esa-sceva/satcom-chunk-collection huggingface dataset:
# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 6 Deep Learning Architectures for Pedestrian and Cyclist Detection  
DL approaches for pedestrian and cyclist detection can be one of the following two categories: a two stages (region proposal approach) detector or a single stage (non-region proposal approach) detector. The single stage detector aims to remove the need for traditional region proposal feature extraction by processing these steps within a single network. The single stage detector can be simpler  
Figure 5: CNN architecture.  
to train, with a higher computational efficiency [5]. In this approach, a proposal of regions is first completed and then the deep network conducts the classification.  
With the progress of DL and its development and success in pedestrian detection, detection accuracy has improved. The DL techniques used for pedestrian detection can include region proposal as part of the system. Some of the region proposal-based techniques include Region-CNN (R-CNN) [79], Regional-Fast Convolutional Network (R-FCN) [83] and Faster R-CNN [59]. Non-region proposal-based techniques include Single Shot Detector (SSD) [84; 85; 86] and You Only Look Once (YOLO) [87]. All of these pedestrian detection techniques are based on CNN, which has become the standard for pedestrian detection. For the task of classification, detection techniques can be placed into one of these families: DPM variants, decision forests and deep neural networks [47; 48]. These techniques can also be applied for cyclist detection as they are visibly similar to pedestrians [25].  
CNN is a popular technique for object classification in pedestrian detection systems [59; 65]. An in-depth review of DL techniques is provided in later sections. A region proposal technique (such as the hand-crafted techniques described in the previous section) can be used alongside CNNs for object detection [59; 65; 66; 67; 79]. The region proposal technique is used to suggest where an object may exist in the image. The proposed regions are fed into a classifier (e.g., CNN) to determine the class of the object. Studies of the use of deep networks for pedestrian detection applications can be found in [88; 89; 90; 91; 92].  
There are also non-region proposal-based DL techniques [59; 79; 83]. Figure 6 depicts the difference between the two types of architecture for object detection. Figure 6a is a detector based on traditional region proposal and feature extraction techniques, where only the classifier is the deep network. Figure 6b represents a deep network that is able to complete region proposal and feature extraction as well as classification in a single step. This is known as a single step detector. In 2009 the Caltech dataset was introduced for benchmarking the various techniques for pedestrian detection. The ConvNet (a CNN-based approach) was introduced in 2013 with competitive results when compared to previous pedestrian detection techniques mentioned [43].  
Figure 6: _Cont._
## 4 Use Case: Smart Buildings
### Implementation
#### 4.2.2 SDN Controller Integration  
The Ryu SDN framework is a widely acclaimed open-source SDN controller, implemented in the Python programming language, known for its user-friendly and highly extensible nature [37]. Leveraging Ryu's Software Development Kit (SDK), developers can craft sophisticated SDN applications with ease, enabling network monitoring, data plane statistics collection, and the enforcement of intricate network policies [38]. Figure 12 illustrates the core modules of the Ryu framework structured in a layered fashion. The lower layers house basic protocols and message handlers, while the middle layer consists of core modules like event dispatcher, responsible for delivering events to registered applications, core libraries such as topology, and basic classes offering a unified API. At the higher levels, Ryu built-in applications and user-defined applications (RyuApps) reside. RyuApps are SDN applications that perform routing, load balancing, security, or expose a REST API for third-party tools and services. Finally third-party SDN applications can also interact with the Ryu SDN controller using the REST API.  
Figure 12: Ryu-based SDN controller integrating RINA capabilities.  
The RyuApp developed in this work extends the existing REST API framework to support RINA commands. The RyuApp communicates with the RINA SBI driver using a UNIX socket and exposes common commands to create and destroy RINA objects, and obtain information from the RINA-based data plane using the commands of the SBI driver detailed in Section 3.2 RINA SBI Driver.
## 4 5G Slicing Energy Evaluation
Based on this implementation, a set of experimental tests were performed which measured the latency for various packets sizes of a 5G system, after having allocated an eMBB slice and then enforcing different URLLC slices over it. The second set of results are shown in Figure 5, where an eMBB slice of 100 Mbits/s was allocated at the backhaul, and a set of URLLC slices were enforced and measured in terms of latency, for srPeriod values of [1,10,40] and slot Period values of [2,5, 5]. As can be deduced, the RAN modifications can greatly affect the latency of the system for all packet sizes, and in some cases an improvement of up to 57%, as in the case of srPeriod = 40, slot Period = 5, srPeriod = 1, and slot Period = 2.5 for the packet size of 128 bytes. Furthermore, in the third set of experimental tests an eMBB slice of 200 Mbits/s was allocated, and the same set of RAN uRLLC slice parameters were enforced. The results, as depicted in Figure 6, indicate a similar behavior to the eMBB slice results of 100 Mbits/s, with the case of 128 byte packets again demonstrating the largest improvement. Figure 5: Latency results for a 100 (Mbits/s) eMBB slice and 6 different uRLLC slices. Figure 6: Latency results for a 200 (Mbits/s) eMBB slice and 6 different URLLC slices. It is evident from both set of results that the case of the URLLC slice of srPeriod = 1 and slot Period = 2.5 display the best performance in terms of latency for a URLCC slice, with an average minimum latency of ~12 ms. These values indicated the best achievable latency performance measured in the proposed 5G platform for end-to-end measurements. The last set of experiments was performed over the different combinatory EMBB and URLLC slices, in order to comprehensively investigate the effect of different packet sizes on different data paths and analyze their energy impact. As can be deduced, the main factor contributing to higher energy consumption is diminutive packet size, which is expected, as this contrivance performs significantly more transmissions to obtain the same level of bitrate, to procure the slice bitrate requisite. Furthermore, the most consequential conclusion of these experiments is that a lower latency requires more energy, as can be clearly seen from Figure 7. The configuration for minimum latency (sr = 1, slot = 2.5) consumes more energy in all different packet-size cases. Consequently, the trade-off between energy consumption and latency subsists in the 5G domain. Moreover, it should be noted that 5G low-latency communications are energy efficient, as minimal additional energy consumption can significantly minimize communication latency, a critical factor for emergency communications. Another minor conclusion drawn is that a higher bitrate additionally consumes more energy in all cases, which is a logical conclusion as the channel transmits more data, therefore consumes more energy.
# Study of the Briquetting Process of Walnut Shells for Pyrolysis and Combustion
## 3 Results and Discussion  
### Characterization of Initial Biomass  
As Table 1 indicates, the Net Calorific Values were 16.9 and 17.1 MJ/kg (on a dry basis) for walnut shells and walnut shells containing 8% of walnuts, respectively. The presence of moisture slightly decreased these values, which are typical for biomass feedstock [30]. Therefore, the proposed materials are suitable to be used as fuels. These Net Calorific Values could increase up to 24 MJ/kg after a torrefraction process to obtain biochars, closer to that of coal, as [PERSON] et al. [11] and other authors observed.  
Their low moisture content, 8.4 and 6.8%, respectively, avoids the need for a drying process previous to briquetting or direct combustion, thus saving the energy needed. For instance, when reducing the moisture content in woodchips from the initial 40% to 15% (thus making them suitable for densification), the energy required was 1328 kJ/kg of initial wet material, according to the work of [PERSON] et al. [9], almost twice the energy required for pelletizing the woodchips, which was 757 kJ/kg.  
Figure 1: Rotating drum used for the durability determination, adapted from the UNE-EN ISO 17831-2:2016 standard [29].  
The ash contents were 1.1 and 1.2%, respectively, which are again really low values compared to other waste biomass such as maize straw at 9.4% [11], sewage sludge at 54%, or furfural residue at 11% [3]. This fact agrees with the low metal and minor element content shown in Table 2, which means that this kind of material produces clean combustion and is therefore highly suitable as fuel for many purposes. It will produce low or negligible toxic emissions. This also reduces the corrosion problems in furnaces caused by sulfur content in fuels, since its content is almost negligible at 0.009%. Other waste biomass contains around 1% of sulfur [3]. The same fact occurs with alkali metals such as potassium, with relatively high content in herbaceous plants, 1.8% for maize straw [11], which requires the need for phosphorous-rich additives, whereas walnut shells contain only 0.36%.  
According to the UNE EN ISO 17225-3:2021 standard [31] about specifications and classes of wood briquettes, walnut shell waste showed chemical characteristics similar to virgin wood. These properties make this kind of waste appropriate for fuel in briquette form.
## 3 Simulation Performance Analysis
### Modal Analysis of Rayleigh and Sezawa Modes  
Finding the vibration mode of the acoustic waves on the IDT/ZnO/SiO\({}_{2}\)/diamond structure designed in this paper is crucial, as there are multiple modes, including Rayleigh, Sezawa, and high-order Sezawa modes, which coexist in the same layered structure. The propagation velocity of acoustic waves in the ZnO piezoelectric film is lower than that of acoustic waves in the diamond substrate.  
Figure 8 shows the Rayleigh and Sezawa mode displacement field distributions in the z direction versus normalized depth when h1, h2, and h3 are 0.4 \(\lambda\), 0.2 \(\lambda\), and 0.08 \(\lambda\), respectively. Figure 8 shows a typical simulated Y11 response of the SAW resonator with this structure. It can be clearly seen that the device exhibits two different resonance frequencies. The acoustic field of the Rayleigh mode is primarily dispersed on the ZnO surface, whereas the acoustic field of the Sezawa mode is primarily spread throughout the ZnO piezoelectric layer and the SiO\({}_{2}\) layer, as seen in Figure 8. Figure 9 shows the proposed device's displacement against substrate depth in both Rayleigh and Sezawa modes.  
Figure 8: (**a**) The admittance of the IDT/ZnO/SiO\({}_{2}\)/diamond structure, and the corresponding resonant mode, where h3/\(\lambda\) = 0.08, h2 = 0.2 \(\lambda\), and h1/\(\lambda\) = 0.4. (**b**) z-direction displacement field distributions of Rayleigh and Sezawa waves as a function of depth.  
Figure 7: Rayleigh and Sezawa modes’ \(k^{2}\) and \(v_{\text{p}}\) dependence on electrode normalized thickness (h3/\(\lambda\)): (**a**) Rayleigh wave; (**b**) Sezawa wave.
# Applications of Nanomaterials in RFID Wireless Sensor Components
## 2 RFID System  
Radio Frequency Identification (RFID) is a type of contactless wireless communication technology that utilizes radio waves as a transmission medium [41; 42; 43]. It consists of three components: tags, RFID readers, and IT systems (Figure 1). Tags, which contain unique information, record data through an integrated circuit chip (IC chip) and transmit and receive wireless signals via an antenna [44; 45]. The wireless interaction between tags and RFID readers is based on digital or backscattered modulation, which involves altering the amplitude, phase, and frequency of radio waves [46]. Specifically, when a tag exists within the permissible range of a RFID reader, the tag receives the transmitted signal from the reader, modifies it, and sends it back as a modulated reflected signal through the antenna. The RFID reader converts the returned signal into digital format, decodes the embedded information, and transmits it to IT systems. Ultimately, IT systems collect data from distributed readers, manage information, and control the overall system.  
Compared to traditional barcode recognition systems, RFID systems can process relatively large amounts of data over long distances due to their reliance on radio waves [21]. Additionally, RFID does not require direct or physical patterns, enabling recognition regardless of vertical or horizontal arrangements. Consequently, RFID can capture signals over a wide range and in all directions without direct contact. Furthermore, using electronic circuits (IC chips) instead of visual pattern arrays allows RFID to store more information than barcodes [47].  
Figure 1: Radio Frequency Identification (RFID) device composed of three components: Tag, RFID reader and IT system.  
The operating frequency range of the tag is broadly categorized into three ranges, determining the communication method and antenna type with the RFID reader (Figure 2a). For low operating frequencies, specifically in the range of 120-140 kHz (LF), the tag and RFID reader operate through inductive coupling, with the antenna used adopting a coil shape. In the case of an operating frequency of 13.56 MHz (HF), the tag and RFID reader operate through inductive or capacitive coupling, and the antenna used primarily employs a planar inductor [48; 49]. In a high operating frequency range of 868-962 MHz (UHF), the tag and RFID reader are radiatively coupled according to the backscattering principle, and the antenna used in this case is either a dipole or microstrip [50].
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 3 AO-AOMDV Routing Protocol  
### Problem Statement  
With the rapid development of the UAV industry, there are fewer routing protocols available for high-speed mobile UAV swarms. The majority of the proposed research studies have focused on the performance and lifetime of FANETs with respect to changes in density while neglecting the significant impact of velocity changes on UAV swarms. Therefore, it is necessary to consider how to enhance the routing performance and network lifetime of UAV swarms at varying velocities. We not only need to select stable and optimal paths but also ensure a minimal routing processing time in order to increase the packet delivery rate and network lifetime and reduce the end-to-end delay. Thus, it is necessary to consider a routing protocol that utilizes optimization algorithms for route selection during the communication process.
# Research on Multi-Strategy Routing Protocol in Flying Ad Hoc Networks
## 3 Forwarding Policy Selection and Handover Design  
In the RGF protocol, each packet contains a packet header to control the packet forwarding policy. The main fields of the packet header are shown in Figure 4. The source node needs to determine the initial forwarding policy for these packets, and the intermediate node must also change the forwarding policy according to the situation.
# A Next-Generation Core Network Architecture for Mobile Networks
## 5 Basic Operations
### UE Attachment and SIP Registration
#### 5.1.1 Scenario with eNodeB  
In the current cellular architecture, when an eNodeB receives an attach request by a UE, it needs to discover which MME to connect to. This is usually done by querying an internal Domain Name System (iDNS) server which returns the IP address of the MME to use. Usually, one or more IP addresses of such DNS servers are either statically configured at each eNodeB or are provided to the eNodeB by a DHCP server, for example. Once the eNodeB connects to the MME, during the UE attach, the  
Figure 10: SN tunnels data from older TCP connections to the UE via the VCN.  
Figure 9: VCN high-level architecture (SIP Proxy and EPC are multi-homed).  
MME queries the iDNS server in order to discover which P-GW and S-GW to use in order to serve the UE [20].  
Figure 11 shows, at a high level, how UE attachment and SIP registration (A simplified SIP registration flow is shown for improved readability.) work in the new architecture when using a legacy eNodeB. In particular, when receiving an attach request from a UE, the eNodeB queries the network iDNS server to discover which SN (i.e., its MME) to talk to for that UE and, once discovered, UE and network perform the usual attach procedure. In particular, the eNodeB is not aware of talking to an SN, but, as far as it is concerned, it is \"talking\" to a traditional MME which, as per standard protocol, will direct it to a P-GW and S-GW. Once the attachment procedure completes, SIP authentication and registration can take place as per IMS standard [21]. In the proposed architecture, the DNS server nodes are also nodes of the P2P core network and, while they expose a standard DNS interface to the eNodeBs, they resolve DSN queries by interrogating the DHT of the P2P core network. This can be achieved by translating between the DNS protocol and the P2P protocol (i.e., RELOAD) used by the core network and vice versa.
## 5 Conclusions  
This paper proposes a method to improve FAPAR estimation based on the Beer-Lambert's law considering FVC, and conducts a case study using FAPAR and LAI from the BigFoot Project as well as from field measurements of grass savanna in Senegal, West Africa, and corresponding MODIS products. The results show that the accuracy in FAPAR\({}_{\text{FVC}}\) is clearly better than those based on FAPAR\({}_{\text{LAI}}\) as well as FAPARMOD, and FAPAR\({}_{\text{LAI}}\) accuracy is also clearly better than FAPARMOD. The improvements in accuracy and the decrease in overestimation for FAPAR\({}_{\text{FVC}}\) were clearer with decreasing FVC compared to FAPAR\({}_{\text{LAI}}\). The results indicate that the FAPAR\({}_{\text{FVC}}\) method can improve the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional FAPAR and GPP simulations, providing more accurate inputs for understanding their tempo-spatial patterns and its drivers. Although the FAPAR\({}_{\text{FVC}}\) method can improve FAPAR estimation, there is still a systematic overestimation which may be related to the error of MODIS LAI data, and thus more in-depth study is required to reduce the error of MODIS LAI.  
Conceptualization, R.L.; methodology, R.L. and B.L.; software, Y.Y.; validation, Y.Y., W.L. and J.Q.; formal analysis, B.L.; investigation, Y.Y., W.L., J.Z., J.Q., H.L., G.M., Y.J., Y.L. and Q.T.; resources, J.Q., H.L. and G.M.; writing--original draft preparation, R.L.; writing--review and editing, B.L.; visualization, W.L.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number U2243206; National Key Research and Development Plan of China, grant number 2022 YC3800803; and National Natural Science Foundation of China, grant number 41701475.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
**Acknowledgments:** Constructive criticisms and comments from professor editors and anonymous referees are acknowledged. We thank [PERSON] for his insightful advice.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract
To the best of our knowledge, there is no previous study that analyzes multiple disorders based on EEG data while integrating multiple visualization techniques. EEG signals are recognized as multivariate time series data, comprising a sequence of spatial and temporal information ordered over time. The signals encompass diverse local and global information, enabling the identification of distinctive patterns for various health conditions and the tracking of trends in individual health conditions. This information is particularly valuable for capturing unique characteristics related to neurological disabilities. However, extracting significant features from EEG signals is an essential yet challenging task in neurological studies. Although a few previous studies have demonstrated the potential to differentiate neurological disabilities using classification algorithms, there exists room for performance enhancement and the advancement of knowledge to accurately differentiate disorders from one another. Due to similar symptoms between ADHD and other psychiatric disorders, identifying ADHD through the analysis of neurophysiological signals remains a challenging task [11]. Therefore, this study introduces an approach that integrates machine learning and visualization to determine differences among patients with neurological disabilities. We emphasize the importance of identifying critical features from EEG signals associated with individual neurological disabilities and determining their unique patterns, as this enhances our ability to understand the disabilities. In summary, the primary contributions of this study are outlined as follows:  
* We designed an efficient feature extraction approach, utilizing signal processing techniques to analyze EEG data associated with multiple neurological and mental disorders with short-term EEG segmentation, such as 1 s and 2 s segmentation intervals. * Our work is the first study utilizing visualization techniques to enhance understanding of the extracted features to distinguish the disorders. * We conducted multiple data classification analyses utilizing machine learning (ML) algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), and logistic regression (LR). * We also performed an extensive evaluation to determine the optimal wavelets and decomposition levels for analyzing and differentiating neurological disabilities. The rest of the paper is organized as follows: First, we discuss prior research in neurological disorders utilizing EEG in the Previous Works section. Then, the proposed approach is explained in greater detail, emphasizing the importance of utilizing wavelet transform and integrating visualization. After presenting the research findings and implications, we conclude this paper with possible future works.