esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Overall PSCDR-BMPNet Pipeline Summary  
Having detailed the SGCBP dataset (Section 2.1), the proposed Point Supervised Contrastive Deep Regression (PSCDR) method (Section 2.2) and the architecture of the BioMixerPoint Network (BMP_Net), including its constituent modules (Section 2.3), Figure 7 provides a holistic view of the entire PSCDR-BMPNet pipeline for point cloud biomass prediction.  
As illustrated, the process initiates with Raw LiDAR Point Cloud data. This data first undergoes Data Preprocessing, which includes steps such as point cloud sampling and any necessary cleaning or normalization.  
During the Training Phase, the preprocessed data is utilized by both the PSCDR: Contrastive Learning module and the BMP_Net: deep regression network. The PSCDR module (Section 2.2) generates augmented data views and computes a contrastive loss that encourages the learning of discriminative features sensitive to biomass variations. This contrastive objective effectively guides the BMP_Net training process, supplementing its primary regression task of predicting biomass. The BMP_Net (Section 2.3) itself processes the point cloud features through its specialized architecture to learn the mapping to biomass.  
Figure 6: Overall architecture of the BioMixerPoint Network (BMP_Net). Input point clouds go through ‘Embedding’, initial ‘N2P Attention’, and stacked ‘Blocks’ (PFMixer, PTransDown, N2P Attention, BatchNorm) for hierarchical feature extraction. ‘Global Pool’ and ‘MLP’ regress biomass output, with residual connections aiding information flow.  
In the subsequent Inference Phase, only the trained BMP_Net is active. New, preprocessed point cloud data is fed directly into the BMP_Net, which then outputs the Biomass Prediction Result. The PSCDR mechanism contributes to the robustness and accuracy of the BMP_Net through the knowledge embedded in its trained weights during the joint training phase. The following section will present the experimental results validating this approach.
## 3 Conclusions  
We have successfully optimised an all-on-resin synthetic strategy to prepare cathelcidin-PY (**1**) by rapid flow Fmoc-SPPS chemistry, paired with an on-resin iodine-mediated disulfide bridge formation. Furthermore, the benefit of modified coupling conditions for racemisation prone residues (Cys/His) under high temperature flow Fmoc-SPPS was highlighted. The optimised methodology was adopted in preparing a library of eight analogues. Surprisingly, alanine substitution of the Cys3 to Cys7 disulfide in linearised analogue **2** had only a modest impact upon secondary structure, biological activity, and serum stability, suggesting that disulfide is non-essential and also amenable to chemical modification. This led to the design of lactamised analogues **4-9** with varying bridge lengths. On the other hand, we rationalised that a more constrained head-to-tail bicyclised analogue (**3**) may offer benefits towards both activity and peptide stability based on the calculated 3D structure of the native peptide. To our surprise, this bicyclisation abolished both bioactivity and secondary structure with respect to the native peptide, suggesting the secondary structure and bioactivity are closely linked. Selected lactam bridged analogues, however, remained similarly potent towards Gram-negative pathogens, retained similar secondary structure, and yielded a modest improvement in serum stability. Like the native compound (**1**), all synthetic derivatives also failed to demonstrate antimicrobial activity towards the Gram-positive pathogen _S. aureus_ and the fungal pathogen _C. albicans_, contrary to the initial report of cathelcidin-PY (**1**). In the presence of physiologically relevant levels of divalent cations, cathelicidin-PY (**1**) and select lactam bridged analogues (**4**, **6**, and **8**) demonstrated selective potency towards _E. coli._ Such selectivity is encouraging, given the growing need for selective antimicrobials that can both preserve the microbiome and avoid resistance development in off-target species.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Time Point and \% Sample Remaining**} \\ \hline
**Test Compound** & **t\({}_{\text{10 min}}\)** & **t\({}_{\text{1 h}}\)** & **t\({}_{\text{3 h}}\)** & **t\({}_{\text{6 h}}\)** \\ \hline Cathelicidin-PY & 84.7\% & 20.8\% & 8.8\% & 2.4\% \\
**2** & 92.9\% & 61.6\% & 8.1\% & 7.4\% \\
**8** & 93.9\% & 64.6\% & 13.4\% & 10.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stability assay of cathlcidin-PY, **1**, and analogues **2** and **8** in human serum at various time points as determined by RP-HPLC (214 nm).
## 4 Privacy-Aware Selection Modes
### Privacy-Aware Selection Modes
#### 4.2.3 Familiar Then Stranger Selection Mode  
The \"familiar then stranger\" next-hop selection mode acts as follows:  
1. The source node first selects a familiar, well-trusted neighbor to start message forwarding.
2. Subsequent intermediary nodes prioritize strangers to relay messages to further restrict propagation.  
This approach suits contexts where sources want to limit spread beyond the community. To limit sender recognition, the user will choose a trusted node to forward, and this one chooses a stranger from his neighbors to maximize the probability of not being familiar to the sender but to any relay node in the path (see Figure 2c). This mode can be applied in a context where the sender is a well-known person and has an important number of social bonds. He wants the nodes carrying his information to be strangers to each other to avoid their discussion or disclosure. As before, relationship scores are calculated using two metrics--the number of common friends and exchanged messages. The scores divide nodes into stranger/weak ties/familiar ranges. This method limits propagation by leveraging the source's trusted ties while intermediaries route through strangers. It prevents messages from spreading among the source's trusted contacts. The multi-metric scoring and thresholds support more complex notions of familiarity and trust than binary selections.  
In all modes, the existing routing logic stays unchanged. Only the final next-hop selection decision is modified to enforce privacy policies by prioritizing nodes satisfying the mode to be selected. Indeed, when the node cannot find relay nodes satisfying the selection mode criteria, its common routing strategy proceeds to balance the message privacy preservation and delivery time. Proceeding like so allows privacy preservation integration without affecting the overall routing. The selection modes support diverse social situations and privacy needs--from high confidentiality to anonymous transmission and restricted propagation. The next section evaluates these modes through simulations for different sample scenarios.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Physical Processes
#### 4.1.4 Vitrification  
Vitrification is a thermal technique at high temperatures (>1500 \({}^{\circ}\)C) to reduce the mobility of heavy metals and metalloids by fixing them into vitreous material. During the process, contaminated soil is heated with high-voltage electricity, an external heat source, or via electrical discharge-induced gas plasma. The soil is melted into molten lava, which, after cooling, is transformed into a vitrified structure. The heavy metals/metalloids are encapsulated in this glassy matrix, while the other contaminants are destroyed [69]. Vitrification is an efficient technique, but it is destructive for soil, expensive, and very complex. Moreover, it is not applicable to soil with high organic matter content, high moisture content, and soil contaminated by volatile or flammable organics.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.3 Communication Model  
The different messaging strategies followed by the approaches require the support of a centralized, client-server model.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Aspects** & **HTTP** & **CoAP** & **QUIC** & **AMQP** & **MQTT** & **DDS** & **OPC UA** & **NDN** \\ \hline
**Messaging pattern** & RR & RR & RR & Broker & RR \({}^{1}\), Broker & RR & RR, PS \({}^{2}\) & PS \\ \hline
**Communication** & CS & CS & CS & CS & CS & Dec & CS & Dec \\
**Model** & & & & & & & & \\ \hline
**Security** & DTLS, & DTLS, & & & & & & \\  & OSCORE, etc. & OSCORE, etc. & own encryption & TLS/SSL & TLS/SSL & Specific & Specific & By design \\ \hline
**Binary Payload** & No & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
**Support** & & & & & & & & \\ \hline
**QoS** & Best Effort & Confirmable, & Congestion & High availability, & Best Effort, & Parameters on & & & \\  & non-confirmable & control & High availability, & Guaranteed & durability, lifespan, & Best Effort & Forwarding \\  & & & & & & & & \\  & & & & & & & & \\ \hline
**Data persistence** & No & No & Yes & Yes & No & Yes & No & Yes \\ \hline
**Data discovery** & Manual & Manual and & & & & & & \\  & registration & & & & & & & \\ \hline
**Applicability** & D2E, E2E, E2C & D2E & D2E, E2C & D2E & D2E & D2D, D2E, E2E & D2E, D2C, D2D \\ \hline
**Complexity** & High & Low & Medium & Low & Low & Low & Low & Low \\ \hline \hline \end{tabular}  
* As described in Section 3.2, MQTT can support a RR pattern supporting one-to-one messaging, for instance, for specific critical scenarios where an acknowledgement is required. \({}^{2}\) under development.  
\end{table}
Table 1: Comparison of IoT communication protocols and architectures.  
From a communication model perspective, the broker-based publish/subscribe is also based on a client/server communication approach.  
Therefore, from the analysed protocols, there are only two that integrate a notion of decentralised data exchange, which is a relevant architectural aspect for heterogeneous and dynamic IoT environments: DDS, and NDN. DDS implements a Bus model and requires registration of entities before communication can be held. While NDN relies on the publish/subscribe receiver-driven model, and hence, achieves a higher degree of decentralisation. A main advantage of the NDN publish/subscribe approach is that it can support more control: a broker cannot control aspects such as packet loss or polling, while with NDN forwarding strategies it is possible to adapt the rate of Interest packets sent and as a consequence, improve _Quality of Experience (QoE)_.
## 4 Experiments
### Ablation Study
#### 4.6.2 Original SqueezeNet with LGA or CCNet  
We compared the performance of the SqueezeNet architecture with CCNet and LGA is incorporated. The results are presented in Table 5. As compared to the original version, LGA, indeed, adds extra computation and storage demands but significantly fewer than CCNet. This was also discussed in Section 3.2. Moreover, it is noteworthy that the performance of SqueezeNet with LGA is better than with CCNet in terms of both mIoU and accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MODEL** & **Squeeze [34]** & **Shuffle [15]** \\ \hline Original model & 41.5 & 36.9 \\ With LGA & 44.6 & 44.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study 1: Improvement in mIoU (%) for different architectures after incorporating LGA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Final (\(\downarrow\))**} & \multicolumn{3}{c}{**Clean (\(\downarrow\))**} \\ \cline{2-7}
**MODEL** & **EPE** & **EPE** & **EPE** & **EPE** & **EPE** \\  & **All** & **Matched** & **Unmatched** & **All** & **Matched** & **Unmatched** \\ \hline UFlow [41] & 6.498 & 3.078 & 34.398 & 5.205 & 2.036 & 31.058 \\ FastFlowNet [42] & 6.080 & 2.942 & 31.692 & 4.886 & 1.789 & 30.182 \\ MDFlow-fast [16] & 5.994 & 2.770 & 32.283 & 4.733 & 1.673 & 29.718 \\ UnsupSimFlow [43] & 6.916 & 3.017 & 38.702 & 5.926 & 2.159 & 36.655 \\ ARFlow [26] & 5.889 & 2.734 & 31.602 & 4.782 & 1.908 & 28.261 \\ LGA & **5.502** & **2.604** & **29.142** & **4.109** & **1.597** & **24.626** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optical flow estimation comparison on the MPI Sintel benchmark [40] for final and clean sets. The proposed LGA method achieved superior end-point error (EPE) scores in the two-frame unsupervised flow estimation task.
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 4 Validation of the Solution
### Test Environment
Two types of devices have been used to perform the validation:  
* Real devices like Raspberry Pis (model 3B and model Zero) [78] and Arduino Yun boards [79] connected to a set of sensors widely used in connected home environments to measure temperature, humidity and barometric pressure (DHT11 [80], DHT22 [81], BMP180 [82], BMP280 [83] and BME280 [84]). As stated before, the code executed in Raspberry Pis and Arduino Yun board is based on Python [73; 77]. Those devices are used to validate the correct operation of the solution in a real setup. Figure 5: Test environment. * Emulated devices using JMeter Apache load tester application [85] with an MQTT plug-in [86] configured to send SenML formatted random temperatures. This application is used to send traffic to validate that the solution works and to stress the system in order to obtain latency and throughput figures. Apart from the IoT gateway and the IoT server installed in the Kubernetes clusters, some other container-based applications are also deployed in order to obtain the measurements that are used to validate the solution. Some of those measurements are shown in next section. InfluxDB open-source time series database [87] has been deployed together with the IoT gateway and the IoT server, and also Grafana, an open-source platform for time-series analytics [88]. Although both Edge X gateway and Mainflux server already supported the integration with InfluxDB for analytics, some small updates on the existing code were required [68; 70] as it was shown in Table 1.  
In relation to the connectivity of the environment, two Ethernet LANs and one wireless LAN (WLAN) were installed in the laboratory. The wireless LAN supports the communications between the Raspberry Pi cluster and the sensor devices (needed in slice 1); and between the sensor devices and the commercial routers (needed in slice 2). The two Ethernet LANs connect, respectively: the router and the laptop where the IoT slice orchestrator is running; and the router with the computer where the JMeter device emulator is executed.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 5 Analysis of Hardware Implementation and Verification Results  
### Hardware Budget  
In order to implement the proposed design, an Intel-Altera Cyclone IV EP4 CE115 FPGA was used. In Table 7, the amount of resources, maximum frequency, and throughput, are shown for two representative cases of the QSM communication systems. The number in brackets represents the percentage of resources used out of the total available in the FPGA device.  
Table 8 presents a breakdown of post-synthesis resources used by the different modules of the architecture for the 2 \(\times\) 2 QPSK and 2 \(\times\)2 16-QAM configurations, respectively. Naturally, the amount of resources went up as the order of modulation and the number of antennas increased.  
According to the results, the SORT block was the module with the greatest amount of hardware resources. It used only combinational components (as it was based on sorting networks) [27] to make a comparison between elements and increased in size as the number of elements to sort rose. Given these characteristics, the critical route of the whole implementation was established by this module and affected the general performance of the architecture.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input** & **Size (Bits)** \\ \hline Sorting\_Elements & \(WL\times NT\times HXQC\) \\ Elements & \(WL\) \\ Index & \(log_{2}(NT\times HXQC)\) \\ \hline
**Output** & **Size (Bits)** \\ \hline Sorted\_Elements & \(WL+log_{2}(WL\times HXQC)\times E2S\times NT\times HXQC\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Inputs and outputs of sort.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **Logic Elements** & **Embedded Multipliers** & **Max Frequency** & **Throughput** \\ \hline
2 \(\times\) 2, QPSK & 2385 (2\%) & 28 (5\%) & 37.11 MHz & 416,966 ops \\
2 \(\times\) 2, 16-QAM & 6863 (5\%) & 36 (6\%) & 20.74 MHz & 171,404 ops \\ \hline \hline \end{tabular}
\end{table}
Table 7: Overall implementation results of the detector in a Cyclone IV FPGA.  
Due to the algorithm being inherently recursive, the number of clock cycles was data dependent. Table 7 shows the max frequency considering the slow 1200 mV 0\({}^{\circ}\)C model; and throughput, which is the number of detection processes that can be done per second in the worst case.
## 7 Conclusions  
This paper introduces an innovative two-loop online distributed actor-critic reinforcement learning algorithm designed to optimize multi-RIS-assisted mobile ad-hoc networks (MANETs) within a finite time, particularly in the presence of uncertain and time-varying wireless channel conditions. Unlike conventional approaches, this algorithm maximizes the potential of multi-pair MANETs and RIS by dynamically learning optimal RIS selection and resource allocation policies through online training. Leveraging the two-loop online distributed actor-critic reinforcement learning and decentralized multi-player multi-armed bandit (Dec-MPMAB) algorithm, the developed method not only identifies the most suitable RIS to support communication between distributed MANET transmitters and receivers but also learns the optimal transmit power and RIS phase shift. This real-time optimization enhances the wireless MANET network quality, including factors such as energy efficiency, even in the face of uncertainties arising from time-varying wireless channels.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **n** & **Mean** & **SD** & **Median** & **Skew** & **Kurtosis** & **SE** \\ \hline D-UCB & 500 & 300.379 & 12.658 & 300.964 & \(-\)0.019 & \(-\)1.452 & 1.808 \\ \hline MAB & 500 & 352.055 & 10.459 & 353.225 & \(-\)0.199 & \(-\)1.106 & 1.494 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics for cumulative regret of D-UCB and MAB algorithms with 20 arms.  
Figure 6: An illustration of the variation in EE and SE with varying transmit power using various methods. (**a**) Average EE versus time steps under \(P_{max}\) = 20 dBm, 22 dBm, 24 dBm. (**b**) Average SE versus time steps under \(P_{max}\) = 20 dBm, 22 dBm, 24 dBm.  
The simulation results, when compared with existing algorithms, attest to the efficacy of our proposed approach.  
Conceptualization, H.X. and Y.Z.; Methodology, H.X. and Y.Z.; writing--original draft preparation, H.X. and Y.Z.; writing--review and editing, H.X. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
The support of the National Science Foundation (Grants No. 2128656) is gratefully acknowledged.  
Due to the involvement of our research data in another study, we will not provide details regarding where data supporting the reported results can be found.  
The authors declare no conflicts of interest.
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
## 5 Conclusions  
In this work, a comparative analysis of the TTFF robustness of the four available GNSSs (BeiDou, Galileo, GLONASS, and GPS), based on their performance against different levels of signal attenuation, up to 20 dB, is presented. This study is limited to a representative low-cost receiver, specifically the u-blox ZED-F9P, and the main GNSS band, as these are typical conditions of many mass-market applications. Due to these constraints, the results are not discussed in absolute terms but as a comparative assessment of the four GNSSs.  
Nonetheless, since the test proposed here relies on the well-known GSV and GGA NMEA messages, it can be easily reproduced with other receivers and bands.  
The results indicate that GLONASS achieves the fastest TTFF when attenuation is low enough, but it also presents the largest horizontal error. On the other hand, BeiDou and Galileo exhibit higher TTFF levels, but constrain better the positioning error. Furthermore, all these three show a significant increase in their TTFF for high attenuation values. Finally, GPS provides the best trade-off between TTFF and accuracy for all attenuation levels. Thus, it can be concluded that GPS is nowadays the GNSS that offers the most robust TTFF for low-cost single-frequency receivers.  
Conceptualization, C.H.-R.; methodology, C.H.-R.; software, C.H.-R., [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
In order to access the dataset of recorded signals, please contact the corresponding author directly. Due to the large size of the files, it is not feasible to store them in a public repository. Arrangements for data transfer will be agreed upon request.  
This research was supported by the Space Security Centre of the General Subdirectorate of Space Systems of the Spanish National Institute for Aerospace Technology.  
The authors declare no conflicts of interest.
# Satellite-Derived Bathymetry for Selected Shallow Maltee Coastal Zones
## 4 Results
### SDB Maps  
Four maps were produced for each date in order to have a visual representation of how the predicted depth values of the four separate machine learning algorithms vary as one moves away from the coastline and during different months of the year. These are shown in Figures 6-8.  
Figure 5: Scatter plots displaying the accuracy of the predicted depth values compared to the in situ depth values for the month of February 2021.  
Figure 6: SDB maps for the month of July 2020.  
The computed bathymetry of Golden Bay and Ghajn Tuffieha Bay displayed noticeable variations from July 2020 to February 2021 as evident from Figures 6-8. The initial data collected in July 2020 indicated the presence of shallower waters even at locations away from the shore. Furthermore, there was a slight alteration in the bathymetry between July and August 2020, with deeper depths observed further offshore but with some shallow areas still visible. Comparing the February 2021 maps with those of August 2020 revealed a clear increase in bathymetric depth further offshore with a gradual transition from shallow depths situated close to the shore to deeper depths further offshore.  
The observed bathymetric changes may be the result of various processes. Sediment runoff could be one of the primary causes of bathymetric changes particularly those near the coastline. This is because both Ghajn Tuffieha Bay and Golden Bay are pocket beaches that are surrounded by natural vegetated regions and globigerina limestone headlands, which are highly susceptible to erosion caused by wave action. Additionally, during the six-month period, seasonal changes between summer and winter could have also contributed to the bathymetric changes further offshore. Storm surges and large waves could have also occurred between August 2020 and February 2021, causing sediment movement and seabed erosion, significantly impacting the offshore waters and consequently influencing the water depth.