esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
## 3 Proposed Scheme
### Partial Spectrum Scan Scheme  
This study proposes a rapid spectrum scanning mechanism, which relies primarily on the IEEE 802.22 standard agreed to make improvements. First, the BS regularly broadcasts the current usage status of all spectrums, and then PU and SU will receive this message, and then PU and SU will choose to use the active (proactive) or passive (reactive) spectrum scanning method to decide which spectrum to switch. The current scanning method, as shown in Figure 5a, can update the most recent spectrum information at any time. If the optimal spectrum changes, SU will perform spectrum handover. In contrast, the passive scanning method is shown in Figure 5c. If the spectrum quality is lower than a threshold, then it will start the process of scanning the spectrum. The spectrum quality is usually based on the strength of the received signal strength (RSSI), idle time, and the available bandwidth or the packet error rate (PER). The traditional method is to scan all spectrums (full scan), but later some scholars [26] proposed a partial scan method to shorten the scan time.  
The fast spectrum scanning mechanisms of this paper are based on active partial scanning and passive partial scanning. If the spectrum changes greatly, the active scanning method may cause constant switching of spectrums; if the spectrum change is small, the passive scanning method may not maximize the spectrum performance. Therefore, we can decide which scanning method to use, according to the stability of the spectrum change. Figure 5 shows an example of a fast spectrum scanning mechanism. In Figure 5b,d, we can see that using partial spectrum scanning can indeed reduce the scanning time and increase the data transmission time after a successful connection, thereby improving the overall network performance. In Figure 5b, the connection results are different each time (that is, \(1\to 2\to 3\)) because the SU selects the spectrum with the best quality to connect each time. As shown in Figure 5d, when the resource of the spectrum is lower than the threshold (the red dotted line in the figure), the scanning procedure will be performed to switch from spectrum 1 to spectrum 4.
## 3 Experimental Analysis
### Directional Decays and Dominant Reflection Directions
Likewise, the elevation values of the dominant reflection locations 'D' and 'E' in Figure 12 are nearly the same when the source is at \(\mathbf{y}_{o}=(1.90^{\circ},120^{\circ})\). Additionally, the location of 'F' is in the close vicinity of the source position. Thus, the dominant reflection locations are principally determined by the source position and source directivity. For locations with same source-to-wall distance, the dominant reflections will depend on the reflectivity of the surface materials. Figure 11: Directional decay times inside Room-1 for the peak frequencies when source is located at (**a**) \(\mathbf{y}_{o}=(1,90^{\circ},40^{\circ})\) (**b**) \(\mathbf{y}_{o}=(1,90^{\circ},120^{\circ})\). Based on the above observations, the analysis of both directional decay and directional power is essential in characterizing the room reflections. This is particularly important while managing the features of early reflections and late reverberations to achieve desired perception quality. Since the early reflections undergo very few boundary reflections [45], they are mainly defined by the source directivity and source-to-wall distance. Hence, we can use the dominant reflection directions to characterize the behavior of early reflections. The late reverberation undergoes multiple boundary reflections, and they are integrated both spatially and temporally before reaching the receiver [45]. Since the late reverberation characteristics are primarily characterized by the surface absorption and room shape [45; 46], we can analyze the directional decay rates to study their behavior. We can further visualize  
Figure 12: Dominant reflection directions inside Room-1 for the peak frequencies when source is located at **(a) \(\mathbf{y}_{o}=(1,90^{\circ},40^{\circ})\) (b) \(\mathbf{y}_{o}=(1,90^{\circ},120^{\circ})\)**. Figure 13: Mapping of dominant reflection directions in Room-1. The letters \(A\) to \(C\) and \(D\) to \(F\) represent the directions of highest reflection powers with respect to Figure 12a,b, respectively. the power spectrum of \(P_{R}(t,k,\hat{y}|\mathbf{y}_{o})\) across time for an extensive analysis of the variations in the anisotropic spatial properties between the early reflections and late reverberations. The precise knowledge of frequencies and surfaces contributing to the salient features of these reflections will be useful for defining the perceptual targets for modal control methods [47], optimizing room mode redistribution to improve acoustic quality [48], and devising active [49] and passive [50; 51] room acoustic treatment methods.
## 4 Simulation Setup and Experimental Results
### Affected Network vs. Resilience Time  
Regarding resilience time, we determined the performance of our proposed SDAAA approach and other contending approaches such as SD, EEHA, HAS, IIF, and RHC. We generated three malicious scenarios and diagnosed the affected network. We noticed in the results, as depicted in Figure 10, that when a network is affected by malicious nodes, then the resilience time also increases. However, our proposed SDAAA approach obtains minimum resilience time compared to the other contending secure data aggregation approaches like SD, EEHA, HAS, IIF, and RHC. Our proposed SDAAA approach yielded a 2.45% effected network with 0.9 maximum resilience time, whereas competing approaches like SD, EEHA, HAS, IIF, and RHC produced a 2.98-3.62% affected network with 0.9 full resilience time.  
Figure 11 depicts a malicious scenario involving 5% malicious nodes. Based on the results, our proposed SDAAA approach has an affected network of 2.62%, with 0.9% resilience time, whereas other competing approaches like SD, EEHA, HAS, IIF, and RHC have effected network of 3.68-4.38%, with 0.9% resilience time. In Figure 12, the ratio of malicious nodes' is increased up to 10%. This increment highly affects other contending approaches such as SD, EEHA, HAS, IIF, and RHC, which lead to a high network effect of 4.47-4.99%, while our proposed SDAAA is not significantly effected has only 2.67%. We surmise that the better performance of our proposed SDAAA protocol is due to the use of authentication, authorization, and a data-freshness paradigm, which helps the data aggregation nodes to identify false aggregated data accurately. Our SDAAA approach was unaffected, while the other contending secure data aggregation approaches did not provide authorization, authentication, or energy efficiency.  
Figure 8: Energy consumption of proposed SDA and other contending approaches (EEHA, SD, HAS, IIF, and RHC) by different event-monitoring nodes in the presence of malicious node.  
Figure 9: Energy consumption of proposed SDA and other contending approaches (EEHA, SD, HAS, IIF, and RHC) by different event-monitoring nodes in the presence of malicious node.
# Microbial Dynamics in Sour-Sweet Wine Vinegar: Impacts on Chemical and Sensory Composition
## 3 Traditional Balsamic Vinegar Yeasts and Alcoholic Fermentation
### Non-Zygosaccharomyces Strains
\begin{table}
\begin{tabular}{c c c} \hline
**Microorganisms** & **General Metabolic Features** & **Sensory Properties** & **Ref.** \\ \hline _Zygosaccharomyces_ species & & \\ \hline _Z. hulli,_ & High resistance to SO\({}_{2}\) and sorbate; & Redberry aroma, due to the \\ _Z. nauxi_ & High tolerance to alcohol, low pH, & production of linear esters; \\ _Z. melli_ & low water activity, and presence of & Blackberry aroma, due to the \\ _Z. bisporus_ & toxic weak acids, such as lactic, & production of branched esters; \\ _Z. pseudarouxii_ & ascorbic, and acetic acids; & Pungent aroma, due to higher \\ _Z. sspae_ & Fructophilic character (possesses & alcohol content; \\ _Z. lentus_ & FFZ genes that encode specific & Rose-like aroma, due to the \\ _Z. osmophilus_ & fructose facilitators); & biosynthesis of 2-phenyl ethanol via \\ _Z. seidelii_ & Halotolerant, osmotolerant, and & the Shikimate or Ehrlich pathways \\  & non-psychrotolerant. & (mainly _Z. rouxii,i_) \\ \hline \end{tabular}
\end{table} 
Table 1: Main _Zygosaccharomyces_ and non-_Zygosaccharomyces_ strains found in traditional balsamic vinegar fermentations, metabolic features, and sensory properties.
## 5 A Case Study on Reliable Pipeline Monitoring Using Cyber Physical Sensor Networks
### Energy Efficiency and Lifetime Concerns  
Continuous condition monitoring requires a continuous, uninterrupted power supply. Wireless transceivers utilize most of the battery in a sensor node. Hence, increased battery life and optimal utilization of the power is of critical value especially for under water and buried pipelines. The commercially available sensor nodes usually provide a discrete, limited set of tunable power levels making dynamic adaptation less beneficial. The communication itself carried out by the sensor nodes in case of pipeline monitoring can be either periodic or event-based. More intelligent pipeline monitoring schemes use aggregated sensing [31] where the only primary sensing mechanism runs periodically, triggering other mechanisms and waking up nodes from sleep only when anomalies are detected. Some schemes incorporate intelligent node algorithms which perform correlation of the recently sensed data with the previous reading and transmit the reading only when the readings differ from the normal measurements [70]. A vibration energy harvester utilizes the energy available in kinetic form and converts it into electrical energy, usable for the sensor nodes. Underground energy scavenging can be done by converting seismic vibrations or thermal gradients to usable energy forms [29, 30]. Electric power can also be generated using turbines embedded in the pipelines.
## 4 Discussion and Conclusions
The gPCE-MDA is obtained at the cost of computing the gPCE coefficients only, and by replacing the forward model runs required at any MDA step with fast polynomial evaluations. We demonstrate that MDA may outperform the standard ES and this option can become particularly attractive if combined with the gPCE approximation, which reduces considerably the required computational effort. Our results show different parameter-constrain capabilities of the proposed approach depending on the choice of the reference \"true\" point in the parameter space. Figure 10 illustrates this behaviour in the space defined by parameters \(\lambda^{*}\) and \(R\). We randomly sampled 500 \"true\" reference parameter sets and perform the data assimilation by means of the gPCE-ES with \(n_{\text{MC}}=3000\) realizations and constant noise standard error. Figure 10a,b show the error \(e=|\mu^{u}-p^{t}|\) between the mean updated value and the true value for parameters \(\lambda^{*}\) and \(R\), respectively. Figure 10c shows the maximum vertical displacements assimilated at final simulation time. Clearly the \(\text{u}_{z,max}\) increases by increasing \(\lambda^{*}\) and diminishing \(R\). Generally, \(\lambda^{*}\) (Figure 10a) presents higher errors with \(1.35<R<1.45\) and the parameter seems to better approach the true reference value with increasing values of \(\text{u}_{z,max}\). The constrain on parameter \(R\) is more complex to analyse. A poor match is obtained at the four corners of the parameter space and minimum errors are obtained for \(\text{u}_{z,max}\simeq-0.6\) m and \(\text{u}_{z,max}\simeq-0.3\) m. Based on these results, further analyses would be needed to investigate the role of the parameter interactions on the model outcome linked to the influence of the varying time-behaviour parameter importance, with model non-linearity and measurements noise providing for additional crucial factors affecting the inverse problem solution. Figure 9: Mean and standard deviation for the updated displacements on the observation point varying the size of the ensembles. **Author Contributions:** Conceptualization, C.Z., L.G., and M.F. ; methodology, C.Z. ; software, C.Z. and L.G. ; data curation, L.G. ; writing-original draft preparation, C.Z. and L.G. ; writing-review and editing, M.F. and P.T. ; supervision, M.F. and P.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** The Authors wish to thank the University of Padova Strategic Research Infrastructure Grant 2017 \"CAPRI: Calcolo ad Alte Prestazioni per la Ricerca e l'Innovazione\" for the availability of the computational resources used in this work. **Conflicts of Interest:** The authors declare no conflict of interest.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
###### Abstract  
This article presents a latency-optimized implementation of the SIMECK lightweight block cipher on a field-programmable-gate-array (FPGA) platform with a block and key lengths of 32 and 64 bits. The critical features of our architecture include parallelism, pipelining, and a dedicated controller. Parallelism splits the digits of the key and data blocks into smaller segments. Then, we use each segmented key and data block in parallel for encryption and decryption computations. Splitting key and data blocks helps reduce the required clock cycles. A two-stage pipelining is used to shorten the critical path and to improve the clock frequency. A dedicated controller is implemented to provide control functionalities. For the performance evaluation of our design, we report implementation results for two different cases on Xilinx 7-series FPGA devices. For our case one, the proposed architecture can operate on 382, 379, and 388 MHz frequencies for Kintex-7, Virtex-7, and Artix-7 devices. On the same Kintex-7, Virtex-7, and Artix-7 devices, the utilized Slices are 49, 51, and 50. For one encryption and decryption computation, our design takes 16 clock cycles. The minimum power consumption is 172 mW on the Kintex-7 device. For the second case, we targeted the same circuit frequency of 50 MHz for synthesis on Kintex-7, Virtex-7, and Artix-7 devices. With minimum hardware resource utilization (51 Slices), the least consumed power of 13.203 mW is obtained for the Kintex-7 device. For proof-of-concept, the proposed SIMECK design is validated on the NEXYS 4 FPGA with the Artix-7 device. Consequently, the implementation results reveal that the proposed architecture is suitable for many resource-constrained cryptographic applications.  
cryptography; lightweight; block cipher; SIMECK; RFID; WSNs; IoTs +
Footnote †: journal: Applied
## 3 Validating Physical Identity Using Third-Party Observation  
The solution to the attack mechanism identified in Section 2.3 is to require that the logical device joining proves its _physical_ identity to the system at the time of the join operation.  
The mechanism to enable this (described in previous work [2]) requires that the C2 server sends the device a message encrypted using its SRUP public key--containing a randomly determined 128-bit cryptographic nonce value. The device must then respond by _showing_ the value back to the server--via a channel external to the protocol itself. This technique guarantees that only the logical device involved in the join request is in possession of the correct nonce value; and since the third-party is interacting with the physical device in the real-world, if the physical device is able to provide this value to the _observer_ then the logical device must correspond to the physical device.  
Whilst this approach is still theoretically susceptible to an attack scenario where an attacker is able to cause the joining of a malicious device to a C2 network, such an approach requires the device to be co-located with the deployed system. For critical systems, such as those posing a hazard to life (or systems for which an elevated threat is suspected), there would still therefore be a requirement to adopt traditional techniques of manual inspection to determine the authenticity of the physical device, before the join process is initiated. The automated observation approach does however prevent a remote adversary from being able to join the system: and as always in security, if the attacker has physical access to facilities or other locations, there are a great many other (simpler) forms of attack that they could exploit.  
The third-party observer is required to be able to receive information from the device, via an external channel to the SRUP protocol. Previous work identified mechanisms for displaying 128-bit nonce values to enable easy comparison by a human observer--including pictographs, and word lists.  
Implementations of both of these techniques were examined as a part of this work, and examples of each can be seen in Section 4).  
Further work was conducted to examine a number of technologies to enable machine-based observation: including machine-readable visual codes, and very short-range Radio Frequency communications protocols.
## 2 Methods
### Scaling Technological Foundations
#### 2.4.2 Versatile Mode (Local or Small-Scale Deployments)  
Designed for environments with limited user bases, such as individual workstations or small laboratory servers, the versatile mode offers a lightweight yet powerful solution using vLLM and FastAPI:  
Figure 1: The workflow ensures a streamlined process for enhancing the JAVIS system through user feedback, maintaining a secure environment within the hospital network, and leveraging containerization technology for efficient deployment.  
* **Rapid deployment** facilitates the swift deployment of single model instances with minimal configuration, allowing researchers or clinicians to integrate LLMs into their workflows quickly without extensive technical overhead.
* **Easy duplication** supports hosting multiple models in parallel by simply copying the container definition and running additional instances. This feature is ideal for testing, demonstrations, or specialized use cases within small groups.
* **Robust performance** ensures consistent performance and low latency on personal computers or small servers, maintaining reliability for a limited number of users.
## 5 Conclusions  
A novel NB-IoT Mixed spread spectrum and clustering Approach with Network Coding (N-MANC) is presented in this research article to enhance the NB-IoT network scalability while improving its energy efficiency, network reliability, and network lifetime performance. The N-MANC is proposed as a step towards enabling the massive connections of IoT objects to the cellular network (5G and beyond) within the licensed band. After establishing through literature survey that this enhanced scalability of the NB-IoT network is a cross-layers effort, the present research work has proposed a mixed approach which at the Physical layer (PHY) proposes an adaptive frequency hopping spread spectrum technique using a lightweight pseudo-random sequence generator coupled with the clustering and network coding techniques at the network layer. The proposed approach is described in detail in terms of its implementation and underlying concepts.  
After evaluation of the proposed approach, the obtained and discussed results led to the conclusion that the proposed N-MANC approach helps mitigates the scarcity of the available spectrum coupled with the interlink interference to enable more NB-IoT nodes to join the network cell while ensuring an offloading of the BS using an energy and RSSI aware formation of network clusters and selection of CHs. It can be further concluded that the offloading of the BS together with the aggregation of data packets at each CH using the proposed RLNC technique, has demonstrated the ability to significantly improve the energy efficiency, network reliability and network lifetime of the network as compared to the N-AFHSS (with adaptive frequency hopping spread spectrum only),  
Figure 7: Network lifetime: remaining average energy versus round of transmission packets.  
the N-LEACH (with energy-efficient clustering only), and N-MANNC (Mixed approach with no network coding) respectively.
## 4 Experimental Results of the Field Trial
While different from our previous field trial using DP-QPSK 400 Gb/s OTN transceivers over 106 km 4-core fiber cable, which still reserved a greater than 5.5-dB OSNR margin for each fiber core and wavelength channel, the worst channel (the channel at 1524.89 nm of the first fiber core) of the 800 Gb/s CDM-WDM transmission has already approached the FEC limit and there is insufficient OSNR margin left [25]. It should be noted that the experimental results are not only related to the impacts of fiber transmission such as span loss, inter-core crosstalk, and SRS, but also related to the performance of the OTN equipment such as the OTUs, WSS, and EDFAs. The performance of signals at lower wavelengths within the C-band is somewhat worse, which is mainly caused by the performance of the OTUs and the EDFA, as well as the impact of the SRS. Compared to the previous results using 400 Gb/s OTUs, the BER performance of different core channels seems to be different, which is also mainly due to the performance differences between the 400 Gb/s and 800 Gb/s OTUs. The fiber core channel employed with better OTUs usually has better transmission performance. This field trial also demonstrates the feasibility of real-time 256 Tb/s (800 Gb/s \(\times\) 80 \(\times\) 4) unrepeated 106 km CDM-WDM transmission over deployed 4-core MCF cable. Compared to previous experimental results using 400 Gb/s OTN transceivers, which achieve real-time bit rates of 128 Tb/s (400 Gb/s \(\times\) 80 \(\times\) 4) and 224 Tb/s (400 Gb/s \(\times\) 80 \(\times\) 7) over deployed 4-core and 7-core MCF cable, respectively, this work demonstrates a higher bit rate [25]. This improvement is mainly attributed to the higher spectral efficiency of the 800 Gb/s OTN transceivers with the PCS-16 QAM modulation format. The received normalized optical spectrums of the 800 Gb/s 4-core 80-\(\lambda\) transmission system are shown in Figure 5. We found that the power of the L-band signals at the receiver was slightly higher than that of the C-band signals, which is also caused by the SRS. Figure 4: Measured BER performances of the 800 Gb/s 4-core 80-\(\lambda\) CDM-WDM transmission system over the C+L band.