esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
## 3 System and Network Model
### Network Model  
The network is composed of a collection of infrastructure nodes that form a multi-hop network. In this paper, the Network Manager and the gateway are merged into a centralized controller \(g\), which is responsible for network management. The set of nodes is denoted as \(V=\{v_{1},v_{2},...\}\), and one  
Figure 1: The WirelessCAN system prototype including fixed and portable devices.  
of these nodes is in the moving state, we use \(MN\) to denote it. Thus, every node can be turned into a \(MN\) possibly. Two kinds of flows are defined to support monitoring and control applications. We use \(f_{i}^{P}:\langle v,p_{i},\varphi_{i},d_{i}\rangle\) to denote the periodical upstream flow, where \(v\) is the generator of flow \(f_{i}^{P}\) and \(\varphi_{i}\) is the phase of dedicated slot allocation in the superframe. Each flow \(f_{i}^{P}\) periodically generates a packet at period \(p_{i}\) which originates at \(v\) and has to be delivered to \(g\) within the deadline \(d_{i}\). \(f_{i}^{C}:\langle v,d_{i}\rangle\) represents an event-based flow of control applications which routes from \(g\) to \(v\).  
All transmissions in each flow are assigned 10 ms slots by Network Manager according to WirelessHART, which construct a periodical superframe. The superframe consists of three parts, which are broadcast slots, management slots and data slots as shown in Figure 2. To guarantee the reliable and real-time transmission, the part of data slots scheduling is according to the SSA algorithm in [31], which is divided into the segments by the hop of transmission for all flows. Each segment comprises dedicated slots and shared slots. Two ensuring nodes to do the half-duplex communication occupy the dedicated slot. While the shared slot is reserved for the nodes which need retransmission. Each transmission can be tried for three times at most, including the initial transmission in dedicated slot and two retransmissions in shared slots. Generally, all flows should complete transmission within a superframe, thus the length of superframe \(L\) depends on the sampling period and the deadline, where it needs to satisfy \(d_{i}\leq L\leq p_{i}\) regardless of monitoring and control flow. Spectrum diversity gives the network access to several channels defined in the IEEE 802.15.4 physical layer and allows per-time slot channel hopping. All available channel in channel hopping scheme is evaluated by all the time-slot transmission. Nodes periodically report the performance of each transmission to Network Manager, which will construct a channel blacklist. Therefore, the inspection of available channels is supported by the original WirelessHART standard.
## 3 Materials and Methods  
### Materials  
\(\varepsilon\)-Caprolactone (2-Oxepanone, CL, 97%, Aldrich, Poznan, Poland), zirconium(IV) acetylacetonate (tetrakis(2,4-pentanedionato)zirconium(IV), Zr(acac)\({}_{4}\), 97%, Aldrich, Poznan, Poland), poly(ethylene glycol) 200 (PEG 200, M\({}_{n}\) = 200 g/mol, pure, Fluka, Warsaw, Poland), poly(ethylene glycol) 600 (PEG 600, M\({}_{n}\) = 600 g/mol, pure, Fluka, Warsaw, Poland), poly(ethylene glycol) 1000 (PEG 1000, M\({}_{n}\) = 1000 g/mol, pure, Sigma-Aldrich, Poznan, Poland), poly(ethylene glycol) 1500 (PEG 1500, M\({}_{n}\) = 1500 g/mol, pure, Sigma, Poznan, Poland), poly(ethylene glycol) 2000, (PEG 2000, M\({}_{n}\) = 2000 g/mol, pure, TCI, Zwijndrecht, Belgium), dichloromethane (DCM, CH\({}_{2}\)Cl\({}_{2}\), \(\geq\)99.8%, POCh, Gliwice, Poland), hydrochloric acid (HCl, ChemPur, Piekary Slaskie, Poland), Dulbecco's Modified Eagle Medium (DMEM, Thermo Fisher Scientific, Warsaw, Poland), 5-fluorouracilouracil (5-fluoropyrimidine-2_4_-[1H,3H]-dione, 5-FU, 99%, Sigma-Aldrich, Poznan, Poland), phosphate-buffered saline (PBS, pH = 7.40 \(\pm\) 0.05, ChemPur, Piekary Slaskie, Poland), phosphate-buffered saline for cell cultures (PBS, GIBCO, Dublin, Ireland), acetonitrile (ACN, HPLC grade min. 99.9%, POCH, Gliwice, Poland) and trifluoroacetic acid (TFA, 99%, Sigma-Aldrich, Poznan, Poland). Prior to use, PEGs compounds were heated in a vacuum for 2 h at 80 \({}^{\circ}\)C to remove residual water. Other reagents were used as received.
# An Analysis of Commercial Noni Juice: LC-MS/MS Phenolic Profiles and Cytotoxic Activity
## 3 Results and Discussion  
### Phytochemical Composition and Ascorbic Acid Content  
Table 4 summarises the general phytochemical composition of the five noni juice brands, as measured using benchtop spectrophotometric methods. The Fijian noni juice showed both the highest TPC (1761 mg GAE L\({}^{-1}\)) and antioxidant capacity (817 mg TE L\({}^{-1}\)). The range of TPCs found here (1106-1761 mg GAE L\({}^{-1}\)) were slightly higher than the TPC of commercial noni juice reported by [PERSON] et al. [14] (919 mg GAE L\({}^{-1}\)), but a little lower than the 2100 mg GAE L\({}^{-1}\) reported by [PERSON] et al. [13] in freshly produced noni juice from Guam. The same authors found that TPC values reduced significantly with storage [13], thus appearing to explain why the TPC values found in the present commercialsamples were lower than those typically found in fresh juice. However, it is important to note that the ripeness of the noni fruits used to manufacture the juice can also influence their TPC and antioxidant capacity [28].  
There was a positive correlation between the TPC and antioxidant capacity; however, this correlation was not significant (r\({}_{3}\) = 0.81; \(p>\) 0.05). However, previous research has reported a correlation between TPC and DPPH antioxidant capacity in this matrix [13].  
In the crude crystals, the highest TPC was seen in DHN, although there was no significant variation between the different brands. The crystals from CIN also showed a very high FRAP (6389 mg TE 100 g\({}^{-1}\)), although this brand had the lowest FRAP for the neat juice. In contrast to the results for the juice, the TPC and FRAP of the crude crystal extracts were not strongly correlated (r\({}_{4}\) = 0.18).  
There was a lower level of variation in the ascorbic acid content of the different noni juice brands (Table 4). The FN and TON noni juice brands showed the highest ascorbic acid contents (20.1 and 19.7 mg L\({}^{-1}\), respectively), while DHN had the lowest ascorbic acid (12.2 mg L\({}^{-1}\)). The ascorbic acid content was only reported on the label of one of the noni juice bottles (TON--16.67 mg L\({}^{-1}\)), which was slightly lower than the experimentally determined value.  
The U.S. Department of Agriculture (USDA) FoodData central has reported a vitamin C content of 30 mg/100 mL in the \"Pure Noni\" brand of Noni juice sourced from Florida [29], which was significantly higher than the values quantified in this study. In a review, the ascorbic acid content in Noni fruit on a wet weight basis was reported to be in the range of 53.2-76.2 mg/100 g (approximately 5-8 mg L\({}^{-1}\)) [30]. Previous research has indicated that illumination during storage often affects the chemical properties and components of noni juice, including vitamin C. Although minimal exposure to light would have occurred during shipping or storage at the laboratory, the storage conditions post-manufacture are unknown for these samples.
# [PERSON] on Korean Block Ciphers
## 3 Proposed Method
### Hight
#### 3.1.1 Key Schedule  
In order to reduce the use of qubits in HIGHT, rather than generating all the round keys in advance, the key schedule is performed simultaneously during the round. In the key schedule, the \(\delta_{0}=\{s_{6},s_{5},s_{4},s_{3},s_{2},s_{1},s_{0}\}\) is used first, and then a new \(\delta_{i}\) must be generated for \(1\leq i\leq 127\). At this time, the number of qubits can be saved by recycling the existing qubits. For example, when creating a \(\delta_{1}\) in Equation (2), a new \(s_{7}\) is generated, and the \(s_{0}\) is not included in new \(\delta_{i}\). Therefore, we do not allocate a new qubit for \(s_{7}\), but operate on the existing \(s_{0}\) and treat it as a new value. The detailed process for this is shown in Algorithm 1.  
```
0:\(\delta_{i-1}=\{s_{(i+5)\%},s_{(i+4)\%},s_{(i+3)\%},s_{(i+2)\%},s_{(i+1)\% },s_{\%},s_{(i-1)\%}\}\)
0:\(\delta_{i}\)
1:\(s_{(i-1)\%}\leftarrow\) CNOT(\(s_{(i+2)\%},s_{(i-1)\%}\))
2:return\(\delta_{i}=\{s_{(i-1)\%},s_{(i+5)\%},s_{(i+4)\%},s_{(i+3)\%},s_{(i+2)\%},s_{( i+1)\%},s_{\%}\}\)
```  
**Algorithm 1** Generate \(\delta_{i}\) of HIGHT.  
As mentioned earlier, to reduce the number of qubits, the key schedule is performed simultaneously with the function of the round. Rather than generating all round keys at once, the key schedule is performed in units of 16 keywords. Instead of generating a round key in new qubits, the operation is performed on the initial key \(K=\{K[0],K[1],\ldots,K[15]\}\) to convert it to round key \(RK=\{RK[0],RK[1],\ldots,RK[15]\}\). The detailed process for this is described in Algorithm 2.  
```
0:\(\delta_{16\cdot(i-1)}\), \(K\)
0:\(K\)
1:for\(j=0\) to 7 do
2: Generate \(\delta_{16\cdot(i-1)+j}\)
3:\(RK[16\cdot(i-1)+j]\leftarrow\) ADD(\(\delta_{16\cdot(i-1)+j}\), \(K[j-i+1\bmod 8]\))
4: Use \(RK[16\cdot(i-1)+j]\) in round function
5: Reverse : ADD(\(\delta_{16\cdot(i-1)+j}\), \(K[j-i+1\bmod 8]\))
6:endfor
7:for\(j=0\) to 7 do
8: Generate \(\delta_{16\cdot(i-1)+j+8}\)
9:\(RK[16\cdot(i-1)+j+8]\leftarrow\) ADD(\(\delta_{16\cdot(i-1)+j+8}\), \(K[(j-i+1\bmod 8)+8]\))
10: Use \(RK[16\cdot(i-1)+j+8]\) in round function
11: Reverse : ADD(\(\delta_{16\cdot(i-1)+j+8}\), \(K[(j-i+1\bmod 8)+8]\))
12:endfor
13:return\(K=\{K[0],K[1],\ldots,K[15]\}\)
```  
**Algorithm 2**\(i\)-th key schedule of HIGHT.  
The \(RK\) of 16 keywords is used for 4 rounds. After use, \(RK\) is initialized to \(K\) by reverse operation. In addition, again, the key schedule is performed to generate the round key \(RK=\{RK[16],RK[17],\ldots,RK[31]\}\) to be used for the next four rounds.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 7 Simplified Observation Model for Outdoor Use
Note that the simple observation model can be used both for initially locating the APs from RTT measurements, and later for locating the UE from RTT measurements. The above shows what to do with positive evidence that the AP may be in certain grid locations based on a response to a ranging request from the UE. There may also be negative evidence: an AP is unlikely to be nearby if it does not respond to a ranging request (i.e., the absence of an RTT response is also an \"observation\"). In this situation, one may want to _reduce_ the probabilities of cells near the current UE positions--by an amount that decreases with distance. Experiments with a difference-of-Gaussian probability distribution for this did not prove especially beneficial in the cases described below.
In order to determine the natural facts of oceanic resources and collect scientific data for monitoring, AUVs and UUVs are equipped with underwater sensors that may also be viewed. Various problems can be resolved using underwater sensor networks because of the technology's effectiveness. The network's diverse technologies, such as localization and energy efficiency, help to solve issues such as node scattering, high attenuation, and absorption impact [4].  
High concentrations of saline water, electromagnetic, optical, and radio waves can travel great distances underwater and be dispersed in numerous directions [5]. Because of this, the situation can be managed by utilizing various methods, such as a subterranean setting, and data might be easily conveyed using an acoustic transmission is depicted in Figure 2. Multi-hop networks are required because underwater sensor nodes are more significant, consume more power, and must be replaced frequently [6]. It is challenging to replace nodes and batteries in multi-hop networks that penetrate downward at the surface and transfer data once or several times. Information can be advanced to onshore control stations through data sinking. Higher bandwidth-demanding routing techniques have considerable end-to-end delays; hence, they should not be used in these conditions. Underwater communication is challenging due to propagation delay, a high bit error rate, and a limited bandwidth [7, 8].
# Confidence Learning for Semi-Supervised Acoustic Event Detection
###### Abstract  
In recent years, the involvement of synthetic strongly labeled data, weakly labeled data, and unlabeled data has drawn much research attention in semi-supervised acoustic event detection (SAED). The classic self-training method carries out predictions for unlabeled data and then selects predictions with high probabilities as pseudo-labels for retraining. Such models have shown its effectiveness in SAED. However, probabilities are poorly calibrated confidence estimates, and samples with low probabilities are ignored. Hence, we introduce a confidence-based semi-supervised Acoustic event detection (C-SAED) framework. The C-SAED method learns confidence deliberately and retrains all data distinctly by applying confidence as weights. Additionally, we apply a power pooling function whose coefficient can be trained automatically and use weakly labeled data more efficiently. The experimental results demonstrate that the generated confidence is proportional to the accuracy of the predictions. Our C-SAED framework achieves a relative error rate reduction of 34% in contrast to the baseline model.  
semi-supervised learning; acoustic event detection; confidence estimates +
Footnote †: journal: Applied Sciences
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 4 SALMA Data-Plane  
After promoting the idea of middlebox integration by presenting some of the shortcomings of middlebox deployment in current enterprise networks, we are now ready to discuss how these shortcomings are addressed in our work. Our proposed solution has two types of data-plane devices: the edge switches and the middlebox subnet switches. The incoming flows to any of the edge switches can be classified into in-scope and out-scope flows. The former need to be forwarded to one of the subnet replicas of the middleboxes, whereas the latter are not required to be forwarded to the middlebox subnet. Instead, these flows should be forwarded directly to the destination. The edge data-plane devices must be able to recognize which flow belongs to which class, and this is the duty of the middlebox manager, as explained in detail in the next section. When an unknown flow is received by an edge data-plane device, it sends a flow setup request to the middlebox manager, which in turn installs a flow entry on the edge switch to decide whether this flow is an in-scope or out-scope flow. In the case of in-scope flow, the data plane forwards the flow packets to the middlebox subnet. Otherwise, the packets are forwarded to the destination. In simple words, the middlebox manager classifies the unknown flows based on the policy configuration that was installed by the network administrator into the policy sequence module. The second type of data-plane device is the middleboxes' subnet switch. When the middlebox manager receives the flow-setup request from the edge switch and considers it as in-scope flow, it configures the middleboxes' subnet switch with multiple flow entries to maintain the policy sequence. At the same time, it checks the collected statistics and the number of assigned flows to each middlebox's subnet replicas to balance the traffic load among them.
## 5 Conclusions, Limitations, and Future Recommendations  
### Conclusions  
This study began with a literature review on Sybil attacks and their targeting of different types of networks. This review delved into the protection and prevention algorithms employed to secure networks vulnerable to Sybil attacks. It also included an overview of reputable systems and an outline of our research methodologies. This study concluded by detailing the steps taken, analyzing results, explaining research limitations, and proposing future research directions. In this study, we developed an algorithm to detect Sybil nodes in an online social network, specifically Twitter, using the number of direct connections as a criterion. This algorithm can function as part of a reputation system within online social networks to identify Sybil nodes. The algorithms yielded high detection accuracy rates, ranging between 96% and 97% for the KNN algorithm, 97% and 100% for the SVM algorithm, and 98.4% to 99.6% for the random forest algorithm. Notably, these algorithms exhibited reliability with minimal standard deviations, all under 0.0061%, with the random forest algorithm showing the lowest deviation of 0.0029%. These outcomes are attainable using standard workstation or desktop computers.  
The algorithm developed in this study depends solely on connection data and their directions. In contrast, the approach by [PERSON] and [PERSON] [PERSON] and [PERSON] (2016) necessitates more parameters to ascertain whether a node is a Sybil or not. Our accuracy results are notably high. Furthermore, the scalability of this algorithm is noteworthy. We efficiently divided large datasets into smaller matrices, enhancing computation speed and enabling parallel execution. This matrix division also significantly reduces the total size of the matrices used for training, contributing to improved performance and stability.
## 3 Results and Discussion
As an example, a  
Figure 6: Magnetoelectric voltage versus AC magnetic field frequency characteristics for sample S2 and reference R: **(a)** for DC field in the range of 0–0.011 T, **(b)** for DC field between 0.058 and 0.4 T.  
comparison of resonant frequencies for the samples exposed to 0.133 T is shown in Figure 7. It is clearly visible that all the resonant peaks for the sample S2 (higher mass) are shifted towards lower frequencies in comparison with the peaks for the sample S1. The results of magnetoelectric voltage versus DC magnetic field measurements for the composite S2 are shown in Figure 8. The characteristics follow the same trend as in the case of the sample S1. As expected, U\({}_{\text{OUT}}\) attains larger values when the sample contains multiple Terfenol-D layers. The voltage increases linearly along with the field, reaches a maximum value, and then decreases. The only exception is the measurement at f\({}_{\text{res1}}\) = 29.3 kHz. At this frequency, which corresponds to the first resonant peak in Figure 6, the magnetoelectric voltage drops as the DC field reaches 0.034 T. The voltage decreases again after reaching a maximum value. This dependence can be observed as the reorientation of the first peak in Figure 6 (downwards for 0 and 0.011 T, upwards for stronger DC fields). In contrast to the sample S1, the first peak for 0.058 T is directed upwards. The magnetoelectric coupling coefficient \(\alpha\) was calculated from Equation (1) for selected resonant frequencies for both composite samples. The characteristic U\({}_{\text{OUT}}\)(f) values for 0.133 T were chosen as the largest magnetoelectric voltages recorded for that DC field. The results are collected in Table 1.  
Figure 4:0 Magnetoelectric voltage versus DC magnetic field characteristics: (**a**) for sample S2 and reference R at different frequencies; (**b**) for sample S2 at two different resonant frequencies. Figure 4:1 Comparison of resonant frequencies for samples S1 and S2. The highest coupling coefficient was observed for the sample S1 (\(\alpha\) = 555 mVcm\({}^{-1}\) Oe\({}^{-1}\)) in a resonance state at 69.2 kHz. Despite the larger observed U\({}_{\text{OUT}}\) for the sample S2, the sample S1 is approximately three times thinner, which had a significant impact on the final result. The experimental results were obtained with magnetic field lines perpendicular to the face of the samples (longitudinal ME effect). Available mathematical models show that a much larger \(\alpha\) can be expected with AC field and DC bias lines parallel to the sample plane (transverse ME effect) [12; 32]. For comparison, values up to 5.9 Vcm\({}^{-1}\) Oe\({}^{-1}\) for different PZT/Terfenol-D composite setups were reported in the literature [5; 11; 33].
# Security of IoT Application Layer Protocols: Challenges and Findings
## 5 Service Discovery Protocols
### mDNS  
Multicast Domain Name System (mDNS) is an open protocol widely used presently for service discovery and name resolution on local links [62]. This protocol, coupled with DNS-based Service Discovery (DNS-SD) [63], offers the flexibility required by environments where it is necessary to automatically integrate new devices and perform DNS-like operations without the presence of a conventional DNS server.  
Unlike messaging protocols, the mDNS protocol does not provide any built-in security service. Therefore, similarly to DNS, mDNS environments are exposed to security attacks. Recent efforts to improve DNS security, such as DNSSEC [64] and DNS over TLS [65], are in general too complex for self-configuring networked environments.  
From the analysis of the potential security threats of mDNS, we identified and classified the attacks as follows:  
* _Denial of Service attacks_: attackers flood mDNS-enabled nodes with messages that exploit specific characteristics of the protocol. These messages could make nodes unresponsive or unavailable by invalidating cache entries or blocking the probing process;
* _Poisoning attacks_: attackers spoof mDNS response messages and advertise fake services frequently exploited for further attacks towards unaware nodes;
* _Remote attacks_: attackers exploit mDNS-enabled nodes responding to queries from outside to abuse services for various purposes, e.g., Distributed Denial of Service reflection attacks, collection of sensitive information.  
To understand the vulnerabilities that might be behind these attacks, we analyzed the 29 CVEs affecting products and services based on mDNS. This analysis reveals that nodes that inadvertently respond to unicast queries with source addresses outside the local link allow attackers to cause Denial of Service or obtain potentially sensitive information via UDP packets over port 5353 (e.g., CVE-2015-1892, CVE-2017-6519, CVE-2017-6520). Similarly, a Denial of Service attack can be performed by sending malformed or maliciously crafted packets (e.g., CVE-2015-0650).  
Moreover, the multicast nature of the communications and the lack of any encryption mechanism might lead to security and privacy issues that often remain undetected. In fact, messages frequently disclose personally identifiable information as well as sensitive information about the nodes of the network and the services being provided. For example, [PERSON] et al. [66] show that in their Wi-Fi campus network, most mDNS-enabled devices include as part of their identifiers the real names of the users. This information could be easily used for any malicious purpose. Therefore, it is necessary to increase awareness of privacy risks associated with service announcements that contain sensitive information.