esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey
## 3 Methodology
### Survey Design and Pilot Testing  
The questionnaire was characterized by 18 mixed open and closed questions, and it was structured into four sections. The first section aimed to collect general information and a registry about the company and respondents. The second section asked about the level of knowledge for each solution proposed; the description of each technological solution was provided through a \"link\" button to the respondents to provide the same interpretation of technology meaning and avoid bias related to ambiguous questions. The third section inquired about the company's perceptions of the benefits of Agri 4.0. Finally, the fourth section investigated the challenges and obstacles in adopting the Agri 4.0 paradigm.  
To reach the highest number of respondents, a web survey was administered for conducting the research [42]. The trend of conducting surveys online has grown in recent times because they can offer many benefits over paper-based surveys. Indeed, with respect to face-to-face and e-mail surveys, web surveys do not require the manual transfer of responses into a database; the cost is minimal compared with other means of distribution, and greater anonymity is guaranteed, helping to avoid interviewer biases [42]. Online survey research can also allow researchers to isolate specific groups of participants who share common features [43].  
Subsequently, to test possible question bias, translation accuracy, and the logical flow of the survey, pilot testing was performed before survey distribution [44]. In the first step, a group of colleagues was involved to check the readability and help pinpoint whether the questionnaire was within the study objectives. After refining the survey, the second step then involved sending the questionnaire to seven beta-tester companies to get feedback from them for further possible improvement. The pilot testing helped assess the content of the questionnaire and guaranteed the validity for the official launch.  
Concerning the survey sample, the unit of analysis refers to Italian agricultural companies and farms. Moreover, this research involves all types of agricultural companies--except livestock farms--with no limits concerning their size and cultivation sector. The survey was carried out from January to October 2021, and repeated waves of reminders and recall activities were conducted with the support of the main Italian agricultural associations. The analysis started with a total number of 1273 responses before eliminating incomplete responses, duplicate responses, and test responses conducted internally by the team. As a result, a sample of 670 companies was validated. The survey respondent is the owner of the company to which the questionnaire was sent (or the decision maker in their place), who, therefore, has an overview of their farm.
# Qutrit Control for Bucket Brigade RAM Using Transmon Systems
## 3 Results  
This paper presented a methodology for designing efficient pulses on transmon systems that realize gates that are encountered in the Bucket Brigade Algorithm for QRAM. It focused on transitions between basis states and controlled operations for conditionally altering the state of one qutrit based on the other. In order to avoid the need of designing pulses that control the first hyperexcited state (\(|2\rangle\)), proper encodings were used, together with additional pulses of one qutrit.  
Ignoring the relaxation and dephasing times, and focusing only on errors introduced by the Hamiltonian dynamics, such as crosstalk terms, the infidelities theoretically computed by the simulation are depicted in Table 3, where the indices of the gates correspond now to the logical states introduced in Table 1. Each gate is described by its relevant pulse. For economy, composite gates that are performed by a series of pulses are listed by their gate names in subsequent pulse schedules after they are first introduced (for example, the \(T_{0,1}\) gate is described as a series of \(T_{1,2}T_{0,2}\) pulses in the third row, but is subsequently listed as \(T_{0,1}\) in pulse schedules). Furthermore, the gates \(\sqrt{X}\), \(\sqrt{Z}\) and the maximally entangling cross-resonance gate that act on the qubit subspace are listed as gates though, strictly speaking, they are not part of the model's gate set. This happens to list their infidelity separately and for better visualizing the pulse schedules.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gate** & **Pulse Realization** & **Infidelity** \\ \hline \(T_{0,2}\) & \(T_{0,2}\) & \(9.81\times 10^{-7}\) \\ \(T_{1,2}\) & \(T_{1,2}\) & \(9.87\times 10^{-7}\) \\ \(T_{0,1}\) & \(T_{1,2}T_{0,2}\) & \(1.90\times 10^{-6}\) \\ \(P_{3}\) & \(T_{0,1}T_{0,2}\) & \(3.70\times 10^{-6}\) \\ \(Z^{-1/2}\) & \(Z_{0,1}^{-1/2}\) & \(9.58\times 10^{-7}\) \\ \(X^{-1/2}\) & \(T_{0,2}^{-1/2}\) & \(9.81\times 10^{-7}\) \\ \(XZ^{-1/2}\) & \(XZ^{-1/2}\) & \(1.30\times 10^{-6}\) \\ \(CT_{0,2}\) & \((IZ)^{-1/2}(XZ)^{-1/2}(XI)^{-1/2}\) & \(2.81\times 10^{-6}\) \\ \(CT_{0,1}\) & \((I_{3}P_{3})CT_{0,2}(I_{3}P_{3})^{2}\) & \(3.23\times 10^{-5}\) \\ \(CT_{1,2}\) & \((I_{3}P_{3})^{2}CT_{0,2}(I_{3}P_{3})\) & \(3.63\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correspondence between physical and logical state evolution under a two-qubit CNOT.  
Figure 6: Pulse parameters (frequency and phase) for the \(\sqrt{Z}\) (**a**) and \((XZ)^{-1/2}\) (**b**) pulses as computed by using Boulder’s Opal optimization.  
All infidelities, except the last three, were obtained by performing the simulations described in Section 2. The last three were obtained by applying the pulse parameters in Boulder Opal and taking the average of infidelities of an ensemble of input states. The results agree with what was expected on the basis of the constituent pulse infidelities.
# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
## 2 System Model and Problem Formulation
### IoT-UAV Data Gathering  
Considering there may be occlusion between UAVs and devices, according to [22; 23], the average path-loss between IoT device \(i\) and UAV \(u\) can be expressed as  
\[L_{iu}=20\lg\left(\frac{4\pi f_{\text{c}}d_{iu}}{v}\right)+P_{iu}\eta_{\text{LOS }}+(1-P_{iu})\eta_{\text{NLOS}}, \tag{1}\]  
where \(f_{\text{c}}\) (Hz) is the carrier frequency, \(v\) (m/s) is the velocity of light in a vacuum, \(\eta_{\text{LOS}}\) and \(\eta_{\text{NLOS}}\) (dB) are the average additive losses caused by the free space path-loss for LOS and NLOS links, respectively, \(d_{iu}=\sqrt{(x_{u}^{\text{U}}-x_{i}^{\text{I}})^{2}+(y_{u}^{\text{U}}-y_{i}^{ \text{I}})^{2}+h_{\text{U}}^{2}}\) is the distance between IoT device \(i\) and UAV \(u\), and \(P_{iu}\) is the LOS probability, which can be expressed as  
\[P_{iu}=\frac{1}{1+\phi e^{-\varphi(\theta_{iu}-\phi)}}, \tag{2}\]  
where \(\phi\) and \(\varphi\) are constants greater than zero determined by the environment. \(\theta_{iu}\) is the elevation angle between IoT device \(i\) and UAV \(u\). It can be seen that the probability of the line-of-sight link is closely related to the elevation angle. The higher the elevation angle, the  
Figure 1: System model of SAG-IoT networks.  
greater the probability of line-of-sight link, and the greater the channel gain. The expression for elevation angle \(\theta_{iu}\) is  
\[\theta_{iu}=\arctan\left(\frac{h_{\mathrm{U}}}{\sqrt{(x_{u}^{U}-x_{i}^{I})^{2}+(y _{u}^{U}-y_{i}^{I})^{2}}}\right). \tag{3}\]  
Therefore, the channel gain \(g_{iu}\) between IoT device \(i\) and UAV \(u\) can be expressed as  
\[g_{iu}=10^{\frac{-L_{iu}}{10}}. \tag{4}\]  
Suppose that an IoT device can only communicate with one UAV at the same time. The rate at which IoT device \(i\) transmits data to UAV \(u\) can be expressed as  
\[r_{iu}=a_{iu}B\log_{2}\left(1+\frac{p_{i}g_{iu}}{BN_{0}}\right), \tag{5}\]  
where \(p_{i}\) is the transmit power of IoT device \(i\). \(N_{0}\) is the additive white Gaussian noise (AWGN) power spectral density at the UAV receiver. \(a_{iu}\) is 0, 1 binary variable. \(a_{iu}=1\) indicates that there is communication between device \(i\) and UAV \(u\), and \(a_{iu}=0\) is the opposite.
## 3 DAC Integral/Differential Nonlinearities--New Normalized Error Vector Metrics
### Integral Error Vector (IEV) as INL Variant Better Suited for Communication Modeling
\begin{array}{l}J_{1}=\langle I_{1}\rangle+\text{IEV_{J}}[1]\langle I_{u} \rangle\Rightarrow\langle I_{1}\rangle+\langle\text{IEV_{J}}[1]\rangle \langle I_{u}\rangle\Rightarrow\left\langle\text{IEV_{J}}[1]\right\rangle= \frac{\langle I_{1}\rangle-\langle I_{1}\rangle}{\langle I_{u}\rangle}=0\\ \\ \text{Specializing }J_{c}=\langle I_{1}\rangle+\big{[}(c-1)+\text{IEV_{J}}[c] \big{]}\langle I_{u}\rangle\text{ (\ref{eq:IEV_J}) to }c\text{:= }C,\text{ and taking means:}\\ \\ J_{C}=\langle I_{1}\rangle+\big{[}(C-1)+\text{IEV_{J}}[C]\big{]}\langle I_{u} \rangle\Rightarrow\langle I_{C}\rangle=\langle I_{1}\rangle+\big{[}(C-1)+ \langle\text{IEV_{J}}[C]\rangle\big{]}\langle I_{u}\rangle\\ \\ \text{Solving the last equation in (\ref{eq:IEV_J}) to }\langle\text{IEV_{J}}[C]\rangle\text{ yields}\\ \\ \left\langle\text{IEV_{J}}[C]\right\rangle=\frac{1}{\langle I_{u}\rangle}[ \langle I_{C}\rangle-\langle I_{1}\rangle]-(C-1)\end{array}  \tag{334}\]  
but \(\langle I_{C}\rangle-\langle I_{1}\rangle=(C-1)\langle I_{u}\rangle\), which is plugged into (334), yielding \(\langle\text{IEV_{J}}[C]\rangle=0\). To recapitulate, by virtue of the IEV definition, we have the interesting property that the first sample of the IEV (the IEV of the LSL level) and the last sample of the IEV (the IEV of the MSL level) are always null:  
\[ \begin{array}{l}\text{The LSL and MSL IEV samples have zero mean :}\langle\text{IEV_{J}}[1]\rangle=0=\langle\text{IEV_{J}}[1]\rangle.\end{array}  \tag{335}\]  
Note: the LSL and MSL IEV null out only in the mean, over the entire ensemble of eDACs, i.e., generally the RVs \(\text{IEV_{J}}[1],\text{IEV_{J}}[C]\) are non-zero. In fact, property (335) is the counterpart of the INL property \(\text{INL}_{J}[1]=0=\text{INL}_{J}[C]\) (307). However, for the INL metrics, each realization of the ensemble of eDACs satisfies (307), whereas for the IEV metrics, (335) only holds in the mean, over the entire ensemble. An important observation regarding the IEV \(|\) DEV metrics is that they can only be evaluated when an entire ensemble is specified, as the IEV \(|\) DEV definitions require the mean unary current \(\langle I_{u}\rangle\). Given an individual eDAC, without specifying which eDAC ensemble it has been drawn from, and what its first-order statistics is, the IEV \(|\) DEV metrics remain undefined. Not so with the INL \(|\) DNL metrics, which are well defined for any given sample of an eDAC regardless of its batch or lot.
## 4 Conclusions  
In this work, we studied a hybrid double cladding EYDF amplifier with an SMS active fiber. The SMS fiber structure consists of three segments of EYDF. The power performance and the spectra properties were theoretically and experimentally analyzed. The results for the hybrid fiber amplifier were compared with those of the uniform fiber amplifier. The simulation results demonstrate that the Yb-ASE of the hybrid fiber amplifier can be suppressed by over 10 dB due to high transmission loss in the SMS structure in the 1-\(\upmu\)m band. The slope efficiency can also be improved because of the extra gain of multimode fiber. Based on the theoretical analysis, the hybrid EYDF amplifier and the typical uniform fiber amplifier were compared using experimental analysis. The Yb-ASE intensity of the hybrid fiber amplifier was suppressed by more than 12 dB, and the slope efficiency was increased by 2.7% compared with the typical uniform fiber amplifier. Although the Er-ASE increased about 2-3 dB, this hybrid EYDF amplifier has great advantages in regard to Yb-ASE suppression. This provides a possible technical path to suppress Yb-ASE and improve the amplification efficiency in the EYDF amplifier.  
Conceptualization, X.B.; Formal analysis, M.W. and Y.Y.; Methodology, X.B.; Resources, X.B.; Software, M.W.; Supervision, Z.L. and W.J.; Writing--review and editing, X.B. All authors have read and agreed to the published version of the manuscript.  
Figure 5: (**a**) Output spectra in the range of 1000 to 1100 nm. (**b**) The output spectra of the hybrid and uniform fiber amplifier in the 1530 to 1560 nm region. The insertion is the zoomed spectra in the range of 1534 to 1540 nm.  
**Funding:** This work was supported by the Natural Science Foundation of the Inner Mongolia Autonomous Region of China (2020 BS06002, 2021 BS06002), the Key Technology Research projects in the Inner Mongolia Autonomous Region (2021 GG0274) and the scientific research projects of the Inner Mongolian higher educational system (NJZY21290).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 1 Introduction  
The improvement in energy efficiency has not played an explicitly relevant role in the operation of many manufacturing systems in the past since minimizing the make-span has often been the priority. Today, with the increasing prices of energy and the new environmental protection regulations, the energy-saving issue in workshops has become an important research field for universities backed by industrial organizations in advanced countries. The relation between industrial management styles, in particular Lean Management (LM) and its variants, and energy savings has been of recent concern [1], while new formulations of the job-shop scheduling problem, including the issue of energy, have been proposed. Further, authors such as those in [2; 3; 4] discussed the possibility of noticeably reducing energy consumption in manufacturing spaces with limited capital investment by rearranging the production process for a given demand through adequate machine selection and operation sequences. Also, in [5], it was shown that, by adjusting the power for each operation in a job-shop environment, relevant energy savings can be obtained. In the case of flexible job shops where machines can competitively perform different operations with differentiated energy costs [6], it is expected that energy-saving opportunities can be made more effective through appropriate machine allocation and the sequencing of operations in the job shop.
## 2 MANTiS-32 Wireless Measurement System Design
### MANTIS-32 Wireless Networking Architecture
#### 2.3.2 Communication Between the MANTiS-32 Hub and AP  
The communication architecture between the MANTiS-32 hub and the AP is implemented using a hierarchical tree topology over Wi-Fi, enabling scalable and structured data aggregation from distributed sensing units. Each MANTiS-32 hub collects vibration data packets from its connected MANTiS-6050 sensor nodes and assembles them into predefined packet blocks for transmission. As illustrated in Figure 5, each packet block consists of a header containing key metadata--such as Hub Number (#), Packet Number (P), and Block Size (B)--followed by the sensor data payload. Sensor readings are encoded in hexadecimal ASCII format and delimited by a colon (:) to ensure clear separation and traceability.  
Data transmission is performed sequentially on a per-block basis. For example, once Hub #1 initiates its transmission without delay, subsequent hubs monitor the RS485 or Wi-Fi network for a terminal semicolon (:) indicating the end of the preceding packet block. Upon detection, they introduce a fixed 10 ms delay before commencing their own transmissions. During this interval, each hub performs a brief wireless channel assessment to avoid collisions, ensuring reliable and orderly communication even under intermittent network instability. This time-synchronized, block-based transmission strategy ensures that sensor data from multiple hubs is delivered without overlaps, maintaining both temporal  
Figure 4: Data packet transmission protocol of the MANTiS-6050 sensor module.  
Figure 5: Packet block transmission scheme of the MANTiS-32 system.  
coherence and high data fidelity. The sequential protocol not only mitigates data loss and transmission conflict but also supports scalable expansion across a wide range of monitoring environments.  
The MANTiS-32 system is architected as a high-performance, wireless SHM network, specifically tailored for deployment in spatially distributed infrastructure, such as long-span bridges, high-rise buildings, and industrial facilities. In such applications, multiple MANTiS-32 hubs can be deployed across key structural zones, each wirelessly transmitting synchronized, high-resolution sensor data to a centralized AP. This configuration enables the accurate reconstruction of global dynamic behavior (including mode shapes and natural frequencies) in a time-aligned manner, thereby supporting real-time damage detection and early-warning capabilities essential for condition-based maintenance and operational safety.
## 2 Materials and Methods  
### Location of the Study Area and Source Materials  
The research on the subject of study was conducted in the Upper Silesian Coal Basin. This area is located in the southern part of Poland and in the Ostrava-Karvina region in the Czech Republic (Figure 1a). The total area of the basin is approximately 7250 km\({}^{2}\), including approximately 5600 km\({}^{2}\) in Poland. The total area of the documented deposits in this area is over 3049 km\({}^{2}\). Currently, 80.06% of the documented balance resources of Polish hard coals are located in this basin. The research was carried out for the areas of hard coal mines (active, closed, or in the process of being closed) adjacent to each other, located in the Polish part of the Upper Silesian Coal Basin (Figure 1b). A total of 14 mining areas of the above-mentioned mines were studied. For the purposes of implementing the transformation task, the coordinates of the boundary points of the above-mentioned mining areas and shafts located in individual areas were collected. The coordinates of boundary points were obtained in the local system (Sucha Gora 1901, 1926--Bessel ellipsoid), while the coordinates of shafts were obtained in the local system (Sucha Gora, 1901, 1926) and in the national system (PL-2000--GRS 80 ellipsoid). The data were made available by the State Mining Office (SMO) and the SMO Documentation Archive. Overall, the data were collected for 248 boundary points and 52 shafts. Figure 2 shows the boundaries of mining areas with boundary points (tie points) and the location of shafts. Table 1 contains information on the affiliation of individual areas to hard coal mines.  
Figure 1: (**a**) Location of the Upper Silesian Coal Basin. (**b**) Map of the analyzed mining areas. Own study based on reference [36].  
Figure 2: Boundaries of the studied mining areas with boundary points and shafts.
## 2 System Design
### Software and Usage  
The main Python script checks system and user settings and controls all actions. Data and settings are stored in an SQLite database. Software will be released under the GNU GPLv3 (GNU's Not Unix General Public License version 3) at the GitHub page: www.github.com/hypermaq/panthyr.  
PANTHYR has one SQLite database which contains the measurement protocol, system settings, logs, and measurement results, as well as a task queue. The user can use a (remotely) connected laptop to access a webpage where the contents of the database can be viewed, changed, and exported (Figure 3). This serves as the main user interface for system configuration.  
A \"worker script\" regularly checks the \"queue\" database table for tasks that need to be performed. These tasks range from executing a measurement cycle to setting up station parameters.  
To perform a measurement cycle, the system gets the necessary settings from the \"settings\" table and read the first line in the \"protocol\" table. Each line in this table contains the parameters (instrument, zenith angle, azimuth offset, number of repetitions) describing one sub-cycle in the measurement cycle. Measurement results are written to the \"measurements\" table. When all scans in the protocol are finished, the system resumes a standby state where it regularly checks for new tasks. In the event of a failure, a log entry is created in the \"logs\" table. After three failed attempts, a task is ignored to prevent it from blocking the system.  
Apart from the database frontend, PANTHYR also hosts an FTP (File Transfer Protocol) server to download log files and pictures, as well as an SSH (Secure SHell) server to allow low-level system maintenance. To prevent provider restrictions enforced on cellular networks, PANTHYR initiates  
Figure 2: Engineering drawings of mounting structures. (**a**) Support providing a flexible connection between a platform structure, e.g., horizontal railings or a vertical post fixed through the swivel couplings, and pan/tilt unit to be mounted on uppermost horizontal surface; (**b**) sensor mounting bracket for attaching radiometers to top of pan/tilt unit.  
an outgoing OpenVPN connection to an onshore server. Users can connect to the same server and communicate with PANTHYR as if it were next to them.
## 4 M2M Communication
### Bluetooth Low Energy  
Bluetooth SMART v. 4.0 [120] is a PAN technology that was introduced to the market by the Bluetooth Special Interest Group in 2010. Bluetooth SMART consists of two distinctly different transports called Basic and Enhanced data rate (BR/EDR) and Low energy (LE). The following focuses briefly on the LE technology and use for web applications in battery operated sensor applications.  
LE supports two modes of connection: Non-connected, unidirectional advertisements (broadcast, unicast and scan support), and Connected, bidirectional and reliable (maximum application throughput is 270 kbit/s). The network topology for connection is star/bus where the collector device is typically a master and sensors are slaves. LE uses three channels for advertisements and 37 channels for data. One reason for having the master role assigned to the collector is that collocation of multiple radios in a handset requires operation between all radios. The Bluetooth co-existence controller is specified in Core specification addendum 4 (CSA4) [121] and it is particularly important in combination with 4G networks as LTE TDD. LE also improves co-existence and robustness with nearby networks by using adaptive frequency hopping (AFH).  
The main layers of Bluetooth are shown in the following Figure 12 and consist of the following components: Radio and link layer (LL) with an AES-128 bit encryption unit, Multiplexer--Logical link control and adaption layer protocol (L2 CAP) providing fixed and connection oriented channels together with fragmentation and reassembly (FAR), Security Manager (SM), Host Controller Interface (HCI)--Connects application processor and Bluetooth controller, The General Access profile (GAP)--Contains a collection of standard procedures, and Generic attribute profile (GATT), which provides an interoperable framework with service discovery and operation. Bluetooth SIG defined service data is characterized by 16 bit universally unique identifiers (uuids) while proprietary extensions use randomized 128 uuids.  
Figure 12: The Bluetooth SMART core 4.0.  
All packets in LE have the same layout and the maximum length of a packet is 47 Octets. Depending on mode of connection the payload may be max 31 Bytes, for an advertisement, implying an efficiency of 31 Bytes/47 Bytes * 100% = 66%. A connection can have max 27 Bytes per packet implying an efficiency of 27 Bytes/47 Bytes * 100% = 57% for a raw L2 CAP channel without encryption. The efficiency drops to 20 Bytes/41 Bytes * 100 = 49% when using GATT with notification and encryption.