esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Assessment of the Developed Approach
### Datasets and Thresholds Used for the Assessment
#### 4.1.2 Review of Involved Thresholds  
The developed approach involves several thresholds which can be classified into the following categories:  
Figure 11: Datasets used for the assessment of the approach: indoor point clouds resampled at 1 cm.  
* Thresholds related to **spatial resampling** of point clouds
* Thresholds related to **space dimensions**
* Thresholds related to **constraints and quality criteria**  
A category of thresholds can be associated to several steps of the approach. Moreover, some thresholds are a combination of categories. Thresholds are often related to both space dimensions and spatial resampling. It is for example the case for the threshold defining the number of points of the smallest 3D region to keep. A synthesis of the main thresholds encountered in the developed approach is proposed hereafter in Table 1. Some of default values of those thresholds are also indicated. In addition, Figure 12 shows where thresholds are used in the approach.  
Some thresholds are defined manually. Thresholds related to constraints and quality criteria have to be defined regarding the project specifications and thus they cannot be automated. These thresholds are notably involved in plane segmentations: the maximum distance between a plane and its inliers, the horizontal and vertical constraints and the threshold related to the RMSE of planes. Default values are proposed to the user for such thresholds. Other thresholds are defined manually and are intended to be applied to buildings in general as for example the smallest volume or the smallest plane which have to be taken into account. Default values of those thresholds are also proposed and should be adapted to several datasets of various buildings.  
By considering all information given by the user, some thresholds are defined automatically as for example for the minimum number of points of a 3D region. Considering a spatial resampling of 1 cm and a minimum volume of 1.8 cubic metres (1 metre by 1.8 m height), the smallest 3D region would count 92,000 points.  
The approach was obviously developed so that it can be applied to all datasets having characteristics described in project framework. The approach was applied to the two presented datasets with the proposed default values of thresholds. The results will allow concluding about the generalisation of the use of thresholds defined manually but also about the adaptation of thresholds established automatically. In this way, the transferability of the approach is investigated.  
Figure 12: Steps of the developed approach and associated thresholds.
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
###### Abstract  
With the advent of 6G technologies, satellite communication networks are in urgent need of innovative bearer technologies to meet the demands of government and enterprise private lines as well as computing power networks. We propose optical service unit-based optical inter-satellite links (OISL-OSU) as a solution to address the current limitations in fine-grained service bearing within optical transport networks (OTNs), thereby enhancing the flexibility and efficiency of satellite optical networks. Comparative tests were conducted between OISL-OSU and existing packet-switching technologies in multi-service satellite optical transport networks. Through hardware-in-the-loop simulation verification, key performance indicators such as delay optimization, bandwidth utilization rate, and flexible resource adjustment capability were systematically evaluated. Experimental results demonstrate that OISL-OSU technology exhibits superior performance in delay optimization and fine-grained service bearing. The flexible mapping and multiplexing mechanism of OISL-OSU significantly improves resource utilization efficiency, decreases transmission delay, and strengthens hard-pipe connection capabilities.  
optical service unit; satellite optical network; delay optimization; bandwidth utilization
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 6 Validation of the Proposed Protocol
### Routing Protocol Requirements Analysis
#### 6.1.2 Consistency  
As authors in [73; 74] have mentioned, a routing protocol is consistent if each node consistently follows the packet forwarding decision. For example, if a node \(N_{n}\) has selected a parent \(N_{p}\) and the path chosen is \(p\)<\(N_{n}\), \(N_{1}\)> where each node on this path should forward packets via sub-paths of this selected path. According to the specifications, it is observable that SMTrust is consistent. For source routing, packet headers are used to forward packets by the on-path nodes, thus the routing consistency is satisfied. Similarly, the consistency of hop-by-hop forwarding is satisfied as the tree or topology in SMTrust is constructed downwards from root node to leaf nodes. This indicates that each transitional node is selected as the preferred parent of any sender node on a specified path. Thus, if a neighbouring node \(N_{p}\) is selected as a preferred parent by node \(N_{k}\), a sub-path towards sink node is inevitably selected because the sender node forward packets to the parent node first, that is, to node \(N_{p}\). The same method for packet forwarding is followed by each node on the sub-path. Thus, the SMTrust-based RPL network fulfils the routing requirement of consistency or network convergence.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 9 Case Study 2: Cisco System Breach by Yanluowang Ransomware Gang
### Future Directions
#### 9.7.7 Adversarial Machine Learning: Safeguarding Intrusion Detection Systems  
As intrusion detection systems become more sophisticated, cyber-criminals may attempt to subvert them using adversarial attacks.  
* **Defending Against Adversarial Attacks:** Focusing on developing robust techniques to defend against adversarial machine learning attacks targeting intrusion detection systems.
* **Enhancing Resilience:** Exploring adversarial training, ensemble methods, and anomaly detection approaches to bolster the resilience of intrusion detection systems against such attacks.
## 4 Robust Distributed Rendezvous Control
### Visiting UIUVs along the Path to the Root, Based on Signal Strength Measurements
In order to set the heading of \(u_{i}\) as the gradient direction, \(u_{i}\) sets its new heading using the following equation:  
\[\mathbf{h}_{i}(k+1)=\mathbf{R}(\psi(k),\theta(k),\phi(k))*\frac{\mathbf{gr}_{ i}(P)}{\|\mathbf{gr}_{i}(P)\|}. \tag{16}\]  
Note that a UUV does not have to measure its attitude \(\phi,\theta,\psi\) for moving towards the signal source. The UUV can move in the gradient direction \(\mathbf{gr}_{i}(P)\) in its body\(-\)fixed frame. For instance, suppose that the gradient field \(\mathbf{gr}_{i}(P)\) is estimated as [1,0,0] in the UUV's body\(-\)fixed frame. Using (16), the UUV's control command is generated for moving in the direction of [1,0,0] in its body\(-\)fixed frame. We next discuss how \(u_{i}\) can detect the moment when it encounters \(\mathbf{p}_{j}\). If \(P(u_{i})\) in Equation (6) exceeds \(P^{e}\) in Equation (7), then we assume that \(u_{i}\) encountered \(\mathbf{p}_{j}\). Thus, \(u_{i}\) begins moving towards \(\mathbf{p}_{j+1}\) if it exists. For collision avoidance, we make \(u_{i}\) slow down as it gets closer to \(p_{j}\). The UUV's linear speed \(s_{i}(k)\) is set as follows. If \(P(u_{i})\) in Equation (6) is larger than \(P^{e}\), then \(u_{i}\) begins moving towards \(\mathbf{p}_{j+1}\). Otherwise, we set the following:  
\[s_{i}(k)=min(s^{max},\beta*(P^{e}-P(u_{i}))). \tag{17}\]  
where \(\beta>0\) is a tuning parameter. In MATLAB simulations, we use \(\beta=10\). In Equation (17), \(min(a,b)\) returns a smaller value between \(a\) and \(b\). When a UUV \(u_{i}\) is sufficiently close to \(\mathbf{p}_{j}\in\mathbb{R}^{3}\), it begins moving towards the next UUV \(\mathbf{p}_{j+1}\). In this way, \(u_{i}\) avoids collision with UUVs on its path to the root. In the case where \(u_{i}\) encounters the root, \(u_{i}\) stops moving. This study considers a spherical UUV. The authors of [16; 19; 20; 21] showed that by adopting vectored water jets, a spherical UUV can maneuver freely in any direction. The control of a spherical UUV appeared in [22], which used fuzzy PID controllers to independently control the robot's movement in all directions. While \(u_{i}\) maneuvers, a moving obstacle may abruptly appear in practice. In this case, the UUV \(u_{i}\) can avoid collision using reactive collision avoidance controls. We acknowledge that any reactive control method can be applied for this evasion [63; 64; 65].
# From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies
## 5 Experiments and Results
### Synthetic Scores
#### 5.1.2 Results  
Figure 4 shows the performances of traditional thresholding methods as well as ThresNets on synthetic data. The generated datasets are not imbalanced so the _macro_- and _micro_-averaged F1 scores are very similar and only _macro_-F1 scores are presented. For each FPR-FNR combination, a new data generator instance was created. The methods were then trained on 10,000 samples, and tested on 1000 new samples generated from the same instance. The same train and test samples are used across all the methods. The test metrics are shown.  
As can be seen, for all combinations of FPR and FNR, neural thresholding performs comparable to--or much better than--the competition. Traditional methods work reasonably well when scores have high quality, i.e., the Gaussian noise added to scores is low; FPR and FNR are also very low. The performances of the baseline methods quickly drop when the score variances increase or the scores are corrupted.  
The important thing to notice here is the asymmetry of the impact of FPR versus FNR on the results of the traditional methods. If FPR is small (\(\leq\)0.1) and the score noise is low, they work reasonably well, even for FNR, up to 0.3. However, when FNR is low and FPR is high, this is not the case. Even for \(\text{FPR}=0.1\), the results are poor. Fortunately, in manually labeled real large/extreme datasets, the first scenario is much more prevalent: failing to assign all relevant labels to a given sample out of the plethora of possibilities is much more frequent than assigning an incorrect label. Still, for all \(\text{FNR}>0\), the traditional methods are worse than ThresNet in this experiment.  
The explanation is that due to the simplicity of traditional methods, they do not have any reconstruction abilities and they rely solely on the quality of per-label scores in isolation from the global view of label scores. In the case where a reconstruction from bad scoring is possible, normal thresholding methods are at a clear disadvantage compared to more powerful neural networks. ThresNets exhibit _rethinking_ abilities (to use phrasing from [38]), but they do it effectively, without directly modeling all \(L^{2}\) relations. The question pertains to the extent of inter-score relationships observed in real datasets obtained using authentic scorers.
## 6 Results and Discussion
### Nonparametric Validation (Wilcoxon Signed-Rank Test)  
The Wilcoxon signed-rank test was employed as a nonparametric alternative to validate the \(t\)-test results, addressing potential outliers or minor violations of normality. All items showed significant differences from neutrality (\(p<0.05\)), confirming the robustness of the findings. Nonparametric tests, being particularly useful for small samples like this study, offer a conservative check on the results [79]. Table 14 summarizes the hypothesis test.  
Each hypothesis test in the table assesses whether a specific aspect of the framework has a median score of 3.000 (indicating a neutral rating). The decision to reject or retain the null hypothesis is based on the significance (Sig.) value. All \(p\)-values are below 0.05, demonstrating that the differences between the observed medians and the neutral value of 3.000 are statistically significant. Consequently, the null hypothesis was rejected for all items, indicating that participants rated each aspect of the framework significantly different from neutral. Given that the earlier one-sample statistics table reported mean scores predominantly above 4.000, these results collectively suggest that the framework was generally perceived positively.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 4 Bit Error Rate and Packet Error Rate Simulations  
The bit error rate (BER) and packet error rate (PER) of the proposed algorithms are estimated through simulation for an idealized channel as described in Section 2.3. We define the BER in two ways: By comparing each bit in the order of demodulation through the exclusive or (XOR) operation  
\[r_{\text{BE}}=\sum_{n}(d_{\text{est}}\oplus d)+|N_{\text{est}}-N|, \tag{45}\]  
and by cross-correlation (XCorr), which returns the maximum match between the transmitted sequence \(d\) and demodulated sequence \(d_{\text{est}}\)  
\[r_{\text{BExc}}=\max_{n}\{d_{\text{est}}\,\,\,\&\,\,d\}+|N_{\text{est}}-N|, \tag{46}\]  
both of which include differences in the number of bits to account for additional or missing bits. The former (XOR) we regard for data transmission, where the content of the sequence is not known at the receiver, while the latter (XCorr) indicates the performance, if a known set of codes is expected. The PER is defined through the relative number of erroneous packets compared to the total number of sent packets, where a packet error is any packet that includes at least one bit error. For the PER we consider bit errors according to (45).  
Figure 8: Spectrograms showing the intermediate frequency over time of parts of the signal to illustrate the effects of the channel and undersampling. (**a**) Single band signal after up-conversion before transmission; (**b**) Multi-band signal after up-conversion before transmission; (**c**) Single band after reception before down-conversion; (**d**) Multi-band after reception before down-conversion. Each package is transmitted three times in the experiment.  
Figure 9: Averaged spectral power plots of the raw received signals. (**a**) Single band communication; (**b**) Dual-band communication. The colored area marks the \(\pm\,1\,\sigma\) region of each frequency bin.  
The probability of errors approximately follows the error function (_erfc_) over the SNR [63]. While there are closed-form approximations for LoRa [62; 64], to ease comparisons we approximate those through superposed error functions  
\[\mathrm{P}_{\mathrm{be|pe}}(\gamma)=\sum_{q}\biggl{(}A_{q}\,\mathrm{erfc}\biggl{\{} \left(B_{q}\,10^{7/10}\right)^{1/2^{q}}\biggr{\}}\biggr{)}, \tag{47}\]  
which were fitted manually for the coefficients in Tables 3 and 4 for the BER and PER simulation as shown in Figure 11, respectively.
# Risk Assessment Method of Solar Smart Grid Network Security Based on TimesNet Model
## 2 Related Works
It effectively simulates multi period changes in time series, including intra-period and inter-period changes, by converting 1D time series into 2D tensors. [PERSON] et al. proposed a hybrid load forecasting model based on TimesNet and time convolutional network, considering the multi periodicity of load data for short-term power load forecasting. The results indicate that the proposed model reduces the average absolute percentage error of the ISO-NE dataset by 20% to 43% [16]. [PERSON] et al. proposed a broadband oscillation localization model based on TimesNet and Autoformer algorithms to address the stability issue of integrating new energy generators into the power grid. The simulation experiment verified the accuracy of the proposed model for broadband oscillation source localization [17]. [PERSON] et al. proposed a basic model of power time series data and TimesNet to address the issue of external factors affecting power time series, which helps maintain the stability of the power grid [18]. In summary, although the security issues of smart grids have been extensively studied and the predictive performance of TimesNet models has been affirmed, the security assessment of smart grids is facing new challenges with the integration of distributed energy sources such as solar energy and the increase in network attack types and concealment. Therefore, further verifying and improving the performance of the TimesNet model, and strengthening the network security analysis of the power grid to adapt to the new network security environment, remains an important research direction. To this end, this study proposes an SGTBD model based on TimesNet and an SGID model based on Bi-LSTM to detect physical and information threats faced by smart grids, thereby promoting the efficient implementation of SSG security assessment work.
# Capturing Protein Domain Structure and Function Using Self-Supervision on Domain Architectures
## 3 Materials and Methods
### Building Domain Architectures  
The InterPro database contains functional annotations for super-family, family, and single domains, as well as functional protein sites. Hereafter, we will refer to all such functional annotations as InterPro annotations. Furthermore, we will denote by _domain architectures_ the ordered arrangement of domains in a protein. We consider two distinct strategies to represent a protein based on its domain architecture, consisting of either _non-overlapping_ or _non-redundant_ annotations. For both annotation types, we insert each annotation, based on the annotation's beginning and end, in an interval tree \(T_{hit}\). For each entry of the \(T_{hit}\), we save the annotation InterPro identifier, significance score, and length. Based on the annotation type, we apply the following two distinct strategies to create the linear domain architectures:  
Figure 1: Summary of our approach divided in four parts, building two forms of domain architectures, training domain embeddings, performing intrinsic and extrinsic evaluation of _dom2 vec_ embeddings.  
**Non-overlapping annotations**. For each overlapping region in a protein, we keep the longest annotation out of all overlapping ones. Annotations of non-overlapping regions are included.  
**Non-redundant annotations**. For each overlapping region in a protein, we keep all annotations with a distinct InterPro identifier. We break ties for annotations with the equal InterPro identifier by filtering in the longest one. Similarly, we keep annotations of non-overlapping regions.  
For both annotation types, we sort the filtered-in annotations by their starting position. Finally, following the approach of [5], we also added the \"GAP\" domain to annotate more than 30 amino acid sub-sequences, which does not match any InterPro annotation entry.  
An example of the resulting domain architectures for the _Diphthine synthase_ protein is shown in Figure 2. All domains are overlapping, with the largest one colored in blue, and the non-overlapping annotation is the single longest domain (IPR035966). All other domains have a unique InterProID; therefore, the set of non-redundant InterPro annotations includes all presented domains which are sorted with respect to their starting position, and colored in green.  
Applying the previous steps for all annotated proteins produces the _domain architectures_, constituting the input corpus to the following embedding module.
# A Long-Distance Communication Architecture for Medical Devices Based on LoRaWAN Protocol
## 2 Related Works
After great research efforts in the field, many solutions have been proposed for transmitting data and parameters either medically or from sensors, which are transmitted either via Bluetooth, USB, Wi-Fi, GSM, or serial (which allows the transmission or reception of data on short distances), but there are very few solutions or proposals for remote and real-time transmission. This article comes with an architectural proposal that is based on remote transmission using LoRa technology. Based on specialized literature and researches in the field [2; 6; 7; 8; 9; 10; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35], we can affirm that the LoRaWAN protocol is the new trend in long-distance communication started from medical devices, automotive, industrial networks, and so on. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Bandwidth [kHz]** & **Bit Rate [bps]** & **Spreading Factor** \\ \hline
125 & 293 & 12 \\
125 & 537 & 11 \\
125 & 977 & 10 \\
125 & 1758 & 9 \\
125 & 3125 & 8 \\
125 & 5469 & 7 \\
250 & 10,938 & 7 \\ – & 50,000 & FSK \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The bit rates used in Europe [30; 31].
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### System Model and Performance Analysis
\(x(t)\) is transmitted signal at time \(t\) and \(x_{s}(t)\) is the transmitted signal by the relay at time \(t\). \(y_{SD}\) represents the signal received at D from S. \(y_{SR_{i}}\) is the signal received at \(R_{i}\) from S and \(y_{R_{i}D}\) is the received signal at D from \(R_{i}\). Since AF relaying technique is used, so, we define G as an amplification factor as:  
\[G=\sqrt{\frac{1}{E_{b}h_{SR_{i}}+N_{0}}} \tag{5}\]  
Figure 1: System model. where, \(N_{0}\) is the power spectral density of AWGN noise and \(E_{b}\) is the energy of transmitted signal. Using MRC, the signal at D is given as:  
\[y_{D}(t)=y_{SD}(t)+y_{R_{i}D}(t) \tag{6}\]  
\(y_{D}(t)\) represents the combined signal of direct and relayed transmission at D. The relays retransmit if and only if the reception at D is erroneous.