esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 3 Polymer-Inorganic Coordinated Hybrids: Complexation-Induced Properties
### Pyridine-Based Polymers  
Poly(4-vinylpyridine) (P4 VP) and poly(2-vinylpyridine) (P2 VP) are the two most studied polymers in coordination chemistry due to the nitrogen lone pair in their pyridine ring, which can form stable complexes with a variety of transition metals. The nitrogen atom acts as an electron donor, creating strong bonds with metal centers, resulting in new properties such as enhanced thermal stability and catalytic activity [92]. However, there are relatively fewer reports on P2 VP coordinating with transition metals, likely due to its increased steric hindrance compared to P4 VP. The nitrogen atom in P2 VP is less accessible for coordination, making it less favorable for forming metal complexes than P4 VP.  
These complexes are particularly known for their applications in catalysis [93], environmental remediation [94; 95], and sensing [96; 97]. The structure of P4 VP allows for easy coordination with metals such as copper, ruthenium, chromium, iron, zinc, aluminum, cobalt, nickel, and rare earth metals, making it a popular choice for creating hybrid materials used in diverse fields [96; 98; 99].
# Feature-Level Camera Style Transfer for Person Re-Identification
## 4 Using Transferred Feature for Person Re-ID
### Joint Training Stage  
The joint training stage jointly trains the baseline re-ID model and the generative model. The training process is shown in Figure 4. During this stage, the parameters of identity feature extractor \(E_{I}\) are unfrozen, so that \(E_{I}\) can be further trained with help of augmented data. Because camera classification is a relatively easy task, camera style feature extractor \(E_{C}\) remains frozen in this stage. The generator \(G\) is stable after the previous training stage, so discriminator \(D\) is removed from this training stage, and \(G\) continues to update with a small learning rate.  
Figure 3: In generative model training stage, identity feature \(f^{I}\) and camera style feature \(f^{C}\) are input into GAN, the generated feature \(f^{\prime}\) maintains the same identity label as \(f^{I}\) and the same camera style label as \(f^{C}\).  
Identity classifier \(C_{I}\) is used for both real input features and generated features, cross-entropy losses \(\mathcal{L}^{r}_{id}\) (for real features) and \(\mathcal{L}^{f}_{id}\) (for generated features) are calculated as follows:  
\[\mathcal{L}^{r}_{id}=\mathbb{E}_{f^{f}_{i}\sim\mathcal{P}_{I}}[- \log C_{I}(E_{I}(x_{i}|y_{i}))] \tag{11}\] \[\mathcal{L}^{f}_{id}=\mathbb{E}_{f^{r}\sim\mathcal{P}_{G}}[- \log C_{I}(G(E_{I}(x_{i}|y_{i}),f^{\rm C}_{j}))] \tag{12}\]  
where \(f^{I}_{i}=E_{I}(x_{i}|y_{i})\) is the real identity feature, and \(f^{\prime}=G(f^{I}_{i},f^{\rm C}_{j})\) is the generated feature. \(\mathcal{P}_{I}\) and \(\mathcal{P}_{G}\) denote the distributions of real features and generated features, respectively.  
For the IDE baseline, triplet loss is also used to enhance the performance of the re-ID model. The generative model can provide abundant positive and negative samples for hard sample mining. With the help of the generated feature, the triplet loss used in this stage can be written as:  
\[\mathcal{L}^{r+f}_{trip}=\frac{1}{N}\sum_{i=1}^{N}ReLU([min(d(f_{i},f_{p})- max(d(f_{i},f_{n}))+m)]) \tag{13}\]  
where \(N\) is the batch size, positive sample \(f_{p}\) and negative sample \(f_{n}\) are both chosen from the union set of real features and generated features, and \(m\) is the margin for triplet loss.  
The total loss function is as follows:  
\[\mathcal{L}_{total}=\mathcal{L}^{r}_{id}+\lambda_{gen}\mathcal{L}^{f}_{id}+ \mathcal{L}^{r+f}_{trip} \tag{14}\]  
where \(\lambda_{gen}\) controls the contribution of generated features; the impact of this weight is studied in Section 5.3.  
Through the above multi-stage training process, the proposed CST approach can help improve the performance of the baseline re-ID model by two means: First, the camera style transfered features can alleviate the difficulties brought by camera style variance. Second, the generated features also act as data augmentation, providing more training samples, which is especially helpful for hard sample mining.
## 6 Future Research Directions
### CSI in NOMA  
Most of the research is carried out by assuming a perfect CSI in NOMA systems, but less number of users are considered imperfect CSI. The CSI plays an important role in user pairing and decoding the users with SIC receivers in the NOMA system. To increase the system performance and spectrum efficiency of a dynamic system, the estimation of CSI is very important. For a dynamic channel, the estimation of CSI using machine learning and game theory algorithms is one of the important future research directions in NOMA systems.
These messages, which have a predefined format [19], do not contain real information but can be used for assessing demodulation performance. Statistics on the TTRD were derived using the methodology developed in [7; 8]. Average daily statistics were computed for three months of data and the TTRD was evaluated considering a variable number of Galileo-connected satellites, that are satellites actually broadcasting HAS corrections. The results are consistent over the observation period, having a daily Page Error Rate (PER) always below one percent and mean TTRD values below ten seconds when more than three satellites are connected. Moreover, the paper findings agree with the preliminary results presented in a previous conference paper [20]. This paper is an extended version of [20], which examined only one week of data. In this work, three months of continuous observations were analyzed. During the three-month period, the vessel reached higher latitudes than those analyzed in [20]. Moreover, the analysis of such a long period allowed the estimation of additional metrics so as to provide a more complete performance assessment of the E6B demodulation performance at high latitudes. It is worth underlining that a very long test on a ship at high latitudes is a unique experiment, particularly for the Galileo E6B signal, and has been rarely considered in the literature. This paper is organized as follows: Section 2 introduces the Galileo E6B signal format, the different coding and dissemination layers introduced by the Galileo HAS, and the demodulation performance metrics. The experimental setup is detailed in Section 3, and experimental results are provided in Section 4. Finally, Section 5 concludes the paper.
# Numerical Simulation of a Submerged Floating Tunnel: Validation and Analysis
## 2 CEL Method for One- and Two-Way Fluid-Structure Coupling Validation
### CEL Method
#### 2.1.1 Fluid Motion Equations (Eulerian Equations)  
The Eulerian equations describe the motion of the fluid, typically in the form of the Navier-Stokes equations. For incompressible fluids, the Navier-Stokes equations [24] can be written as  
Momentum conservation equation:  
\[\frac{\partial\mathbf{u}}{\partial t}+(\mathbf{u}\cdot\
abla)\mathbf{u}=-\frac {1}{\rho}\
abla\bigg{(}p_{0}+\frac{1}{2}\rho|\mathbf{u}|^{2}\bigg{)}+\
u\
abla ^{2}\mathbf{u}+\mathbf{g} \tag{1}\]  
Here, \(\rho\) is the density, \(\mathbf{u}\) is the velocity vector, \(\
u\) is the dynamic viscosity coefficient, \(\
abla\mathbf{u}\) is the velocity gradient, \(p_{0}\) represents the static pressure, and \(\mathbf{g}\) is the gravitational acceleration vector.
# Insights into the Issue of Deploying a Private LoRaWAN
## 2 Background and State of the Art
In spite of that, they belong to the most frequently used channels in the unlicensed 868 MHz band (863-870 MHz) with a high probability of collision and high level of radio noise. There are several works focusing on collisions in the 868 MHz band, i.e., [9; 60; 59]. The paper [9] shows decreasing network performance, which comes with the growing number of communicating devices, i.e., decreasing number of received packets, decreasing packet delivery success ratio, and decreasing maximal throughput, to mention only a few. Moreover, the paper [59] shows the growing probability of collisions and packet loss, which comes with the increasing number of communicating nodes operating with different spreading factors. Further, the paper [60] summarizes the relations between the increasing number of communicating devices and the probability of channel occurrence, and the probability of collision. Furthermore, the number of wireless devices is growing in the 868 MHz band every day, which increases the noise background. These are, for example, fire alarm systems, intruder alarm systems, automation systems, access and remote control systems, smart meters, telemetry networks, automotive systems, and many others. The frequency band of 868 MHz is a free-licensed band, and we cannot completely evade the possibility of collision or a higher noise level. The paper [61] summarizes the level of interference experiences for selected channels of the 868 MHz band in different areas: shopping area, business park, hospital complex, industrial area, and residential area. Moreover, the same authors also published the paper [62], which focuses on the impact of interferences on the LoRaWAN and SigFox technologies. The interferences significantly impact the service quality and network coverage. For this reason, the three recommended channels of LoRaWAN will not be sufficient in future. As one can see from the discussion above, none of the previous works has offered a comparison of the different LoRaWAN deployment options (i.e., private versus public). Meanwhile, this decision is critical and has to be often made by the application developers and service providers. Therefore, to bridge this gap, in the following we discuss the different aspects of the two network deployment options along the three tracks: the communication performance, the security, and the costs. We hope that these results will equip an interested reader with clear understanding of advantages and disadvantages of the two approaches, and assist him or her when deciding whether to go for a private or a public LoRaWAN network.
# Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images
## 4 Experimental Results  
### Model Configuration  
We implement the proposed method in Pytorch by leveraging the MMdetection object detection pipeline [51]. Our composite backbone ResNeXt-101 [46] is pre-trained on the MS-COCO dataset [52]. The pre-trained feature extraction network facilitates our object detection algorithm to adapt from the domain of natural scenes to documents. We scaled the input document images to 1200 \(\times\) 800 but maintained the original aspect ratio. The training starts with a learning rate of 0.0025, which is reduced after every eighth epoch. We train the network for a total of 20 epochs for both of the datasets. The IoU threshold values for cascaded bounding boxes are set to [0.5, 0.6, 0.7]. We employed three different anchor ratios of [0.5, 1.0, 2.0] with only one anchor scale of [8] since FPN [24] itself performs the multi-scale detection owing to its top-down architecture. We operated with a batch size of one to train our network. The models for both of the datasets are trained on an NVIDIA GeForce RTX 101 Ti GPU with 12 GB memory.
# 6G Mobile Networks: Key Technologies, Directions, and Advances
## 3 6G Market Overview and Current Status
On the other hand, 6G offers 1 Tbps for both the uplink and the downlink. 3. Latency--5G offers a latency of around 1 ms, whereas 6G aims to achieve a latency of less than 1 us. 4. Architectural Style--The 5G architecture uses Mmwave tiny cells with a range of roughly 100 metres and dense sub-6 GHz smaller BSs with umbrella macro BSs. While 6G design comprises cell-free smart surfaces operating at higher frequencies, transient hotspots generated by BSs placed on drones, and tests with miniature THz cells. 5. Device Type--5G comprises devices like Smartphones, Sensors and Drones. 6G consists of gadgets including smart implants, CRAS, XR, and BCI technology, as well as DLT gadgets. 6. Reliability--5G has a reliability of 10-5 and 6G has 10 to 9. 7. Accuracy in localization--For 5G networks, 10 cm on 2D, and 1 cm on 3D for 6G based networks. 8. Customer engagement--50 Mbps 2D anywhere for 5G and for 6G network it is 10 Gbps 3D everywhere.
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
## 2 Related Work
### Threat Models
#### Attacks against Confidentiality  
This category is where an attacker eavesdrops on the communication channel between the mobile money user and the mobile money application server or bank to tap information like the mobile money PIN that is used to compromise the system. Confidentiality attacks in mobile money are classified into four, namely, eavesdropping attacks, brute force attacks, guessing attacks, and shoulder surfing attacks.  
* Eavesdropping attacks: This is where an attacker secretly overhears information transmitted over the communication channel between the mobile money application server and mobile money user, the mobile money application server and mobile money agent, or the bank server and mobile money application server, without being authorised by the trusted parties. The attacker takes advantage of unsecured network communications to access sensitive information. This is common when the data transmitted over the communication channel are in plaintext [21; 54]. [PERSON] [52] observed that attackers use network sniffer software such as Wireshark to intercept data in transit. Furthermore, attackers also take advantage of weak encryption keys used and the weak secure scl/transport layer security (TLS) implementation, which gives them the opportunity to snoop the communication channel [23].
* Brute force attacks: This is where an adversary guesses the PIN of a mobile money user or agent to gain access to their mobile money account. It is a simple attack method and has a high success rate because MMS uses numeric PINs to authenticate users [8; 9; 11; 23; 46; 52; 55]. [PERSON] and [PERSON] [19] also observed that, in authenticating users using a smartphone, when the PIN is entered several times, it might leave a greasy residue or scratches on the touch screen, which may make it easy for the attacker to guess. [PERSON], [PERSON], and [PERSON] [56] and [PERSON], [PERSON], and [PERSON] [57] concluded that using a PIN for authentication is vulnerable to a brute force attack.
* Guessing Attack: This attack occurs when an adversary happens to see the mobile money PIN of the subscriber during the authentication process. The PIN used for authenticating the user has only four or five digits and is entered when unmasked, thus making it guessable [9; 23; 52; 55].
* Shoulder Surfing attack: This is where an attacker obtains information such as PINs and other confidential data by looking over the victim's shoulder while performing a mobile money transaction. Attackers take advantage of crowded places where mobile money agents operate and where mobile money users perform transactions. The mobile money PIN used for authenticating a user is simple and unmasked, thus making it easy for the attacker to see and memorise [19; 57; 58; 59].
# Blockchain Technology for Intelligent Environments
## 5 Blockchains for IoT Systems
### Data Delivery and Storage  
#### 5.2.1 Streamr  
_Streamr_ is a platform that utilizes blockchain technology to allow the creation of real-time data decentralized applications (Dapps) for IoT. Compared to IOTA, which was designed specifically for IoT, it leverages existing blockchain technology and builds upon that. It is, in essence, a decentralized peer-to-peer (P2P) network that claims to offer scalability, low-latency, untamperable data delivery, and persistence. The solution that the Streamr platform offers is _decentralized messaging_ and _event processing_, while it aims to replace platforms such as Azure EventHub [67] and Azure StreamAnalytics [68].  
The Streamr platform stack consists of the following five layers:  
* **Streamr Editor:** A graphical user interface offering non-blockchain technology experts a toolbox for the development of Dapps.
* **Streamr Engine:** An event processing and analytics decentralized engine.
* **Streamr Data Market:** A universe of data streams that can be published or subscribed to. It allows the exchange and monetization of data. The data that traverse throughout the network are events (timestamped data) batched into streams; payments are made in _DATAcoin_, Streamr's internal cryptocurrency.
* **Streamr Network:** The data transport backbone and core of the platform. A P2P network whose basic software building block is the Broker Node.
* **Streamr Smart Contracts:** This is how the platform utilizes the blockchain technology. Smart contracts are used for incentivization, network coordination, permissioning, and integrity checking.  
The Streamr Network layer combines the characteristics of systems such as Kafka [69], ZeroMQ [70], etc., which are scalable Cloud-based real-time data transport systems and decentralized P2P systems such as Whisper, Bitmessage, etc. In that way, it can offer high throughput for real-time data applications that the former type of systems achieve, while at the same time being able to effectively route data, discover peers, etc. that the latter systems support.  
Through the Streamer Smart Contracts layer, the Network uses an underlying Ethereum stack (not limited to this specific technology), which is not used for data storage but rather for the following functions:  
* **Stream registry:** Storing administrative information about data streams.
* **Network partitioning and coordination:** Reaching consensus on splitting the network into partitions and assigning them to specific broker nodes.
* **Incentivization:** Using DATacoin as the network's usage token to subscribe to data streams and to reward broker nodes for carrying out their tasks; data integrity service via checksums and data delivery service.
* **Event persistence:** Storing the series of events offering robustness and fault tolerance.
* **Data provenance and data licensing:** Cryptographically sign data with a private key and grant access only to authorized users for a specific time period.
# Measurement and Analysis of Vibration Levels for Truck Transport Environment in Korea
## 3 Results and Discussion
### Composite PSD Profiles for the Measured Vibration Level in Truck Transport
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Breakpoint No.**} & \multicolumn{6}{c}{**Leaf-Spring Truckfor the Top 30\% of The Data Recorded**} \\ \cline{2-9}  & **Frequency (Hz)** & **PSD (G\({}^{2}\)/Hz)** & **dB** & **Oct** & **dB/oct** & **Area** & **Grms** \\ \hline
1 & 1 & 0.005 & - & - & - & - & - \\
2 & 2 & 0.03 & 7.78 & 1.00 & 7.78 & 0.02 & 0.12 \\
3 & 3 & 0.03 & 0 & 0.58 & 0 & 0.05 & 0.21 \\
4 & 7 & 0.003 & \(-\)10.00 & 1.22 & \(-\)8.18 & 0.09 & 0.29 \\
5 & 13 & 0.003 & 0 & 0.89 & 0.00 & 0.10 & 0.32 \\
6 & 25 & 0.0002 & \(-\)11.76 & 0.94 & \(-\)12.47 & 0.11 & 0.34 \\
7 & 30 & 0.0002 & 0 & 0.26 & 0 & 0.12 & 0.34 \\
8 & 40 & 0.0007 & 5.44 & 0.42 & 13.11 & 0.12 & 0.35 \\
9 & 120 & 0.0007 & 0 & 1.58 & 0 & 0.18 & 0.42 \\
10 & 250 & 0.0002 & \(-\)5.44 & 1.06 & \(-\)5.14 & 0.22 & 0.47 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Breakpoint No.**} & \multicolumn{6}{c}{**Air-Ride Truckfor the Top 30\% of the Data Recorded**} \\ \cline{2-9}  & **Frequency (Hz)** & **PSD (G\({}^{2}\)/Hz)** & **dB** & **Oct** & **dB/oct** & **Area** & **Grms** \\ \hline
1 & 1 & 0.01 & - & - & - & - & - \\
2 & 2 & 0.03 & 4.77 & 1.00 & 4.77 & 0.02 & 0.14 \\
3 & 4 & 0.002 & \(-\)11.76 & 1.00 & \(-\)11.76 & 0.04 & 0.19 \\
4 & 12 & 0.002 & 0 & 1.58 & 0 & 0.05 & 0.23 \\
5 & 20 & 0.0002 & \(-\)10.00 & 0.74 & \(-\)13.57 & 0.06 & 0.24. \\
6 & 100 & 0.0002 & 0 & 2.32 & 0 & 0.07 & 0.27 \\
7 & 250 & 0.00006 & \(-\)5.23 & 1.32 & \(-\)3.96 & 0.09 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Recommended high-level composite PSD profiles for the top 30% of the data recorded. higher frequencies. Generally, the frequency band associated with damage of packaged products in transit is the low frequency band. The difference in the high frequency band suggested by existing researchers is believed to be due to the road characteristics of Korea with many curves.
# Performance of the Dual-Chamber Fungal Fuel Cell in Treating Tannery Wastewater
## 2 Materials and Methods
### Training of Fungal Strains on Hexavalent Chromium Solution  
The relative toxicity of _A. niger_ to hexavalent chromium became obvious at the higher concentration. This test indicated the possible adaptation of _A. niger_ to hexavalent chromium stress conditions and their potential for use in extracting metal ions from the solution. Hexavalent chromium is considered one of the most toxic chromium states for microorganisms, so our study focused on measuring the toxicity level of different hexavalent chromium concentrations and the effect of training of the fungal strain on fungal growth rate and adsorption ratio. The _A. niger_ strain was trained with high studied hexavalent chromium ion levels ranging from 10 to 100 ppm. A. niger without adaptation to hexavalent chromium stress is called the control strain. The data analysis for biosorption of the studied metal ions by the control and the trained _A. niger_ was completed in light of different isothermal models. The results showed that the _A. niger_ trained strain had higher maximum removals than the control strain. It also showed that the total growth (G) and the growth rate (\(\upmu\)) of the control and the trained strain were gradually inhibited with an increase in hexavalent chromium concentration, with the control strain being more sensitive as shown in Table 2. In Cr\({}^{6+}\) assays, the control _A. niger_ biotypes grew at 100 ppm with a growth rate of 4.6 mg/h and removal efficiency of 67.5%; meanwhile, the trained strain grew at 100 ppm with a growth rate of 7.3 mg/h and a removal efficiency of 81.8%, as shown in Figures 4 and 5. Further studies are recommended for evaluation of post-treatment and disposal of the Cr-adsorbed fungal strain as a final bio-waste product.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Trained Strain**} & \multicolumn{3}{c}{**Control Strain**} \\ \cline{2-7}
**Cr\({}^{6+}\)Conc.**, & **Total** & **Growth** & **Cr\({}^{6+}\)** & **Total** & **Growth** (G),** & **Rate** (\(\upmu\)), **(mg/h) & **Cr\({}^{6+}\)** \\
**(mg/L)** & **Growth** & **Rate** (\(\upmu\)), **(mg/h)** & **Removed,** (\%)** & **Growth** (G), **(mg/L)** & **Growth** (mg/h) & **Removed,** (\%) \\ \hline
0 & \(5600\pm 74.8\) & \(33.3\pm 5.8\) & — & \(5600\pm 74.8\) & \(33.3\pm 5.8\) & — \\
10 & \(5420\pm 73.6\) & \(32.3\pm 5.7\) & \(97.0\) & \(5000\pm 70.7\) & \(29.8\pm 5.5\) & 88.0 \\
25 & \(5030\pm 70.9\) & \(29.9\pm 5.5\) & \(90.8\) & \(3950\pm 62.8\) & \(23.5\pm 4.8\) & 81.6 \\
50 & \(4100\pm 64.0\) & \(24.4\pm 4.9\) & \(87.2\) & \(3000\pm 54.8\) & \(17.9\pm 4.2\) & 76.4 \\
75 & \(3200\pm 56.6\) & \(19.0\pm 4.4\) & \(84.8\) & \(1970\pm 44.4\) & \(11.7\pm 3.4\) & 72.1 \\
100 & \(1220\pm 34.9\) & \(7.3\pm 2.7\) & \(81.8\) & \(780\pm 27.9\) & \(4.6\pm 2.1\) & 67.5 \\ \hline \end{tabular}
\end{table}
Table 2: Control and trained _A. niger_ strain growth behavior under different hexavalent chromium stress conditions.  
Figure 3: Prevalence of fungal communities in tannery wastewater.