by batch normalization and a ReLU activation function. The input (identity-shortcut) is passed through \(1\times 1\) convolution, followed by batch normalization, and added with the output using element-wise summation.  
Each residual unit is defined as [27]:  
\[y_{i}=h(x_{i})+F(x_{i},W_{i}) \tag{1}\]  
\[x_{i+1}=f(y_{i}) \tag{2}\]  
where \(x_{i}\) and \(x_{i+1}\) are the input and output of the \(i\)-th residual unit, respectively; \(F(.)\) is the residual function; \(f(y_{i})\) is the activation function; and \(h(x_{i})\) is an identity-mapping function. \(W_{i}\) is the weight vector of the feature map at the \(i\)-th residual unit.
# Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions +
###### Abstract  
[PERSON]'s stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth's surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a frequency-selective generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingraa sea clutter database is performed, and good agreement is shown.  
Keywords:airborne radar; radar clutter; radar signal processing; stochastic systems; time-varying systems; maximum entropy +
Footnote †: This paper is an extended version of our paper published in the 2018 IEEE Radar Conference.
# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
###### Abstract  
Gun violence and mass shootings kill and injure people, create psychological trauma, damage properties, and cause economic loss. The loss from gun violence can be reduced if we can detect the gunshot early and notify the police as soon as possible. In this project, a novel gunshot detector device is developed that automatically detects indoor gunshot sound and sends the gunshot location to the nearby police station in real time using the Internet. The users of the device and the emergency responders also receive smartphone notifications whenever the shooting happens. This will help the emergency responders to quickly arrive at the crime scene, thus the shooter can be caught, injured people can be taken to the hospital quickly, and lives can be saved. The gunshot detector is an electronic device that can be placed in schools, shopping malls, offices, etc. The device also records the gunshot sounds for post-crime scene analysis. A deep learning model, based on a convolutional neural network (CNN), is trained to classify the gunshot sound from other sounds with 98% accuracy. A prototype of the gunshot detector device, the central server for the emergency responder's station, and smartphone apps have been developed and tested successfully.  
convolutional neural network; Mel Frequency Cepstral Coefficients (MFCC); Jetson Nano; smartphone app; SQL database; server; Bluetooth; Wi-Fi; gunshot; push notification +
Footnote †: journal: Medical  
Individuals affected by gun violence, including witnesses, bystanders, and neighbors, may undergo feelings of stress, depression, anxiety, and post-traumatic stress disorder (PTSD). More than 5% of America's children have witnessed a shooting and it causes them psychological distress [6]. The proposed device can lead the police to the crime scene as soon as possible and can give peace of mind.
* Solely in 2010, emergency rooms received 36,000 firearm assault victims, with 25,000 requiring hospital admission, resulting in a staggering USD 630 million in medical expenses. The overall economic impact of gun violence on the American economy is estimated to be at least USD 229 billion annually [6]. The proposed device will bring the police quickly and reduce medical costs and property damage.  
The rest of this paper is organized as follows. The related works and comparison with other works are discussed in Section 2. In Section 3, materials and methods are discussed for the custom deep learning model for gunshot detection, and prototype system architecture consisting of the device, central server, and smartphone apps. Results of the deep learning model training, validation, and testing as well as the performance of the prototype system are discussed in Section 4. In Section 5, the discussion and future works are presented. Finally, Section 6 presents the conclusion.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 5 Localization in Smart Applications
### Smart Home and Smart Infrastructure  
A smart home is a residence that interlinks and controls smart devices to offer everyday activities of non-directional data to residents. It provides facilities and pertinence for upgraded health, ease and security. Few smartphone applications involve real-time user localization, which is especially imperative for elders and physically disabled people. Moreover, the localization permits smart devices to perform elementary tasks and executes complex commands through flexible interactions with user [79].  
The indoor localization approach developed in [80] is accurate and is modified according to the environmental dynamics. It does not entail surveys and the primary training phase. The approach also captures interferences, broadcast turbulences and dimensional errors. In a smart home, all the gadgets in a house are supervised automatically and concurrently with respect to the resident's location. It includes communication among the localization scheme and the remote controller service area. Gadgets in the smart home execute inevitably with respect to in and out moves of the resident. It includes services like closing and opening shutters, security alarms, adjustments of humidity sensor nodes and radiators, automatic control of temperature and electricity, etc. In addition to this, it informs directly to the resident of any unusual observations.  
Bluetooth is used in [81] for indoor localization in a smart building. Best accomplishment strategies applied in this scheme lead to a closer estimate of the object's location. The object distance improved by 22% and established 66% additional rewards compared to the supervised deep reinforcement learning model. The time-based localization scheme proposed in [82] uses Wi-Fi to evaluate the position of an object. It is structured to process smart devices, which smears the time intermission as the difference in the positions and the variable quantity of the Wi-Fi location to spot time points.  
A positioning scheme based on Bluetooth technology to outline an association between the RSSI and the device location [83] utilizes the activities of a user for narrowing the exploration space iteratively to locate the desired object.
## 2 Materials and Methods
### Parasitism of Planthopper and Leafhopper Eggs  
In order to quantify parasitism rates under different nitrogen levels, IR62 and IR64 plants infested with planthopper or leafhopper eggs were exposed in the experimental plots. The IR64 and IR62 plants were initially sown to seed beds in a greenhouse and, at 28 DAS, were individually transplanted into pots of 5 \(\times\) 5 cm (H \(\times\) diam) with zero-added nitrogen or with nitrogen equivalent to 150 kg ha\({}^{-1}\). The pots were individually covered within mylar cages of 45 \(\times\) 10 cm (H \(\times\) diam), each with a mesh top. At 50 DAS, five female and five male BPH, WBPH or GLH from the colonies were placed into the cages (each plant with a single herbivore species). The females were allowed to lay eggs in the plants for 24 h after which the adults were removed. Three plants of each variety, each plant with eggs of one of the herbivore species, were exposed in the field subplots each season (i.e., 3 species \(\times\) 2 varieties \(\times\) 2 nitrogen levels \(\times\) 6 replicates = 72 plants per season). The potted plants were placed in subplots with the corresponding variety and nitrogen level. A set of not field-exposed, control plants (i.e., 3 insect species \(\times\) 2 varieties \(\times\) 2 nitrogen levels \(\times\) 5 replicates = 60 pots) were maintained in a screenhouse. The control plants were used to verify that eggs were not parasitized before transfer to the field plots. We also used the controls to assess egg laying by each herbivore species as a response to variety and nitrogen level.  
After 4 days, the field-exposed plants, and control plants that were maintained in the screenhouse were collected. The plants were removed from their pots and the roots rinsed under tap water. The roots were then wrapped in moistened paper towels. Each plant was stored in a test tube of 200 \(\times\) 25 mm (H \(\times\) diam). Tubes were wrapped with carbon paper and a translucent vial was placed at the top of each tube to attract the hatched insects to the light (i.e., to the vial). Fourteen days after collection, the tubes with the plants were placed in a freezer at -20 \({}^{\circ}\)C. The plant samples were dissected under a stereo microscope (Nikon SMZ-2B, Tokyo, Japan) and the numbers of eggs, hatched herbivores and parasitoids (free-living adults and parasitized herbivore eggs) were recorded. Parasitoid species were differentiated by the color of the eggs: orange eggs = _Gonatocerus_ spp., reddish-brown eggs = _Anagrus_ spp., yellow eggs = _Digosita_ spp.; white colored eggs were regarded as unparasitized (leafhopper and planthopper eggs). This color-based identification method is regarded as effective for assigning a genus to the parasitoids, but not for determining species [55]. However, emerged adult parasitoids were identified to the species level.
## 4 Theoretical Error Analysis  
All measurement errors can be divided into two types: extrinsic and intrinsic. Extrinsic errors cover the physical effects on the measurement channel, such as multipath effects and changes in the signal propagation speed due to changes in the surrounding environment. On the other hand, intrinsic errors are caused by defects of hardware or software. Extrinsic errors are harder to handle or predict in realistic deployment, but intrinsic errors cause many complications. Position estimation is strictly influenced by even relatively small measurement errors [36]. Some extrinsic errors for our proposed localization scheme will be addressed in Section 5.3. In this section, we analyze RLS in detail and point out the possible localization errors which affect its accuracy.
# Mode-Locking Dynamics in an All-PM Figure-Nine Tm-Doped Fiber Laser
## 2 Experimental Setup  
The experimental setup is depicted in Figure 1. The all-PM Tm-doped oscillator was optically pumped by a self-made CW laser operating at 1565 nm. The pumping laser was based on a pair of fiber Bragg gratings with reflectivity of 10% and 99%, 2.5 m of Er/Yb-co-doped double-clad fiber (Nufern SM-EYDF-6/125), and a multimode fiber-coupled laser diode operating at 976 nm (BWT K976 AB2 RN-9.000W). The pumping laser delivered up to 1.8 W to the oscillator via the PM wavelength division multiplexer (PM WDM). The entire Tm-doped oscillator was composed exclusively of PM fibers and components. The fiber part of the laser incorporated 1560/2000 nm PM WDM, asymmetrically placed 35 cm of PM highly doped active fiber (Nufern PM-TSF-5/125, \(\beta_{2}=-20.0\) ps\({}^{2}\)/km, TDF) [11]. We used a C-band Panda-type PM fiber (Nufern PM1550-XP, \(\beta_{2}=-76.0\) ps\({}^{2}\)/km, SMF) as a passive fiber in the entire cavity [11]. The linear arm was connected with the loop via a fiber-based polarization beam combiner (PBC) and consisted of a reflective-type NRPS. The former combined beams from the loop segment into the free-space segment in two orthogonal polarizations. The latter introduced intensity-independent phase bias between beams via a set including a Faraday rotator (FR), a quarter-wave plate (QWP\({}_{1}\)), and a half-wave plate (HWP). The fast axis of each of the wave plates corresponds to an angle of zero degrees. In addition, the fast axis of the fiber entering the collimator is parallel to the fast axis of the plates in the zero degrees setting. By adjusting the angle of these waveplates, we changed phase bias, modulation depth, and the ratio of light reflected by the free-space polarization beam splitter (PBS), thus constituting a reject port [48]. By using QWP\({}_{2}\) placed between the end mirror and PBS, we controlled the amount of light returning to the loop part and the amount of laser radiation leaving the output port. The total length of the oscillator was 404.5 cm, including 23.0 cm of the free-space segment. Both active and passive fibers possessed anomalous dispersion in the region around 1985 nm, resulting in the net group delay dispersion of approximately \(-0.091\) ps\({}^{2}\). Regarding the asymmetry of the active fiber placement, between the beginning of the Tm-doped fiber and the end of the collimator there were about 97.0 cm and 226.5 cm for clockwise and counterclockwise directions of propagation, respectively, as shown in Figure 1.
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 4 Evaluation
### Evaluation Results
#### 4.3.1 Comparing the Availability of the Three Types of PV-NoN Model
Although with type-SD traffic on one VN does not interfere with the other VN, neither VN can use its allocated physical resources fully because of the existence of the partition. Meanwhile, with type-UD the VNs can use their resources fully, but traffic between the VNs interferes with each other. Type-DD combines the characteristics of the other two types, thereby overcoming their shortcomings and improving the availability. Another notable characteristic is observed in the simulation with biased traffic, as shown in Figure 4b,d. Because there is no resource partition among the VNs in type-UD, traffic congestion in one VN influences the other interdependent VNs in a cascading manner. Therefore, of the three dotted lines, only the red one drops as the arrival rate \(\lambda\) increases. The extent to which cascades occur with type-UD is expected to increase further if the overlapping area of physical resources shared among the VNs increases. Meanwhile, even though type-DD also shares the physical resources of P-interfaces, the cascading of the performance degradation is prevented by the complementary dependence inspired by the B-NoN model. Figure 5 shows the simulation results for the packet delay. The performance of the packet delay can basically be explained in correspondence with the GCS results, and we confirm that the delay is lowest with type-DD. Type-DD can be said to be superior to the other two types regarding practical performance as a communication network. Another notable point is that the type-SD packet-delay performance degrades when compared with the GCS results in Figure 4. No matter how large the buffer utilization (i.e., \(\sum_{l\in\mathcal{V}}n_{r,s}^{V_{l}}(t)/\mathcal{B}_{r,s}^{P}\)), the availability \(\sigma_{r,s}^{V_{k}}(t)\), which is the basis of the GCS, is expressed as a binary state that does not take a negative value. On the other hand, the packet delay is expressed by taking any positive value reflecting the buffer utilization as it is. Based on these characteristics and results, we assume that interference among VNs with type-UD is likely to generate a number of moderately loaded V-interfaces to degrade the availability. Meanwhile, inefficient buffer allocation with type-SD is likely to generate rather few highly loaded V-interfaces to degrade the communication performance. Figure 4: Variation of giant component size (GCS) with arrival rate \(\lambda\). The vertical axis represents the GCS: 1 indicates that all the nodes on a VN are connected, whereas 0 indicates that they are completely disconnected. The solid lines in (**a**,**c**) correspond to the average GCS of the two VNs, whereas the GCS for each VN is plotted separately in (**b**,**d**). The horizontal axis represents the arrival rate \(\lambda\). In (**b**,**d**), note that \(\lambda\) is changed on VN1 but fixed on VN2.