esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Information Bottleneck Decoders for Irregular LDPC Codes Using Message Alignment
### Message Alignment -- A Graphical Perspective  
In this subsection, some consequences of Definition (4) will be sketched, resulting in what we call the _message alignment_ problem. Please recall that LLRs are not needed in the entire information bottleneck decoder during decoding, because only cluster indices \(t\) are exchanged. However, analyzing LLRs interpretation of a particular cluster index \(t\) in density evolution allows the limiting factor of the considered decoding method to be graphically illustrated for irregular codes. Figure 5a visualizes the meanings of certain cluster indices \(t\) with respect to \(x\) before and after averaging over the degree distribution according to (4). Here, \(L_{2}(x|t)=\log\frac{p_{2}(X=0|t)}{p_{2}(X=1|t)}\) and \(L_{4}(x|t)=\log\frac{p_{4}(X=0|t)}{p_{4}(X=1|t)}\) denote the conveyed meanings of a certain cluster \(t\) with respect to \(x\) of variable nodes with degree two and four _before_, that is, without averaging according to (4).  
Both compression variables have the same event space \(\mathcal{T}\). However, the meaning conveyed by a certain cluster with respect to \(X\) differs. This means that the cross and dot for the same index \(t\) in Figure 5a do not superimpose. For instance, if \(t=13\) and \(d=2\), \(L_{2}(x|t)\approx 2.88\) and if \(d=4\), \(L_{4}(x|t)\approx 4.02\) (cf. arrows in Figure 5a). After application of (4), the _averaged_ meaning, that is, \(L_{d}(x|t)=\log\frac{p_{d}(X=0|t)}{p_{d}(X=1|t)}\) (cf. black triangles in Figure 5a) lies somewhere in between and neither matches the originally intended belief of the variable node with degree two nor the one with degree four. Nevertheless, a closer look at Figure 5a leads to another interesting observation. Although the meanings corresponding to the same indices can differ significantly (i.e., \(L_{2}(x|T=t)\
eq L_{4}(x|T=t)\)), quite similar meanings are expressed by different cluster indices. That is, \(L_{2}(x|T=t_{2})\approx L_{4}(x|T=t_{4})\) for some \(t_{2}\
eq t_{4}\). The explanation of this phenomenon is as follows. The clusterings \(p_{d}(t|\mathbf{y})\) are determined independently for different \(d\). Although by construction the realizations of the compression variables \(t_{d}\in\mathcal{T}\) coincide, the realizations have no natural or physical interpretation which enable a meaningful order or relation (e.g., between \(t_{2}\in\mathcal{T}_{2}\) and \(t_{4}\in\mathcal{T}_{4}\)). In other words, the _messages_ are not _aligned_ with respect to their belief on \(x\). We advocate that this misalignment inhibits a straightforward application of (4). Therefore, knowing the node degrees \(d\) is extremely important to recapture \(L_{d}(x|t)\). However, this node degree is not available at a receiving node in message passing decoding. At this point, we defer further evidence to Section 5, where it is shown that information bottleneck decoders without message alignment exhibit a notable performance degradation.
###### Abstract  
Green wave control is an important technology that synchronizes traffic signals to improve traffic flow on urban arterial roads. Current research has focused on optimizing and evaluating control schemes; however, their operational effect is easily affected by a variety of traffic and travel factors. This means it is important to study methods to identify the factors influencing the operational effect of the green wave on arterial roads. In this study, we conducted innovative research to identify these factors and made breakthroughs in optimizing and evaluating schemes of green wave control. We use the number of stops, travel time, and delays as representative evaluation indicators to assess the effects of four influencing factors: design speed, signal timing, pedestrian crossing, and heavy vehicles. An association analysis that combines sensitivity analysis and grey relational analysis was used to rank these factors in their degree of correlation. A case study was conducted based on the traffic data on Eshan Road in Wuhu City to verify the proposed method. The results of simulations in Vissim 7.0 showed that pedestrian crossing and heavy vehicles were the more important factors influencing the operational effect of the green wave. Moreover, implementing measures related to traffic management helped improve the effect to an extent greater than by optimizing the scheme for green wave control alone. Additionally, optimizing control schemes in the context of implementing measures related to traffic management significantly improved the operational effect of the green wave.  
green wave control; evaluation indicators; influential factors; association analysis; identification
## 5 Evaluation
### SDN Controller Evaluation
#### 5.1.1 Functional Testing  
The main objective was to validate the SDN controller's ability to control RINA-based network devices. The functional testing is divided into two parts: (i) validation of DAF management with RINA manager and MAs, and (ii) validation of RINA SBI to translate requests from the SDN controller.  
The first validation consisted of verifying the IRATI console of the edge node to check if the IPCP was enrolled into the management DAF. During enrollment, the RINA manager interchanges CDAP messages with the MA to establish the DAF management, as described in Section 3.1. Figure 13 shows the edge node console, and we can notice in the last line that the _edgeNode1.management_ was assigned to DIF management. In this case, the _edgeNode1_ MA was accepted as part of the management DAF and interchanged CDAP messages with the _terminet.manager-1_. Furthermore, the RINA SBI registered MA as a new system and assigned a system ID. This register is relevant to keep track of the MA connected into the DAF and to return as systems information when the SDN controller requests the list_systems API.  
The second validation consisted of creating a slice (RINA DIF) using the SDN controller. The RINA SB driver translates the SDN controller data model into the RINA data model to create DIFs. The DIFs' creation request is sent to the _edgeNode1_. The RINA SB driver interchanges CDAP messages with the _edgeNode1_, as part of the creation process. Figure 14 shows the CDAP messages interchanged with the _edgeNode1_ (M_CREATE and M_CREATE_R). The first CDAP message (1740 Bytes) contained the IPCP configuration description (IPCP name, N-1 DIF, and name of the DIF to be enrolled) and the DIF configurations (QoS Cube, routing, namespace directory, enrollment task, resource allocator, and EFCP configurations). Once the IPCP was created and enrolled successfully, the _edgeNode1_ agent sent the result of the request (result:0 means success) using an M_CREATE_R. Figure 15 shows the console of  
Figure 13: _EdgeNode1_ IRATI management console.  
the _edgeNode1_, and we can notice in the last line that there is a new IPCP (_edge1.slice1_) and it was assigned into the _slice1_ DIF.  
The other functionalities (destroy DIF, destroy IPCP, create IPCP) were tested following the same procedure as the create DIF showed. In those cases, we validated that the RINA SBI drive translated the SDN controller requests into CDAP messages and sent them to the _edgeNode1_ using the CDAP messages (M_CREATE and M_CREATE_R).
## 3 Results and Discussion
### Black Space curve  
Figure 14 shows the Black Space curves for the control, PG 70-28, 5%PCM, 5%GPCM, 7%GPCM, 5%GPCM, 5%GPCM-SBS, and 7%GPCM-SBS binders. As can be seen, the Black Space curves were diverse for all the tested binders, especially at low frequencies. It was noticed that the viscous behaviour for the control binder was dominant at low frequencies and high temperatures, at which the phase angle reaches 90\({}^{\circ}\). The 5%PCM, 5%GPCM, and 7%GPCM binders exhibited the same viscous behaviour as the control binder at the same temperatures and frequencies.  
On the other hand, the PG70-28 binder showed lower phase angles throughout the test, indicating that the elastic behaviour of the binder was dominant. However, adding 5% and 7%GPCM in the PG70-28 binder increased the phase angle values, leading to a decrease in the elastic behaviour of the binder.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 4 Incremental Frequent Itemset Mining with Map-Reduce
### The Algorithms
#### 4.2.3 Early Pruning Example  
The following is an example of \"minimal count\" (_mincount_) and \"maximal count\" (_maxcount_) for a small DB with 1001 transactions (\(|\mathcal{D}|~{}=~{}1001\)). In case we have two Mappers, the MR framework would split the DB to two Splits of roughly equal sizes. Table 4 is a table showing the calculation per two Splits with the support ratio of 20%. For example, for an itemset to be frequent in \(\mathcal{D}\), it needs to be contained in at least \(1001*0.2~{}=~{}201\) transactions. This example will examine itemsets A \(~{}=~{}\{\text{a}\}\), and B \(~{}=~{}\{\text{b}\}\), and their possible appearance in \(\mathcal{D}\).  
Table 4 shows how A is being preemptively pruned in \(\mathcal{D}\). Table 5 shows an example of incremental computation with \(\Delta\). If \(\Delta\) consists of only five similar transactions {a} {\(|\Delta|\)= 5, minimum support level is 1}, then FI(\(\Delta,minSup)\)= {a}. Also \(|\mathcal{D}^{+}|\)= 1006, and the new minimum threshold is 202. The following table shows how decisions for A and B are being made:  
This example shows that B cannot be frequent in \(\mathcal{D}^{+}\), so it will not even be resent to \(\Delta\) for recounting (if we would omit this optimization, then we would have to go over \(\Delta\) again to count B).  
By using early pruning optimization, we are able to reduce the number of candidates which reduces the output of MR, and saves CPU resources in future jobs that otherwise would require counting of the non-potential \"candidates\". The above optimization is valid for any distributed framework.  
Next, we show an optimization which is tailored specifically to Map-Reduce.
# PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments
## 5 Construction of PriSign
### Challenges and Intuitions  
The schemes described in [33; 34; 35; 36; 38] form the basis of our construction; the challenge is to combine and adapt them to ensure that the final scheme satisfies the system and security model proposed in Section 4. As shown in Figure 7, we summarize the dependencies between the building blocks used by PriSign along with a roadmap highlighting our contributions in gray boxes.  
First, we construct an attribute-based credential with traceability (ABCT) scheme from the anonymous credential in [34] and utilize it to create users' credentials. Because our ABCT scheme supports attribute disclosure proofs with unlinkability, the ticket issuer can issue different types of tickets for users according to different selective disclosure policies in the PriSign scheme, thereby realizing fine-grained access control of services. In addition, this enables PriSign to support traceability of users.  
Figure 5: Unlinkability of tokens.  
Figure 6: Token-hiding.  
Second, we construct an attribute-based credential with blindness (ABCB) scheme from the anonymous credential in [38] and utilize it to issue tokens for users. Our ABCB scheme supports issuing credentials based on both unblinded and blinded attributes simultaneously. Therefore, when issuing a ticket, the double-spending identity of the ticket can be used as the blinded attribute and the ticket's validity period as the unblinded attribute.  
Third, we construct a threshold inner-product functional encryption (TIPFE) scheme from the inner-product functional encryption (IPFE) in [35] and predicate encryption scheme in [36]. We use TIPFE's encryption algorithm to hide tokens, TIPFE's inner product predicate as the matching function to designate verifiers, and TIPFE's threshold key issuing to achieve stable policy key generation for verifiers in cloud environments. Finally, the SPS [33] scheme enables us to implement public key credential issuance of the ticket issuer.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Resistance Capability of ECSSP-SC against Attacks
#### 6.2.6. Resistance to Known Key Attacks  
An _Attacker A_ somehow obtains the session key \(K\) and then tries to get the other session key during a known session key attack. In the ECSSP-SC, the session key \(K\) is generated by using secret random number \(v\), which is selected fresh in every session. This ensures that all the session keys do not depend on each other. Thus, the proposed ECSSP-SC is safe from any known key attack.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 3 Results and Discussion
### The Yaw Angle Differences and the Duration of Eclipse
#### 3.2.3 GALILEO-FOC Satellites  
During the period of July 2017, no GALILEO IOV satellites are in eclipse. Therefore, only the yaw angle differences of GALILEO FOC satellites are analyzed. According to the official description, when the GALILEO-FOC satellites approach the orbit noon or the midnight, the modified yaw steering law is adopted to keep the yaw change rate low. Different from other satellite systems, the sign of the yaw angle rather than the sun angle, at the time of the switch, must be stored. As shown in Figure 7, the noon maneuver of GALILEO FOC satellites looks like that of the GLONASS-M satellites, whose attitudes are symmetrical distribution centered around the noon. The maximum duration of noon maneuver for GALILEO FOC satellites is about 47 min. During the first 7 min and the last 7 min of the noon maneuver, the nominal and the modeled yaw angles agree within a few degrees, even for a very small sun angle (0.02\({}^{\circ}\)). As shown in Figure 8, when the absolute sun angle is larger than 2.0\({}^{\circ}\), the maximum differences will be within 20\({}^{\circ}\).  
Figure 6: The yaw angle of R10 (M) during shadow crossing for different sun angles: (**a**) beta = 0.24\({}^{\circ}\); (**b**) beta = 4.77\({}^{\circ}\); and (**c**) beta = 13.79\({}^{\circ}\). The nominal yaw angle is shown in green, while the modeled one is in red.  
Figure 7: The yaw angle of E01 (FOC) during noon maneuver for different sun angles: (**a**) beta = \(-1.99^{\circ}\); (**b**) beta = \(-1.19^{\circ}\); and (**c**) beta = 0.02\({}^{\circ}\). The nominal yaw angle is shown in green, while the modeled one is in red.
# Frequency Range Optimization for Continuous Wave Terahertz Imaging
###### Abstract
The materials were all polyethene plastic plates of varying density, with different additives and with the same geometry. Fifty frequency scans were acquired in the range between 50 GHz and 1210 GHz for each material. The WSD algorithm was applied for every frequency scan, and the CNN was trained to classify the materials. The optimized frequency range was determined using occlusion sensitivity and predetermined criteria. The results were evaluated for three frequency ranges, and they were compared to the results in the preliminary work [17]. The results show a decrease in acquisition time, while frequency resolution and contrast stayed reasonable. Frequency tuning error, bias towards lower frequencies and phase shift because of frequency tuning were all mitigated in the proposed frequency range optimization algorithm.
## 5 Simulation Results and Discussion
### TSC-TCR's Cost-Effectiveness  
Surprisingly, TSC-TCR is an expensive tool that is becoming increasingly important in energy systems due to key features such as high speed, efficiency, and real-power management capabilities, among others [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. Depending on their potential benefits and the environment, parts of the TSC-TCR system are expected to offer better storage and use to facilities in the future [36; 37; 38; 39; 40]. While the price of the TSC-TCR components may appear high at the moment, long-term research and development is expected to lower prices and improve grid security technology.
## 4 Validation of Proposed Method
### Validation of Frame Synchronization  
The frame synchronization process in the proposed method was evaluated by carrying out a frame synchronization test. The accuracy of frame synchronization \(A_{cc}\) was considered to be correctly frame synchronized at 100%. This is defined as  
\[A_{cc}=\frac{I_{\mathrm{CA}}}{I_{\mathrm{ALL}}}\times 100, \tag{15}\]  
where \(I_{\mathrm{CA}}\) is the number of analysis frames that can be completely frame-synchronized, and \(I_{\mathrm{ALL}}\) is the number of all analysis frames. The number of reference frames is 1, 2, 4, 8, 16, 32, 64, 128, and All (202). The total number of speech signal frames used in this evaluation is \(K/2\), that is, 202.  
Figure 5 shows the averaged accuracy of frame synchronization, where the horizontal axis represents the number of references frames and the vertical axis represents the accuracy of frame synchronization. Figure 5 shows the front-side and back-side forms. We can see here that the frame synchronization processes of the front-side and back-side forms have no significant difference in the accuracy of frame synchronization. In addition, the accuracy of frame synchronization increases with the increase of the number of reference frames and embedding-strength level. Specifically, the frame synchronization processes of the front-side and back-side forms have 100% accuracy of frame synchronization when the embedding-strength level is \(-15\) dB and the number of reference frames is \(K/2\), that is, 202.  
Next, we evaluated the frame synchronization process of the front-side and back-side forms for robustness to the sample-cut attack. The first few samples of the signal with embedded bit strings are randomly deleted in the range of \(1\sim 2N\). The signal is utilized as input to analyze the accuracy of frame synchronization using Equation (15). The number of samples to be deleted is 14, 57, 109, 127, 144, 320, 406, 439, 480, 487, 494, and 611.  
Figure 6 shows the averaged accuracy of frame synchronization, where the horizontal axis represents the number of references frames \(K/2\) and the vertical axis represents the accuracy of frame synchronization. Figure 6 shows the front-side and back-side forms. We can see here that the frame synchronization process of the front-side and back-side forms have no significant difference in the accuracy of frame synchronization. In addition, the accuracy of frame synchronization increases with the increase of the number of reference frames and embedding-strength level. Specifically, the frame synchronization accuracy in the front-side and back-side forms is 100% when the number of reference frames is All and the embedding-strength level is \(-15\) dB.  
Figure 5: Accuracy of frame synchronization with respect to the number of reference frames: (**a**) front-side and (**b**) back-side forms. Each line shows the results for a different embedding-strength level \(L_{\mathrm{SSL}}\).
## 4 EEG Signal Acquisition and Motor Imagery Training  
### EEG-Based Signal Acquisition  
Accessing the Neural Network: The interface between brain and world is established through electrodes, either in non-invasive surface contact or via implanted subdermal conduits. The non-invasive method relies on conductive gel bridging the gap with the scalp, facilitating signal acquisition but introducing setup time and low-frequency attenuation. An alternative, dry electrodes, offer swift application but increased susceptibility to movement artifacts [41]. EEG data acquisition employs two primary modes: unipolar and bipolar. In unipolar mode, each electrode relays its information relative to a common reference, creating individual EEG channels. Conversely, bipolar mode focuses on potential differences between specific electrode pairs, each pair constituting a channel [42]. To ensure standardized data gathering and inter-session comparability, the international 10-20 system governs electrode placement across the scalp. This systematic approach fosters reliable communication and collaborative efforts within the field of brain-computer interfaces [43].  
Figure 2 reveals the international 10-20 system, a topographical blueprint for deciphering the brain's electrical signatures. Similar to carefully arranged instruments in an orchestra, this framework meticulously guides the placement of EEG sensors across the scalp. Once these sensitive outposts detect the subtle fluctuations in cerebral potential, the signal undergoes amplification, a process similar to tuning in to the faintest whispers of the neural ensemble. Subsequently, this amplified signal is adapted into the digital domain, analogous to transcribing the intricate melody of brain activity into a language computers can comprehend. By employing higher sampling rates, measured in Hertz, researchers can capture more fleeting neural fluctuations. Increasing the number of EEG sensors, analogous to expanding the observation network, provides a broader spatial perspective on brain activity. This combination of greater temporal and spatial resolution allows for a more detailed and accurate representation of the dynamic processes unfolding within the cerebral cortex.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 3 System Design
### Environment Configuration
#### 3.4.1 Traffic Map  
In the scenario described in Section 3.1, UAVs should move to appropriate locations and provide network services autonomously. In order to do this, we built various network environments with different traffic distributions for the agent to perform learning from diverse experience. Figure 3 shows an example network traffic map, and darker color means higher traffic on the ground. In the figure, red and blue points with a number mean UAVs and satellites, respectively. We set the traffic map to periodically change the traffic distribution whenever a certain number of episodes are finished, during the training process. By doing so, the agent can perform learning in various environments, which alleviates any bias which can be formed from a specific environment.