esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Issues in Implementing URLLC
### Error Handling  
Owing to the faulty nature of the data channel [26], the handling of packet errors is another issue to meet along with the defined latency requirements for URLLC; for example, the 1 ms latency deadline [17]. The current LTE provides a very low error rate at the cost of higher latency, which is not suitable for URLLC [27].  
As shown in Figure 6, when data arrives at the base station (BS) buffer, a request for resource grant (RG) is transmitted to the target UE. As data is received, the UE decodes the data and responds with either a positive or negative acknowledgment (ACK/NACK) based on the success of the data decoding. In a case where the UE fails to respond within the allocated time, the BS retransmits the data. Compared with the LTE, URLLC operates in a shorter TTI and requires a faster response from the UE to avoid retransmission. Wireless channel impairments can be another concern. If the BS does not receive an ACK/NACK within the mentioned deadline as a result of channel fading, the BS retransmits the data. This can increase latency, and hence the wastage of resources [27]. To overcome this issue, the stronger channel coding and multiple antenna technologies can be considered. However, the stronger channel coding may require longer decoding latency, for example, more iterations in an iterative channel decoding scheme. As a result of high power consumption and space limitation, multiple antennas cannot be equipped on the Internet of things (IoT) devices.
## 4 The Proposed Selective Retransmission Mechanism  
The energy consumption model described in Section 3 refers to a fully-reliable hop-by-hop retransmission mechanism where all packets have the same treating by the nodes and every packet is acknowledged. When the packet error rises, the quality of the data received by the sink is preserved, but the energy consumption also increases, potentially impacting the expected network lifetime.  
Energy is a crucial resource that must be saved as much as possible in order to extend the sensor network lifetime. Considering the context of wireless image sensor networks, we propose a selective hop-by-hop retransmission mechanism that can save energy by avoiding retransmission of some packets by the cost of the quality of the received image at the sink. It is expected that sensor nodes may be interconnected by error-prone wireless links where burst of errors may be constant, but temporary. In such context, we argue that some visual monitoring applications can tolerate some loss of quality in the received images, since the energy resources of intermediate nodes are preserved. Moreover, we expect that such applications are delay-tolerant and immune to jitter, since all parts of an image must to be buffered at the sink to allow the decoding process and typical transmission rate of visual information collected from the monitored field should be one snapshot per second or lower. However, the decision not to retransmit some corrupted packet results in less than average end-to-end delay, but we do not consider it to be crucial for some types of applications.  
The proposed selective hop-by-hop retransmission mechanism is based on the relevance of the data resulted when we apply DWT over original images, where only the packets that carry the most significant information are retransmitted if corrupted. Doing so, we achieve a tradeoff between energy consumption and image quality, bringing a valuable contribution to wireless image sensor networks.
## 4 Experimental Section
### Tem  
TEM was performed for overexpressing OrtT as described previously [42]. For preparing cell samples for TEM, overnight cultures of BW25113/pCA24N-_ortT_ and BW25113/pCA24N-(_-gfp_) were refreshed to a turbidity at 600 nm of 0.05, grown in LB-chloramphenicol to a turbidity at 600 nm of 0.2, and then 1 mM of IPTG was added to induce OrtT production for 4 h. Cells were harvested and fixed with primary fixative (1.5% paraformaldehyde and 2.5% glutaraldehyde, and 4% sucrose in 100 mM of sodium cacodylate buffer, pH 7.4) at room temperature for 15 min, then 4 \(\,\mathrm{\SIUnitSymbolCelsius}\) for 12 h. Cell pellets were then washed three times with buffer (0.1 M sodium cacodylate buffer, pH 7.4) for 5 min. Secondary fixation was carried by fixing the cell pellets with 1% osmium tetroxide in buffer for 1 h in dark, followed by three washes with buffer and one wash with sterile MilliQ water. Cells were then subjected to en bloc staining by treating with 2% uranyl acetate in the dark for 1 h. Next, dehydration step was performed by applying a graded ethanol series (50%, 70%, 85%, 95% and three times with 100% ethanol, for 5 min each), and 100% acetone (three times, 5 min each). The dehydrated cells were then embedded into epoxy resins after three epoxy exchange for at least 12 h at 60 \(\,\mathrm{\SIUnitSymbolCelsius}\). The hardened cell samples were then sectioned into thin specimens (70 nm thick) using an ultramicrotone (UC6, Leica, Buffalo Grove, IL, USA). Positive staining was performed by staining the specimens with uranyl acetate for 6 min and lead citrate for another 6 min. The stained specimens were then examined on a FEI Tecnai G2 Spirit BioTwin TEM (Penn State Microscopy and Cytometry Facility, University Park, PA, USA) at an accelerating voltage of 120 kV.
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
###### Abstract  
This paper brings forward a Deep Learning (DL)-based Chaos Shift Keying (Dlcsk) demodulation scheme to promote the capabilities of existing chaos-based wireless communication systems. In coherent Chaos Shift Keying (CSK) schemes, we need synchronization of chaotic sequences, which is still practically impossible in a disturbing environment. Moreover, the conventional Differential Chaos Shift Keying (DCSK) scheme has a drawback, that for each bit, half of the bit duration is spent sending non-information bearing reference samples. To deal with this drawback, a Long Short-Term Memory (LSTM)-based receiver is trained offline, using chaotic maps through a finite number of channel realizations, and then used for classifying online modulated signals. We presented that the proposed receiver can learn different chaotic maps and estimate channels implicitly, and then retrieves the transmitted messages without any need for chaos synchronization or reference signal transmissions. Simulation results for both the AWGN and Rayleigh fading channels show a remarkable BER performance improvement compared to the conventional DCSK scheme. The proposed DLCSK system will provide opportunities for a new class of receivers by leveraging the advantages of DL, such as effective serial and parallel connectivity. A Single Input Multiple Output (SIMO) architecture of the DLCSK receiver with excellent reliability is introduced to show its capabilities. The SIMO DLCSK benefits from a DL-based channel estimation approach, which makes this architecture simpler and more efficient for applications where channel estimation is problematic, such as massive MIMO, mmWave, and cloud-based communication systems.  
chaos shift keying; deep learning; LSTM; multi-antenna +
Footnote †: journal: _Sensors_
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### Common Experiment Setup and Configurations  
We managed to run the experiments with three different topologies: linear, multipath, and partial mesh multipath. Furthermore, for each of the three scenarios, we experimented seven times to obtain precise results with 100 packets transmitted from the source with the lowest transmit power set to make the testbed manageable. We had nine sources and a sink in Scenario 1 and a single source and a sink in Scenarios 2 and 3. Furthermore, Table 2 shows the general experiment setup and configurations for the experiments.
# Machine Learning Techniques for Non-Terrestrial Networks
## 4 Non-Terrestrial Network Architectures
### NTN Architectures
#### 4.2.1 Transparent NTN Architecture  
In the transparent architecture, gNB is located at the ground, thus after the NTN ground station (see Figure 2). The NTE is transparent to the signal sent by the UE, which is retransmitted to the NTN gateway without any processing on-board (except possible frequency translation). The gNB is connected to the 5 GC and then the signal is sent to the external packet data network (PDN).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**NTN Service** & **5G Category** & **Ssuited NTEs** & **Required Data Rate** \\ \hline Mobility cell connectivity & & eMBB & HAPS, LEO & 50 Mbit/s/person \\ Unserved/Under-served and & & & & \\ isolated areas. & & & & \\  • Fixed cell connectivity & & eMBB & HAPS, LEO & 50 Mbit/s/person \\ • Wide area IoT & & & & \\ • Sensor/ actuators in local & mMTC & HAPS, LEO & 0.1–100 kbit/s/sensor \\ remote areas & & mMTC & UAV, HAPS, LEO & 1–10 Mbit/s per local hot spot \\  & & eMBB & & \\ Throughput increase & & & UAV, (HAPS) & 100–200 Mbit/s/hot spot \\ Secondary link for backup & & uRLLC * & HAPS, LEO & 0.01–1 Mbit/s \\ Disaster relief & & eMBB, uRLLC * & UAV, HAPS & 10–500 Mbit/s \\ Broadcasting/ Multicasting: & & & & \\ • TV & & eMBB & LEO, (HAPS) & e.g., video 4K \\ • Alerting UEs for possible & & & & \\ disaster warning & & mMTC & LEO, HAPS & 10 kbit/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Service requirements for NTN networks.  
In Figures 3 and 4, the protocol stack of this NTN architecture for UP and CP, respectively, are reported. Since the NTE (and the NTN gateway) are transparent, protocol messages are forwarded to the terrestrial gNB (from the physical layer (PHY) to the service data adaptation protocol (SDAP)) and to the user plane function (UPF) (i.e., upper layers, such as IP, TCP and applications). In the CP, protocols from PHY to radio resource control (RRC) close to gNB. The non-access stratum (NAS), which is made up by the mobility management (MM) and the session management (SM) functionalities, closes to the access and mobility function (AMF) and session management function (SMF) in the 5 GC, respectively. In the same figure, the CP protocol stack is also completed by other two functions/nodes of 5 GC as the policy control function (PCF) and application function (AF). The AF enables the external service by interfacing with the NTN or in general with the 5G system through the N5 interface. Then, over-the-top services, such as those related to vehicular, health or industry, can be provided to users. Finally, note that between gNB and UPF in the UP, the generic tunneling protocol (GTP) over the user datagram protocol (UDP) is adopted to create a tunnel, while between the gNB and the AMF in the CP, the NG application protocol (NG-AP) over the stream control transport protocol (SCTP) is adopted for tunneling in the N2 interface.  
Figure 3: Protocol stack of the user plane in the transparent NTN architecture [27].  
Figure 2: Transparent NTN architecture.
# An SSI-Based Solution to Support Lawful Interception
###### Abstract  
Lawful Interception refers to the acquisition of the contents of communications between private individuals or organizations by subjects authorized by law. It involves three actors: the network operator (NO), the Law Enforcement Agency (LEA), and the Law Enforcement Monitoring Facility (LEMF). In the literature, standards and scientific solutions are proposed for the interception procedure and the interaction between the NO and the LEMF. However, no standard has been proposed for the interaction between the LEMF and the LEA. The absence of standards for controlling LEA (or a delegated agency) access to intercepted contents stored by the LEMF is a significant gap that should be overcome. This prevents the implementation of secure, interoperable, and automated procedures, leading to inefficiencies and security risks. In this paper, we propose to cover the above gap by adopting the Self-Sovereign Identity (SSI) paradigm. The adopted research methodology follows a multi-phase approach that includes studying existing solutions, system design, and technical feasibility testing. The study first examines existing standards and identity management frameworks and their limitations. Next, an SSI-based architecture is proposed to manage the interactions between LEA (or a delegated agency) and LEMF. Finally, a proof of concept of the proposed solution written in Python and using the Hyperledger Indy blockchain has been implemented to assess whether our proposal is technically feasible. The proposed solution enhances automation, security, and interoperability in lawful interception. Indeed, it enables machine-readable authorizations, reducing errors and improving efficiency by eliminating manual operations. Additionally, verifiable credentials and decentralized identifiers strengthen security and standardize interactions across jurisdictions, ensuring privacy-preserving identity management. By standardizing interactions between LEA and LEMF, this research contributes to a more secure, privacy-preserving, and legally compliant lawful interception process.  
S +
Footnote †: journal: applied sciences  
Article  
An SSI-Based Solution to Support Lawful Interception
# An ML-Based Solution in the Transformation towards a Sustainable Smart City
## 5 Discussion  
SSCs that are socially inclusive, safe (also from an energy and cybersecurity point of view), resilient, and take into account the health and well-being of residents are a key element of the Sustainable Development Goals (SDG) and the New Urban Agenda [45; 46; 47; 48]. The operation of such a system is predictable in the short term, allowing the ongoing management of SSCs based on current status, historical data and forecasts made 24 hours in advance. Predictions for longer periods depend more strongly on the accuracy of other predictions (e.g., weather forecasts) and can be shaped by long-term factors (e.g., seasonal variation, seasonal employment, etc.); hence, they require greater management flexibility.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **RSsquared** & **Absolute Loss** & **Squared Loss** & **RMS Loss** \\ \hline FastForestRegression & 0.7820 & 6.98 & 100.00 & 10.00 \\ LightGbmRegression & 0.8150 & 6.06 & 84.85 & 9.21 \\ FastTreeRegression & 0.7627 & 7.02 & 108.89 & 10.44 \\ FastForestRegression & 0.7759 & 7.05 & 102.82 & 10.14 \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._The entire algorithm selection process is novel. We have provided a framework that not only sets the stage for future work on integrating large data sets but also uses machine learning techniques to synthesise data from different sources in SSCs. In particular, we have described the use of seven carefully selected algorithms, selected based on criteria such as prediction and classification accuracy, as well as computational efficiency. The selection process involved systematically applying different algorithms and identifying those that consistently delivered optimal results at each stage of the data processing process. These algorithms, numbered 1 to 7, are divided into three distinct stages to facilitate a structured approach to handling data:  
* Algorithms 1-3 are dedicated to edge computing, where initial data refinement and feature extraction occur;
* Algorithms 4-6 focus on data aggregation, where information from multiple sources is consolidated;
* Algorithm 7 is used in the final stage to develop a global model that synthesises the aggregated data into actionable insights.  
This methodical approach enables the most effective algorithms to be applied at each stage, which maximises overall system performance and reliability when handling large-scale data integration tasks in SSC environments.
## 3 Results and Discussion  
### Screening of Different AOPs  
#### 3.1.1 Fenton Reagent  
The Fenton reaction has been used as a treating process to degrade sulfonamides, as it generates hydroxyl radicals through the catalytic reaction between Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\)[33]. However, prior to the oxidation experiments with the Fenton reagent, reference tests needed to be performed in order to investigate the prospect of direct reaction between SDX and H\({}_{2}\)O\({}_{2}\). For these experiments, it was highly important to have H\({}_{2}\)O\({}_{2}\) in excess, in order to ensure that there will be enough reagent for a potential full degradation of SDX.  
Although H\({}_{2}\)O\({}_{2}\) is a strong oxidizing agent, it was not effective in oxidizing SDX by itself, as negligible degradation of SDX was observed. In contrast, when Fe\({}^{2+}\) was added to the solution, significant and rapid degradation of SDX occurred, with the contaminant being completely degraded in a remarkably short time frame of only 30 s (Figure 1). However, while the Fenton reaction demonstrated rapid removal of the contaminant, the overall mineralization efficiency of the process was modest, with TOC removal reaching a level of 28%. The pH of the treated solution is critical for Fenton AOPs to avoid the precipitation of iron as hydroxide [33; 34]. In this case, the initial pH was within the acceptable range (pH 3) and no further adjustment needed to be taken.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 5 Evaluation
### Evaluation of the Attack Effectiveness
#### 5.4.1 Impact on the Flow Table Utilization  
We measure the flow table consumption of the victim switch with and without the attacks. In the attack scenario, we measure the number of flow rules generated by attack flows and benign background flows, respectively. As shown in Figure 11, without the attack traffic, the number of flow rules ranges between 500 and 600, which is about half of the capacity. When the attack occurs in the attack scenario, as shown in Figure 12, the number of flow rules can increase to 1000 at around 252 s. The result demonstrates that the saturation attack can effectively overflow the flow table.  
We can observe that after the flow table is full, the number of rules fluctuates rather than staying at 100% all the time. The reason is that the background traffic fluctuates, and the controller cannot immediately insert new flow rules when the old ones expire. In our settings, the controller stops installing rules when it detects that the switch has no storage space. The flow table storage space can only be released after the installed rules in the switch expire due to a timeout. Then the controller detects the freed space by receiving the Flow-Removed messages, which is not a real-time process.  
Figure 11: The number of flow rules with only benign background traffic.  
We also find that the number of flow rules for benign flows is less than that without the attack. After the flow table is saturated, the number of attack flows gradually increases while the number of benign flows decreases. The reason is that benign flows must compete with attack flows for the flow table space when the flow table is overflowed. However, the attack flow rules are persistently stored and occupy the storage space. Therefore, the controller cannot handle the benign requests.
## 4 Discussion
### Comparison of Model Applicability  
In this section, the new model was applied to the sample data (with changes) shared by the ESTARFM designer to evaluate the performance of the new model on reflectance data [21]. The results are shown in Figure 12.  
Upon comparing the results of the two models and the actual observations, the new model was found to obtain values closer to the actual values due to the inclusion of numerical conversion. However, segmented fitting reduces spatial variability. Compared with the ESTARFM, it blurs some spatial details and may cause inconsistency in the image,  
Figure 10: Detailed comparison of the areas near Shicheng Island and Wangjia Island.  
as seen in the yellow box (a) in Figure 13. The ESTARFM fully expresses the spatial texture through spatial retrieval and conversion.  
The results of the reflectance data derived from the two models show that the new model is more suitable for a single type of data with a larger range of variation. For terrestrial areas with sudden changes in land cover types, the ESTARFM scheme is more robust.  
Figure 11: The variation in _RMSE_ and CC of ESTARFM, ESTARFM_p, and ESTARFM_p-one results with time intervals.  
Figure 12: Scatter plots of the Landsat and results of ESTARFM and ESTARFM_p for green (**a**), red (**b**), and NIR-infrared bands (**c**).
## 3 Results  
### Case Study  
The case study in this research is based on a real power system. The power system includes two 20 KV feeders, which are called the main feeder and adjacent feeder. The DG is embedded on the main feeder, and the adjacent feeder can be fed by the main feeder. Figure 2 depicts the topology of the case study consisting of the two feeders. The length of the main feeder is 3.64 km, and it has more energy consumption due to an industrial area. The maximum current of the main feeder is about 102 A. Moreover, the adjacent feeder has a longer length (26.76 km) with residential loads. The maximum current of the adjacent feeder has been recorded 73 A. The main feeder consumes more energy, while the adjacent feeder line is much longer. The DG is connected to the substation by the feeders. According to Equation (3), an impedance more than 28.85 \(\Omega\) causes blind areas, and the three-phase short-circuit current is around 12.5 kA. There are two 132/20 kV transformers rated at 30 MVA in the substation. The simulations are conducted in DIGSILENT software (version 15.1, DIgSILENT GmbH, Gomaringen, Germany, 2016) [35].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Impedance** & **Point 1** & **Point 2** & **Point 3** \\ \hline DG & 0.523 \(\Omega\) & 1.089 \(\Omega\) & 8.463 \(\Omega\) \\ Feeder & 1.077 \(\Omega\) & 1.65 \(\Omega\) & 9.27 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Path impedances of fault points without fault impedance. DG: distributed generation.
# SmartX Multi-View Visibility Framework
## 2 Background and Requirements of Flow-Centric Visibility
### Flow-Centric Visibility Requirement for Unified Monitoring  
We proposed SmartXMVF to deal with the multiple layers of visibility with associated visualization support for SDN-enabled multisite cloud [13]. Among multiple visibility layers, as shown in Figure 2, flow-layer is becoming crucial because it can help us to smoothly integrate several layers of visibilities together and verify packets that are flowing through the playground. A network flow is the sequence of packets that belong to a certain network session between two endpoints. Typically, flows are identified by a five-tuple consisting of source IP address, source port, destination IP address, destination port, and transport layer protocol. Flow information is useful for understanding network behavior by denoting source address and destination address to understand flow origin, port information to characterize application traffic utilization, device interface identification to indicate traffic utilization for specific network devices, and matched packets and bytes to show the amount of traffic flow. This view of the flow-centric visibility can be used to instantly highlight congested links and identify the source of the flow and the associated application-level conversations. To enable a practical, sustainable version of flow-centric visibility to effectively collect, integrate, and visualize visibility data of multiple layers together, the following key requirements should be fulfilled.